{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-06T14:01:22.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-06T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":2052,"title":"provide the numerical answer to these number questions...","description":"return a row matrix containing the sorted numbers which answer the following questions:\r\n\r\n a) what is the first Knuth number to repeat 3 times?\r\n b) how many Platonic solids are there?\r\n c) what base is the decimal number system in?\r\n d) what is the additive identity?\r\n e) what is the multiplicative identity?\r\n f) what is the smallest perfect number?\r\n g) in the Fibonacci sequence, what is the larget cube number?\r\n h) what is the number of spatial dimension we live in?\r\n i) what is the square of the only even Prime?\r\n j) what is the only even Prime?\r\n k) any number can be divided by this digit if the repeated sum of the digits is this digit\r\n","description_html":"\u003cp\u003ereturn a row matrix containing the sorted numbers which answer the following questions:\u003c/p\u003e\u003cpre\u003e a) what is the first Knuth number to repeat 3 times?\r\n b) how many Platonic solids are there?\r\n c) what base is the decimal number system in?\r\n d) what is the additive identity?\r\n e) what is the multiplicative identity?\r\n f) what is the smallest perfect number?\r\n g) in the Fibonacci sequence, what is the larget cube number?\r\n h) what is the number of spatial dimension we live in?\r\n i) what is the square of the only even Prime?\r\n j) what is the only even Prime?\r\n k) any number can be divided by this digit if the repeated sum of the digits is this digit\u003c/pre\u003e","function_template":"function amat = numtest()\r\namat(1)=100;\r\namat(2)=2;\r\n% etc\r\nend","test_suite":"%% intentionally obfuscated\r\ny_correct = -4.6:.1:-3.6;\r\nassert(isequal(numtest(),int32(10*y_correct+'0'-log10(100)) ))","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":17471,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":59,"test_suite_updated_at":"2017-08-18T16:07:56.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-15T06:20:43.000Z","updated_at":"2026-04-07T13:37:29.000Z","published_at":"2013-12-15T06:20:43.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ereturn a row matrix containing the sorted numbers which answer the following questions:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ a) what is the first Knuth number to repeat 3 times?\\n b) how many Platonic solids are there?\\n c) what base is the decimal number system in?\\n d) what is the additive identity?\\n e) what is the multiplicative identity?\\n f) what is the smallest perfect number?\\n g) in the Fibonacci sequence, what is the larget cube number?\\n h) what is the number of spatial dimension we live in?\\n i) what is the square of the only even Prime?\\n j) what is the only even Prime?\\n k) any number can be divided by this digit if the repeated sum of the digits is this digit]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50018,"title":"Number Puzzles - 010","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 21px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 10.5px; transform-origin: 407px 10.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of fifteen (15) consecutive five-digit numbers whose sum is a palindrome.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_010()\r\n  y = 1:15;\r\nend","test_suite":"%%\r\ny=puzzle_010();\r\nassert(isequal(length(num2str(y(1))),5))\r\nassert(isequal(y(15)-y(1)+1,15))\r\nassert(isequal(size(unique(y),2),15))\r\naa=num2str(sum(y));\r\nassert(strcmp(aa,fliplr(aa)))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-28T01:33:03.000Z","updated_at":"2026-02-25T10:48:18.000Z","published_at":"2021-01-28T01:33:03.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of fifteen (15) consecutive five-digit numbers whose sum is a palindrome.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2027,"title":"Consecutive Powers","description":"Return 2 numbers and 2 powers such that their difference is 1\r\n\r\nA 4 element row vector is expected: x where \r\n\r\n x(1)^x(2) - x(3)^x(4) = 1;\r\n","description_html":"\u003cp\u003eReturn 2 numbers and 2 powers such that their difference is 1\u003c/p\u003e\u003cp\u003eA 4 element row vector is expected: x where\u003c/p\u003e\u003cpre\u003e x(1)^x(2) - x(3)^x(4) = 1;\u003c/pre\u003e","function_template":"function [x,y,p1,p2] = conpow()\r\n% your code here\r\nend","test_suite":"%%\r\nx=conpow();\r\nd = x(1)^x(2)-x(3)^x(4);\r\ny_correct = 1;\r\nassert(isequal(d,y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":17471,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":98,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-30T12:48:23.000Z","updated_at":"2026-02-25T10:57:25.000Z","published_at":"2013-11-30T12:57:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn 2 numbers and 2 powers such that their difference is 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA 4 element row vector is expected: x where\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x(1)^x(2) - x(3)^x(4) = 1;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50392,"title":"Number Puzzle - 073","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 42px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 21px; transform-origin: 407px 21px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of a pair of four-digits prime numbers which has the following form ##XX and YY## (where # could be any single digit number) and whose sum is a square. Your answer should be y=[##XX YY##].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_073()\r\n  y = [1234 5678];\r\nend","test_suite":"%%\r\ny=puzzle_073();\r\na=num2str(y(1));\r\nassert(isequal(a(3),a(4)))\r\nb=num2str(y(2));\r\nassert(isequal(b(1),b(2)))\r\nc=y(1)+y(2);\r\nassert(isequal(sqrt(c),floor(sqrt(c))))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-02-16T18:48:55.000Z","updated_at":"2026-01-30T15:49:42.000Z","published_at":"2021-02-16T18:48:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of a pair of four-digits prime numbers which has the following form ##XX and YY## (where # could be any single digit number) and whose sum is a square. Your answer should be y=[##XX YY##].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":50008,"title":"Number Puzzles - 008","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 21px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 10.5px; transform-origin: 407px 10.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of five prime numbers whose product is a palindrome.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_008()\r\n  y = [1 2 3 4 5];\r\nend","test_suite":"%%\r\ny=puzzle_008();\r\nassert(isequal(size(unique(y),2),5))\r\nn=num2str(prod(y))\r\nassert(isequal(n,fliplr(n)))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-28T01:02:08.000Z","updated_at":"2026-03-17T07:45:10.000Z","published_at":"2021-01-28T01:02:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of five prime numbers whose product is a palindrome.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2770,"title":"Probability of Choosing a Red Ball","description":"Given two jars of red and blue balls, find the probability of choosing a red ball from Jar 1 after going through the steps. \r\n\r\n  Step 1: Choose a random ball from Jar 2 and add it to Jar 1\r\n\r\n  Step 2: Choose a random ball from Jar 1\r\n\r\n  Step 3: Calculate the probability of the final ball being red\r\n\r\n*Example:* \r\n\r\nGiven inputs for Jar 1 and Jar 2\r\n\r\nJar 1: (r1,b1) = (2,7)\r\n\r\nJar 2: (r2,b2) = (5,5)\r\n\r\nChoose a ball from Jar 2 and add it to Jar 1. \r\n  \r\n   _Note: Jar 1 could now have either 3 blue and 7 red or 2 blue and 8 red._ \r\n\r\nTaking into consideration both possibilities, the likelihood of the final ball being red is *0.25* . ","description_html":"\u003cp\u003eGiven two jars of red and blue balls, find the probability of choosing a red ball from Jar 1 after going through the steps.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eStep 1: Choose a random ball from Jar 2 and add it to Jar 1\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eStep 2: Choose a random ball from Jar 1\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eStep 3: Calculate the probability of the final ball being red\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven inputs for Jar 1 and Jar 2\u003c/p\u003e\u003cp\u003eJar 1: (r1,b1) = (2,7)\u003c/p\u003e\u003cp\u003eJar 2: (r2,b2) = (5,5)\u003c/p\u003e\u003cp\u003eChoose a ball from Jar 2 and add it to Jar 1.\u003c/p\u003e\u003cpre\u003e   _Note: Jar 1 could now have either 3 blue and 7 red or 2 blue and 8 red._ \u003c/pre\u003e\u003cp\u003eTaking into consideration both possibilities, the likelihood of the final ball being red is \u003cb\u003e0.25\u003c/b\u003e .\u003c/p\u003e","function_template":"function prob = probRedBall(r1,b1,r2,b2)\r\n  prob = r1/(r1+b1);\r\nend","test_suite":"%%\r\nr1 = 2; b1 = 7; r2 = 5; b2 = 5; \r\nprob_correct = 0.2500;\r\nassert(isequal(probRedBall(r1,b1,r2,b2),prob_correct))\r\n\r\n%%\r\nr1 = 0; b1 = 5; r2 = 0; b2 = 5; \r\nprob_correct = 0.0000;\r\nassert(isequal(probRedBall(r1,b1,r2,b2),prob_correct))\r\n\r\n%%\r\nr1 = 0; b1 = 3; r2 = 1; b2 = 3; \r\nprob_correct = 0.0625;\r\nassert(isequal(probRedBall(r1,b1,r2,b2),prob_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":32736,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":66,"test_suite_updated_at":"2014-12-10T17:44:29.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2014-12-10T17:13:21.000Z","updated_at":"2026-03-05T15:56:58.000Z","published_at":"2014-12-10T17:35:42.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two jars of red and blue balls, find the probability of choosing a red ball from Jar 1 after going through the steps.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Step 1: Choose a random ball from Jar 2 and add it to Jar 1\\n\\nStep 2: Choose a random ball from Jar 1\\n\\nStep 3: Calculate the probability of the final ball being red]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven inputs for Jar 1 and Jar 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJar 1: (r1,b1) = (2,7)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJar 2: (r2,b2) = (5,5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChoose a ball from Jar 2 and add it to Jar 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   _Note: Jar 1 could now have either 3 blue and 7 red or 2 blue and 8 red._]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTaking into consideration both possibilities, the likelihood of the final ball being red is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0.25\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50232,"title":"Number Puzzle - 044","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 136px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 68px; transform-origin: 407px 68px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA four-digit number N can be written as abcd where a, b, c, and d do not have to be unique. Find all four-digit numbers that satisfy the following condition:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 55px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 27.5px; text-align: left; transform-origin: 384px 27.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzY1AACSkgACAAAAAzY1AADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAyOjEwIDE3OjE3OjE3ADIwMjE6MDI6MTAgMTc6MTc6MTcAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAyLTEwVDE3OjE3OjE3LjY0OTwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIADEBGwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiivH9W+LGv6B8bIfDGow6ZNoEl3DbPeRQSJLC86MYkZjIVJ4BJ24Iz0oA9gorifif4l8QeF9J0u48L/2bJc3uoxWAgv4ZH8xpThdpR1xjBJznI9Mc9haLcrZwrfSRS3IQCV4YyiM2OSqksQM9iT9TQBNRRRQAUUUUAFFFFABRRRQAUVi2vimyu/Gl/4ZjiuBe2NrHdSSMq+WVckAA5znjngVtUAFFFeb+ENY8QX3xn8XaVca5Nf6FpEUKxwy28K7JpQH270RSQoDDk9+cnmgD0aWVIYXlmdUjRSzMxwFA5JNctF8TvCMslqF1VlhvJfJtruS0mS2mfptWdkEZPB6NXUyRpNE0cqK8bqVZGGQwPUEdxXnniXS4fHjW3hDRYI4PD+mXMbajdwqFSMxEFbWAAY3ZA3EcIOOpwAD0WiiigAorEv/ABVZad4w0nw3NFcNearFNLA6KpjURAFtxzkHnjAP4Vt0AFFFFAEV1dQWNnNd3k0cFvBG0kssjBVjRRksSegAGc1zlp8R/C17fWVrDqMiNqDbbKWezmhhuiegjldAjk9grHNdJc20F5ay2t5DHPbzIY5YpUDJIpGCrA8EEHBBryf4hvdxeI/DS+MtPtrbwja6zEbNtMmMkn2gblgMwZU2R4JJVA2OmT3APXKKKKACisSXxVZQ+OYPCrRXBvp7Fr5ZAq+UI1fYQTnO7J9Me9bdABRRRQAVm+IPEWleFdFm1bX7xLKxhxvlcFuScAAAEk57AGuH0PWtfuvj7r2h/wBuTXmhabYJPJbSQQjyZ5SpSMOqBioUkjJJ9SetcX8W9ZOueHfE93q2n6tbxadEbXSba40m6ERcsFkumk8vywSpKpluBk9WxQB75HIssSSRnKOoZTjqDTqxvC2sx61oyyw2d/axwlYVN7avbmbCKd6q4DbcsRkgZKntgnZoAK8A1Xw2/jf4T+OvEVuGe8uNbm1HTpE67LT90m31JRJAP96vaPFNzqtr4Yvn8PafJqOpmFktoI5Uj+cjAYs7KAAeTznjisj4YaRd6H8MtH0fVtMfTrqzg8me3kkjk3NklmBRmBDEk9c89KAOMsvEq/EfxD8MnQo6i2n1q+ReiSxJ5K4+krv+Vew15F8Ivhlqngjxp4luNSjxpy4tdFfzFbNu0ryMMA5Xkp1xk5r12gDE8V6Xrur6VHB4Y8R/8I9drMHe6+wpdb02sCmxyAMkqc9flx3rkf8AhCfid/0Vz/y2rb/4quu8V+DtC8b6VHpviex+3WkUwnSPznjw4VlByjA9GbjOOa5H/hnz4Y/9Cz/5P3P/AMcoAvaL4T8f2OtW1zrHxL/tSyjfM1n/AGDbw+cv93epyv1Fd3XCaL8FfAHh7WrbVtH0D7Pe2r74Zftlw+1umcM5B69xXd0AeaeNdUu/EfxO0f4e2F1NaWb2ralrEtu5SR4AdqQqw5UMww2MHBHPUHnPGPhPRrf4zeANC8NWMOmo0kt9qMFkoiWaOEo8TSBfvfMjDJ5561teI/D3i/RfjSnjPwlo0GuW97pn9n3UEt4tsYTvDb9zZyPlU8AnqMdKzZfBvj+w+Lkfie0hsdRuNR0k2k95LcbYdMlMm7KRH5nVVAAUY3EkkqSaAPZq4/xR8Qf+EX1cWH/CI+KtYzEJPtGk6Z58IySNu7cPmGOR7iusgR47eNJpTNIqAPIVALnHJwOBn2qSgDwLS/iZ5Pxn13Vv+EJ8YSfaNMtofsUek5uYtrH53Tdwpzwc816v4S8a/wDCWS3Sf8I14i0T7OqndrNh9nEuc8J8xyRjn6isjSNOvYvjx4i1CSzuEsptJtY47lomEbsGbKhsYJHcV3tACMyopZyFVRkknAArwj4X+FB8SNH8Q614kkuDoetavcXMVpDM0JuhnapkZSG2JghUzjO4nOFx694yg1O68EazbaDF5upXFnLDbKXC/OylQckgDGc/hXDeGLDxr4c+Gdn4P0zw4ttqdvA9uNVe8iNpGWYnzQAxlJ+Ytt2DnvQBX+ATXd18OdZ0yS8uPs1nq11ZWNx5m6SOIKmNpII4LEg8jPbip1+AmmLpjaafGnjRtOcMr2Z1ZfJcMSWBTy8YJJz65Ndt4J8J2fgjwfY6DYMZEtU/eTMMGWQnLufqSeOwwO1b1ABXN+LvGX/CJfY/+Kc8Qa39q3/8gWx+0+Tt2/f+Ybc7uPXB9K6SigDwHXviZ9q+MHhPVf8AhCvGEP2O1vE+xzaVtuJ96qMxpu+YLj5jnivTPDPxE/4SXWRp/wDwh/izSMoz/adV0zyIRjtu3Hk9hVPxHp19P8cPBl/DZ3ElnbWd8s9wkTGOIsi7QzYwCe2etd9QAE4GTwK8p8CxRfFWbVPFfiaJb/R2u3tdH024G+3WGM4MrRn5Wdj3YHGMCvU5olngkifO2RSpx6EYrxzwNpPxM8A6GfB2neGtMvbaGaU2uuz6iEiVXYsC8IBkJBPQY9M96AO70v4caDpHg/VfDNqk/wDZmqSzyTRGQAoJeCiYA2qAAAOwHeoYvhvZSf2bHrOtavrdppcqTWlnfyRGON0GEYlI1aQrnjeze+a6nT4bm3023hv7r7ZdJGqzXHlhPNbHLbRwMntVigArl/Fvjj/hE7q3h/4RjxJrXnoX8zRtP+0LHg4wx3DBrqKKAPAbr4mb/jlYaz/whXjBfL0OS3+wtpWLpszBvMEe7lB0LZ616j4U8f8A/CVarJY/8In4o0by4TN9o1jTfs8TYZRsDbjlvmzj0B9KzrzTr5v2hNO1FbO4Ninh6WFroRN5SyGcEIXxgNjnGc131AEdxcR2trLcTtsihQu7HsoGSa8s+HumwfE7S5/Gvja1j1GG/uJV0zTrtA8FnbIxQYjOVLkgkuRnpjAr0vWNPGraFf6cX8sXltJAXA+7vUrn9a8n8FeGfiFB4CHgXVtPtNHsbVJYTq8V6JJLqNmYhY0UZTOcFyQQvQBuQAP/AGetMgXTPE+vWu4wanq8kdqzMW3W0WRHyew3MB9K9F8Y+Fbbxr4ZuNCv7y7tLS5K+c1oYw7qDnbl0YAZAPAB469a474OeH/F3hzwzY6P4hsbXSbLTYpYxDFMs8l7I8hfzWYcIoBwFBJJOTjAFem0AVdNsjp2l21m11PdmCMR+fcbfMcDjLbQoz9AKtUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q==\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_044(x)\r\n  y = [1234 5678];\r\nend","test_suite":"%%\r\na=puzzle_044();\r\nassert(length(a)\u003e=3)\r\nassert(length(unique(a))\u003e=3)\r\nassert(sum(a)==5409)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-02-10T22:20:17.000Z","updated_at":"2026-01-30T15:01:34.000Z","published_at":"2021-02-10T22:20:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA four-digit number N can be written as abcd where a, b, c, and d do not have to be unique. Find all four-digit numbers that satisfy the following condition:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"49\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"283\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.jpeg\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.jpeg\",\"contentType\":\"image/jpeg\",\"content\":\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzY1AACSkgACAAAAAzY1AADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAyOjEwIDE3OjE3OjE3ADIwMjE6MDI6MTAgMTc6MTc6MTcAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAyLTEwVDE3OjE3OjE3LjY0OTwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIADEBGwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiivH9W+LGv6B8bIfDGow6ZNoEl3DbPeRQSJLC86MYkZjIVJ4BJ24Iz0oA9gorifif4l8QeF9J0u48L/2bJc3uoxWAgv4ZH8xpThdpR1xjBJznI9Mc9haLcrZwrfSRS3IQCV4YyiM2OSqksQM9iT9TQBNRRRQAUUUUAFFFFABRRRQAUVi2vimyu/Gl/4ZjiuBe2NrHdSSMq+WVckAA5znjngVtUAFFFeb+ENY8QX3xn8XaVca5Nf6FpEUKxwy28K7JpQH270RSQoDDk9+cnmgD0aWVIYXlmdUjRSzMxwFA5JNctF8TvCMslqF1VlhvJfJtruS0mS2mfptWdkEZPB6NXUyRpNE0cqK8bqVZGGQwPUEdxXnniXS4fHjW3hDRYI4PD+mXMbajdwqFSMxEFbWAAY3ZA3EcIOOpwAD0WiiigAorEv/ABVZad4w0nw3NFcNearFNLA6KpjURAFtxzkHnjAP4Vt0AFFFFAEV1dQWNnNd3k0cFvBG0kssjBVjRRksSegAGc1zlp8R/C17fWVrDqMiNqDbbKWezmhhuiegjldAjk9grHNdJc20F5ay2t5DHPbzIY5YpUDJIpGCrA8EEHBBryf4hvdxeI/DS+MtPtrbwja6zEbNtMmMkn2gblgMwZU2R4JJVA2OmT3APXKKKKACisSXxVZQ+OYPCrRXBvp7Fr5ZAq+UI1fYQTnO7J9Me9bdABRRRQAVm+IPEWleFdFm1bX7xLKxhxvlcFuScAAAEk57AGuH0PWtfuvj7r2h/wBuTXmhabYJPJbSQQjyZ5SpSMOqBioUkjJJ9SetcX8W9ZOueHfE93q2n6tbxadEbXSba40m6ERcsFkumk8vywSpKpluBk9WxQB75HIssSSRnKOoZTjqDTqxvC2sx61oyyw2d/axwlYVN7avbmbCKd6q4DbcsRkgZKntgnZoAK8A1Xw2/jf4T+OvEVuGe8uNbm1HTpE67LT90m31JRJAP96vaPFNzqtr4Yvn8PafJqOpmFktoI5Uj+cjAYs7KAAeTznjisj4YaRd6H8MtH0fVtMfTrqzg8me3kkjk3NklmBRmBDEk9c89KAOMsvEq/EfxD8MnQo6i2n1q+ReiSxJ5K4+krv+Vew15F8Ivhlqngjxp4luNSjxpy4tdFfzFbNu0ryMMA5Xkp1xk5r12gDE8V6Xrur6VHB4Y8R/8I9drMHe6+wpdb02sCmxyAMkqc9flx3rkf8AhCfid/0Vz/y2rb/4quu8V+DtC8b6VHpviex+3WkUwnSPznjw4VlByjA9GbjOOa5H/hnz4Y/9Cz/5P3P/AMcoAvaL4T8f2OtW1zrHxL/tSyjfM1n/AGDbw+cv93epyv1Fd3XCaL8FfAHh7WrbVtH0D7Pe2r74Zftlw+1umcM5B69xXd0AeaeNdUu/EfxO0f4e2F1NaWb2ralrEtu5SR4AdqQqw5UMww2MHBHPUHnPGPhPRrf4zeANC8NWMOmo0kt9qMFkoiWaOEo8TSBfvfMjDJ5561teI/D3i/RfjSnjPwlo0GuW97pn9n3UEt4tsYTvDb9zZyPlU8AnqMdKzZfBvj+w+Lkfie0hsdRuNR0k2k95LcbYdMlMm7KRH5nVVAAUY3EkkqSaAPZq4/xR8Qf+EX1cWH/CI+KtYzEJPtGk6Z58IySNu7cPmGOR7iusgR47eNJpTNIqAPIVALnHJwOBn2qSgDwLS/iZ5Pxn13Vv+EJ8YSfaNMtofsUek5uYtrH53Tdwpzwc816v4S8a/wDCWS3Sf8I14i0T7OqndrNh9nEuc8J8xyRjn6isjSNOvYvjx4i1CSzuEsptJtY47lomEbsGbKhsYJHcV3tACMyopZyFVRkknAArwj4X+FB8SNH8Q614kkuDoetavcXMVpDM0JuhnapkZSG2JghUzjO4nOFx694yg1O68EazbaDF5upXFnLDbKXC/OylQckgDGc/hXDeGLDxr4c+Gdn4P0zw4ttqdvA9uNVe8iNpGWYnzQAxlJ+Ytt2DnvQBX+ATXd18OdZ0yS8uPs1nq11ZWNx5m6SOIKmNpII4LEg8jPbip1+AmmLpjaafGnjRtOcMr2Z1ZfJcMSWBTy8YJJz65Ndt4J8J2fgjwfY6DYMZEtU/eTMMGWQnLufqSeOwwO1b1ABXN+LvGX/CJfY/+Kc8Qa39q3/8gWx+0+Tt2/f+Ybc7uPXB9K6SigDwHXviZ9q+MHhPVf8AhCvGEP2O1vE+xzaVtuJ96qMxpu+YLj5jnivTPDPxE/4SXWRp/wDwh/izSMoz/adV0zyIRjtu3Hk9hVPxHp19P8cPBl/DZ3ElnbWd8s9wkTGOIsi7QzYwCe2etd9QAE4GTwK8p8CxRfFWbVPFfiaJb/R2u3tdH024G+3WGM4MrRn5Wdj3YHGMCvU5olngkifO2RSpx6EYrxzwNpPxM8A6GfB2neGtMvbaGaU2uuz6iEiVXYsC8IBkJBPQY9M96AO70v4caDpHg/VfDNqk/wDZmqSzyTRGQAoJeCiYA2qAAAOwHeoYvhvZSf2bHrOtavrdppcqTWlnfyRGON0GEYlI1aQrnjeze+a6nT4bm3023hv7r7ZdJGqzXHlhPNbHLbRwMntVigArl/Fvjj/hE7q3h/4RjxJrXnoX8zRtP+0LHg4wx3DBrqKKAPAbr4mb/jlYaz/whXjBfL0OS3+wtpWLpszBvMEe7lB0LZ616j4U8f8A/CVarJY/8In4o0by4TN9o1jTfs8TYZRsDbjlvmzj0B9KzrzTr5v2hNO1FbO4Ninh6WFroRN5SyGcEIXxgNjnGc131AEdxcR2trLcTtsihQu7HsoGSa8s+HumwfE7S5/Gvja1j1GG/uJV0zTrtA8FnbIxQYjOVLkgkuRnpjAr0vWNPGraFf6cX8sXltJAXA+7vUrn9a8n8FeGfiFB4CHgXVtPtNHsbVJYTq8V6JJLqNmYhY0UZTOcFyQQvQBuQAP/AGetMgXTPE+vWu4wanq8kdqzMW3W0WRHyew3MB9K9F8Y+Fbbxr4ZuNCv7y7tLS5K+c1oYw7qDnbl0YAZAPAB469a474OeH/F3hzwzY6P4hsbXSbLTYpYxDFMs8l7I8hfzWYcIoBwFBJJOTjAFem0AVdNsjp2l21m11PdmCMR+fcbfMcDjLbQoz9AKtUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q==\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1154,"title":"Identify the heavier bag","description":"There are N=2^n bags of rice looking alike, N-1 of which have equal weight and one is slightly heavier. The weighing balance is of unlimited capacity. Using the balance, the minimum number of weighing required to identify the heavier bag is?","description_html":"\u003cp\u003eThere are N=2^n bags of rice looking alike, N-1 of which have equal weight and one is slightly heavier. The weighing balance is of unlimited capacity. Using the balance, the minimum number of weighing required to identify the heavier bag is?\u003c/p\u003e","function_template":"function y = heavier(N)\r\n  y = N;\r\nend","test_suite":"%%\r\nN = 2;\r\ny_correct = 1;\r\nassert(isequal(heavier(N),y_correct))\r\n%%\r\nN = 4;\r\ny_correct = 2;\r\nassert(isequal(heavier(N),y_correct))\r\n%%\r\nN = 8;\r\ny_correct = 2;\r\nassert(isequal(heavier(N),y_correct))\r\n%%\r\nN = 128;\r\ny_correct = 6;\r\nassert(isequal(heavier(N),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":4,"created_by":9317,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-12-30T18:11:08.000Z","updated_at":"2024-11-12T06:50:37.000Z","published_at":"2012-12-30T18:11:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are N=2^n bags of rice looking alike, N-1 of which have equal weight and one is slightly heavier. The weighing balance is of unlimited capacity. Using the balance, the minimum number of weighing required to identify the heavier bag is?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2359,"title":"Integer Sequence - 1","description":"Check the test suite to determine the relationship between input integer scalar and output integer scalar.","description_html":"\u003cp\u003eCheck the test suite to determine the relationship between input integer scalar and output integer scalar.\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  \r\nend","test_suite":"%%\r\nx = 7;\r\ny_correct = 354;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 12;\r\ny_correct = 1739;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 21;\r\ny_correct = 9272;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n\r\n%%\r\nx = 35;\r\ny_correct = 42886;\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":68,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-06-11T18:12:27.000Z","updated_at":"2026-04-02T10:34:45.000Z","published_at":"2014-06-11T18:13:19.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck the test suite to determine the relationship between input integer scalar and output integer scalar.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50078,"title":"Number Puzzle - 022","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 135px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 67.5px; transform-origin: 407px 67.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of m and n that satisfy the following conditions:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzkyAACSkgACAAAAAzkyAADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAxOjMxIDE3OjU4OjI1ADIwMjE6MDE6MzEgMTc6NTg6MjUAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAxLTMxVDE3OjU4OjI1LjkxNzwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIAGMBJAMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiiigAorltT+I3hvS/F+n+F5L3z9YvpvKW2twHMPGcyHOFHt156V0tzcQ2drLc3UqwwQoZJJHOFRQMkk9gAKAJKK4IfF/Q1trXULvTtYs9EvJRFBrNxahbVyThSfm3qp7MyAe9dZruvab4a0K51jWrpbaxtU3ySnn2AAHJJJAAHUmgDRorjbL4l6bNren6Xqulavoc2qZ+wPqduqJckfwgqzbWx/C+08jjJArZ8TeKtN8J6fDc6o0rvczLb2ttbxmSa5lb7qIo6k/gPegDZorl9D8fadrHiKTQLmy1DR9YSD7QtjqUSo8sWcb0ZGZWGfRs9eODjqKACiiigAoqOC4huoRNazRzREkB42DKSDg8j0II/CpKACiiigAooooAKKKKACiiigAoqNriBLhLd5o1mkUskZYBmA6kDqQMipKACiuW8WfEbw34MubW01i9zfXcqRw2UADzNubAYrn5V9zgccZPFdTQAUVwdx8XdEhtbrUIdN1i80WzlMVxrNtah7ZCDtYj5t7KD1ZVI967A6vp40T+2DeQ/2d5H2n7Vu+Tytu7fn0xzQBcorg4/i5onl2F3e6brGn6TqUqxWmrXdqEtpS33TkMXUN2LKoPXpzXVeIfEOm+FtDuNX1u4FvZ24G5sFiSTgKAOSSSAAKANKiuQ0/4j6dc+ILLRdV0vVtCvdRVmsV1S3VFudoyQrK7ANj+FsHpxkitTxP4s03wpa20moCeae8mEFpaWsfmTXMh/hRf6kgDuaANuiuY0Dx5p2ua9caFNaX2k6zbxCdrDUYlSRoicb1KsysueOGOKl8Q+NbDQNVtNJFre6nq14jSw6fp8SvKYwcFzuZVVc8ZZhk9KAOiorn/C3jPS/FovY7Bbi2vdPl8m9sbyPy57du25ckYODggkH1roKACiiigApGUMpVuhGDS0UAeL+PNF03Qviz8KrXSLOK0hF5eZEa8sdsRyx6sSSSSSSSTW/+0DfTWPwS1s2zFGm8mFmH91pVDD8RkfjXN/ETWZtU+KPgbUtL8PeJLuy0O5uHvZ49DugFD+WBtDIC2NhJwPpmu78daGnxJ+FepaZp4mhe9i3W32y2kt3Ekbhl3JIoZQWTGSOhyODQBm/EfSbc/s96rYCNfJtdHUxqRwvlKrL+W0Vw3inULjVvhz8HrO8kZ01PUtOF0Sc+bhVHPrndn61saxqXiTxZ8KI/BkPhjVrXxDdQRWN5Ld2pS1gClRJL5x+RlIUkBSTz04rW+I/w/vbj4d6BbeFIxcah4VntriyhYhTOsKhSmT0JAB+oxQBT/aQDW/w1tNVgwt1pmrW9zA/dWG4cH8R+VSeLHa9/aY8D2U5JgtLC6u407eYyuuf/AB0H8Kj8XC/+LH/CP6JZ6Dq2nWEV/HfarPqdm1uIkjB/dLvxvZi2MpkDGc4rQ+I+g6pbeO/C3jvQ7CbUm0ZpYL60tlBme3kUjcg/iK7m+XqcjHegDP8AimGsvjD8MdStsJM19PaSMOrI4QY+mGf/AL6r1uvL5bC9+IPxY8O65/Zd/p+h+G4ppUk1C2a3kubiQAbVjcBgq7VO4jBxgeteoUAcRrHxHC67deH/AAho134i1q1IW4SL9zb2pPTzZm4HHOFDE4NUT4A8Q+LiZPiP4hZrRuuh6KWgtsekkn+sl+nyjitnxF8ONC1/UP7VjWfSdaA+XVdMlME//AiOHHA4YHjisY6l8Q/BZI1axj8a6Sv/AC96cghv4x6tD92Tt9wgmgDutL0qx0TS4NN0m1jtLO3XZFDGMKgrmPidqWsaL4J1XVtK1RdMSxsZJllSJJJHmGPLXDqVCE8HjJyMEY56PQ9ZtvEGi22qWKzJBcKSqTxGORSCQVZTyCCCPwriPjfp+pa74FtvD+k2l3cNq+p21tO1tCz+RDv3NI5AO1QVXJPHNAHK+LPGvxAtPhfZeO7PUrLTLaGK1b+z3sQ76hvKqzsxP7tSWJVV528lgTgaHijxR490rU/C2ti7trHT9Z1e3sB4ea0V5RFLk75JTyHwOVXAUkDLY52PijpN5rN34K8O6dp9xJp76xFcXskMLNFDBAM7XYDCg7hjPUr7VH8QLXUJ/ip4Hun02+u9G05rm4lNnbNN/pHl4iDbR8nOMM2F5OSOoAPTKwfF82oWvh+6urHUV02G1tpri4uhGskiBELDYGBXqMksDwMAc5GZdeOrmy8f+GvC11ou2fWrSW4mmS53C0MaFiuAvzjI25yOtQfGT+05PhNrVpoVjdX99exrbRw2sLSMQ7gOcAHjZu5oA4N/GnxC1b4Kx+L7PVrTSF0+yE0s01iryalIpwwAPyxp0AIXLMDjauCfYvDl9c6n4W0q/wBQiEN3dWUM08Y6I7ICw/AkivPvif4avYPgrp3hnR7O5vLaGSytbtLOJpJDbRldzBVG48qp4BNejaTdyXtl50lhJYx7ysMUvDmMcBiv8GeoU8gYzg5AAKXirxbpPgzR11HXZnjiklWCFIomkkmlYEqiqBkscH8q5Vrv4h+MwV062j8FaU//AC8XirPqDr7RA7Is8j5iSOtdrrOh6X4i0x9P12wgv7STloZ0DDPYj0I9RyK4pvBHifwmDJ8O/EDS2icjRNcZp4Mekc3+sj9gSwyaANvwv8PNE8LX0upwm61DWJ02T6pqM5muJBnOMnhRwOFA6V1Ncj4Y8b3Wrau2ieIfDmoaDq6RtJslXzbeZQQCY51+Vuo4ODXXUAeL/HTQ9M0nw74el0+yiglufFltLPKBl5WZZSSzHk9u/AAA4Ar0P4jX02m/DHxJd2jFJ4tMuDG46q3lnBH061578c9QuNasdG0zRNB1/UbjTtchvLhrfR7gxhI1cHa5QK+SwxtJB9a9HuTZ+O/Bmp2KRXttDfW8to4vbGW2kQumMhJVUnG4cjIyODxQBzfgLSbeb9nfTNOMamG60Rg6sOD5iEtn8WNeT3Os3n/DFFsDK255/shbPJjFyxA/IBfpXbaBqniXw98J5fBVz4W1afxFa28thayQ2pa0nDbhHL5/3FUAjIYg8dOa0tS+E80n7O6eBbWSNtQgt1lR+itcB/NYZPQFiy59DQBofGbSbdvgTrlkqL5dpaRtFkfd8t1Ix6fdxXH+Lb+fWtI+C8F85dNSu7K7uRn/AFjrFG3Pr99vzrR8S6h4h8ffDSHwfb+GtWsNavhDb6jNe2jR29oqMpkkEp+WQHbwFJJz2Na3xN8EX0/hjw1c+FLf7VfeE7y3uba2yA08UeAUBPf5VPvjHpQBR/aGDW3hbw7q1thLrT/EFtLFJ3HD8D8Qp/CpNdc3n7VHhm0n+aKw0Ka7hU9BJI0kbH8lH5UniiK9+Kuq+GdOtdD1XTtK0/UU1PUrjU7NrbaY1IWFQ+C5O5gSuQOuTVvx/oeqab8S/DXj7R9OuNTj0+KSy1K1tE3TGBw210Xq+0ux2jnp70AUfiIGsPj78Nb+3wsl0bu0kI6sgVeD6j94SPepPBcjX/7R3j+5uDueytbO1g/2IygYgfVlz+NWI9OvfHPxg0jxK+l3un6J4etZRbvf27QSXVxKMHEbYYKowckDJHFRXmnaj4H+Neo+KotKvtR0PxBZxxXR063aeS1njChWMa5YqQvUA8sc+4BBYbtP/az1OKDCx6l4eSadR/E6uqqx9wFx9DXrdeb+C9D1DVPihr/j3VNPn02G6to9P022uk2TGFdpeR16rllGAecdff0igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDKi8OafH4on8Qujy6jLAtsskrZEMQ52IP4QTye5P0AGrRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFUrjWdLtNSt9OutSs4L65/wBRayTqssv+6pOW/Ckj1vSptWk0qHU7OTUYl3yWa3CGZF9Smcge+KAL1czqPxE8L6VPdR3movizk8q7mhtJpobZ/wC7LKiFIzz0Yitiy1zSdRvbiz0/U7O7urU4uIILhHeH/eUHK/jXM+J7SDWbXUvCGgwwWv8AaCs2s3kcSqtvHIPnJ7NM69M9B8zcbQwB2MM0dxBHNA6yRSKHR0OQykZBB9KfVPR4rKDQ7GLSpFlsY7aNbaRJN6vGFAUhh94EY571coAKKKKACiiqNtrelXl7c2dpqdnPdWYzcwRXCM8H++oOV/GgC9XLH4leExIg/tQ+TJN5CXgtZjamTONn2jZ5Wc8Y3VuaZrOl61FJLo+pWeoRxPske1nWUI390lScH2ry/wCKEJ0vwnDqqtpl74Isbu3ll0XT7fyXkBlUZEqsyviVt21VTPQk85APXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPHvixYC78deCNA0TZa6jqesPqUt0F3OnkRAeZznJC52g8ZQdqzPEngbSf+F4eEdD8LxjSJYLC5vNSu7UkXE8D/ALv5pPvM7Heu8ncN5OcgV6XceDDdfFO08Y3F/uSz01rK3sfI+47OS0u/d1IJXG38aWw8GG1+Juq+MJ7/AM972yisre28nb9mjXBYb9x3bmGegx70AcR4Y8P6RY/tJamnhmwt9Ps9I8PxW11HbRhVaeWTeC2OpKY5PJ2iui1f4JfD/XdYutU1bQWub27kMs0rX9wCzH2EmAPYcAcCrOnfD+bTfG2ua3b63KttrVxBc3FskO2XdCPlTzt3+rOeV25xxnBIPaUAVtN0+10jSrTTdPi8m0s4Uggj3FtiIoVRkkk4AHJOa5v4l/8ACH/8IZL/AMLF/wCQJ50e/wD1338/L/qvm6/hXW0UAfNv/GMX+f7Trqvhx/wo/wD4Ta2/4V7/AMh3y5PJ/wCP37u07/8AW/J93PX8K9oooAa6CSNkbO1gQcEg/mK8UsvDln4p+Pmv6dFEkOg6HpFrp1zbxDaJwT5qwkj+DIO4d9gB4Jr22uW8HeDD4V1DxDfT3/2+61zUnvXfyPL8pD9yL7xyF55469KAOQ+Eel2MPj/4h6joVtDZ6SdRisILe2QJErwIfMIUcfefPHqfWuxtvhz4XtZo3h06Ty4pvPjtXvJnto5M7t6wM5jDZ5yF61S8F+AJ/B890q65Jc2Mt/PfR2yQeUd8vBErbj5gUdOFGTkgkLjtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/2Q==\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [m n] = puzzle_022()\r\n  [m n] = [1 1];\r\nend","test_suite":"%%\r\ny = puzzle_022()\r\nassert(min(y)\u003e1000)\r\nassert(max(y)\u003c10^5)\r\nassert(isequal(y(1)^2,y(2)^3))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-31T23:08:58.000Z","updated_at":"2026-01-29T21:56:32.000Z","published_at":"2021-01-31T23:08:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of m and n that satisfy the following conditions:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"99\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"292\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.jpeg\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.jpeg\",\"contentType\":\"image/jpeg\",\"content\":\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzkyAACSkgACAAAAAzkyAADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAxOjMxIDE3OjU4OjI1ADIwMjE6MDE6MzEgMTc6NTg6MjUAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAxLTMxVDE3OjU4OjI1LjkxNzwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIAGMBJAMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiiigAorltT+I3hvS/F+n+F5L3z9YvpvKW2twHMPGcyHOFHt156V0tzcQ2drLc3UqwwQoZJJHOFRQMkk9gAKAJKK4IfF/Q1trXULvTtYs9EvJRFBrNxahbVyThSfm3qp7MyAe9dZruvab4a0K51jWrpbaxtU3ySnn2AAHJJJAAHUmgDRorjbL4l6bNren6Xqulavoc2qZ+wPqduqJckfwgqzbWx/C+08jjJArZ8TeKtN8J6fDc6o0rvczLb2ttbxmSa5lb7qIo6k/gPegDZorl9D8fadrHiKTQLmy1DR9YSD7QtjqUSo8sWcb0ZGZWGfRs9eODjqKACiiigAoqOC4huoRNazRzREkB42DKSDg8j0II/CpKACiiigAooooAKKKKACiiigAoqNriBLhLd5o1mkUskZYBmA6kDqQMipKACiuW8WfEbw34MubW01i9zfXcqRw2UADzNubAYrn5V9zgccZPFdTQAUVwdx8XdEhtbrUIdN1i80WzlMVxrNtah7ZCDtYj5t7KD1ZVI967A6vp40T+2DeQ/2d5H2n7Vu+Tytu7fn0xzQBcorg4/i5onl2F3e6brGn6TqUqxWmrXdqEtpS33TkMXUN2LKoPXpzXVeIfEOm+FtDuNX1u4FvZ24G5sFiSTgKAOSSSAAKANKiuQ0/4j6dc+ILLRdV0vVtCvdRVmsV1S3VFudoyQrK7ANj+FsHpxkitTxP4s03wpa20moCeae8mEFpaWsfmTXMh/hRf6kgDuaANuiuY0Dx5p2ua9caFNaX2k6zbxCdrDUYlSRoicb1KsysueOGOKl8Q+NbDQNVtNJFre6nq14jSw6fp8SvKYwcFzuZVVc8ZZhk9KAOiorn/C3jPS/FovY7Bbi2vdPl8m9sbyPy57du25ckYODggkH1roKACiiigApGUMpVuhGDS0UAeL+PNF03Qviz8KrXSLOK0hF5eZEa8sdsRyx6sSSSSSSSTW/+0DfTWPwS1s2zFGm8mFmH91pVDD8RkfjXN/ETWZtU+KPgbUtL8PeJLuy0O5uHvZ49DugFD+WBtDIC2NhJwPpmu78daGnxJ+FepaZp4mhe9i3W32y2kt3Ekbhl3JIoZQWTGSOhyODQBm/EfSbc/s96rYCNfJtdHUxqRwvlKrL+W0Vw3inULjVvhz8HrO8kZ01PUtOF0Sc+bhVHPrndn61saxqXiTxZ8KI/BkPhjVrXxDdQRWN5Ld2pS1gClRJL5x+RlIUkBSTz04rW+I/w/vbj4d6BbeFIxcah4VntriyhYhTOsKhSmT0JAB+oxQBT/aQDW/w1tNVgwt1pmrW9zA/dWG4cH8R+VSeLHa9/aY8D2U5JgtLC6u407eYyuuf/AB0H8Kj8XC/+LH/CP6JZ6Dq2nWEV/HfarPqdm1uIkjB/dLvxvZi2MpkDGc4rQ+I+g6pbeO/C3jvQ7CbUm0ZpYL60tlBme3kUjcg/iK7m+XqcjHegDP8AimGsvjD8MdStsJM19PaSMOrI4QY+mGf/AL6r1uvL5bC9+IPxY8O65/Zd/p+h+G4ppUk1C2a3kubiQAbVjcBgq7VO4jBxgeteoUAcRrHxHC67deH/AAho134i1q1IW4SL9zb2pPTzZm4HHOFDE4NUT4A8Q+LiZPiP4hZrRuuh6KWgtsekkn+sl+nyjitnxF8ONC1/UP7VjWfSdaA+XVdMlME//AiOHHA4YHjisY6l8Q/BZI1axj8a6Sv/AC96cghv4x6tD92Tt9wgmgDutL0qx0TS4NN0m1jtLO3XZFDGMKgrmPidqWsaL4J1XVtK1RdMSxsZJllSJJJHmGPLXDqVCE8HjJyMEY56PQ9ZtvEGi22qWKzJBcKSqTxGORSCQVZTyCCCPwriPjfp+pa74FtvD+k2l3cNq+p21tO1tCz+RDv3NI5AO1QVXJPHNAHK+LPGvxAtPhfZeO7PUrLTLaGK1b+z3sQ76hvKqzsxP7tSWJVV528lgTgaHijxR490rU/C2ti7trHT9Z1e3sB4ea0V5RFLk75JTyHwOVXAUkDLY52PijpN5rN34K8O6dp9xJp76xFcXskMLNFDBAM7XYDCg7hjPUr7VH8QLXUJ/ip4Hun02+u9G05rm4lNnbNN/pHl4iDbR8nOMM2F5OSOoAPTKwfF82oWvh+6urHUV02G1tpri4uhGskiBELDYGBXqMksDwMAc5GZdeOrmy8f+GvC11ou2fWrSW4mmS53C0MaFiuAvzjI25yOtQfGT+05PhNrVpoVjdX99exrbRw2sLSMQ7gOcAHjZu5oA4N/GnxC1b4Kx+L7PVrTSF0+yE0s01iryalIpwwAPyxp0AIXLMDjauCfYvDl9c6n4W0q/wBQiEN3dWUM08Y6I7ICw/AkivPvif4avYPgrp3hnR7O5vLaGSytbtLOJpJDbRldzBVG48qp4BNejaTdyXtl50lhJYx7ysMUvDmMcBiv8GeoU8gYzg5AAKXirxbpPgzR11HXZnjiklWCFIomkkmlYEqiqBkscH8q5Vrv4h+MwV062j8FaU//AC8XirPqDr7RA7Is8j5iSOtdrrOh6X4i0x9P12wgv7STloZ0DDPYj0I9RyK4pvBHifwmDJ8O/EDS2icjRNcZp4Mekc3+sj9gSwyaANvwv8PNE8LX0upwm61DWJ02T6pqM5muJBnOMnhRwOFA6V1Ncj4Y8b3Wrau2ieIfDmoaDq6RtJslXzbeZQQCY51+Vuo4ODXXUAeL/HTQ9M0nw74el0+yiglufFltLPKBl5WZZSSzHk9u/AAA4Ar0P4jX02m/DHxJd2jFJ4tMuDG46q3lnBH061578c9QuNasdG0zRNB1/UbjTtchvLhrfR7gxhI1cHa5QK+SwxtJB9a9HuTZ+O/Bmp2KRXttDfW8to4vbGW2kQumMhJVUnG4cjIyODxQBzfgLSbeb9nfTNOMamG60Rg6sOD5iEtn8WNeT3Os3n/DFFsDK255/shbPJjFyxA/IBfpXbaBqniXw98J5fBVz4W1afxFa28thayQ2pa0nDbhHL5/3FUAjIYg8dOa0tS+E80n7O6eBbWSNtQgt1lR+itcB/NYZPQFiy59DQBofGbSbdvgTrlkqL5dpaRtFkfd8t1Ix6fdxXH+Lb+fWtI+C8F85dNSu7K7uRn/AFjrFG3Pr99vzrR8S6h4h8ffDSHwfb+GtWsNavhDb6jNe2jR29oqMpkkEp+WQHbwFJJz2Na3xN8EX0/hjw1c+FLf7VfeE7y3uba2yA08UeAUBPf5VPvjHpQBR/aGDW3hbw7q1thLrT/EFtLFJ3HD8D8Qp/CpNdc3n7VHhm0n+aKw0Ka7hU9BJI0kbH8lH5UniiK9+Kuq+GdOtdD1XTtK0/UU1PUrjU7NrbaY1IWFQ+C5O5gSuQOuTVvx/oeqab8S/DXj7R9OuNTj0+KSy1K1tE3TGBw210Xq+0ux2jnp70AUfiIGsPj78Nb+3wsl0bu0kI6sgVeD6j94SPepPBcjX/7R3j+5uDueytbO1g/2IygYgfVlz+NWI9OvfHPxg0jxK+l3un6J4etZRbvf27QSXVxKMHEbYYKowckDJHFRXmnaj4H+Neo+KotKvtR0PxBZxxXR063aeS1njChWMa5YqQvUA8sc+4BBYbtP/az1OKDCx6l4eSadR/E6uqqx9wFx9DXrdeb+C9D1DVPihr/j3VNPn02G6to9P022uk2TGFdpeR16rllGAecdff0igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDKi8OafH4on8Qujy6jLAtsskrZEMQ52IP4QTye5P0AGrRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFUrjWdLtNSt9OutSs4L65/wBRayTqssv+6pOW/Ckj1vSptWk0qHU7OTUYl3yWa3CGZF9Smcge+KAL1czqPxE8L6VPdR3movizk8q7mhtJpobZ/wC7LKiFIzz0Yitiy1zSdRvbiz0/U7O7urU4uIILhHeH/eUHK/jXM+J7SDWbXUvCGgwwWv8AaCs2s3kcSqtvHIPnJ7NM69M9B8zcbQwB2MM0dxBHNA6yRSKHR0OQykZBB9KfVPR4rKDQ7GLSpFlsY7aNbaRJN6vGFAUhh94EY571coAKKKKACiiqNtrelXl7c2dpqdnPdWYzcwRXCM8H++oOV/GgC9XLH4leExIg/tQ+TJN5CXgtZjamTONn2jZ5Wc8Y3VuaZrOl61FJLo+pWeoRxPske1nWUI390lScH2ry/wCKEJ0vwnDqqtpl74Isbu3ll0XT7fyXkBlUZEqsyviVt21VTPQk85APXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPHvixYC78deCNA0TZa6jqesPqUt0F3OnkRAeZznJC52g8ZQdqzPEngbSf+F4eEdD8LxjSJYLC5vNSu7UkXE8D/ALv5pPvM7Heu8ncN5OcgV6XceDDdfFO08Y3F/uSz01rK3sfI+47OS0u/d1IJXG38aWw8GG1+Juq+MJ7/AM972yisre28nb9mjXBYb9x3bmGegx70AcR4Y8P6RY/tJamnhmwt9Ps9I8PxW11HbRhVaeWTeC2OpKY5PJ2iui1f4JfD/XdYutU1bQWub27kMs0rX9wCzH2EmAPYcAcCrOnfD+bTfG2ua3b63KttrVxBc3FskO2XdCPlTzt3+rOeV25xxnBIPaUAVtN0+10jSrTTdPi8m0s4Uggj3FtiIoVRkkk4AHJOa5v4l/8ACH/8IZL/AMLF/wCQJ50e/wD1338/L/qvm6/hXW0UAfNv/GMX+f7Trqvhx/wo/wD4Ta2/4V7/AMh3y5PJ/wCP37u07/8AW/J93PX8K9oooAa6CSNkbO1gQcEg/mK8UsvDln4p+Pmv6dFEkOg6HpFrp1zbxDaJwT5qwkj+DIO4d9gB4Jr22uW8HeDD4V1DxDfT3/2+61zUnvXfyPL8pD9yL7xyF55469KAOQ+Eel2MPj/4h6joVtDZ6SdRisILe2QJErwIfMIUcfefPHqfWuxtvhz4XtZo3h06Ty4pvPjtXvJnto5M7t6wM5jDZ5yF61S8F+AJ/B890q65Jc2Mt/PfR2yQeUd8vBErbj5gUdOFGTkgkLjtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/2Q==\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60536,"title":"Jigsaw 001: Intro 2x2 square. Pieces 128x128","description":"This challenge is to re-assemble camerman.tif in grayscale from four 128x128 pieces into a 256x256 image.\r\n\r\n\r\nThe pointer layout of the image is [1 3; 2 4].\r\nReturn a four value vector that remaps the scrambled image into an original form.\r\nThe displayed scramble is [2 4 1 3] making the solution [3 1 4 2].\r\nThe four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\r\n\r\n\r\nThis series will explore various puzzle pieces, orientations, sizes,double sided, and ultimately DARPA shredder data.\r\nMultiple methods are provided in the template to achieve re-mapping. Which will work and which will fail?","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 522.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 261.25px; transform-origin: 407px 261.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 338px 8px; transform-origin: 338px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to re-assemble camerman.tif in grayscale from four 128x128 pieces into a 256x256 image.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 222.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 111.25px; text-align: left; transform-origin: 384px 111.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.5px 8px; transform-origin: 137.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe pointer layout of the image is [1 3; 2 4].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256px 8px; transform-origin: 256px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn a four value vector that remaps the scrambled image into an original form.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 203px 8px; transform-origin: 203px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe displayed scramble is [2 4 1 3] making the solution [3 1 4 2].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 340px 8px; transform-origin: 340px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.5px 8px; transform-origin: 363.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis series will explore various puzzle pieces, orientations, sizes,double sided, and ultimately DARPA shredder data.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 326.5px 8px; transform-origin: 326.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMultiple methods are provided in the template to achieve re-mapping. Which will work and which will fail?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = jigsaw001(pc,nr,nc,pnr,pnc)\r\n% pc cell array of jigsaw pieces, all the same size and double\r\n% nr,nc  Jigsaw piece counts rows and columns\r\n% pnr,pnc Jigsaw piece size row pixels, col pixels\r\n\r\n %Four scoring methods created\r\n % 1 sum of abs delta       \r\n % 2 sum of (abs delta)^2   \r\n % 3 sum of (abs delta)^0.5 \r\n % 4 median of abs delta    \r\n %\r\n\r\n% Brute force\r\n %Try all piece permutations and score the piece edges\r\n  v=1:nr*nc; % Total pieces\r\n  vperms=perms(1:nr*nc);\r\n  [vnr,vnc]=size(vperms);\r\n  m=zeros(nr*pnr,nc*pnc); %matrix to hold created image\r\n  \r\n  best_score=inf;\r\n  for i=1:vnr % cycle thru all permutations\r\n   vidx=vperms(i,:);\r\n   m=[pc{vidx(1)} pc{vidx(3)};pc{vidx(2)} pc{vidx(4)}];\r\n   \r\n % Four scoring methods created\r\n   score=sum(abs(m(pnr,:)-m(pnr+1,:)))+sum(abs(m(:,pnc)-m(:,pnc+1))); % Method 1\r\n   %score=sum(abs(m(pnr,:)-m(pnr+1,:)).^2)+sum(abs(m(:,pnc)-m(:,pnc+1)).^2); % Method 2\r\n   %score=sum(abs(m(pnr,:)-m(pnr+1,:)).^.5)+sum(abs(m(:,pnc)-m(:,pnc+1)).^.5); % Method 3\r\n   \r\n   %sort_score=sort([abs(m(pnr,:)-m(pnr+1,:)) [abs(m(:,pnc)-m(:,pnc+1))]']); %Method 4\r\n   %score=sort_score(256);  %Median delta                                    %Method 4\r\n   \r\n   %fprintf('%i %i %i %i Score: %.2f\\n',vidx,score);\r\n   if score\u003cbest_score\r\n    v=vidx;\r\n    best_score=score;\r\n    %fprintf('New Best score\\n');\r\n   end\r\n   \r\n  end % i vperms vnr\r\n  \r\nend %jigsaw001","test_suite":"%%\r\n% all imdata 2019 are hosted for cody at https://drive.google.com/drive/folders/1TZkBMEEKHiFJExqVoJgj5VVeHbvOfTYB\r\n% a Text file of matlab urlwrite links will be added in the future\r\n%camerman.tif\r\n%https://drive.google.com/uc?export=download\u0026id=1WNWSIp29e_BM47RSiNwom9zMSUTUVKv7\r\n% future will show cody matlab imdata location and how to access\r\n\r\nurl='https://drive.google.com/uc?export=download\u0026id=1WNWSIp29e_BM47RSiNwom9zMSUTUVKv7'; \r\nfname='cameraman.tif';\r\n%tic\r\nurlwrite(url,fname);\r\n%toc\r\n%dir\r\n%figure;imshow('cameraman.tif') % valid\r\n\r\nm_cameraman=imread('cameraman.tif');\r\nm_cameraman=double(m_cameraman);\r\n\r\n%{\r\nd1=sum(abs(m_cameraman(:,128)-m_cameraman(:,129)));\r\nd2=sum(abs(m_cameraman(:,1)-m_cameraman(:,256)));\r\nfprintf('col delta scr: 128:129 %.2f  256:1 %.2f\\n',d1,d2);\r\n\r\nd1=sum((abs(m_cameraman(:,128)-m_cameraman(:,129))).^.5);\r\nd2=sum((abs(m_cameraman(:,1)-m_cameraman(:,256))).^.5);\r\nfprintf('col root scr: 128:129 %.2f  256:1 %.2f\\n',d1,d2);\r\n%}\r\n\r\n\r\nfprintf('Original image\\n');\r\nfigure;imagesc(m_cameraman);colormap gray %\r\n\r\n%{\r\nfigure;plot(1:256,abs(m_cameraman(:,128)-m_cameraman(:,129)));hold on\r\nplot(1:256,sort(abs(m_cameraman(:,128)-m_cameraman(:,129))));\r\n\r\nfigure;plot(1:256,abs(m_cameraman(:,1)-m_cameraman(:,256)));hold on\r\nplot(1:256,sort(abs(m_cameraman(:,1)-m_cameraman(:,256))));\r\n\r\nfigure;plot(1:256,(abs(m_cameraman(:,128)-m_cameraman(:,129))).^.5);hold on\r\nplot(1:256,sort((abs(m_cameraman(:,128)-m_cameraman(:,129))).^.5));\r\n\r\nfigure;plot(1:256,(abs(m_cameraman(:,1)-m_cameraman(:,256))).^.5);hold on\r\nplot(1:256,sort((abs(m_cameraman(:,1)-m_cameraman(:,256))).^.5));\r\n%}\r\n\r\n%size(m_cameraman) % 256 256\r\n\r\nnr=2;nc=2;\r\npnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=m_cameraman(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\n% scrambled image\r\njigsaws=[jpc{1} jpc{3};jpc{2} jpc{4}];\r\nfprintf('Scrambled image\\n')\r\nfigure;imagesc(jigsaws);colormap gray %\r\n\r\nv = jigsaw001(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=[jpc{v(1)} jpc{v(3)};jpc{v(2)} jpc{v(4)}];\r\n\r\nfprintf('Final image\\n');\r\nfigure;imagesc(jigsawf);colormap gray %\r\n\r\nassert(isequal(jigsawf,m_cameraman))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-06-14T22:50:25.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-14T16:43:17.000Z","updated_at":"2025-03-02T14:00:42.000Z","published_at":"2024-06-14T22:50:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to re-assemble camerman.tif in grayscale from four 128x128 pieces into a 256x256 image.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe pointer layout of the image is [1 3; 2 4].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn a four value vector that remaps the scrambled image into an original form.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe displayed scramble is [2 4 1 3] making the solution [3 1 4 2].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis series will explore various puzzle pieces, orientations, sizes,double sided, and ultimately DARPA shredder data.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMultiple methods are provided in the template to achieve re-mapping. Which will work and which will fail?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image2.png\",\"relationshipId\":\"rId2\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":3042,"title":"Fill-a-pix - Solution Checker","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/fill-a-pix/rules Fill-a-pix\u003e is a logic puzzle game similar to Pic-a-Pix (aka Logic art) and Minesweeper. An example starting board is shown below on the left with the completed board shown to its right. Each number in the board indicates how many surrounding cells, including itself, are to be filled in. There are (up to) nine total cells associated with each number: four immediately adjacent (up, down, left, and right), four diagonally adjacent (one touching each corner), and the central cell (where the number is located).\r\n\r\nBased on this logic, all 0's and 9's are determinant, as all nine cells are either empty or filled, respectively, for these numbers. Also, all 6's on the board edges and 4's in the corners should be completely filled in, as they only involve 6 or 4 cells, respectively.\r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/1418.gif\u003e\u003e \r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/1420.gif\u003e\u003e\r\n\r\nFor this problem, you will be provided with various boards and solutions to each board. Write a function to determine if the solution is correct for the given board. The board will be filled with NaN's where there are no number clues; these cells should not be checked. The solution board will be filled with 1's (filled) and 0's (empty).\r\n\r\nA related problem is \u003chttps://www.mathworks.com/matlabcentral/cody/problems/3043-fill-a-pix-solver-basic Fill-a-pix - Solver (basic)\u003e.","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/fill-a-pix/rules\"\u003eFill-a-pix\u003c/a\u003e is a logic puzzle game similar to Pic-a-Pix (aka Logic art) and Minesweeper. An example starting board is shown below on the left with the completed board shown to its right. Each number in the board indicates how many surrounding cells, including itself, are to be filled in. There are (up to) nine total cells associated with each number: four immediately adjacent (up, down, left, and right), four diagonally adjacent (one touching each corner), and the central cell (where the number is located).\u003c/p\u003e\u003cp\u003eBased on this logic, all 0's and 9's are determinant, as all nine cells are either empty or filled, respectively, for these numbers. Also, all 6's on the board edges and 4's in the corners should be completely filled in, as they only involve 6 or 4 cells, respectively.\u003c/p\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/1418.gif\"\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/1420.gif\"\u003e\u003cp\u003eFor this problem, you will be provided with various boards and solutions to each board. Write a function to determine if the solution is correct for the given board. The board will be filled with NaN's where there are no number clues; these cells should not be checked. The solution board will be filled with 1's (filled) and 0's (empty).\u003c/p\u003e\u003cp\u003eA related problem is \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/3043-fill-a-pix-solver-basic\"\u003eFill-a-pix - Solver (basic)\u003c/a\u003e.\u003c/p\u003e","function_template":"function [tf] = fill_a_pix_solution_check(board,solution)\r\n\r\ntf = 1;\r\n\r\nend\r\n","test_suite":"%%\r\nboard = [-1,-1,-1,-1,-1,-1,-1,-1,0,-1; -1,8,8,-1,2,-1,0,-1,-1,-1; 5,-1,8,-1,-1,-1,-1,-1,-1,-1; -1,-1,-1,-1,-1,2,-1,-1,-1,2; 1,-1,-1,-1,4,5,6,-1,-1,-1; -1,0,-1,-1,-1,7,9,-1,-1,6; -1,-1,-1,6,-1,-1,9,-1,-1,6; -1,-1,6,6,8,7,8,7,-1,5; -1,4,-1,6,6,6,-1,6,-1,4; -1,-1,-1,-1,-1,-1,3,-1,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 0,1,1,0,0,0,0,0,0,0; 0,0,0,0,0,1,1,1,1,1; 0,0,0,1,1,1,1,1,1,1; 0,0,0,1,0,1,1,1,1,1; 0,1,1,1,1,1,1,1,1,1; 0,1,0,1,1,1,0,1,0,1; 0,0,1,0,0,0,1,0,1,0];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,2,3,-1,-1,0,-1,-1,-1,-1; -1,-1,-1,-1,3,-1,2,-1,-1,6; -1,-1,5,-1,5,3,-1,5,7,4; -1,4,-1,5,-1,5,-1,6,-1,3; -1,-1,4,-1,5,-1,6,-1,-1,3; -1,-1,-1,2,-1,5,-1,-1,-1,-1; 4,-1,1,-1,-1,-1,1,1,-1,-1; 4,-1,1,-1,-1,-1,1,-1,4,-1; -1,-1,-1,-1,6,-1,-1,-1,-1,4; -1,4,4,-1,-1,-1,-1,4,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,1,1; 0,0,0,1,0,0,0,1,1,1; 0,0,1,1,1,0,0,1,1,1; 0,1,1,0,1,1,0,1,0,0; 0,1,0,0,0,1,1,1,1,0; 1,1,0,0,1,1,0,0,1,1; 1,0,0,0,1,0,0,0,0,1;  1,0,0,0,1,0,0,0,0,1; 1,1,0,0,1,1,0,0,1,1; 0,1,1,1,1,1,1,1,1,0];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [0,-1,-1,4,3,2,1,-1,-1,-1,-1,-1,3,-1,-1; -1,-1,5,-1,-1,4,-1,-1,4,4,-1,-1,-1,-1,3; -1,5,4,5,4,5,5,-1,5,3,-1,1,2,-1,3; 4,-1,-1,-1,4,-1,-1,4,2,-1,1,-1,-1,-1,-1; -1,-1,5,4,-1,2,2,-1,1,0,-1,-1,7,5,-1; -1,-1,-1,5,-1,-1,0,-1,-1,-1,-1,4,5,-1,2; 4,-1,-1,5,4,2,0,0,-1,-1,-1,5,6,-1,-1; 5,-1,-1,6,5,-1,-1,-1,-1,-1,3,3,3,-1,3; -1,-1,5,-1,5,3,-1,-1,-1,-1,-1,-1,3,-1,-1; 5,-1,-1,6,5,-1,3,5,-1,6,-1,-1,0,-1,0; -1,-1,5,-1,4,3,2,4,5,-1,4,-1,-1,1,-1; -1,7,-1,-1,5,-1,-1,1,-1,5,5,5,-1,-1,-1; -1,-1,6,4,4,4,3,1,2,4,-1,-1,6,4,-1; -1,5,-1,6,-1,-1,-1,-1,-1,4,6,-1,-1,-1,-1; -1,-1,-1,-1,-1,-1,3,2,0,-1,4,4,3,-1,2];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,0,0,0,0,0,1,1,1,1,0; 0,0,1,0,1,0,0,1,1,1,0,0,0,0,1; 1,1,1,1,0,1,1,0,1,0,0,0,0,0,1; 1,0,0,0,1,0,1,1,0,0,0,0,1,1,0; 0,1,1,1,0,0,0,0,0,0,0,1,1,0,0; 0,1,0,1,0,0,0,0,0,0,0,1,1,1,0; 1,1,1,0,1,0,0,0,0,0,0,0,0,0,1; 1,0,0,1,1,0,0,0,0,0,1,1,1,1,1; 1,1,1,1,0,1,0,0,1,1,0,0,0,0,0; 1,0,0,1,0,0,1,1,1,1,0,0,0,0,0; 1,1,1,1,1,0,0,0,1,0,1,0,0,0,0; 1,1,0,0,1,0,0,0,0,1,0,1,1,0,0; 0,1,1,1,0,1,0,0,0,1,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,0,1,1,0,1,0; 0,0,1,1,1,0,1,0,0,0,1,1,0,0,1];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,3,3,-1,-1,-1,-1,-1,-1; 3,-1,-1,-1,-1,-1,0,-1,0,-1; -1,-1,3,4,-1,3,-1,-1,-1,-1; 3,-1,4,-1,-1,-1,-1,3,-1,-1; 2,3,-1,5,-1,4,4,-1,-1,4; -1,-1,5,4,6,6,-1,4,-1,4; -1,-1,-1,-1,-1,3,3,-1,-1,4; -1,3,-1,-1,5,6,5,-1,-1,4; -1,-1,-1,7,-1,-1,-1,7,-1,5; -1,4,-1,-1,6,-1,6,-1,5,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,1,1,0,0,0,0,0,0; 0,1,0,0,1,0,0,0,0,0; 1,1,0,0,1,0,0,0,0,0; 0,0,1,0,1,0,0,1,0,1; 0,1,0,1,1,1,1,0,1,1; 0,1,0,1,0,1,0,1,0,1; 0,1,0,0,1,1,1,0,0,1; 0,0,1,0,0,0,0,0,1,1; 0,0,1,1,1,1,1,1,1,0; 1,1,1,1,1,1,1,1,1,1];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,-1,4,-1,-1,4,-1,6,-1,5,4,-1,-1,1; -1,4,-1,-1,-1,-1,-1,-1,-1,7,-1,-1,-1,-1,-1; -1,-1,4,-1,-1,0,1,-1,4,-1,5,-1,6,-1,-1; 4,-1,-1,0,-1,0,-1,3,-1,-1,4,-1,5,-1,4; -1,-1,1,-1,-1,2,-1,3,5,4,-1,4,5,-1,-1; -1,2,-1,-1,3,-1,5,-1,-1,5,5,5,-1,-1,-1; -1,-1,1,2,-1,5,-1,3,4,-1,-1,-1,-1,-1,5; -1,0,0,1,-1,-1,5,-1,6,-1,7,-1,6,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,5,5,-1,-1,6,-1,-1; -1,0,-1,-1,4,-1,6,-1,-1,-1,6,-1,7,-1,-1; -1,-1,-1,-1,-1,8,-1,8,7,-1,-1,-1,7,-1,3; -1,-1,5,-1,7,-1,8,-1,7,7,-1,-1,5,-1,-1; -1,2,-1,8,-1,8,-1,-1,-1,6,5,-1,-1,-1,5; -1,1,-1,5,-1,5,-1,3,-1,-1,5,-1,3,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,3,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,1,1,1,1,1,1,1,0,0,0; 0,1,1,1,0,0,0,1,1,1,1,0,1,1,0; 1,1,0,0,0,0,0,0,0,0,1,1,0,1,1; 1,0,0,0,0,0,0,0,1,0,0,1,1,0,1; 1,0,0,0,0,0,0,1,1,1,0,0,1,0,1; 0,1,0,1,1,1,0,0,0,1,0,0,1,1,1; 0,0,0,0,0,1,1,1,0,1,1,1,1,0,1; 0,0,0,0,1,0,0,1,0,0,1,1,0,1,1; 0,0,0,0,0,0,0,1,1,1,1,0,1,1,0; 0,0,0,0,1,1,1,0,1,0,0,1,1,0,0; 0,0,0,1,0,1,1,1,1,1,1,1,1,1,0; 0,0,1,1,1,1,1,1,1,1,1,0,1,1,1; 0,0,1,1,1,0,1,1,0,0,1,0,0,0,1; 0,0,0,1,1,1,1,0,0,1,1,0,0,1,1; 0,0,0,0,0,0,0,0,0,1,0,1,0,1,0];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [0,-1,-1,4,3,2,1,-1,-1,-1,-1,-1,3,-1,-1; -1,-1,5,-1,-1,4,-1,-1,4,4,-1,-1,-1,-1,3; -1,5,4,5,4,5,5,-1,5,3,-1,1,2,-1,3; 4,-1,-1,-1,4,-1,-1,4,2,-1,1,-1,-1,-1,-1; -1,-1,5,4,-1,2,2,-1,1,0,-1,-1,7,5,-1; -1,-1,-1,5,-1,-1,0,-1,-1,-1,-1,4,5,-1,2; 4,-1,-1,5,4,2,0,0,-1,-1,-1,5,6,-1,-1; 5,-1,-1,6,5,-1,-1,-1,-1,-1,3,3,3,-1,3; -1,-1,5,-1,5,3,-1,-1,-1,-1,-1,-1,3,-1,-1; 5,-1,-1,6,5,-1,3,5,-1,6,-1,-1,0,-1,0; -1,-1,5,-1,4,3,2,4,5,-1,4,-1,-1,1,-1; -1,7,-1,-1,5,-1,-1,1,-1,5,5,5,-1,-1,-1; -1,-1,6,4,4,4,3,1,2,4,-1,-1,6,4,-1; -1,5,-1,6,-1,-1,-1,-1,-1,4,6,-1,-1,-1,-1; -1,-1,-1,-1,-1,-1,3,2,0,-1,4,4,3,-1,2];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,0,0,0,0,0,1,1,1,1,0; 0,0,1,0,1,0,0,1,1,1,0,0,0,0,1; 1,1,1,1,0,1,1,0,1,0,0,0,0,0,1; 1,0,0,0,1,0,1,1,0,0,0,0,1,1,0; 0,1,1,1,0,0,0,0,0,0,0,1,1,0,0; 0,1,0,1,0,0,0,0,0,0,0,1,1,1,0; 1,1,1,0,1,0,0,0,0,0,0,0,0,0,1; 1,0,0,1,1,0,0,0,0,0,1,1,1,1,1; 1,1,1,0,0,1,0,0,1,1,0,0,0,0,0; 1,0,0,1,0,0,1,1,1,1,0,0,0,0,0; 1,1,1,1,1,0,0,0,1,0,1,0,0,0,0; 1,1,0,0,1,0,0,0,0,1,0,1,1,0,0; 0,1,1,1,0,1,0,0,0,1,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,0,1,1,0,1,0; 0,0,1,1,1,0,1,0,0,0,1,1,0,0,1];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,-1,-1,-1,-1,-1,-1,0,-1; -1,8,8,-1,2,-1,0,-1,-1,-1; 5,-1,8,-1,-1,-1,-1,-1,-1,-1; -1,-1,-1,-1,-1,2,-1,-1,-1,2; 1,-1,-1,-1,4,5,6,-1,-1,-1; -1,0,-1,-1,-1,7,9,-1,-1,6; -1,-1,-1,6,-1,-1,9,-1,-1,6; -1,-1,6,6,8,7,8,7,-1,5; -1,4,-1,6,6,6,-1,6,-1,4; -1,-1,-1,-1,-1,-1,3,-1,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 0,1,1,0,0,0,0,0,0,0; 0,0,0,0,0,1,1,1,1,1; 0,0,0,1,1,1,1,1,1,1; 0,0,0,1,0,1,1,1,1,1; 0,1,1,1,1,1,1,1,1,1; 0,1,0,0,1,1,0,1,0,1; 0,0,1,0,0,0,1,0,1,0];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,2,3,-1,-1,0,-1,-1,-1,-1; -1,-1,-1,-1,3,-1,2,-1,-1,6; -1,-1,5,-1,5,3,-1,5,7,4; -1,4,-1,5,-1,5,-1,6,-1,3; -1,-1,4,-1,5,-1,6,-1,-1,3; -1,-1,-1,2,-1,5,-1,-1,-1,-1; 4,-1,1,-1,-1,-1,1,1,-1,-1; 4,-1,1,-1,-1,-1,1,-1,4,-1; -1,-1,-1,-1,6,-1,-1,-1,-1,4; -1,4,4,-1,-1,-1,-1,4,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,1,1; 0,0,0,1,0,0,0,1,1,1; 0,0,1,1,1,0,0,1,1,1; 0,1,1,0,1,1,0,1,0,0; 0,1,0,1,0,1,1,1,1,0; 1,1,0,0,1,1,0,0,1,1; 1,0,0,0,1,0,0,0,0,1;  1,0,0,0,1,0,0,0,0,1; 1,1,0,0,1,1,0,0,1,1; 0,1,1,1,1,1,1,1,1,0];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,3,3,-1,-1,-1,-1,-1,-1; 3,-1,-1,-1,-1,-1,0,-1,0,-1; -1,-1,3,4,-1,3,-1,-1,-1,-1; 3,-1,4,-1,-1,-1,-1,3,-1,-1; 2,3,-1,5,-1,4,4,-1,-1,4; -1,-1,5,4,6,6,-1,4,-1,4; -1,-1,-1,-1,-1,3,3,-1,-1,4; -1,3,-1,-1,5,6,5,-1,-1,4; -1,-1,-1,7,-1,-1,-1,7,-1,5; -1,4,-1,-1,6,-1,6,-1,5,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,1,1,0,0,0,0,0,0; 0,1,0,0,1,0,0,0,0,0; 1,1,0,0,1,0,0,0,0,0; 0,0,1,0,1,0,0,1,0,1; 0,1,0,1,1,1,1,0,1,1; 0,1,0,1,0,0,0,1,0,1; 0,1,0,0,1,1,1,0,0,1; 0,0,1,0,0,0,0,0,1,1; 0,0,1,1,1,1,1,1,1,0; 1,1,1,1,1,1,1,1,1,1];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,-1,4,-1,-1,4,-1,6,-1,5,4,-1,-1,1; -1,4,-1,-1,-1,-1,-1,-1,-1,7,-1,-1,-1,-1,-1; -1,-1,4,-1,-1,0,1,-1,4,-1,5,-1,6,-1,-1; 4,-1,-1,0,-1,0,-1,3,-1,-1,4,-1,5,-1,4; -1,-1,1,-1,-1,2,-1,3,5,4,-1,4,5,-1,-1; -1,2,-1,-1,3,-1,5,-1,-1,5,5,5,-1,-1,-1; -1,-1,1,2,-1,5,-1,3,4,-1,-1,-1,-1,-1,5; -1,0,0,1,-1,-1,5,-1,6,-1,7,-1,6,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,5,5,-1,-1,6,-1,-1; -1,0,-1,-1,4,-1,6,-1,-1,-1,6,-1,7,-1,-1; -1,-1,-1,-1,-1,8,-1,8,7,-1,-1,-1,7,-1,3; -1,-1,5,-1,7,-1,8,-1,7,7,-1,-1,5,-1,-1; -1,2,-1,8,-1,8,-1,-1,-1,6,5,-1,-1,-1,5; -1,1,-1,5,-1,5,-1,3,-1,-1,5,-1,3,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,3,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,1,1,1,1,1,1,1,0,0,0; 0,1,1,1,0,0,0,1,1,1,1,0,1,1,0; 1,1,0,0,0,0,0,0,0,0,1,1,0,1,1; 1,0,0,0,0,0,0,0,1,0,0,1,1,0,1; 1,0,0,0,0,0,0,1,1,1,0,0,1,0,1; 0,1,0,0,1,1,0,0,0,1,0,0,1,1,1; 0,0,0,0,0,1,1,1,0,1,1,1,1,0,1; 0,0,0,0,1,0,0,1,0,0,1,1,0,1,1; 0,0,0,0,0,0,0,1,1,1,1,0,1,1,0; 0,0,0,0,1,1,1,0,1,0,0,1,1,0,0; 0,0,0,1,0,1,1,1,1,1,1,1,1,1,0; 0,0,1,1,1,1,1,1,1,1,1,0,1,1,1; 0,0,1,1,1,0,1,1,0,0,1,0,0,0,1; 0,0,0,1,1,1,1,0,0,1,1,0,0,1,1; 0,0,0,0,0,0,0,0,0,1,0,1,0,1,0];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-02-25T02:55:09.000Z","updated_at":"2025-12-31T18:50:57.000Z","published_at":"2015-02-25T02:55:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/fill-a-pix/rules\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFill-a-pix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle game similar to Pic-a-Pix (aka Logic art) and Minesweeper. An example starting board is shown below on the left with the completed board shown to its right. Each number in the board indicates how many surrounding cells, including itself, are to be filled in. There are (up to) nine total cells associated with each number: four immediately adjacent (up, down, left, and right), four diagonally adjacent (one touching each corner), and the central cell (where the number is located).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased on this logic, all 0's and 9's are determinant, as all nine cells are either empty or filled, respectively, for these numbers. Also, all 6's on the board edges and 4's in the corners should be completely filled in, as they only involve 6 or 4 cells, respectively.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem, you will be provided with various boards and solutions to each board. Write a function to determine if the solution is correct for the given board. The board will be filled with NaN's where there are no number clues; these cells should not be checked. The solution board will be filled with 1's (filled) and 0's (empty).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA related problem is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/3043-fill-a-pix-solver-basic\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFill-a-pix - Solver (basic)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"},{\"partUri\":\"/media/image2.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"}]}"},{"id":44757,"title":"Lights Out 6 - 5x5, 13 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require 13 moves to solve. For example, if\r\n\r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\n\r\nan answer is:\r\n\r\n moves = [1:5 8 13 18 21:25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44756 5x5, 10 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44759-lights-out-7-5x5-x-moves 5x5, x moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require 13 moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\u003c/pre\u003e\u003cp\u003ean answer is:\u003c/p\u003e\u003cpre\u003e moves = [1:5 8 13 18 21:25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44756\"\u003e5x5, 10 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44759-lights-out-7-5x5-x-moves\"\u003e5x5, x moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_6(board) % 5x5 board, 13 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_6(board); % [1:5 8 13 18 21:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          0 1 0 1 0  \r\n          0 1 1 1 0  \r\n          0 0 0 0 0  \r\n          1 1 1 0 0];\r\nmoves = lights_out_6(board); % [1:13]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_6(board); % [1:2:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 1 1 1 0  \r\n          1 1 0 0 0  \r\n          1 1 0 1 0  \r\n          0 1 0 0 1  \r\n          1 0 1 1 0];\r\nmoves = lights_out_6(board); % [1:3 6 8:11 16:19 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 0 1 1 1  \r\n          0 0 0 0 1  \r\n          0 0 1 0 1  \r\n          1 0 0 0 0];\r\nmoves = lights_out_6(board); % [1 4 6:9 12:16 19 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          0 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 1 0 0  \r\n          0 1 0 1 1];\r\nmoves = lights_out_6(board); % [1 3 9 11 14 16 19 20:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_6(board); % [1:2 4:6 10 13 16 20:22 24:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          1 1 1 0 1  \r\n          1 0 1 0 1  \r\n          1 0 1 1 1  \r\n          1 1 1 0 0];\r\nmoves = lights_out_6(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 1 0 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 1 1  \r\n          0 0 0 1 1];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          1 1 0 1 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 0 0 1];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          0 0 0 0 0  \r\n          0 1 1 1 0  \r\n          0 1 0 1 0  \r\n          0 1 1 0 1];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 0 0 1 0  \r\n          0 1 1 0 0  \r\n          0 1 0 1 1  \r\n          1 1 1 0 0];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":"2018-11-15T13:42:34.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T19:15:14.000Z","updated_at":"2025-11-29T13:54:32.000Z","published_at":"2018-11-15T13:38:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require 13 moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 1 0  \\n          1 0 1 0 1  \\n          0 1 1 1 0  \\n          1 0 1 0 1  \\n          0 1 0 1 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ean answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1:5 8 13 18 21:25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44756\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 10 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44759-lights-out-7-5x5-x-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45329,"title":"Castling-01","description":"Given the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Castling\u003e","description_html":"\u003cp\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Castling\"\u003ehttps://en.wikipedia.org/wiki/Castling\u003c/a\u003e\u003c/p\u003e","function_template":"function y = castling_01(a)\r\n  y = x;\r\nend","test_suite":"%%\r\na={'Ra1','Ka7'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rh1'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rh1','Kh8'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rc1','Kh5'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rh1','Ke1'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Ra1','Rh1','Ke1'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rh1','Ke1'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rh5','Ke5'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra8','Ke8'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rd8','Rh8','Ke8'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rh8','Kd8'}\r\nassert(isequal(castling_01(a),0))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":"2020-02-15T23:25:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-15T12:59:18.000Z","updated_at":"2026-01-23T13:34:49.000Z","published_at":"2020-02-15T23:08:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Castling\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Castling\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44709,"title":"Toads and Frogs Puzzle","description":"On a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\r\n\r\n      T T T T X F F F\r\n\r\nToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\r\n\r\nWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:  \r\n\r\n     F F F X T T T T\r\n\r\n*ALGORITHM:* To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\r\n\r\n*ILLUSTRATION:* Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\r\n\r\n T T X F\r\n T X T F   Slide\r\n T F T X   Jump\r\n T F X T   Slide\r\n X F T T   Jump\r\n F X T T   Slide\r\n\r\nHence, a total of five moves is required for n = 2 toads and m = 1 frog.","description_html":"\u003cp\u003eOn a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\u003c/p\u003e\u003cpre\u003e      T T T T X F F F\u003c/pre\u003e\u003cp\u003eToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\u003c/p\u003e\u003cp\u003eWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:\u003c/p\u003e\u003cpre\u003e     F F F X T T T T\u003c/pre\u003e\u003cp\u003e\u003cb\u003eALGORITHM:\u003c/b\u003e To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\u003c/p\u003e\u003cp\u003e\u003cb\u003eILLUSTRATION:\u003c/b\u003e Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\u003c/p\u003e\u003cpre\u003e T T X F\r\n T X T F   Slide\r\n T F T X   Jump\r\n T F X T   Slide\r\n X F T T   Jump\r\n F X T T   Slide\u003c/pre\u003e\u003cp\u003eHence, a total of five moves is required for n = 2 toads and m = 1 frog.\u003c/p\u003e","function_template":"function moves = ToadsFrogs(n,m)\r\n  \r\nend","test_suite":"%%\r\nassert(isequal(ToadsFrogs(0,0),0))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(1,1),3))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(3,4),19))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(2,7),23))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(4,6),34))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(8,3),35))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(4,8),44))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(7,6),55))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(5,9),59))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(9,7),79))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":178544,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2018-09-07T17:35:43.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-08-03T12:58:09.000Z","updated_at":"2026-01-20T13:33:38.000Z","published_at":"2018-08-03T13:42:48.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOn a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[      T T T T X F F F]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     F F F X T T T T]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eALGORITHM:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eILLUSTRATION:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ T T X F\\n T X T F   Slide\\n T F T X   Jump\\n T F X T   Slide\\n X F T T   Jump\\n F X T T   Slide]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHence, a total of five moves is required for n = 2 toads and m = 1 frog.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60551,"title":"Jigsaw 002: Intro 2x2 square. Local Cody images","description":"This challenge is to re-assemble camerman.tif and other grayscale images from four 128x128 pieces into a 256x256 image. The proposed method is best fitting edges to line m=1 and b=0 for 100, 90, and 80 percentiles.\r\nJigsaw001 was to show how data fitting can achieve results but is not a good general method. Tim used an elegant anonymous function of f=@(u)norm(diff(u))/std(mean(u)); along with a couple other methods incorporated into Jigsaw002.\r\nJigsaw is similar to the real world task of In-Scene-Calibration of a scanning sensor which was best solved by smoothing and usage of RobustFit. The template tries to simulate robustfit with edge smoothing to account for gradients\r\n\r\nThe pointer layout of the image is [1 3; 2 4]. Return a four value vector that remaps the scrambled image into an original form. The displayed scramble is [2 4 1 3] making the solution [3 1 4 2].The four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\r\nAssertion is adjacent Chan and Row pairs will best match m=1 and b=0 for 100P,90P,80P of error.\r\nUsing 100/90/80p gives 88% of chans having \u003e95% valid adjacent channel determination. Using 90/70/50p gives 93% of chans having \u003e95% valid adjacent channel determination. Bordered images fail spectacularly.\r\nScoring function 10*abs(m-1)+abs(b/10) used at 100/90/80p with 4 out of 6 voting to determine best. Tie 3/3 leads to a best mean comparison.\r\nSeparation of Vertical and Horizontal performances is consequential. This was also gleaned from Tim.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 841px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 420.5px; transform-origin: 407px 420.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 366.5px 8px; transform-origin: 366.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to re-assemble camerman.tif and other grayscale images from four 128x128 pieces into a 256x256 image. The proposed method is best fitting edges to line m=1 and b=0 for 100, 90, and 80 percentiles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 361.5px 8px; transform-origin: 361.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eJigsaw001 was to show how data fitting can achieve results but is not a good general method. Tim used an elegant anonymous function of f=@(u)norm(diff(u))/std(mean(u)); along with a couple other methods incorporated into Jigsaw002.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377px 8px; transform-origin: 377px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eJigsaw is similar to the real world task of In-Scene-Calibration of a scanning sensor which was best solved by smoothing and usage of RobustFit. The template tries to simulate robustfit with edge smoothing to account for gradients\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 222.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 111.25px; text-align: left; transform-origin: 384px 111.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe pointer layout of the image is [1 3; 2 4]. Return a four value vector that remaps the scrambled image into an original form. The displayed scramble is [2 4 1 3] making the solution [3 1 4 2].The four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 305.5px 8px; transform-origin: 305.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAssertion is adjacent Chan and Row pairs will best match m=1 and b=0 for 100P,90P,80P of error.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eUsing 100/90/80p gives 88% of chans having \u0026gt;95% valid adjacent channel determination. Using 90/70/50p gives 93% of chans having \u0026gt;95% valid adjacent channel determination. Bordered images fail spectacularly.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eScoring function 10*abs(m-1)+abs(b/10) used at 100/90/80p with 4 out of 6 voting to determine best. Tie 3/3 leads to a best mean comparison.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 318px 8px; transform-origin: 318px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSeparation of Vertical and Horizontal performances is consequential. This was also gleaned from Tim.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 222.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 111.25px; text-align: left; transform-origin: 384px 111.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" alt=\"Adj Scatters\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = jigsaw002(pc,nr,nc,pnr,pnc)\r\n %Brute force try all puzzle map permutations \r\n %Try all piece permutations and score the Horiz/Vertical piece edges separately\r\n %RobustFit like scoring with 100p 90p and 80p of edge vs interpolated deltas\r\n \r\n  %mpathc used to display progression of processing\r\n  mpathc = mat2cell(zeros(256*16, 256), ones(1,16)*256, 256); % 16 cells of 256x256 zeros\r\n  ptrc=0;\r\n  \r\n  best_score=inf(1,6);\r\n  for p=perms(1:4)'  %Tim\r\n   m=cell2mat(reshape(pc(p),2,2)); %Tim\r\n   mpv=[(m(:,pnc-1)+m(:,pnc+1))/2 m(:,pnc)];\r\n   mph=[(m(pnr-1,:)+m(pnr+1,:))/2; m(pnr,:)]';\r\n   mpves=create_sort(mpv);\r\n   mphes=create_sort(mph);\r\n   \r\n   score_setv=calc_score_set(mpves);\r\n   score_seth=calc_score_set(mphes);\r\n   scores=[score_setv score_seth]; % set of 6 scores\r\n    \r\n   if nnz(best_score-scores\u003e0)\u003e3  % 4/6 or 5/6 or 6/6 best_score\u003cscore  100p 90p 80p\r\n    v=p;\r\n    best_score=scores;\r\n    fprintf('New Best 4 score VH');fprintf(' %6.3f',scores);fprintf('\\n')\r\n    ptrc=ptrc+1;\r\n    mpathc{ptrc}=m;\r\n   elseif nnz(best_score-scores\u003e0)==3 % Tie  Choose lowest mean\r\n    if sum(scores)\u003csum(best_score) % could select 90s and 80s only\r\n     v=p;\r\n     best_score=scores;\r\n     fprintf('New Best 3 score VH');fprintf(' %6.3f',scores);fprintf('\\n')\r\n     ptrc=ptrc+1;\r\n     mpathc{ptrc}=m;\r\n    end\r\n   end\r\n   \r\n  end % p perms 1:nr*nc\r\n  \r\n  %Displaying images for various New Best configurations\r\n  if ptrc\u003c5\r\n   mpathf=cell2mat(reshape(mpathc(1:4),2,2));\r\n  elseif ptrc\u003c10\r\n   mpathf=cell2mat(reshape(mpathc(1:9),3,3));\r\n  else % hope for 16 or less\r\n   mpathf=cell2mat(reshape(mpathc(1:16),4,4));\r\n  end\r\n  figure;imagesc(mpathf);axis equal;colormap gray; %Display Path to solution\r\n  \r\nend %jigsaw002\r\n\r\nfunction mpes=create_sort(a)\r\n% a matrix [L,2]\r\n mpe=[a abs(diff(a,[],2))]; % Create error column\r\n mpes=sortrows(mpe,-3); % Descending sort of deltas\r\nend % create_sort\r\n\r\nfunction score_set=calc_score_set(mpes)\r\n% calc scores at 100p 90p 80p for a 256 sample matrix\r\n% score 10*abs(m-1)+abs(b)/10\r\n% score_set [1,3]\r\n warning('off','all'); % Some puzzles are poorly conditioned\r\n  mb100 = polyfit(mpes(:,1),mpes(:,2),1); % 256 samples\r\n  mb90 = polyfit(mpes(26:end,1),mpes(26:end,2),1);\r\n  mb80 = polyfit(mpes(52:end,1),mpes(52:end,2),1);\r\n  score_set=[(abs([mb100;mb90;mb80]-[1 0]))*[10;.1]]';\r\nend % calc_score_set","test_suite":"%%\r\n% all imdata 2019 are hosted for cody at https://drive.google.com/drive/folders/1TZkBMEEKHiFJExqVoJgj5VVeHbvOfTYB\r\n% a Text file of matlab urlwrite links will be added in the future\r\n\r\n%To access matlab cody local image folder may be accomplished using cd\r\n%jigsaw002_pwd=pwd;\r\n%dir\r\n\r\ncd (fullfile(matlabroot,'toolbox/images/imdata/'));\r\n\r\nmc=double(imread('cameraman.tif'));\r\n%fprintf('Cameraman size %i %i\\n',size(mc))\r\n%dir *.tif  %*.jpg  *.png\r\n%figure;imshow('cameraman.tif') % valid\r\n\r\n%dir *.mat\r\n%fnmat='imdemos.mat'; %trees cellsequence contours mristack\r\n%fnmat='trees.mat';\r\n%whos('-file',fnmat) %mat file contents name nrxnc size type\r\n%load(fnmat)\r\n\r\n%Possible files to use from imdemos.mat\r\n%  mc=double(circuit4); %\r\n% mc=double(coins2); %\r\n%  mc=double(rice3); %\r\n% mc=double(circles); %Bad image with top/bot black\r\n% mc=double(eight); %\r\n% mc=double(glass2); %\r\n%  mc=double(liftbody256); %\r\n% mc=double(saturn2); %image not great\r\n% mc=double(vertigo2); % Pass 100/80/60  Fails 100/90/80\r\n\r\nnr=2;nc=2;\r\npnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\n%jigsaws=[jpc{1} jpc{3};jpc{2} jpc{4}];\r\njigsaws=cell2mat(reshape(pc(vperm),2,2));  % scrambled image\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n%jigsawf=[jpc{v(1)} jpc{v(3)};jpc{v(2)} jpc{v(4)}];\r\n\r\nfigure;imagesc(jigsawf);colormap gray %Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(circuit4);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(liftbody256);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(coins2);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(rice3);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-06-26T13:41:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-18T22:29:32.000Z","updated_at":"2025-04-23T19:19:12.000Z","published_at":"2024-06-26T13:41:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to re-assemble camerman.tif and other grayscale images from four 128x128 pieces into a 256x256 image. The proposed method is best fitting edges to line m=1 and b=0 for 100, 90, and 80 percentiles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJigsaw001 was to show how data fitting can achieve results but is not a good general method. Tim used an elegant anonymous function of f=@(u)norm(diff(u))/std(mean(u)); along with a couple other methods incorporated into Jigsaw002.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJigsaw is similar to the real world task of In-Scene-Calibration of a scanning sensor which was best solved by smoothing and usage of RobustFit. The template tries to simulate robustfit with edge smoothing to account for gradients\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe pointer layout of the image is [1 3; 2 4]. Return a four value vector that remaps the scrambled image into an original form. The displayed scramble is [2 4 1 3] making the solution [3 1 4 2].The four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssertion is adjacent Chan and Row pairs will best match m=1 and b=0 for 100P,90P,80P of error.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUsing 100/90/80p gives 88% of chans having \u0026gt;95% valid adjacent channel determination. Using 90/70/50p gives 93% of chans having \u0026gt;95% valid adjacent channel determination. Bordered images fail spectacularly.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eScoring function 10*abs(m-1)+abs(b/10) used at 100/90/80p with 4 out of 6 voting to determine best. Tie 3/3 leads to a best mean comparison.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSeparation of Vertical and Horizontal performances is consequential. This was also gleaned from Tim.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Adj Scatters\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image2.png\",\"relationshipId\":\"rId2\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image3.png\",\"relationshipId\":\"rId3\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null},{\"partUri\":\"/media/image3.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45330,"title":"Castling-02","description":"This is a follow up of problem \r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\u003e\r\n\r\n\r\nGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Castling\u003e","description_html":"\u003cp\u003eThis is a follow up of problem\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\u003c/a\u003e\u003c/p\u003e\u003cp\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Castling\"\u003ehttps://en.wikipedia.org/wiki/Castling\u003c/a\u003e\u003c/p\u003e","function_template":"function tf=castling_02(a)\r\n  y = x;\r\nend","test_suite":"%%\r\na={'Rd1','Rh1','Ke1'}\r\nassert(isequal(castling_02(a),1))\r\n%%\r\na={'Rd1','Rd4','Ke1','Bb6'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'d1','Rh8','Kg1'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'Ra1','Rh1','Ke1','Qd1'}\r\nassert(isequal(castling_02(a),1))\r\n%%\r\na={'Ra1','Rh1','Ke1','Qd1','Bg1'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'Ba2','Ra1','Rb1','Ke1','Bg1'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'Na1','Rh1','Ke1','Qd1'}\r\nassert(isequal(castling_02(a),1))\r\n%%\r\na={'Kd1','Qe1','Rh1','Rd8','a4','Nf2'}\r\nassert(isequal(castling_02(a),0))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-15T23:34:53.000Z","updated_at":"2026-01-23T13:53:26.000Z","published_at":"2020-02-15T23:38:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a follow up of problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Castling\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Castling\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45247,"title":"Tell your secret","description":"A secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\r\n\r\n* z(1) -- each person can continue spreading the secret\r\n* z(2) -- each person can tell the secret to only two persons\r\n\r\nn.b. outputs can only be multiples of 5. ","description_html":"\u003cp\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\u003c/p\u003e\u003cul\u003e\u003cli\u003ez(1) -- each person can continue spreading the secret\u003c/li\u003e\u003cli\u003ez(2) -- each person can tell the secret to only two persons\u003c/li\u003e\u003c/ul\u003e\u003cp\u003en.b. outputs can only be multiples of 5.\u003c/p\u003e","function_template":"function t = puzzle_tell_ur_secret(n)","test_suite":"%%\r\nn = 1;\r\nz = [0,0];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 2;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 8;\r\nz = [10,15];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 20;\r\nz = [15,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 31;\r\nz = [20,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 64;\r\nz = [20,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100;\r\nz = [25,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 768;\r\nz = [35,45];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3000;\r\nz = [40,55];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100000;\r\nz = [55,80];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2019-12-27T00:41:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-12-27T00:10:25.000Z","updated_at":"2026-03-13T11:59:30.000Z","published_at":"2019-12-27T00:41:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons? Output will be for two scenarios -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(1) -- each person can continue spreading the secret\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(2) -- each person can tell the secret to only two persons\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en.b. outputs can only be multiples of 5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42769,"title":"GJam March 2016 IOW: Cody's Jams ","description":"This Challenge is derived from \u003chttp://code.google.com/codejam/contest/8274486/dashboard GJam March 2016 Annual I/O for Women Cody's Jam\u003e. This is a mix of the small and large data sets.\r\n\r\nThe GJam story goes that a store is having a 25% off sale and ordered original and sale price labels. Unfortunately the labels came commingled in increasing numeric value. Find the sale prices.\r\n\r\n*Input:* m , Vector length N\u003c=100 with values \u003c=10^9.\r\n\r\n*Output:* v , Vector containing the Sale price tags\r\n\r\n*Examples:* [m] [v]\r\n\r\n  [15 20 60 75 80 100] creates v=[15 60 75]\r\n  [9 9 12 12 12 15 16 20] creates v=[9 9 12 15]\r\n \r\n\r\n*Google Code Jam 2016 Open Qualifier: April 8, 2016*\r\n\r\nComplete Code Jam Input/Output included in Test Suite.\r\nThe women's competition had 500 entrants. The qualifier winner was USA Stacy992 using Java. The top 60 show strength in USA, Russia, China, and South Korea under \u003chttp://code.google.com/codejam/contest/8274486/scoreboard?c=8274486# Contest Dashboard\u003e. ","description_html":"\u003cp\u003eThis Challenge is derived from \u003ca href = \"http://code.google.com/codejam/contest/8274486/dashboard\"\u003eGJam March 2016 Annual I/O for Women Cody's Jam\u003c/a\u003e. This is a mix of the small and large data sets.\u003c/p\u003e\u003cp\u003eThe GJam story goes that a store is having a 25% off sale and ordered original and sale price labels. Unfortunately the labels came commingled in increasing numeric value. Find the sale prices.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m , Vector length N\u0026lt;=100 with values \u0026lt;=10^9.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e v , Vector containing the Sale price tags\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e [m] [v]\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e[15 20 60 75 80 100] creates v=[15 60 75]\r\n[9 9 12 12 12 15 16 20] creates v=[9 9 12 15]\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eGoogle Code Jam 2016 Open Qualifier: April 8, 2016\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComplete Code Jam Input/Output included in Test Suite.\r\nThe women's competition had 500 entrants. The qualifier winner was USA Stacy992 using Java. The top 60 show strength in USA, Russia, China, and South Korea under \u003ca href = \"http://code.google.com/codejam/contest/8274486/scoreboard?c=8274486#\"\u003eContest Dashboard\u003c/a\u003e.\u003c/p\u003e","function_template":"function v=CodyJams(m)\r\n% m is increasing value vector of length 2L\r\n% v is length L vector of Sale Price Tags\r\n v=[];\r\nend","test_suite":"%%\r\nm=[15 20 60 75 80 100 ];\r\nv=CodyJams(m);\r\nvexp=[15 60 75 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[9 9 12 12 12 15 16 20 ];\r\nv=CodyJams(m);\r\nvexp=[9 9 12 15 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[480 640 1047 1396 1638 2184 2481 3308 ];\r\nv=CodyJams(m);\r\nvexp=[480 1047 1638 2481 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[495 660 1953 2559 2604 2685 3412 3580 ];\r\nv=CodyJams(m);\r\nvexp=[495 1953 2559 2685 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[384 512 1005 1340 2037 2716 2973 3964 ];\r\nv=CodyJams(m);\r\nvexp=[384 1005 2037 2973 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[270624780 336144033 360833040 448192044 736130808 745857189 981507744 994476252 ];\r\nv=CodyJams(m);\r\nvexp=[270624780 336144033 736130808 745857189 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[147 196 267 330 356 440 810 1080 ];\r\nv=CodyJams(m);\r\nvexp=[147 267 330 810 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[222 296 1533 1767 2044 2356 2541 3388 ];\r\nv=CodyJams(m);\r\nvexp=[222 1533 1767 2541 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[450 600 804 1072 1137 1497 1516 1996 ];\r\nv=CodyJams(m);\r\nvexp=[450 804 1137 1497 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[1788 2384 2697 2967 2991 3596 3956 3988 ];\r\nv=CodyJams(m);\r\nvexp=[1788 2697 2967 2991 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[4131 5508 7344 7803 8397 8667 9792 10404 11196 11556 13872 14553 14742 14928 15408 17631 18496 19404 19656 19904 20544 20925 21816 23508 23598 23949 25872 26208 27900 29088 31344 31464 31932 34496 34944 37200 38784 41792 41952 42576 49600 51712 55936 56768 7440174 8148762 8601471 8837667 9290376 9664353 9920232 9920232 10865016 10865016 11468628 11468628 11783556 11783556 12387168 12387168 12885804 12885804 13187610 13226976 13226976 13443489 14309541 14486688 14486688 15291504 15291504 15711408 15711408 16516224 16516224 17181072 17181072 17583480 17583480 17635968 17635968 17924652 17924652 19079388 19079388 19315584 19315584 20388672 20388672 20948544 20948544 22021632 22021632 22908096 22908096 23444640 23444640 23514624 23514624 23899536 23899536 25439184 25439184 25754112 25754112 27184896 27184896 27931392 29362176 29362176 30544128 30544128 31259520 31259520 31352832 31352832 31866048 31866048 33918912 33918912 34338816 34338816 36246528 36246528 39149568 39149568 40725504 40725504 41679360 41679360 41803776 41803776 42488064 42488064 45225216 45225216 45785088 45785088 48328704 48328704 52199424 52199424 54300672 54300672 55572480 55572480 55738368 55738368 56650752 56650752 60300288 60300288 61046784 61046784 64438272 64438272 69599232 69599232 72400896 72400896 74096640 74096640 74317824 74317824 75534336 75534336 80400384 80400384 81395712 81395712 85917696 85917696 92798976 92798976 96534528 96534528 98795520 98795520 99090432 100712448 100712448 107200512 107200512 108527616 108527616 114556928 123731968 128712704 131727360 131727360 134283264 134283264 142934016 142934016 144703488 144703488 175636480 179044352 190578688 192937984 ];\r\nv=CodyJams(m);\r\nvexp=[4131 7344 7803 8397 8667 13872 14553 14742 14928 15408 17631 20925 21816 23598 23949 25872 26208 31344 37200 38784 41952 42576 7440174 8148762 8601471 8837667 9290376 9664353 9920232 10865016 11468628 11783556 12387168 12885804 13187610 13226976 13443489 14309541 14486688 15291504 15711408 16516224 17181072 17583480 17635968 17924652 19079388 19315584 20388672 20948544 22021632 22908096 23444640 23514624 23899536 25439184 25754112 27184896 29362176 30544128 31259520 31352832 31866048 33918912 34338816 36246528 39149568 40725504 41679360 41803776 42488064 45225216 45785088 48328704 52199424 54300672 55572480 55738368 56650752 60300288 61046784 64438272 69599232 72400896 74096640 74317824 75534336 80400384 81395712 85917696 92798976 96534528 98795520 100712448 107200512 108527616 131727360 134283264 142934016 144703488 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[47652765 63537020 67915215 69349602 72816921 73024614 82232592 90005040 90280239 90553620 92466136 94414812 97089228 97366152 103779192 109643456 111106431 111164235 120006720 120373652 125886416 135665742 136205178 138372256 138890175 141176913 142430418 148141908 148218980 151625514 165274734 180887656 181606904 185186900 186382386 188235884 189907224 191652879 197100096 200131203 202167352 220366312 228636354 230018664 233064210 248509848 252322995 253203684 255537172 262800128 266522562 266752464 266841604 276629997 284197221 289714236 292147407 298051953 304848472 306691552 310752280 315722328 323249112 326482563 331581687 336430660 337604912 341668035 343831008 350075772 355363416 355669952 368839996 377457843 378929628 380744748 386285648 389529876 394930203 397402604 398376315 398670747 400335744 403277814 417169314 420741072 420963104 429730527 430998816 431599908 435310084 437438880 442108916 446485650 455557380 456522042 456986763 458441344 458579964 462594018 466767696 473333373 473409816 498541914 500694354 502642542 502680435 503277124 507659664 526573604 531168420 531512730 531560996 533780992 537703752 554512398 556085664 556225752 559312521 560988096 572974036 575466544 576554121 582854961 583251840 595314200 603269448 608696056 609315684 609830100 611439952 616792024 622311657 630908808 631111164 631213088 640087608 662978055 664722552 666189831 667592472 668044893 670190056 670240580 670949520 676510731 678398706 680890563 683321925 683396070 686473173 698833314 701501745 704706354 706326441 708683640 712094160 717827469 719130264 721789842 730229028 732531111 739232499 739349864 740488395 741447552 743207979 745750028 747639918 768738828 777139948 804359264 813106800 829748876 841211744 853450144 883970740 888253108 890726524 894599360 902014308 904531608 907854084 911095900 911194760 915297564 931777752 935335660 939608472 941768588 949458880 957103292 958840352 962386456 973638704 976708148 985643332 987317860 990943972 996853224 ];\r\nv=CodyJams(m);\r\nvexp=[47652765 67915215 69349602 72816921 73024614 82232592 90005040 90280239 94414812 103779192 111106431 111164235 135665742 136205178 138890175 141176913 142430418 151625514 165274734 186382386 191652879 197100096 200131203 228636354 230018664 233064210 252322995 253203684 266522562 266752464 276629997 284197221 289714236 292147407 298051953 315722328 323249112 326482563 331581687 341668035 343831008 350075772 377457843 380744748 394930203 398376315 398670747 400335744 403277814 417169314 420741072 429730527 431599908 437438880 446485650 456522042 456986763 458579964 462594018 473333373 473409816 498541914 500694354 502642542 502680435 531512730 554512398 556085664 559312521 576554121 582854961 603269448 609830100 622311657 630908808 640087608 662978055 666189831 668044893 670949520 676510731 678398706 680890563 683321925 683396070 686473173 698833314 701501745 704706354 706326441 712094160 717827469 719130264 721789842 730229028 732531111 739232499 740488395 743207979 747639918 ];\r\nassert(isequal(vexp,v))\r\n%%\r\n% function GJam_IOW_2016a\r\n% % \r\n% fn='A-large-practice.in';\r\n% %fn='A-small-practice.in';\r\n% [data] = read_file(fn); % create cell array\r\n% \r\n% fidG = fopen('A-large-output.out', 'w');\r\n%  \r\n% tic\r\n% for i=1:size(data,2) % Cell array has N rows of cases\r\n%  v = Rd1A(data{i});\r\n%  m=data{i};\r\n%  \r\n%  fprintf(fidG,'Case #%i:',i);\r\n%  fprintf(fidG,' %i',v);fprintf(fidG,'\\n');\r\n%  fprintf('Case #%i:',i);\r\n%  fprintf(' %i',v);fprintf('\\n');\r\n%  \r\n% end\r\n% toc\r\n% \r\n% fclose(fidG);\r\n% end\r\n% \r\n% function v=Rd1A(m)\r\n%  L=length(m);\r\n%  v=zeros(1,L/2);\r\n%  for i=1:L/2\r\n%   vptr=find(m\u003e0,1,'first');\r\n%   v(i)=m(vptr);\r\n%   m(find(m==round(m(vptr)*4/3),1,'first'))=0;\r\n%   m(vptr)=0;\r\n%  end\r\n% end\r\n% \r\n% \r\n% function [d] = read_file(fn)\r\n% d={};\r\n% fid=fopen(fn);\r\n% fgetl(fid); % Total Count ignore\r\n% ptr=0;\r\n% while ~feof(fid)\r\n%  ptr=ptr+1;\r\n%  fgetl(fid); % Data set countIgnore\r\n%  v=str2num(fgetl(fid)); \r\n%  \r\n%  d{ptr}=v;\r\n%  \r\n% end % feof\r\n%  fclose(fid);\r\n% \r\n% end % read_file\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-03-13T05:45:39.000Z","updated_at":"2016-03-15T16:31:00.000Z","published_at":"2016-03-13T06:32:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is derived from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://code.google.com/codejam/contest/8274486/dashboard\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGJam March 2016 Annual I/O for Women Cody's Jam\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. This is a mix of the small and large data sets.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe GJam story goes that a store is having a 25% off sale and ordered original and sale price labels. Unfortunately the labels came commingled in increasing numeric value. Find the sale prices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m , Vector length N\u0026lt;=100 with values \u0026lt;=10^9.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e v , Vector containing the Sale price tags\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [m] [v]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[15 20 60 75 80 100] creates v=[15 60 75]\\n[9 9 12 12 12 15 16 20] creates v=[9 9 12 15]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGoogle Code Jam 2016 Open Qualifier: April 8, 2016\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eComplete Code Jam Input/Output included in Test Suite. The women's competition had 500 entrants. The qualifier winner was USA Stacy992 using Java. The top 60 show strength in USA, Russia, China, and South Korea under\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://code.google.com/codejam/contest/8274486/scoreboard?c=8274486#\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eContest Dashboard\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1668,"title":"Josephus Survivor","description":"The \u003chttp://en.wikipedia.org/wiki/Josephus_problem Josephus Challenge\u003e is to find the position that is the last remaining when every Kth item is removed from a list of N items. The removal wraps from the end to the start.\r\n\r\n*Input:* N, K  where N is the number of players and K is the removal period.\r\n\r\n*Output:* S  the last position remaining\r\n\r\n*Example:* N=4 K=2 produces the sequence\r\n\r\n1 2 3 4; 1 3 4; 1 3; 1\r\n\r\n*Comment:*\r\n\r\nThis is a replication of \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1092-decimation Decimation\u003e by James but has a different Historical story reference.","description_html":"\u003cp\u003eThe \u003ca href = \"http://en.wikipedia.org/wiki/Josephus_problem\"\u003eJosephus Challenge\u003c/a\u003e is to find the position that is the last remaining when every Kth item is removed from a list of N items. The removal wraps from the end to the start.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e N, K  where N is the number of players and K is the removal period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e S  the last position remaining\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e N=4 K=2 produces the sequence\u003c/p\u003e\u003cp\u003e1 2 3 4; 1 3 4; 1 3; 1\u003c/p\u003e\u003cp\u003e\u003cb\u003eComment:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis is a replication of \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\"\u003eDecimation\u003c/a\u003e by James but has a different Historical story reference.\u003c/p\u003e","function_template":"function survivor=solve_josephus(n,k)\r\n survivor=randi(n); \r\nend","test_suite":"%%\r\n% Fixed 6/21/13\r\nn=40;\r\nk=7;\r\ns_expect=24;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=4;\r\nk=2;\r\ns_expect=1;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=40;\r\nk=2;\r\ns_expect=17;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=37;\r\nk=6;\r\ns_expect=10;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=29;\r\nk=28;\r\ns_expect=3;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=101;\r\nk=1;\r\ns_expect=101;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":42,"test_suite_updated_at":"2013-06-21T13:42:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-06-21T04:41:00.000Z","updated_at":"2025-12-02T16:42:13.000Z","published_at":"2013-06-21T05:01:14.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Josephus_problem\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eJosephus Challenge\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is to find the position that is the last remaining when every Kth item is removed from a list of N items. The removal wraps from the end to the start.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e N, K where N is the number of players and K is the removal period.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e S the last position remaining\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e N=4 K=2 produces the sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 2 3 4; 1 3 4; 1 3; 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eComment:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a replication of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eDecimation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e by James but has a different Historical story reference.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":59045,"title":"8 : Find the Solving vector","description":"The Puzzle 8 is a variant of 15 ( Fifteen ). Fifteen is a slider puzzle, also a matlab function, with the goal being to create the matrix [1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 0] where 0 is a hole.\r\nGiven a matrix m [3,3] and a matrix vset [n,k] of n possible solutions of up to length k determine which solution is valid. Return a vector [1,k] that produces [1 2 3;4 5 6;7 8 0]\r\nMovement is of the Zero-0. 3-Up, 0-Down, 1-Left, 2-Right, 4 is Not used/SKIP\r\nSome potential solutions may cause faults so try/catch may be required\r\nThe deepest solutions of 8 is 31 single moves. Solving this will be the next 8 challenge.\r\n[867;254;301] and [647;850;321] have 29 solutions each of length 31\r\nThe Puzzle 8 cases are readily created using a 15 board.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 243px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 121.5px; transform-origin: 407px 121.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 101.5px 8px; transform-origin: 101.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Puzzle 8 is a variant of 15 ( \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/15_Puzzle\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eFifteen\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 252px 8px; transform-origin: 252px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ). Fifteen is a slider puzzle, also a matlab function, with the goal being to create the matrix [1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 0] where 0 is a hole.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a matrix m [3,3] and a matrix vset [n,k] of n possible solutions of up to length k determine which solution is valid. Return a vector [1,k] that produces [1 2 3;4 5 6;7 8 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 241px 8px; transform-origin: 241px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMovement is of the Zero-0. 3-Up, 0-Down, 1-Left, 2-Right, 4 is Not used/SKIP\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 224px 8px; transform-origin: 224px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSome potential solutions may cause faults so try/catch may be required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 272.5px 8px; transform-origin: 272.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe deepest solutions of 8 is 31 single moves. Solving this will be the next 8 challenge.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 218.5px 8px; transform-origin: 218.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e[867;254;301] and [647;850;321] have 29 solutions each of length 31\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 180px 8px; transform-origin: 180px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Puzzle 8 cases are readily created using a 15 board.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = solve8(m,vset)\r\n% m is 3x3 matrix with 0:8 values\r\n% vset is multiple possible solutions to restore m to [1 2 3;4 5 6;7 8 0]\r\n% Movement is of the 0. 3-U, 0-D, 1-L, 2-R, 4 is Not used/SKIP\r\n% some possible solutions may move the 0 off the board so try/catch may be needed\r\n v=vset(1,:);\r\n for i=1:size(vset,1)\r\n  mf=Eight_SolveA(m,vset(i,:));\r\n  \r\n  %check for valid/return_vector\r\n  \r\n end % i\r\n \r\nend % solve8\r\n\r\n\r\nfunction m=Eight_SolveA(m,svec)\r\n%m [3,3]\r\n%svec [1,n] 3U 0D 1L 2R  Movement of the Zero/Hole, 4 is skip\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n  try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    return\r\n  end\r\n  m(zr,zc)=0;\r\n  catch\r\n   return;\r\n  end\r\n end % i  svec(i)\r\nend %Eight_SolveA\r\n","test_suite":"%%\r\nvalid=1;\r\nm=[1 2 3;4 5 6;7 0 8]; %2\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[0 4;2 4;1 1];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n \r\n%%\r\nvalid=1;\r\nm=[3 1 2;4 5 6;7 0 8]; %133201022313200\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[2 1 1 3 3 2 0 1 0 2 2 3 1 3 2;\r\n      1 3 3 2 0 3 0 1 0 2 2 3 1 3 2;\r\n      1 3 3 2 0 1 0 2 2 1 2 3 1 3 2;\r\n      1 3 3 2 0 1 0 2 2 3 1 3 2 0 0];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n \r\n%%\r\nvalid=1;\r\nm=[0 2 3;1 5 6;4 7 8]; %0022\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[2 0 0 1 2 4;\r\n      0 0 2 3 4 4;\r\n      0 0 2 2 4 4;\r\n      0 3 2 1 4 4];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n%%\r\nvalid=1;\r\nm=[2 3 0;1 5 6;4 7 8]; %110022\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[1 1 2 0 0 1 2 4;\r\n      1 1 0 0 2 3 3 3;\r\n      1 1 0 0 2 2 4 4;\r\n      1 1 0 3 2 1 2 1];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n \r\n%%\r\nvalid=1;\r\nm=[2 3 6;1 5 8;4 7 0]; %33110022\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[3 3 1 1 2 0 0 1 2;\r\n      3 3 1 1 0 0 2 3 3;\r\n      3 3 1 1 0 3 2 1 2;\r\n      3 3 1 1 0 0 2 2 4;\r\n      3 3 1 1 0 0 2 3 3];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n ","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-10-02T21:40:49.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-10-02T19:13:59.000Z","updated_at":"2023-10-02T21:40:50.000Z","published_at":"2023-10-02T21:40:50.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Puzzle 8 is a variant of 15 ( \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/15_Puzzle\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFifteen\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e ). Fifteen is a slider puzzle, also a matlab function, with the goal being to create the matrix [1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 0] where 0 is a hole.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix m [3,3] and a matrix vset [n,k] of n possible solutions of up to length k determine which solution is valid. Return a vector [1,k] that produces [1 2 3;4 5 6;7 8 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMovement is of the Zero-0. 3-Up, 0-Down, 1-Left, 2-Right, 4 is Not used/SKIP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSome potential solutions may cause faults so try/catch may be required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe deepest solutions of 8 is 31 single moves. Solving this will be the next 8 challenge.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[867;254;301] and [647;850;321] have 29 solutions each of length 31\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Puzzle 8 cases are readily created using a 15 board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44756,"title":"Lights Out 5 - 5x5, 10 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require ten moves to solve. For example, if\r\n\r\n board = [0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 0 1]\r\n\r\nan answer is:\r\n\r\n moves = [1 2 3 4 5 16 17 18 19 20]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves 5x5, 8 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44757-lights-out-6-5x5-13-moves 5x5, 13 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require ten moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 0 1]\u003c/pre\u003e\u003cp\u003ean answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 2 3 4 5 16 17 18 19 20]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\"\u003e5x5, 8 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44757-lights-out-6-5x5-13-moves\"\u003e5x5, 13 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_5(board) % 5x5 board, 10 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 0 1];\r\nmoves = lights_out_5(board); % [1 2 3 4 5 16 17 18 19 20]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 0 0 1  \r\n          0 0 1 0 1];\r\nmoves = lights_out_5(board); % [1 2 3 11 13 14 16 17 21 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 0 0  \r\n          0 1 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_5(board); % [1 2 3 4 6 7 8 11 12 16]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          0 0 1 1 0  \r\n          1 1 0 1 0  \r\n          1 1 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_5(board); % [3 6:7 11 13:15 19 22:23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          0 1 1 0 0  \r\n          0 0 1 0 0  \r\n          0 1 0 1 0  \r\n          1 0 1 1 0];\r\nmoves = lights_out_5(board); % [2 3 9 10 14 16 17 20 23 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          0 1 0 0 0  \r\n          0 0 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 1 0 1];\r\nmoves = lights_out_5(board); % [2 4 7 9 11 12 17 19 20 21]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 0 0 1 1  \r\n          1 0 1 0 0  \r\n          1 0 1 0 1  \r\n          1 0 0 1 0  \r\n          1 1 0 1 1];\r\nmoves = lights_out_5(board); % [1 4 6 12 14 15 18 21 23 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          0 0 0 1 1  \r\n          1 1 0 0 0  \r\n          1 0 0 1 0  \r\n          1 1 1 1 0];\r\nmoves = lights_out_5(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 0 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 1  \r\n          1 1 0 0 1];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 1 1 1 0  \r\n          0 1 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 1 0  \r\n          0 1 1 1 1];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          0 0 0 1 1  \r\n          1 0 1 0 0  \r\n          1 0 1 1 0  \r\n          0 1 1 0 0];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          0 0 1 1 0  \r\n          0 1 0 0 0  \r\n          0 1 1 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 1 0 1  \r\n          0 0 0 1 1  \r\n          0 1 1 0 0  \r\n          1 1 1 1 0  \r\n          0 0 1 1 0];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 1 1];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)","published":true,"deleted":false,"likes_count":3,"comments_count":8,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":18,"test_suite_updated_at":"2018-11-13T13:07:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T18:53:12.000Z","updated_at":"2025-11-29T13:41:10.000Z","published_at":"2018-11-12T15:53:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require ten moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 1 0 1  \\n          1 1 1 1 1  \\n          1 1 1 1 1  \\n          1 1 1 1 1  \\n          0 1 1 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ean answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 2 3 4 5 16 17 18 19 20]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 8 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44757-lights-out-6-5x5-13-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 13 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44759,"title":"Lights Out 7 - 5x5, x moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve, ranging from 1 to 25 buttons (indices). The one function you write has to solve all of them. An answer is provided for some of the test cases, though there are often multiple possible answers per test case—any correct answer will work.\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44757 5x5, 13 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44760-lights-out-8-5x5-light-only-solution-i 5x5, light-only solution? I\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve, ranging from 1 to 25 buttons (indices). The one function you write has to solve all of them. An answer is provided for some of the test cases, though there are often multiple possible answers per test case—any correct answer will work.\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44757\"\u003e5x5, 13 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44760-lights-out-8-5x5-light-only-solution-i\"\u003e5x5, light-only solution? I\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_7(board) % 5x5 board, any number of moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 0 0 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_7(board); % [13]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 1 0 0 0  \r\n          0 0 1 0 0  \r\n          0 0 0 1 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_7(board); % [7 13 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_7(board); % [7:9 12:14 17:19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_7(board); % [7 9 17 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_7(board); % [1 5 7 9 13 17 19 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0];\r\nmoves = lights_out_7(board); % [1:5 11:15 21:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_7(board); % [7 8 9 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 0 1  \r\n          0 0 1 1 0  \r\n          0 0 1 0 0  \r\n          0 0 1 1 1];\r\nmoves = lights_out_7(board); % [18:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_7(board); % [1:6 10:11 15:16 20:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1];\r\nmoves = lights_out_7(board); % [1:2 4:7 9:10 16:17 19:22 24:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 0 0  \r\n          1 0 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_7(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          0 1 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 1 0  \r\n          0 1 1 0 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 0 1 0  \r\n          0 0 0 1 0  \r\n          0 0 0 0 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 1  \r\n          0 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 0 1 0];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 1 1 0 0  \r\n          0 1 0 0 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 1 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-30T12:52:15.000Z","updated_at":"2025-11-29T15:18:21.000Z","published_at":"2018-12-03T13:24:38.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve, ranging from 1 to 25 buttons (indices). The one function you write has to solve all of them. An answer is provided for some of the test cases, though there are often multiple possible answers per test case—any correct answer will work.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44757\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 13 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44760-lights-out-8-5x5-light-only-solution-i\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46040,"title":"Solve a Weird Calculator puzzle","description":"The September 2012 issue of GAMES Magazine had a Weird Calculator puzzle by Erich Friedman. In this puzzle, the calculator has only four buttons: +13, -7, *2, and /3. The object is to add 1 to get from one number to another with the specified number of button presses. For example, if you were asked to get from 8 to 9 in two button presses, you could press *2 and -7. \r\n\r\nWrite a function to solve the Weird Calculator puzzle. The input will be the starting number a, the desired result b, and the number of button n presses. The output should be a string with the buttons separated by a comma and a space. For the example above, the output should be\r\n\r\n  *2, -7\r\n\r\nEnjoy!","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 217.433px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 108.717px; transform-origin: 407px 108.717px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 365.5px 7.91667px; transform-origin: 365.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe September 2012 issue of GAMES Magazine had a Weird Calculator puzzle by Erich Friedman. In this puzzle, the calculator has only four buttons: +13, -7,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: normal; font-weight: 400; color: rgb(0, 0, 0);\"\u003e×\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 234.133px 7.91667px; transform-origin: 234.133px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2, and /3. The object is to add 1 to get from one number to another with the specified number of button presses. For example, if you were asked to get from 8 to 9 in two button presses, you could press \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: normal; font-weight: 400; color: rgb(0, 0, 0);\"\u003e×\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.6167px 7.91667px; transform-origin: 27.6167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2 and -7.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 280.583px 7.91667px; transform-origin: 280.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve the Weird Calculator puzzle. The input will be the starting number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.85px 7.91667px; transform-origin: 3.85px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003ea\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 59.5167px 7.91667px; transform-origin: 59.5167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the desired result \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.85px 7.91667px; transform-origin: 3.85px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eb\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.1667px 7.91667px; transform-origin: 29.1667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and the number n of button presses. The output should be a string with the buttons separated by a comma and a space. For the example above, the output should be\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 23.1px 7.91667px; transform-origin: 23.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e*2, -7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.45px 7.91667px; transform-origin: 19.45px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEnjoy!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function buttons = weirdCalc(a,b,n)\r\n  buttons = f(a,b,n);\r\nend","test_suite":"%%\r\na = 8;\r\nb = 9;\r\nn = 2;\r\nbtn_correct = '*2, -7';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 29;\r\nb = 30;\r\nn = 3;\r\nbtn_correct = '-7, -7, *2';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 54;\r\nb = 55;\r\nn = 4;\r\nbtn_correct = '/3, +13, *2, -7';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 4;\r\nb = 5;\r\nn = 5;\r\nbtn_correct = '*2, -7, *2, +13, /3';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 39;\r\nb = 40;\r\nn = 6;\r\nbtn_correct = '-7, *2, *2, +13, /3, -7';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 152;\r\nb = 153;\r\nn = 7;\r\nbtn = split(weirdCalc(a,b,n),', ');\r\nx = a;\r\nfor i = 1:n\r\n    x= str2num([num2str(x) btn{i}]); \r\nend\r\nassert(isequal(x,b))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2020-07-11T18:48:50.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-07-11T06:13:43.000Z","updated_at":"2020-07-30T13:30:20.000Z","published_at":"2020-07-11T14:06:38.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe September 2012 issue of GAMES Magazine had a Weird Calculator puzzle by Erich Friedman. In this puzzle, the calculator has only four buttons: +13, -7,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\times\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e2, and /3. The object is to add 1 to get from one number to another with the specified number of button presses. For example, if you were asked to get from 8 to 9 in two button presses, you could press \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\times\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e2 and -7.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve the Weird Calculator puzzle. The input will be the starting number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, the desired result \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and the number n of button presses. The output should be a string with the buttons separated by a comma and a space. For the example above, the output should be\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[*2, -7]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEnjoy!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":3033,"title":"Tic-Tac-Logic - Solution Checker","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/tic-tac-logic/rules Tic-Tac-Logic\u003e is a logic puzzle wherein a rectangular grid containing a certain set of pre-filled squares must be filled in completely to have no more than two consecutive X's or O's in any row or column. Also, the number of X's and O's must be the same in every row and column (equal to half the size of the row or column). Finally, all rows must be unique (compared to other rows) and all columns must be unique (compared to other columns).\r\n\r\nAn example puzzle from Conceptis is included here, wherein the first board represents the starting condition and the second board represents a properly solved board:\r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/3993.gif\u003e\u003e\r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/3994.gif\u003e\u003e\r\n\r\nYou will be provided with a variety of completed boards. X's are represented by ones and O's by zeros. Write a function to test whether the board has been properly solved. Remember to check against all three criteria.","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/tic-tac-logic/rules\"\u003eTic-Tac-Logic\u003c/a\u003e is a logic puzzle wherein a rectangular grid containing a certain set of pre-filled squares must be filled in completely to have no more than two consecutive X's or O's in any row or column. Also, the number of X's and O's must be the same in every row and column (equal to half the size of the row or column). Finally, all rows must be unique (compared to other rows) and all columns must be unique (compared to other columns).\u003c/p\u003e\u003cp\u003eAn example puzzle from Conceptis is included here, wherein the first board represents the starting condition and the second board represents a properly solved board:\u003c/p\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/3993.gif\"\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/3994.gif\"\u003e\u003cp\u003eYou will be provided with a variety of completed boards. X's are represented by ones and O's by zeros. Write a function to test whether the board has been properly solved. Remember to check against all three criteria.\u003c/p\u003e","function_template":"function [tf] = tic_tac_logic_check(board)\r\n\r\ntf = 1;\r\n[m,n] = size(board);\r\n\r\n%check up to only two consecutive\r\n\r\n%check same number in each row/column\r\n\r\n%check unique rows and columns\r\n\r\nend","test_suite":"%%\r\nboard = [0,1,1,0,0,1; 1,0,1,0,0,1; 0,1,0,1,1,0; 0,1,0,1,0,1; 1,0,1,0,1,0; 1,0,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,1,0,1,0,1; 0,1,0,1,0,1; 1,0,1,0,1,0; 1,0,1,0,1,0; 0,1,0,1,0,1; 1,0,1,0,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,1,0,0,1,0,1,1; 0,0,1,0,1,1,0,1; 1,0,1,1,0,1,0,0; 0,1,0,1,0,0,1,1; 1,1,0,0,1,1,0,0; 1,0,1,0,1,1,0,0; 0,0,1,1,0,0,1,1; 1,1,0,1,0,0,1,0];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,1,0,1,0; 1,1,0,0,1,1,0,1,0,0; 1,1,0,0,1,0,1,0,1,0; 0,0,1,1,0,1,0,1,0,1; 1,0,1,1,0,0,1,1,0,0; 1,1,0,0,1,1,0,0,1,0; 0,1,0,1,0,0,1,0,1,1; 0,0,1,0,1,0,1,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0; 0,1,1,0,1,0; 1,0,0,1,0,1; 1,0,0,1,1,0; 0,1,1,0,0,1; 0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,1,1,0,0,1; 1,0,1,0,0,1; 0,1,0,1,1,0; 0,1,1,0,0,1; 1,0,1,0,1,0; 1,0,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,1,0,0,1,1,0,1; 0,0,1,0,1,1,0,1; 1,0,1,1,0,1,0,0; 0,1,0,1,0,0,1,1; 1,1,0,0,1,1,0,0; 1,0,1,0,1,1,0,0; 0,0,1,1,0,0,1,1; 1,1,0,1,0,0,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,1,0; 1,1,0,1,0,0,1,0; 0,0,1,0,1,1,0,1; 0,1,0,1,0,1,0,1; 1,0,1,0,1,0,1,0; 0,0,1,0,1,0,1,1; 1,1,0,1,0,1,0,0; 1,1,0,0,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,0; 0,0,1,1,0,1,0,1; 1,1,0,1,0,0,1,0; 0,0,1,0,1,1,0,1; 0,1,0,1,0,1,1,0; 1,0,1,0,1,0,0,1; 0,1,0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,1,0; 1,1,0,1,0,0,1,0; 0,0,1,1,0,1,0,1; 0,1,0,1,0,1,0,1; 1,0,1,0,1,0,1,0; 0,0,1,0,1,0,1,1; 1,1,0,1,0,1,0,0; 1,1,0,0,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,1,1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,1,0,0; 1,0,0,1,0,1,0,0,1,1; 0,1,1,0,1,0,1,0,0,1; 1,0,1,0,0,1,0,1,1,0; 1,0,0,1,1,0,1,0,0,1; 0,1,0,1,0,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,1; 1,0,0,1,1,0,0,1,0,1; 0,0,1,1,0,1,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,0,0,1,1,1; 1,1,1,0,0,0; 1,0,1,0,1,0; 0,1,0,1,0,1; 1,1,0,0,0,1; 0,0,1,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,1,0,0,1,0,1,0,0,1; 1,0,0,1,0,1,0,1,1,0; 0,1,1,0,1,0,0,1,0,1; 0,0,1,1,0,1,1,0,1,0; 1,0,0,1,1,0,1,0,0,1; 1,1,0,0,1,0,0,1,1,0; 0,1,1,0,0,1,0,1,1,0; 0,0,1,1,0,1,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,1,1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,1,0,0; 1,0,0,1,0,1,0,0,1,1; 0,1,1,0,1,0,1,0,0,1; 1,0,1,0,0,1,0,1,1,0; 1,0,1,0,1,0,1,0,0,1; 0,1,0,1,0,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,1; 1,0,0,1,1,0,0,1,0,1; 0,0,1,1,0,1,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,0,0,1,1; 1,1,0,0,1,1,0,1,0,0; 1,1,0,0,1,0,1,0,1,0; 0,0,1,1,0,1,0,1,0,1; 1,0,1,1,0,0,1,1,0,0; 1,1,0,0,1,1,0,0,1,0; 0,1,0,1,0,0,1,0,1,1; 0,0,1,0,1,0,1,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [1,1,0,0,1,0,1,0,0,1; 1,0,0,1,0,1,0,1,1,0; 0,1,1,0,1,0,0,1,0,1; 0,0,1,1,0,1,1,0,0,1; 1,0,0,1,1,0,1,0,0,1; 1,1,0,0,1,0,0,1,1,0; 0,1,1,0,0,1,0,1,1,0; 0,0,1,1,0,1,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,1,0,0,1,1,0,0; 0,0,1,1,0,1,0,1; 1,0,1,1,0,0,1,0; 0,1,0,0,1,0,1,1; 1,0,1,0,1,1,0,0; 0,1,0,0,1,0,1,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,1,0,0,1,1,0,0; 0,0,1,1,0,1,0,1; 1,0,1,1,0,0,1,0; 0,1,0,0,1,0,1,1; 1,0,1,0,1,1,0,0; 0,1,0,1,0,0,1,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0; 0,1,1,0,1,0; 1,0,0,1,0,1; 1,0,0,1,1,0; 0,1,1,0,1,1; 0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,0; 0,0,1,1,0,1,0,1; 1,1,0,1,0,0,1,0; 1,0,1,0,0,1,0,1; 0,1,0,1,0,1,1,0; 1,0,1,0,1,0,0,1; 0,1,0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":6,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":30,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-02-22T04:12:50.000Z","updated_at":"2025-11-01T18:08:07.000Z","published_at":"2015-02-22T04:12:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/tic-tac-logic/rules\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTic-Tac-Logic\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle wherein a rectangular grid containing a certain set of pre-filled squares must be filled in completely to have no more than two consecutive X's or O's in any row or column. Also, the number of X's and O's must be the same in every row and column (equal to half the size of the row or column). Finally, all rows must be unique (compared to other rows) and all columns must be unique (compared to other columns).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn example puzzle from Conceptis is included here, wherein the first board represents the starting condition and the second board represents a properly solved board:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou will be provided with a variety of completed boards. X's are represented by ones and O's by zeros. Write a function to test whether the board has been properly solved. Remember to check against all three criteria.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,R0lGODlhyADIAPcAAJNz0mY7wGtBwkgVs62V3nFKxdDK0WZQaMa26JmJmlUluDASMot5jdXN1dHE7evj+Lyp5KygrsjByfXy/KCE2DodPObg5ksvTLmk4/Ls+yYHKZWElnpofIVizYJug+3o+HpUybyyvXhkefz4/rGksuHZ9iMDJYFezMC2wSgIKvPx88/Iz0wxTvHu8cK6xNvV22RNZqibqreruO7s7uHb4KOI2kwZtaGSosS5xI5r0OLZ88q56V4xvdjM8aSWpbWg4UIlRHdieN3T8tvQ8dLM0ykJK6aM2risuJ6A1sG4wod1iXNedWE0vrOe4b+t5WFJYn9rgPj4/VA2U1gpui8QMVIht5OClejh9+Xd9n5Zy00cti0PMFQ6Vpp81WtUbLKb4Me36c/B7JZ41I98keLd4oh2isGw5lpCXD4iQcS8xYRyhUQoRqqeq1ksu1Y9WSwNLzYZObCksSsMLUoXtM2+7My96t/a37OptaKTopuNnYxqz56PoF5FX52Onrqwu7Wf4GNMZDIUNbCjsVAet6aZqNXI7qOUpFwvvE81UbKoszMVNXdQyJBu0Ylnz15HYGFKY1cnuVstuzwfP3VPxkQpR1lAWicIKfj4+Ozq7Lart6CRodrT2ruxvKqerOro7JeHma+isOrm6qqdq7Kms83FzcW9xoZ0h+zo7NXP1vz8/NbQ1ujk6Lmvu5qLnczEzLert6CRopiHmfz8//z//OTf5MzFzfj2/Ma+yNjQ2M/Gz7WptqqfrKueq2dQad/Y3a+isa+jsL21vtbP1ujm6Oro6uTb9vb29qKTo0gsSu7q7tXP1bOmte7u8MvEzN3W3c3Gzezq7rWqtoJvhODW86ufraqfraufrPbz9raqt62isKyfrZeGmN3Y3d3Y39/Y3+bi5rGa4JiImaaYpuzm6tPN1Orm6NjS2NjS2qCSoeji5tjS1ubg9t/V8zESM9/a4CcHKVc+WcS5wujf9vz//+Lf45KAlNvW3KmQ28vCy7esuG5ZcLuvu8q76tbQ2JaFl////yH/C1hNUCBEYXRhWE1QPD94cGFja2V0IGJlZ2luPSLvu78iIGlkPSJXNU0wTXBDZWhpSHpyZVN6TlRjemtjOWQiPz4gPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iQWRvYmUgWE1QIENvcmUgNS4wLWMwNjEgNjQuMTQwOTQ5LCAyMDEwLzEyLzA3LTEwOjU3OjAxICAgICAgICAiPiA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPiA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iIHhtbG5zOnhtcE1NPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvbW0vIiB4bWxuczpzdFJlZj0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL3NUeXBlL1Jlc291cmNlUmVmIyIgeG1wOkNyZWF0b3JUb29sPSJBZG9iZSBQaG90b3Nob3AgQ1M1LjEgV2luZG93cyIgeG1wTU06SW5zdGFuY2VJRD0ieG1wLmlpZDpFQzk0QTM1RkVEMzcxMUUyQUQxNzkwMUIzRDRENTc2MSIgeG1wTU06RG9jdW1lbnRJRD0ieG1wLmRpZDpFQzk0QTM2MEVEMzcxMUUyQUQxNzkwMUIzRDRENTc2MSI+IDx4bXBNTTpEZXJpdmVkRnJvbSBzdFJlZjppbnN0YW5jZUlEPSJ4bXAuaWlkOkVDOTRBMzVERUQzNzExRTJBRDE3OTAxQjNENEQ1NzYxIiBzdFJlZjpkb2N1bWVudElEPSJ4bXAuZGlkOkVDOTRBMzVFRUQzNzExRTJBRDE3OTAxQjNENEQ1NzYxIi8+IDwvcmRmOkRlc2NyaXB0aW9uPiA8L3JkZjpSREY+IDwveDp4bXBtZXRhPiA8P3hwYWNrZXQgZW5kPSJyIj8+Af/+/fz7+vn49/b19PPy8fDv7u3s6+rp6Ofm5eTj4uHg397d3Nva2djX1tXU09LR0M/OzczLysnIx8bFxMPCwcC/vr28u7q5uLe2tbSzsrGwr66trKuqqainpqWko6KhoJ+enZybmpmYl5aVlJOSkZCPjo2Mi4qJiIeGhYSDgoGAf359fHt6eXh3dnV0c3JxcG9ubWxramloZ2ZlZGNiYWBfXl1cW1pZWFdWVVRTUlFQT05NTEtKSUhHRkVEQ0JBQD8+PTw7Ojk4NzY1NDMyMTAvLi0sKyopKCcmJSQjIiEgHx4dHBsaGRgXFhUUExIREA8ODQwLCgkIBwYFBAMCAQAAIfkEAAAAAAAsAAAAAMgAyAAACP8A/wkcSLCgwYMIEypcyLChw4cQI0qcSLGixYsYM2rcyLGjx48gQ4ocSbKkyZMoU6pcybKly5cwY8qcSbPjgps4c+rcybOnz59AgwodSrSo0aNEHS5o90aO06dQo0qd2nSq1atQ31TFylXq1q5g5WgNS3Ys2a5Ubip948bLgbdw48qdG7fXI3gwetHdy/dtrzNn9PYdXBcGvEeCCRPu5Qie4sdvK/FJDJluL0AV0irVECPVpc+gQ4seLVpVjGGkU6sOzYrV6tekh8VQBbs2aHzZVNi2TcLFbtjfLmxZsJnXRnvUmHEMEYIjM2v2OOYSNIvjnVsbMQkn3nCBBlEbN3X/wsSREyeOmDpt4kgK1CWOo0ptDLV9M3iN4slvNI9ePXv38MmnEX3D2RfeeOWdl51/G7X33kbxzVdfd98dqJ9G/C24XoMAQihgRgRyx5B392WUX4L9baiRgwFKWCCFJWJ04n4KapSeihmx6KGLIi5EooUoavjfgxpFOOCEI1aIH4I0pjhki0e+mGSMF82IYY0Z3fjkjlH2qNCPS16YUYY2Mrhih0V+iFGIBoYZZJk4YqRjmjy2aSKTVzrJIZEZGQkikj4qeaeYGJGZpZk5otmnmhexCSOQTQq5J5R/ShkolRZZOSaWGGk5KZeVepkQmIO+eWicF825aJ2PuhkpnFvS/9mlnTLiuameZ/KJkZ9rAvqloLUSepGhnSIqp6K7MmqRo1NCmqekuVLaq6W/YlqRpoVyepGn0YI6ragIkRqsqcWiapGqybLarKvPwvqprKHSWqWt2eKaqK4X8dqor6MCO6+wFhG7rbGpIpuvshUxGygJG33DMEcuuNARCd9whAs2HbGywkapsEDtqEVwYIgmJJds8skom3yDFRy0AkvKMMdMMjpqqIGOzDifDEsrHFhxQ85Aa4LOGFDsEXTQUDDw89Exw5IAEJp1Z0kCqjRg9dVYZ6011qig0IorqGwt9thWoxJDDGGTrTbXrrSCQtprr43KK3sQEffdDeARB9x4b/+NCj7IfByuBtps5EsEKnAUTDAcqRCBLxwZ8PBG0UiwUQv8Dm4tRdgOq61F3N4r7b6CHyTuv+QObG5F6B6s7qXO3gqt6N6SDq7p/mZKr+f2HouvRfoumznuVHJjB0PDqGJQ5wF/XlHovo/+DxkvMDQDEQUpXC1BdlSCDPYJfePGBa4UxHxFAoNO8LkGA8/oN4AAkYZC0DwBRBIEad9vjC5IosEFtUBIN86ggXZQ6XwUSd/z1se69lVEXysAggnWML+DrAIQKdhCHvI3PIOcLh+SMAELAlgQehBQEcdY3u6a17uC/e6BynLBGkxACfwVhBkwKAIVNpC9DhbkdP94hf//WGAAgnyDgIFIoQoBhj7nUQR6LpTeP3AwQ0pUUCAtgIEGqGAFg+hPcwfJxBCL+A93IBEPCEHgRBT4RAZSpHXuQ0gSgKABKwokFDB4xw4P8kXiIeQIYwzFGd6hCEMkRI0SYeNEoMi+F1IkeANJwwzX4IJU5HCPfPQhQYA4EEBqABFuSIEi0HjIFTaxhY2U4kCoaIILnEGHPERIHz2YO4LsgxJFKEIFCLEQREZEkRJhZAMdORFIEiQNF3AKHBKgkFn+sJYDWQUXckkJ8JWSiQl04iLdOBE4wnAhKnBEEeSABoklxJmbhOY/jlgEOFSgCCzY2DVTp77VvdGBj0TYQFSQ/0N3FqGG59TkQDj5D1pUQgOKEIcfhmjNJdJzgfbsJj6Lqc9/EAMQW0yAJDWwBn2ic6C1HCBClehJFpTvIL6ECDAjIsx7ElMixsQjLAUSjzVw1IY9LB0tMWXCdwTCkAMBoQjlaT5TZhOVw5RiFlOASYHgQIIAzentdlqQVSDxBgYRogZY0FCBpPQhK4VISyX60ogELxlabOpAXEBHCkqVVgZww0j/6L8L4NSrRl2jNoPJTYl4M58FeYE41UqQJNgUCKzgoE6fGSMJoIEKQP1jBdoBiqJiU69IdenohEEJOcQSIWkAwhuwOpCPCuR0JNgcQY7gA4e+6lSxWlVBWNHahf+gYA+pUOxUGRu7es0uerUT3mLTqVqJfNUhYX3IWP06UZhWVCKm/QdBJ3LchiTXIcuNyF8p+rrtsUt27uoWvL61GTZs5AW7mMFymrORGeyiehvJBTByu5FEYEcjpxDoaTXgg1N44r8ADrCABxzgU6zAB3bwL4EXzGBPnOIOd1BwgycM4FPYwQcrkDCFJ3yKNMRgFRsOsSesEQINi3jAp3CGfqUrhyDk4RMwjrGMZ0xjGYeDAUGwQixqzOMewzgcUIBCOHxM5BnHwgpBYMCQi8zkcJhCBP5gspQ/wQFpLHnKPI5FPYAwXJBWViM0+EVZIYICFHDkEr+gAUeIsIyO5KP/GRu5hsd2S9ze8u63UQxuwlY8XePmNZF7ZWlftdtcsz43ItHtc0Sqy5DrNiS7ENmuc7u7PzuzEM+p1DNFEq1OP18W0JklqyonwuniLvrPvwy0WAm2hy8rBB0RMIikDU1pMH7Xt+EVSAyoAAdgKCQBW6jAXf8xa4gYk9R87vSpP53q3pECESaoQKwPso12mIAPaiZIsR9ybOgm29QQYfRCHM0QKKpCChqogKsHEg5FaOAJoZB1oY19aIiU2tKnxHQD0K1ugiQgEEV4ggUOsm2HdBvR38b3UTH9D3VIIdrTDoe14Y2Qgjfk4PZO+K3vnOuC4OLhFSBBHgJhgieMIyEW/2cIxh9y741fuuMFUYYU2gmHgKdDISlfyMod0vJSvbZcC3nBBYqQgjO0YCE5V8jOG9Lzcf1cdQvpAxxSUAQ0jALp8+Z2vVmucZ+3C7YJSYAiisAHLryjAnHAedYNvnWed93pXwc6QiSugUck4wUgnzbB137xtjP97ah7ej0RUu2S68ccea8431Xud4Y0PfBxh7pBxP7uVRBEFYhIt6/lPWat19qPLs83zP/9jidULOb8XrdAkp6QpTse8LpjtkpVrVxj+WALJngEE8/xcDiwdyCsR4jrF/L42D+0jXFagRT4EO+EYP4JZChI8A8y/GbC/lqonn0LcVEOhtDAG5wftf+3u7xfcD9E3Aoh90Ig/ZDpG6T6ASW/dJUd7uyDlfbYHXSkF6/zxhP/+pxjf8iFf4+mf+3Hf0rnf9Ynf4pWf7J3f6HGXJ3Hdp9HS4KwEbQQB9WxEUkwbBcxC3FACxwhDLrQEUfwDBtxCXOmFEVQBqyQCTAYgzI4gzQog/lACAwQBzJQgzzYgzCYDxuwAfngg0Q4gzIQBwxACENYhEyYD5rAALrAhFKYCWOQB0s4hTwoA6BACVEzIi34gljIgzeYgzsYhmIYhFdohkaIhEqohjXohFDohjxYhWkoh1rIhXQGUheoERm4gRrRgRwBgiK4ESRogiioESrIgPR3fgJoXQT/WG4G6BDuVxDwJ0sASF2N2GiPuH6R2BCTSBCVmEmKaH4OgX4JoX4KwX6SiICtp4Dxl4flp3CYxXCapWnINooKMQF04AQYsAMf0EuZOG6bmIoEc3QNkTjSx4rC54qWiIsHgQViIAA8MAUKcAgBkAM9ME+CB1EF8Q0wsAQL0QKAEATGkIwT2HcVyFsGAQECYANz0AZMwASR4I5MQABpFIzpN4wJAT17kAKWEAQjgBDQAAMmsAB+YI7ih3DOSBB/MAU2wARdsAMlUAJhQAECMAdVUAMohY+nqI8IAT2XAAViEQT0RRAXxFSfBXzKSH3MKIqwOH9UEgZMYAOL4AAGMQQd/6AFU+AErhV5g2cQItliN3RJKamS58h46VhnAyELHTAAASAECPEBkzAABWALlnV827Q6UJBLJIlFadVFe3eU/ZeUIBUjdMADg9AECoEA1IgBV7mNyJcQWymUmJBHhKVtK/l+LelFb0cAcxAAAakQBTAAYvCWPsmNcikWS9ALM4VyeUmJe/lWrSIQYjAAHcAQYjAHJ2CY4AV2CSENC6BM24B1YpmAZBmLAwEAcwAADFEDNrCZBGGKCIGK+9hXocACROcIDPGJAxGKfLmQ/9AFAwCbCqGalxmbHDmbHnkQY0UMMPAGVBCdQUCaCZlxwPkFNsADGbAQAjAAXcCZuOaZBv/BT++wABsgDXJQBCKgdqXZiqcJkwTRA0ygBRSgED9QBVNgBuDJceJJEBe1Rf4gEFtZBEHwQrwpEL4pmeuSmtlZBwgxDe2YBbKwny/Xn3dElAQhkm8QBPMQltXJdcD5D/IQAH/plgWxA+0YCYXQk50pdwSxVHcpkgTqobY4fi8JRDvABHOgACeAAWHgAE6gB23wjiZKoaLXny+AUXcpoLm0BINolB/qdiEqEGEAAoMwBzYwCFVgAzagBQWgnxv5gAPYO4YgFkVZEFCAUL+3eo8JipGpW/IyEBnwBx0gAIcQCQKQBQQgD9p4mHH5oh4AlgqhBvUQmFBaowp5o50mCw//oAM6IA9WqRCyeRC0+ZGduJtt2ptvWlqX6GlYyVcRJYFR+ndTin1i6ogRSGjtuYzv2YCMeKqamKr7t6os2aqLWIrJSanLaRCq6ImZiqCbekfJpnoWEWa0ahBldmZptmZtxhFvFmcr2B0t9mJYxmM3lmM7Vq3WGmRXpq1GhmRK5q015mRQJq41tg0coAbdaq5aBjWK2l8nxmAGhmAmFq8FBmH1aq//ZWEYlq/62mEfpq8MRmL+Gq8plmzmpRHopV4bwRwc4V7wpRHyVZIZYV8bkV+lGoCwKoyyeoDHCpm2SooNMakGUanMeanUiajWqagiyxAkWxAmy6soy56j+noZ/4uJG5uPHbuKH+umISuLQgAOYqAHjFADdTABOkuLoqayIMqy+KYDAHAI7jgHWFoFIIAAHbmzvtqzmvqzLscP7TgIAdAIepADk6AANhAJ9amrWoupXAusXut1AyEEATAAbWAE0zAQHwAGBWADVXAPbKu0osq0Uuq0tyYLJ/CQdHAQE3ACc8ADYVCyu1oQveq2Nft/N+upBGEGCqAAX5AQxdCOjGCoAhGzlDuzjvm2/5CgcDqZcCcQTQkCpGsQ4PCQWACzk0sQlZuy42U7cWqqBGEL7aiRCiEEPKAF/IC7bcu7shUvrgt5/1AMAaAAELAQD1AAWqCWBGG6uou6iqe6rP/LqZm7bASBBdPLkwohlTagvQPBvQOxuzRLuKRquHIbBcO7EOwwn2CgvIKrqpe7gPT7uv9wAgMAAkibEF+gBTxQDPwLc5nWu8IVwND7D05QBZDwAwmxDheZA1HQwBb6wM1LXs9rfAWRBU4ZuQaRAR1gA4ewuB7sorQDwXs2vg5oEPL5uPdQAgMRBWCwCFowCMT7wpInXiHsuyMMvAYBBgHgtwLQCACQAyAwj5CABMq5vPErw5vWqeR7EEIgpFowAGDstwWAvoHrwEklvzYrwSSMED1QA3qwCCfQBWZAsZJrxan7v6/4uxr7qYIWqv6Lxpirxkjsp1kZW+kyK0e8x3D/WcjvUsQRrMc4y8er5sezisfNqKiFoxGHg4wasTiN8ziRMzkaUTmXw2dTUzV9IzZd8zV8k8pZYzZo48qq3DZvI8t+Qzd2Y8tbozetbMt/Ezg3GjIjwzQwszIt8zLEDDM0YzPJDDM70zNL08wncwNEYzTSnDJJczPXXDJO466bIcoX4TAdETETc3oacTEZQ1QY0TE0/KqSXHuU7LGW7JKQrLmEDKqG7DqIvKACzJ8wDFxYfIuCrMj33Mf5HEf7DDuhNxAjoANDIAS/yLH9W8mADMD1vMUHUQIUkAUFEAACsAiMgLVZO9HyTBANsASWoxDkgNJ6Gbf9LBA/sMRzMAg0/20DdgsAEV3HJM2zBQEIraTOBWEPXGACbkDH4Sus7YyrAEMBCjAATKAHEGAGZkAB2GsDk5DT25u77+u9NDoQoMBQB2EHXKABcBBZXZvQ3iW3AoEBCqAFWbCiBYEEDtkB2ynEP0nEAnEEaLBVJDQQqjDWcGAcLY3WlXZrD7DEIBCpBnEPWorBdo2YeJ3XITRCA7EJgC3Yg+28/DzB96AFTGCTCeG4BvzYfxrZAqFVRPQP3HDZ7knYtqbWIDAAerAQYFAFCgCVWW3H35sQYqQBUgAK8EDWmF2rrg16cvsBAjAI9qgQoWsD1ZvbO721CqHXchAIb1ABw03cmq3QclsCAf8ACWQclYuwvqTNyKZNEDGwAFoRDkhZ3FT1XYetAEWKENdrA/P9D+4rEPB7xwnxAvCQnnKACF2V2SK82WssEIPJmgrhAApQBXDdvlqt31wdfgjRD2MtCWpAR5Td2tud1gIsBjYgADqgECAuAFiN3xH+D/u92wch1ML9D0KV2qzq3uqo1kNwCIOg4AeBAG2gBUEM4brd1QThDUMNByWC2gMOtzSulGr9DxRgA1Os2ANhBiRaALdb3vjcyALx1y++WkN0UiC75GVpZ43bpSDwAxMgC7JQBwAwBXPABA6q02ZciwNBDqxtEDGe0j4r5qjZ5P+QATnQ1FUAj4eAtl6avGX//MFnPBA+beQJIUREbdTB+g/FN8gGgQFZ8N1+ywMFQAEnDt1zvrQDIQiIIA4LkQiIUFt73uGF7ecEUQhm0AQQUAefjuUGreX/YHkMoethzuqv/dJH+s95FtA2etE1vMhZft6Axefzl90W8QLVwLAa4bDtVQ0RmxHTQccXcR0Xm2zZsBE0kA3luBHJuhHGkA3ZphFEcHUckQn4sBEqEK0jIgdewACmcO/4nu/6vu/5XgYc4AXSwO8CP/D4rgRLsARlQPAKv+/S4AUckPALH/FlIAJeoAYRf/GmoA9BAPEYL/BK4AGPdaPtkJ65VPImf/Ion/Ikn/Is3/Il7xQuH/MsRL/yMl/zuUTzNh/zMJ/zMp8WN4oUQB/0Qj/0RF/0Rq8TNZH0Sr/0TN/0Tv/0UB/1Uj/1VF/1Vn/1WJ/1Wr/1XN/1ChEQADs=\"},{\"partUri\":\"/media/image2.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"}]}"},{"id":44755,"title":"Lights Out 4 - 5x5, 8 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require eight moves to solve. For example, if\r\n\r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0]\r\n\r\nthe answer is:\r\n\r\n moves = [2 4 6 10 16 20 22 24]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves 5x5, 6 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44756-lights-out-5-5x5-10-moves 5x5, 10 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require eight moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [2 4 6 10 16 20 22 24]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\"\u003e5x5, 6 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44756-lights-out-5-5x5-10-moves\"\u003e5x5, 10 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_4(board) % 5x5 board, 8 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_4(board); % [2 4 6 10 16 20 22 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_4(board); % [7 8 9 12 14 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 1 0 1 1  \r\n          1 0 0 0 1  \r\n          1 1 0 1 0  \r\n          0 0 1 1 0];\r\nmoves = lights_out_4(board); % [1 2 5 10 16 21 24 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_4(board); % [1 5 7 9 17 19 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 0 1 0 1  \r\n          0 0 1 0 0  \r\n          0 1 1 0 0  \r\n          0 1 0 0 0  \r\n          0 1 1 1 0];\r\nmoves = lights_out_4(board); % [4 5 8 12 14 19 22 23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_4(board); % [1 2 3 4 5 7 9 13]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 0 1 0 0  \r\n          1 1 1 0 0  \r\n          1 0 1 1 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_4(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 0 1 1  \r\n          1 1 1 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 0  \r\n          1 0 1 1 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          1 1 0 0 0  \r\n          0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 0 0 0  \r\n          1 1 0 1 0  \r\n          1 0 1 1 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1  \r\n          1 0 1 0 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 1  \r\n          1 1 0 1 0  \r\n          0 1 0 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 1 1 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 0 0  \r\n          1 1 1 1 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 0 1 0 1  \r\n          0 1 0 0 1  \r\n          0 1 0 1 1  \r\n          1 0 1 1 0  \r\n          0 1 0 0 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          1 0 0 0 1  \r\n          0 0 1 1 1  \r\n          0 1 0 1 1  \r\n          1 0 0 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":18,"test_suite_updated_at":"2018-11-09T14:19:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T18:36:00.000Z","updated_at":"2025-11-29T14:28:48.000Z","published_at":"2018-11-09T14:19:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require eight moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 1 0  \\n          1 0 0 0 1  \\n          0 0 0 0 0  \\n          1 0 0 0 1  \\n          0 1 0 1 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [2 4 6 10 16 20 22 24]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44756-lights-out-5-5x5-10-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 10 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44760,"title":"Lights Out 8 - 5x5, light-only solution? I","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state is a potential answer—i.e., if toggling only the starting lights are sufficient to solve the board.\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44759 5x5, any number of moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44761 5x5, light-only solution? II\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state is a potential answer—i.e., if toggling only the starting lights are sufficient to solve the board.\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44759\"\u003e5x5, any number of moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44761\"\u003e5x5, light-only solution? II\u003c/a\u003e\u003c/p\u003e","function_template":"function tf = lights_out_8(board) % 5x5 board, lights-only solution\r\n tf = 0;\r\nend","test_suite":"%% all true cases first\r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nassert(lights_out_8(board)); % [2 4 6 10 16 20 22 24]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 0 0 0 1];\r\nassert(lights_out_8(board)); % [1 5 7 9 17 19 21 25]\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nassert(lights_out_8(board)); % [2 6 8 12 14 18 20 24]\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1];\r\nassert(lights_out_8(board)); % [1:2 4:7 9:10 16:17 19:22 24:25]\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          0 0 1 0 0];\r\nassert(lights_out_8(board)); % [3 7 9 11 13 15 17 19 23]\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1];\r\nassert(lights_out_8(board)); % [1 3 5 11 13 15 21 23 25]\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0];\r\nassert(lights_out_8(board)); % [2:4 6:7 9:11 13 15:17 19:20 22:24]\r\n\r\n\r\n%% false cases start here\r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nassert(~lights_out_8(board)); % [1 2 3 4 5 7 9 13]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 0 0  \r\n          0 1 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 0 0 0];\r\nassert(~lights_out_8(board)); % [1 2 3 4 6 7 8 11 12 16]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          1 0 0 0 1];\r\nassert(~lights_out_8(board)); % on your own\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0];\r\nassert(~lights_out_8(board));\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1];\r\nassert(~lights_out_8(board));\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 1  \r\n          0 1 1 0 0];\r\nassert(~lights_out_8(board));\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0];\r\nassert(~lights_out_8(board));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":52,"created_at":"2018-10-30T14:01:47.000Z","updated_at":"2025-11-29T15:01:02.000Z","published_at":"2019-01-09T15:04:36.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state is a potential answer—i.e., if toggling only the starting lights are sufficient to solve the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44759\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, any number of moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44761\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45237,"title":"Queen's move - 02","description":"In continuation with the problem-45236 ... \r\nIn the previous problem, it was assumed that there were no other chess pieces on the board... Now lets assume there is a number of king present on the board whose location is given in an array *p* . Now, check for the validity of Queen's moves.\r\n\r\n# x={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'}\r\n# p={'Kd4','Ke5','Kh7','Ke7'}\r\n\r\noutput=[1,1,0,1,0,1,1,0,0,0]\r\n\r\nkindly see this problem for understanding\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\u003e\r\n","description_html":"\u003cp\u003eIn continuation with the problem-45236 ... \r\nIn the previous problem, it was assumed that there were no other chess pieces on the board... Now lets assume there is a number of king present on the board whose location is given in an array \u003cb\u003ep\u003c/b\u003e . Now, check for the validity of Queen's moves.\u003c/p\u003e\u003col\u003e\u003cli\u003ex={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'}\u003c/li\u003e\u003cli\u003ep={'Kd4','Ke5','Kh7','Ke7'}\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eoutput=[1,1,0,1,0,1,1,0,0,0]\u003c/p\u003e\u003cp\u003ekindly see this problem for understanding \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\u003c/a\u003e\u003c/p\u003e","function_template":"function z = Queen_move_3(x,p)\r\n  y = x;\r\nend","test_suite":"%%\r\nx={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'};\r\np={'Kd4','Ke5','Kh7','Ke7'};\r\ny_correct = [1,1,0,1,0,1,1,0,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qd1','Qd5','Qf1','Qa8','Qf7','Qb2','Qc7'};\r\np={'Kd4','Ke5','Kh7','Ke7'};\r\ny_correct = [1,0,1,0,1,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qh7','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7'};\r\np={'Kd4','Ke5','Kh7','Ke7'};\r\ny_correct = [0,0,0,0,0,0,0,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qh7','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7'};\r\np={'Ka8','Kb2','Kd7','Kf3','Kg6'};\r\ny_correct = [1,1,1,0,0,0,0,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qh7','Qh1','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qg2','Qg7','Qf1','Qf7','Qf2','Qa2'};\r\np={'Ka8','Kb2','Kd7','Kf3','Kg6'};\r\ny_correct = [1,1,0,0,0,0,0,0,1,0,1,0,1,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-12-20T23:00:44.000Z","updated_at":"2026-01-23T12:47:14.000Z","published_at":"2019-12-20T23:01:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn continuation with the problem-45236 ... In the previous problem, it was assumed that there were no other chess pieces on the board... Now lets assume there is a number of king present on the board whose location is given in an array\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . Now, check for the validity of Queen's moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ep={'Kd4','Ke5','Kh7','Ke7'}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput=[1,1,0,1,0,1,1,0,0,0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ekindly see this problem for understanding\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44753,"title":"Lights Out 3 - 5x5, 6 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require six moves to solve. For example, if\r\n\r\n board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1]\r\n\r\nthe answer is:\r\n\r\n moves = [1 5 11 15 21 25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves 5x5, 4 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves 5x5, 8 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require six moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 5 11 15 21 25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\"\u003e5x5, 4 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\"\u003e5x5, 8 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_3(board) % 5x5 board, 6 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1];\r\nmoves = lights_out_3(board); % [1 5 11 15 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 0 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_3(board); % [4 9 10 16 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_3(board); % [7 8 9 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 1  \r\n          0 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 0 1 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_3(board); % [4 8 11 13 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 0  \r\n          0 0 0 1 0  \r\n          0 1 0 1 0];\r\nmoves = lights_out_3(board); % [7 8 12 14 15 21]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 1 1 0  \r\n          0 1 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_3(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 1 0 1  \r\n          0 1 0 0 1  \r\n          1 0 0 1 0  \r\n          1 1 0 1 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 1 1  \r\n          1 0 0 0 0  \r\n          1 0 0 0 1  \r\n          1 0 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 1  \r\n          0 0 0 1 0  \r\n          0 0 0 0 0  \r\n          1 0 1 1 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 0 0 0 0  \r\n          1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          1 0 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 1 1 1 1  \r\n          1 0 1 0 0  \r\n          1 1 0 0 1  \r\n          0 1 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 1 0 0  \r\n          0 0 0 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":10,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T14:59:29.000Z","updated_at":"2025-11-29T15:04:22.000Z","published_at":"2018-11-05T13:04:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require six moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 1 0 1  \\n          1 0 1 0 1  \\n          0 0 0 0 0  \\n          1 0 1 0 1  \\n          1 0 1 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 5 11 15 21 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 4 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 8 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":51251,"title":"Locate a family on a long street ","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 186.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 93.25px; transform-origin: 407px 93.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.983px 7.91667px; transform-origin: 374.983px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTwo children, Matilda and Labrun, live on a street in which—unlike many streets in the U.S.—the houses on one side are numbered consecutively, starting at 1. They notice that the sum of the numbers on the houses to the left of theirs equals the sum of the numbers to the right. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 82.7083px 7.91667px; transform-origin: 82.7083px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"nmax\" style=\"width: 27px; height: 20px;\" width=\"27\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 281.6px 7.91667px; transform-origin: 281.6px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the maximum number of houses on the street, and returns the largest possible number of houses and the number of the house where the family of Matilda and Labrun lives. For example, if \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"nmax\" style=\"width: 27px; height: 20px;\" width=\"27\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.8667px 7.91667px; transform-origin: 46.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 10, then the function should find that the street has 8 houses and Matilda and Labrun live in house 6 because 1+2+3+4+5 = 7+8 = 15.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 157.158px 7.91667px; transform-origin: 157.158px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHenry Dudeney posed a version of this problem in \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52.9083px 7.91667px; transform-origin: 52.9083px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eStrand Magazine\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 79.3417px 7.91667px; transform-origin: 79.3417px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in the early part of the 20\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5.83333px 7.91667px; transform-origin: 5.83333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 69.4917px 7.91667px; transform-origin: 69.4917px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e century. Although the connections might not be immediately clear, you might try Cody Problems \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/groups/25/problems/1215\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e1215\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.91667px; transform-origin: 3.88333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/8057\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e8057\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 17.5px 7.91667px; transform-origin: 17.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45253\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e45253\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2833px 7.91667px; transform-origin: 25.2833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as well.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [x,y] = houseNumber(nmax)\r\n  % x = number of the house of Matilda and Labrun\r\n  % y = number of houses on the street\r\n  x = f1(nmax)\r\n  y = f2(nmax)\r\nend","test_suite":"%%\r\nnmax = 10;\r\nx_correct = 6;\r\ny_correct = 8;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 100;\r\nx_correct = 35;\r\ny_correct = 49;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%% Dudeney's problem\r\nnmax = 500;\r\nx_correct = 204;\r\ny_correct = 288;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 2000;\r\nx_correct = 1189;\r\ny_correct = 1681;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 10000;\r\nx_correct = 6930;\r\ny_correct = 9800;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 100000;\r\nx_correct = 40391;\r\ny_correct = 57121;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 500000;\r\nx_correct = 235416;\r\ny_correct = 332928;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 2e6;\r\nx_correct = 1372105;\r\ny_correct = 1940449;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\n\r\n%%\r\nnmax = 2e7;\r\nx_correct = 7997214;\r\ny_correct = 11309768;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\n\r\n%%\r\nnmax = 1834432;\r\np_correct = 78376578048;\r\nds_correct = 48;\r\ndp_correct = 1866240;\r\n[x,y] = houseNumber(nmax);\r\nd = [num2str(x)-'0' num2str(y)-'0'];\r\nassert(isequal(x*y,p_correct) \u0026\u0026 isequal(sum(d),ds_correct) \u0026\u0026 isequal(prod(d),dp_correct))","published":true,"deleted":false,"likes_count":8,"comments_count":1,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":"2021-05-16T01:51:40.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2021-03-29T00:43:22.000Z","updated_at":"2026-03-22T23:36:57.000Z","published_at":"2021-03-29T00:53:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTwo children, Matilda and Labrun, live on a street in which—unlike many streets in the U.S.—the houses on one side are numbered consecutively, starting at 1. They notice that the sum of the numbers on the houses to the left of theirs equals the sum of the numbers to the right. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"nmax\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en_{max}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, the maximum number of houses on the street, and returns the largest possible number of houses and the number of the house where the family of Matilda and Labrun lives. For example, if \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"nmax\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en_{max}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e = 10, then the function should find that the street has 8 houses and Matilda and Labrun live in house 6 because 1+2+3+4+5 = 7+8 = 15.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHenry Dudeney posed a version of this problem in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eStrand Magazine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in the early part of the 20\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eth\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e century. Although the connections might not be immediately clear, you might try Cody Problems \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/groups/25/problems/1215\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e1215\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/8057\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e8057\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45253\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e45253\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e as well.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2358,"title":"Word Search Solver","description":"There are n words (minimum of three letters in each word) supplied with a given word search board. The answer will contain n rows where each row contains the row and column indices where the word starts followed by an integer indicating the direction of the word. The direction integer runs from 1 to 8 and starts at 12 o'clock, running clockwise. So, a word spelled to the right (normal fashion) would be indexed as a 3 and facing downward to the left (SW) would be a 6.\r\nThe first board is included here for reference:\r\n board = [\r\n  'xcupa'\r\n  'dyrng'\r\n  'osbaq'\r\n  'exbid'\r\n  'wgamv'\r\n ];\r\n\r\n words = {'aim'; 'bid'; 'cup'; 'doe'};\r\n\r\n loc_ans = [\r\n  3 4 5\r\n  4 3 3\r\n  1 2 3\r\n  2 1 5\r\n ];","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 450.933px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 225.467px; transform-origin: 407px 225.467px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 365px 8px; transform-origin: 365px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are n words (minimum of three letters in each word) supplied with a given word search board. The answer will contain n rows where each row contains the row and column indices where the word starts followed by an integer indicating the direction of the word. The direction integer runs from 1 to 8 and starts at 12 o'clock, running clockwise. So, a word spelled to the right (normal fashion) would be indexed as a 3 and facing downward to the left (SW) would be a 6.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 143.5px 8px; transform-origin: 143.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe first board is included here for reference:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 326.933px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 163.467px; transform-origin: 404px 163.467px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e board = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'xcupa'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'dyrng'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'osbaq'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'exbid'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'wgamv'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e ];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 152px 8.5px; tab-size: 4; transform-origin: 152px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003e words = {\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'aim'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'bid'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'cup'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'doe'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e};\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e loc_ans = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  3 4 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  4 3 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  1 2 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  2 1 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e ];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function loc = WordSearch(board,words)\r\n loc = [-1 -1];\r\nend","test_suite":"%%\r\nboard = [\r\n 'xcupa';\r\n 'dyrng';\r\n 'osbaq';\r\n 'exbid';\r\n 'wgamv';\r\n];\r\nwords = {'aim'; 'bid'; 'cup'; 'doe'};\r\nloc_ans = [\r\n 3 4 5;\r\n 4 3 3;\r\n 1 2 3;\r\n 2 1 5;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))\r\n\r\n%%\r\nboard = [\r\n 'okeanpbirf';\r\n 'qicwnafehu';\r\n 'wniazcgame';\r\n 'egaxjelbiv';\r\n 'bnomelvmcr';\r\n];\r\nwords = {'fair'; 'game'; 'hall'; 'ice'; 'jack'; 'king'; 'lemon'};\r\nloc_ans = [\r\n 2 7 4;\r\n 3 7 3;\r\n 2 9 6;\r\n 3 3 1;\r\n 4 5 8;\r\n 1 2 5;\r\n 5 6 7;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))\r\n\r\n%%\r\nboard = [\r\n 'werojea';\r\n 'cafefls';\r\n 'apufrbw';\r\n 'hrleaan';\r\n 'aoltgbb';\r\n 'aoaevdr';\r\n 'mdzoece';\r\n];\r\nwords = {'able'; 'bare'; 'cafe'; 'door'; 'edge'; 'full'};\r\nloc_ans = [\r\n 4 6 1;\r\n 5 7 8;\r\n 2 1 3;\r\n 7 2 1;\r\n 7 7 8;\r\n 2 3 5;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))\r\n\r\n%%\r\nboard = [\r\n 'anmjwfpnyo';\r\n 'wasgijsaen';\r\n 'akigyqaekl';\r\n 'doorbellci';\r\n 'loiapucfdx';\r\n 'loepalirri';\r\n 'alzhheagle';\r\n 'mgxmsovnpr';\r\n 'aiqtbovgee';\r\n 'juyhctahnr';\r\n];\r\nwords = {'airplane'; 'board'; 'clasp'; 'doorbell'; 'eagle'; 'fiesty'; 'graph'; 'hatch'; 'igloo'; 'jigsaw'; 'key'; 'llama'};\r\nloc_ans = [\r\n 2 2 4;\r\n 9 5 2;\r\n 5 7 1;\r\n 4 1 3;\r\n 7 6 3;\r\n 5 8 6;\r\n 3 4 5;\r\n 10 8 7;\r\n 9 2 1;\r\n 2 6 7;\r\n 3 9 1;\r\n 5 1 5;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":26769,"edited_by":223089,"edited_at":"2022-09-19T13:13:49.000Z","deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2022-09-19T13:13:50.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-06-11T13:58:10.000Z","updated_at":"2025-12-15T20:16:04.000Z","published_at":"2014-06-11T13:58:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are n words (minimum of three letters in each word) supplied with a given word search board. The answer will contain n rows where each row contains the row and column indices where the word starts followed by an integer indicating the direction of the word. The direction integer runs from 1 to 8 and starts at 12 o'clock, running clockwise. So, a word spelled to the right (normal fashion) would be indexed as a 3 and facing downward to the left (SW) would be a 6.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first board is included here for reference:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [\\n  'xcupa'\\n  'dyrng'\\n  'osbaq'\\n  'exbid'\\n  'wgamv'\\n ];\\n\\n words = {'aim'; 'bid'; 'cup'; 'doe'};\\n\\n loc_ans = [\\n  3 4 5\\n  4 3 3\\n  1 2 3\\n  2 1 5\\n ];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44238,"title":"Mastermind III: Solve in 1","description":"\u003chttps://en.wikipedia.org/wiki/Mastermind_(board_game) Mastermind\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\r\n\r\n  Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\r\n[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v) \r\n\r\nwhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\r\n\r\nChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\r\n\r\nTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\r\n\r\nFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Mastermind_(board_game)\"\u003eMastermind\u003c/a\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAnswer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\u003c/pre\u003e\u003cp\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/p\u003e\u003cp\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/p\u003e\u003cp\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/p\u003e\u003cp\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/p\u003e\u003cp\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/p\u003e","function_template":"function [guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n% mguess and mpegs are kx4 and kx2 and will not be empty\r\n% The player gets only one guess\r\n guess=[1 1 1 2];\r\nend","test_suite":"%%\r\nglobal m mpc mc mpc5c v\r\ntic\r\nv=1111:6666;\r\nvL=length(v);\r\nm=zeros(vL,4);\r\nfor i=1:vL\r\n  vp=v(i);\r\n  for k=4:-1:1\r\n   m(i,k)=mod(vp,10);\r\n   vp=floor(vp/10);\r\n  end\r\nend\r\nmdel=sum((m==0)+(m\u003e6),2)\u003e0;\r\nm(mdel,:)=[];\r\nv=m*[1000;100;10;1];\r\nmL=size(m,1);\r\n\r\nmpc=zeros(mL); % 0.030\r\nfor j=1:mL\r\nmpc(:,j)=sum(m==repmat(m(j,:),mL,1),2);\r\nend\r\n\r\nmch=zeros(mL,6); % 0.038\r\nfor i=1:mL\r\n  for k=1:6\r\n   mch(i,k)=nnz(m(i,:)==k);\r\n  end\r\nend\r\n\r\nmc=zeros(mL); % 0.06\r\nfor j=1:mL\r\n  mc(:,j)=sum(min(mch,repmat(mch(j,:),mL,1)),2);\r\nend\r\nmc=mc-mpc; % remove mpc part\r\nmpc5c=5*mpc+mc;\r\nfprintf('Initialization %.3f\\n',toc)\r\n% finished initilaiztion calculation in less than 0.2 sec\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 0\r\n3 6 5 6  2 1\r\n6 6 5 5  4 0]; % case 940\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  3 0\r\n6 5 4 5  4 0]; % case 900\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 2\r\n4 4 1 5  3 0\r\n1 4 5 6  1 3\r\n6 4 1 5  4 0]; % case 850\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  0 3\r\n6 3 2 1  4 0]; % case 816\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  1 2\r\n2 4 5 1  0 2\r\n6 1 2 3  4 0]; % case 750\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 1\r\n4 2 5 6  0 2\r\n6 5 1 5  1 1\r\n5 5 3 2  4 0]; % case 700\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  1 0\r\n5 3 5 5  4 0]; % case 650\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  0 2\r\n3 2 1 5  2 2\r\n3 5 1 2  1 3\r\n5 2 1 3  4 0]; % case 600\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  1 2\r\n4 3 2 5  2 1\r\n4 4 2 3  3 0\r\n4 6 2 3  4 0]; % case 550\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 3\r\n3 4 5 3  2 2\r\n4 3 5 3  4 0]; % case 500\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  0 1\r\n4 1 5 6  2 1\r\n4 5 5 1  1 1\r\n4 1 6 4  4 0]; % case 450\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 3\r\n1 4 4 5  0 2\r\n3 6 1 4  4 0]; % case 400\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 3\r\n1 1 4 3  0 4\r\n3 4 1 1  4 0]; % case 350\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  2 1\r\n1 4 1 5  0 1\r\n3 1 2 2  4 0]; % case 300\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  2 0\r\n1 3 5 5  1 1\r\n2 6 5 3  1 1\r\n2 5 4 5  4 0]; % case 250\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 0\r\n1 5 2 6  0 1\r\n2 2 4 4  1 1\r\n2 3 3 2  4 0]; % case 200\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  1 2\r\n1 1 1 4  3 0\r\n2 1 1 4  4 0]; % case 150\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  1 1\r\n3 2 5 3  0 2\r\n1 5 3 6  3 0\r\n1 5 3 5  4 0]; % case 100\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 1\r\n1 1 3 2  2 1\r\n4 1 1 5  0 1\r\n1 3 2 2  4 0]; % case 50\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  2 1\r\n1 4 2 5  1 0\r\n1 1 1 3  4 0]; % case 1\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-06-18T19:30:49.000Z","updated_at":"2025-12-12T14:19:44.000Z","published_at":"2017-06-18T19:58:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mastermind_(board_game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47518,"title":"Play Outside In with primes","description":"In the word game Outside In,* you start with a word, take the first and last letters, try to think of a word in which the two letters appear together in the interior, and repeat. For example, starting with ‘MATLAB’, you might proceed as follows:\r\nsombrero, dissolution, kidnap, pickpocket, helicopter, birthright, debtor, tawdry, katydid, backdrop, subpoena, anniversary, payback, napkin, mannequin, solemnity, psychosis, lopsided, balderdash, etc. \r\nYou can play alone or with others, and you could add other rules or objectives—e.g., all words must have six or more letters, a person who cannot think of a word is eliminated, players try to generate a list that uses all letters in the alphabet, etc.   \r\nThis game has one problem: without a convenient and stable word list, it does not translate easily to Cody. So instead of words, let’s use prime numbers. In particular, start with a two-digit seed, find the smallest prime for which the two digits appear together in the interior, create a new two-digit number from the first and last digits of the prime, and repeat. \r\nFor example, if the seed is 11, then the list would start 1117, 1171, 2111, 1213, 2131, 1217,… Because the last digits of primes are constrained, you will notice similarities in lists generated from different seeds. Solvers able to plot the numbers might enjoy seeing the patterns for longer lists graphically. \r\nWrite a function to play Outside In with primes: given a two-digit seed , generate a list of  primes following the rules above. \r\n\r\n*I devised the game and the name, but I would be surprised if someone had not already thought of it.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 420px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 210px; transform-origin: 407px 210px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.683px 8px; transform-origin: 368.683px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn the word game Outside In,* you start with a word, take the first and last letters, try to think of a word in which the two letters appear together in the interior, and repeat. For example, starting with ‘MATLAB’, you might proceed as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.39167px 8px; transform-origin: 7.39167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eso\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 10.5px 8px; transform-origin: 10.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003emb\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2833px 8px; transform-origin: 25.2833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003erero, dis\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eso\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.675px 8px; transform-origin: 25.675px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003elution, ki\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.55px 8px; transform-origin: 8.55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edn\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 20.6167px 8px; transform-origin: 20.6167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eap, pic\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ekp\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.9px 8px; transform-origin: 38.9px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eocket, helico\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.60833px 8px; transform-origin: 6.60833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ept\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4417px 8px; transform-origin: 19.4417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eer, birt\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7px 8px; transform-origin: 7px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ehr\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 22.95px 8px; transform-origin: 22.95px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eight, de\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.60833px 8px; transform-origin: 6.60833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ebt\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 20.6083px 8px; transform-origin: 20.6083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eor, taw\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7px 8px; transform-origin: 7px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edr\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.2583px 8px; transform-origin: 14.2583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ey, ka\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.225px 8px; transform-origin: 6.225px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ety\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 24.5083px 8px; transform-origin: 24.5083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003edid, bac\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ekd\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21.3917px 8px; transform-origin: 21.3917px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003erop, su\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.55px 8px; transform-origin: 8.55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ebp\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.45px 8px; transform-origin: 19.45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eoena, anniver\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 8px; transform-origin: 7.78333px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003esa\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 13.0917px 8px; transform-origin: 13.0917px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ery, p\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 8px; transform-origin: 7.78333px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eay\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 26.45px 8px; transform-origin: 26.45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eback, na\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003epk\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.0583px 8px; transform-origin: 19.0583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ein, ma\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.55px 8px; transform-origin: 8.55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enn\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 33.85px 8px; transform-origin: 33.85px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eequin, sole\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 10.5px 8px; transform-origin: 10.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003emn\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.2583px 8px; transform-origin: 14.2583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eity, p\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 8px; transform-origin: 7.78333px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003esy\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.175px 8px; transform-origin: 29.175px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003echosis, lo\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eps\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 24.9px 8px; transform-origin: 24.9px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eided, ba\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.21667px 8px; transform-origin: 6.21667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eld\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.5px 8px; transform-origin: 38.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eerdash, etc. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.05px 8px; transform-origin: 363.05px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can play alone or with others, and you could add other rules or objectives—e.g., all words must have six or more letters, a person who cannot think of a word is eliminated, players try to generate a list that uses all letters in the alphabet, etc. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.283px 8px; transform-origin: 373.283px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis game has one problem: without a convenient and stable word list, it does not translate easily to Cody. So instead of words, let’s use prime numbers. In particular, start with a two-digit seed, find the smallest prime for which the two digits appear together in the interior, create a new two-digit number from the first and last digits of the prime, and repeat. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 318.958px 8px; transform-origin: 318.958px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if the seed is 11, then the list would start 1117, 1171, 2111, 1213, 2131, 1217,… Because \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45994-investigate-the-frequency-of-last-digits-of-prime-numbers\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ethe last digits of primes are constrained\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 306.775px 8px; transform-origin: 306.775px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, you will notice similarities in lists generated from different seeds. Solvers able to plot the numbers might enjoy seeing the patterns for longer lists graphically. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 215.742px 8px; transform-origin: 215.742px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to play Outside In with primes: given a two-digit seed \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 57.5583px 8px; transform-origin: 57.5583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, generate a list of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 82.8583px 8px; transform-origin: 82.8583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e primes following the rules above. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 311.567px 8px; transform-origin: 311.567px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e*I devised the game and the name, but I would be surprised if someone had not already thought of it.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = OutsideInPrimes(x,n)\r\n%  x = two-digit seed\r\n%  n = number of primes in the list\r\n\r\n  y = f(x,n);\r\nend","test_suite":"%%\r\nx = 11;\r\nn = 6;\r\ny_correct = [1117 1171 2111 1213 2131 1217];\r\nassert(isequal(OutsideInPrimes(x,n),y_correct))\r\n\r\n%%\r\nx = 52;\r\nn = 10;\r\ny_correct = [1523 2131 1213 2137 1277 1171 1117 2179 1291 2111];\r\nassert(isequal(OutsideInPrimes(x,n),y_correct))\r\n\r\n%%\r\nx = 88;\r\nn = 10;\r\ny_correct = [1889 1193 2131 1213 2137 1277 1171 1117 2179 1291];\r\nassert(isequal(OutsideInPrimes(x,n),y_correct))\r\n\r\n%%\r\nx = 66;\r\nn = 200;\r\ny = OutsideInPrimes(x,n);\r\nsum_correct = 1167614;\r\ny_correct_p = [10177 11171 11159 11903 11329 11909 11923 11351 11161 11173 11353 11369 11927 11177 11701 12113 11383 11393 11399];\r\nassert(isequal(sum(y),sum_correct))\r\nassert(isequal(y(149:167),y_correct_p))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-09-16T16:09:02.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-15T14:32:53.000Z","updated_at":"2025-11-29T23:35:09.000Z","published_at":"2020-11-15T15:09:15.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the word game Outside In,* you start with a word, take the first and last letters, try to think of a word in which the two letters appear together in the interior, and repeat. For example, starting with ‘MATLAB’, you might proceed as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eso\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003erero, dis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eso\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003elution, ki\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eap, pic\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ekp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eocket, helico\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ept\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eer, birt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehr\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eight, de\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eor, taw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edr\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ey, ka\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ety\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003edid, bac\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ekd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003erop, su\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eoena, anniver\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esa\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ery, p\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eay\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eback, na\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epk\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ein, ma\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eequin, sole\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eity, p\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003echosis, lo\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eps\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eided, ba\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eld\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eerdash, etc. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can play alone or with others, and you could add other rules or objectives—e.g., all words must have six or more letters, a person who cannot think of a word is eliminated, players try to generate a list that uses all letters in the alphabet, etc. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis game has one problem: without a convenient and stable word list, it does not translate easily to Cody. So instead of words, let’s use prime numbers. In particular, start with a two-digit seed, find the smallest prime for which the two digits appear together in the interior, create a new two-digit number from the first and last digits of the prime, and repeat. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if the seed is 11, then the list would start 1117, 1171, 2111, 1213, 2131, 1217,… Because \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45994-investigate-the-frequency-of-last-digits-of-prime-numbers\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe last digits of primes are constrained\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, you will notice similarities in lists generated from different seeds. Solvers able to plot the numbers might enjoy seeing the patterns for longer lists graphically. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to play Outside In with primes: given a two-digit seed \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, generate a list of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e primes following the rules above. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e*I devised the game and the name, but I would be surprised if someone had not already thought of it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44751,"title":"Lights Out 1 - 5x5, 3 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. Pressing any button on the board toggles that button as well as adjacent buttons vertically and horizontally, where present (and no wrapping here), to the opposite state (i.e., on to off and off to on). For example, pressing a corner button toggles three button states, an edge button four button states, and an interior button five button states (as can be visualized by the example below).\r\n\r\nThis first problem uses a 5x5 board and requires that your function return a column-major vector of button presses (indices) that will solve the board by turning off all lights. Since pressing any button twice will return to the same board state, only one move is needed (and allowed) per button. For example, if\r\n\r\n board = [0 1 0 0 0\r\n          1 1 1 0 1\r\n          0 1 0 1 1\r\n          1 0 0 0 1\r\n          1 1 0 0 0]\r\n\r\nthe answer is:\r\n\r\n moves = [5 7 23]\r\n\r\nIn this first problem, all boards have solutions of only three moves.\r\n\r\nNote: while brute-force solutions will solve some problems in this series, they will time out on later ones. Solving the first few problems by developing a robust solver function is encouraged.\r\n\r\nAs a potential starting point, you might check out one or more of the following resources by \u003chttp://www.mat.ucm.es/~vmunozve/lights-out.pdf Vicente Muñoz\u003e; \u003chttp://www.ijritcc.org/download/browse/Volume_5_Issues/August_17_Volume_5_Issue_8/1503304082_21-08-2017.pdf Chen, et al.\u003e; \u003chttp://vprusso.github.io/blog/2017/the-mathematics-of-lights-out/ Vincent Russo\u003e; \u003chttp://mathworld.wolfram.com/LightsOutPuzzle.html Margherita Barile (on wolfram.com)\u003e; \u003chttp://www.keithschwarz.com/interesting/code/?dir=lights-out Keith Schwarz\u003e; or \u003chttps://dc.ewu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/\u0026httpsredir=1\u0026article=1166\u0026context=theses Rebecca Meyer (a master's thesis)\u003e.\r\n\r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves 5x5, 4 moves\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"display: block; min-width: 0px; padding-top: 0px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eLights Out\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e is a logic game wherein all lights need to be turned off to complete each board. Pressing any button on the board toggles that button as well as adjacent buttons vertically and horizontally, where present (and no wrapping here), to the opposite state (i.e., on to off and off to on). For example, pressing a corner button toggles three button states, an edge button four button states, and an interior button five button states (as can be visualized by the example below).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eThis first problem uses a 5x5 board and requires that your function return a column-major vector of button presses (indices) that will solve the board by turning off all lights. Since pressing any button twice will return to the same board state, only one move is needed (and allowed) per button. For example, if\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-bottom: 10px; margin-left: 3px; margin-right: 3px; margin-top: 10px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e board = [0 1 0 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          1 1 1 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          0 1 0 1 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          1 0 0 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          1 1 0 0 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 10px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003ethe answer is:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-bottom: 10px; margin-left: 3px; margin-right: 3px; margin-top: 10px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e moves = [5 7 23]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 10px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eIn this first problem, all boards have solutions of only three moves.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eNote: while brute-force solutions will solve some problems in this series, they will time out on later ones. Solving the first few problems by developing a robust solver function is encouraged.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eAs a potential starting point, you might check out one or more of the following resources by\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eVicente Muñoz\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eChen, et al.\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eVincent Russo\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMargherita Barile (on wolfram.com)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eKeith Schwarz\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e; or\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://dc.ewu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/\u0026amp;httpsredir=1\u0026amp;article=1166\u0026amp;context=theses\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRebecca Meyer (a master's thesis)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eNext:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e5x5, 4 moves\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function moves = lights_out_1(board) % 5x5 board, 3 moves\r\n moves = board;\r\nend","test_suite":"\r\n%% \r\n board = [0 1 0 0 0\r\n          1 1 1 0 1\r\n          0 1 0 1 1\r\n          1 0 0 0 1\r\n          1 1 0 0 0];\r\n %plot_board(board,'Input')\r\nmoves = lights_out_1(board); % should be [5 7 23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2);   %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 1];\r\nmoves = lights_out_1(board); %should be [1 13 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [1 0 0 0 0  \r\n          0 1 0 0 0  \r\n          1 1 0 0 0  \r\n          0 1 0 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_1(board); %should be [2 3 4]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_1(board); %should be [7 12 17]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 1  \r\n          0 0 1 1 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_1(board); %you're on your own now\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 1 1 1 1  \r\n          0 0 1 0 0];\r\n plot_board(board,'Input',[])\r\nmoves = lights_out_1(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2);\r\n    plot_board(board,['Step ',num2str(i)],moves(i))\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n\r\nfunction plot_board(board,txt_title,sq_tap)\r\n sq_on = (board==1); % determine which squares are on\r\n pts = repmat(1:5,[5,1]); x = pts(1:end); % x coordinates for each square\r\n pts = fliplr(pts)'; y = pts(1:end); % y coordinates for each square\r\n figure; hold on\r\n fill([0,0,5,5],[0,5,5,0],'k','FaceAlpha',0.3) % background\r\n plot(x(sq_on)-0.5,y(sq_on)-0.5,'ys','MarkerSize',50, ...\r\n     'MarkerFaceColor','y') % on squares\r\n plot(x(~sq_on)-0.5,y(~sq_on)-0.5,'s','MarkerSize',50, ...\r\n     'Color',[0.9 0.95 1.0],'MarkerFaceColor',[0.9 0.95 1.0]) % off squares\r\n if ~isempty(sq_tap)\r\n  plot(x(sq_tap)-0.5,y(sq_tap)-0.5,'g*','MarkerSize',40, ...\r\n      'LineWidth',5) % tapped square\r\n end\r\n axis square\r\n set(gca,'XTick',0:5); set(gca,'YTick',0:5)\r\n title(txt_title,'FontSize',24)\r\nend","published":true,"deleted":false,"likes_count":5,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2021-04-13T00:54:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-25T19:39:19.000Z","updated_at":"2026-01-06T08:23:35.000Z","published_at":"2018-10-29T14:07:20.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. Pressing any button on the board toggles that button as well as adjacent buttons vertically and horizontally, where present (and no wrapping here), to the opposite state (i.e., on to off and off to on). For example, pressing a corner button toggles three button states, an edge button four button states, and an interior button five button states (as can be visualized by the example below).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis first problem uses a 5x5 board and requires that your function return a column-major vector of button presses (indices) that will solve the board by turning off all lights. Since pressing any button twice will return to the same board state, only one move is needed (and allowed) per button. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 0 0\\n          1 1 1 0 1\\n          0 1 0 1 1\\n          1 0 0 0 1\\n          1 1 0 0 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [5 7 23]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this first problem, all boards have solutions of only three moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote: while brute-force solutions will solve some problems in this series, they will time out on later ones. Solving the first few problems by developing a robust solver function is encouraged.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs a potential starting point, you might check out one or more of the following resources by\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVicente Muñoz\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eChen, et al.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVincent Russo\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMargherita Barile (on wolfram.com)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKeith Schwarz\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e; or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://dc.ewu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/\u0026amp;httpsredir=1\u0026amp;article=1166\u0026amp;context=theses\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRebecca Meyer (a master's thesis)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 4 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":46120,"title":"Solve the Challenger puzzle","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 442.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 221.167px; transform-origin: 407px 221.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 7.8px; transform-origin: 14px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://kingfeatures.com/features/puzzlesandgames/challenger/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eChallenger\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 321.267px 7.8px; transform-origin: 321.267px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e puzzle by Linus Maurer requires the solver to fill a 4x4 matrix of integers from 1 to 9 to match the given sums of the rows, columns, main diagonal, and anti-diagonal. Four of the numbers are given. Numbers can be repeated, and the solution is not necessarily unique. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 376.017px 7.8px; transform-origin: 376.017px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve the Challenger. The input will be a matrix resembling the game board. For example, if the input is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e   NaN  NaN  NaN  NaN  12\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     0    0    0    4  23\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     0    1    0    0  11\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     6    0    0    0  21\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     0    0    2    0  13\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     9   20   22   17  12\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.85px 7.8px; transform-origin: 379.85px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ethen the sums of the four columns are 9, 20, 22, and 17. The sums of the rows are 23, 11, 21, and 13, and the sums of the two diagonals are 12 and 12. The four starting numbers are 6, 1, 2, and 4, and zeros indicate the numbers to be determined. Ignore the NaNs. The output of the function should be the 4x4 matrix of numbers. In the example, a valid solution would be \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80.85px 8.25px; transform-origin: 80.85px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     1    9    9    4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80.85px 8.25px; transform-origin: 80.85px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     1    1    4    5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80.85px 8.25px; transform-origin: 80.85px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     6    3    7    5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 84.7px 8.25px; transform-origin: 84.7px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     1    7    2    3 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.8px; transform-origin: 0px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Challenger(x)\r\n  y = f(x);\r\nend","test_suite":"%%\r\nx = [NaN NaN NaN NaN 12; 0 0 0 4 23; 0 1 0 0 11; 6 0 0 0 21; 0 0 2 0 13; 9 20 22 17 12];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)\r\n\r\n%%\r\nx = [NaN NaN NaN NaN 25; 0 0 0 7 18; 0 9 0 0 29; 0 0 6 0 27; 9 0 0 0 27; 30 21 19 31 28];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)\r\n\r\n%%\r\nx = [NaN NaN NaN NaN 19; 0 0 0 2 16; 5 0 0 0 22; 0 0 4 0 27; 0 2 0 0 11; 19 16 19 22 15];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)\r\n\r\n%%\r\nx = [NaN NaN NaN NaN 20; 0 0 4 0 25; 0 6 0 0 22; 4 0 0 0 21; 0 0 0 4 25; 26 20 22 25 26];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)","published":true,"deleted":false,"likes_count":3,"comments_count":3,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-08-08T15:02:54.000Z","updated_at":"2026-02-11T17:03:32.000Z","published_at":"2020-08-08T15:35:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://kingfeatures.com/features/puzzlesandgames/challenger/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eChallenger\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e puzzle by Linus Maurer requires the solver to fill a 4x4 matrix of integers from 1 to 9 to match the given sums of the rows, columns, main diagonal, and anti-diagonal. Four of the numbers are given. Numbers can be repeated, and the solution is not necessarily unique. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve the Challenger. The input will be a matrix resembling the game board. For example, if the input is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   NaN  NaN  NaN  NaN  12\\n     0    0    0    4  23\\n     0    1    0    0  11\\n     6    0    0    0  21\\n     0    0    2    0  13\\n     9   20   22   17  12]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethen the sums of the four columns are 9, 20, 22, and 17. The sums of the rows are 23, 11, 21, and 13, and the sums of the two diagonals are 12 and 12. The four starting numbers are 6, 1, 2, and 4, and zeros indicate the numbers to be determined. Ignore the NaNs. The output of the function should be the 4x4 matrix of numbers. In the example, a valid solution would be \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     1    9    9    4\\n     1    1    4    5\\n     6    3    7    5\\n     1    7    2    3 ]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59586,"title":"List ways to make a sum in Killer Sudoku","description":"In Sudoku, the subject of several Cody problems, one tries to arrange the digits 1 through 9 in a 9x9 grid of 81 cells (or a 3x3 arrangement of nine 3x3 boxes) so that every row, column, and 3x3 box contains each digit exactly once. In the variant Killer Sudoku, digits are placed in cages that indicate the sum of the digits; digits cannot be repeated in cages. In the example below from Wikipedia, the cages are indicated by colors. \r\nSolving Killer Sudoku with few (or no) given digits requires ready ideas of how to make particular sums. A two-digit cage with a sum of 3 can contain only 1 and 2, and a two-digit cage with a sum of 4 can contain only 1 and 3 because 2 cannot be repeated. A three-digit cage with a sum of 21 can have some arrangement of [9 8 4], [9 7 5], or [8 7 6]. The three-digit cage summing to 17 in the central box of the example has seven possible sets, but if the box already has an 8 outside the 17-cage, then only four of those sets are possible: [9 7 1], [9 6 2], [9 5 3], [7 6 4].\r\nWrite a function to list the ways to make a sum with a given number of digits. If only two arguments are given, then all of the digits 1-9 are possible. If a third argument is given, then it indicates the digits that cannot be used. The output should be a matrix in which the digits decrease along the rows and the rows are sorted in decreasing order of the first digit, then the second digit, then the third, etc., as in the examples. List only one arrangement: in the last example above, only [9 7 1] is given, not [9 1 7], [7 9 1], [7 1 9], [1 9 7], and [1 7 9]. If the sum cannot be made, return [].\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 582.7px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 291.35px; transform-origin: 407px 291.35px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.95px 8px; transform-origin: 78.95px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn Sudoku, the subject of \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems?term=sudoku\u0026amp;submitsearch=\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eseveral Cody problems\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 223.65px 8px; transform-origin: 223.65px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, one tries to arrange the digits 1 through 9 in a 9x9 grid of 81 cells (or a 3x3 arrangement of nine 3x3 boxes) so that every row, column, and 3x3 box contains each digit exactly once. In the variant Killer Sudoku, digits are placed in cages that indicate the sum of the digits; digits cannot be repeated in cages. In the example below from \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Killer_sudoku\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eWikipedia\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 110.075px 8px; transform-origin: 110.075px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the cages are indicated by colors. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 372.65px 8px; transform-origin: 372.65px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSolving Killer Sudoku with few (or no) given digits requires ready ideas of how to make particular sums. A two-digit cage with a sum of 3 can contain only 1 and 2, and a two-digit cage with a sum of 4 can contain only 1 and 3 because 2 cannot be repeated. A three-digit cage with a sum of 21 can have some arrangement of [9 8 4], [9 7 5], or [8 7 6]. The three-digit cage summing to 17 in the central box of the example has seven possible sets, but if the box already has an 8 outside the 17-cage, then only four of those sets are possible: [9 7 1], [9 6 2], [9 5 3], [7 6 4].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.75px 8px; transform-origin: 383.75px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to list the ways to make a sum with a given number of digits. If only two arguments are given, then all of the digits 1-9 are possible. If a third argument is given, then it indicates the digits that cannot be used. The output should be a matrix in which the digits decrease along the rows and the rows are sorted in decreasing order of the first digit, then the second digit, then the third, etc., as in the examples. List only one arrangement: in the last example above, only [9 7 1] is given, not [9 1 7], [7 9 1], [7 1 9], [1 9 7], and [1 7 9]. If the sum cannot be made, return [].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 261.7px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 130.85px; text-align: left; transform-origin: 384px 130.85px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"256\" height=\"256\" style=\"vertical-align: baseline;width: 256px;height: 256px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Killersudoku_color.svg/1024px-Killersudoku_color.svg.png\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = makesum(varargin)\r\n% Argument 1 is the target sum, and argument 2 is the number of digits to be used in the sum.\r\n% If given, argument 3 lists the digits to exclude from the sum.\r\n  y = sum(randi(9,n));\r\nend","test_suite":"%%\r\nx = randi(9);\r\nn = 1;\r\ny = makesum(x,n);\r\ny_correct = x;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 3;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 4;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 5;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [4 1; 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 6;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [5 1; 4 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 7;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [6 1; 5 2; 4 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 8;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [7 1; 6 2; 5 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 9;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [8 1; 7 2; 6 3; 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 10;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 1; 8 2; 7 3; 6 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 11;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 2; 8 3; 7 4; 6 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 12;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 3; 8 4; 7 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 13;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 4; 8 5; 7 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 14;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 5; 8 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 15;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 6; 8 7];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 16;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 7];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 8];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 2;\r\nd = [];\r\ny = makesum(x,n,d);\r\ny_correct = [9 8];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 2;\r\nd = [1 2];\r\ny = makesum(x,n,d);\r\ny_correct = [9 8];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 6;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [3 2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 7;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [4 2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 8;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [5 2 1; 4 3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 8;\r\nn = 3;\r\nd = 2;\r\ny = makesum(x,n,d);\r\ny_correct = [4 3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 9;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [6 2 1; 5 3 1; 4 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 10;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [7 2 1; 6 3 1; 5 4 1; 5 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 11;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [8 2 1; 7 3 1; 6 4 1; 6 3 2; 5 4 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 12;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 2 1; 8 3 1; 7 4 1; 7 3 2; 6 5 1; 6 4 2; 5 4 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 13;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 3 1; 8 4 1; 8 3 2; 7 5 1; 7 4 2; 6 5 2; 6 4 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 13;\r\nn = 3;\r\nd = [1 3];\r\ny = makesum(x,n,d);\r\ny_correct = [7 4 2; 6 5 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 14;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 4 1; 9 3 2; 8 5 1; 8 4 2; 7 6 1; 7 5 2; 7 4 3; 6 5 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 15;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 5 1; 9 4 2; 8 6 1; 8 5 2; 8 4 3; 7 6 2; 7 5 3; 6 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 16;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 6 1; 9 5 2; 9 4 3; 8 7 1; 8 6 2; 8 5 3; 7 6 3; 7 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 16;\r\nn = 3;\r\nd = 3;\r\ny = makesum(x,n,d);\r\ny_correct = [9 6 1; 9 5 2; 8 7 1; 8 6 2; 7 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 7 1; 9 6 2; 9 5 3; 8 7 2; 8 6 3; 8 5 4; 7 6 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 18;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 1; 9 7 2; 9 6 3; 9 5 4; 8 7 3; 8 6 4; 7 6 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 19;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 2; 9 7 3; 9 6 4; 8 7 4; 8 6 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 20;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 3; 9 7 4; 9 6 5; 8 7 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 21;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 4; 9 7 5; 8 7 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 21;\r\nn = 3;\r\nd = 7;\r\ny = makesum(x,n,d);\r\ny_correct = [9 8 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 10;\r\nn = 4;\r\ny = makesum(x,n);\r\ny_correct = [4 3 2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 16;\r\nn = 4;\r\ny = makesum(x,n);\r\ny_correct = [9 4 2 1; 8 5 2 1; 8 4 3 1; 7 6 2 1; 7 5 3 1; 7 4 3 2; 6 5 4 1; 6 5 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 21;\r\nn = 4;\r\ny = makesum(x,n);\r\ny_correct = [9 8 3 1; 9 7 4 1; 9 7 3 2; 9 6 5 1; 9 6 4 2; 9 5 4 3; 8 7 5 1; 8 7 4 2; 8 6 5 2; 8 6 4 3; 7 6 5 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 21;\r\nn = 4;\r\nd = [1 3];\r\ny = makesum(x,n,d);\r\ny_correct = [9 6 4 2; 8 7 4 2; 8 6 5 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 40;\r\nn = 6;\r\ny = makesum(x,n);\r\nassert(isempty(y))\r\n\r\n%% \r\nx = 40;\r\nn = 7;\r\ny = makesum(x,n);\r\ny_correct = [9 8 7 6 5 4 1; 9 8 7 6 5 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 45;\r\nn = 9;\r\ny = makesum(x,n);\r\ny_correct = 9:-1:1;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('makesum.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-01-22T12:22:39.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-22T05:25:55.000Z","updated_at":"2024-01-22T12:22:39.000Z","published_at":"2024-01-22T05:32:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn Sudoku, the subject of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems?term=sudoku\u0026amp;submitsearch=\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eseveral Cody problems\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, one tries to arrange the digits 1 through 9 in a 9x9 grid of 81 cells (or a 3x3 arrangement of nine 3x3 boxes) so that every row, column, and 3x3 box contains each digit exactly once. In the variant Killer Sudoku, digits are placed in cages that indicate the sum of the digits; digits cannot be repeated in cages. In the example below from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Killer_sudoku\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWikipedia\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, the cages are indicated by colors. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolving Killer Sudoku with few (or no) given digits requires ready ideas of how to make particular sums. A two-digit cage with a sum of 3 can contain only 1 and 2, and a two-digit cage with a sum of 4 can contain only 1 and 3 because 2 cannot be repeated. A three-digit cage with a sum of 21 can have some arrangement of [9 8 4], [9 7 5], or [8 7 6]. The three-digit cage summing to 17 in the central box of the example has seven possible sets, but if the box already has an 8 outside the 17-cage, then only four of those sets are possible: [9 7 1], [9 6 2], [9 5 3], [7 6 4].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to list the ways to make a sum with a given number of digits. If only two arguments are given, then all of the digits 1-9 are possible. If a third argument is given, then it indicates the digits that cannot be used. The output should be a matrix in which the digits decrease along the rows and the rows are sorted in decreasing order of the first digit, then the second digit, then the third, etc., as in the examples. List only one arrangement: in the last example above, only [9 7 1] is given, not [9 1 7], [7 9 1], [7 1 9], [1 9 7], and [1 7 9]. If the sum cannot be made, return [].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"256\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"256\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Killersudoku_color.svg/1024px-Killersudoku_color.svg.png\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47463,"title":"Slitherlink II: Gimmes","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 531.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 265.833px; transform-origin: 407px 265.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.8667px 7.91667px; transform-origin: 87.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink II: Gimmes\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 293.283px 7.91667px; transform-origin: 293.283px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.683px 7.91667px; transform-origin: 363.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\n\r\n[sv,valid]=pcheck(s,p,bsegs); \r\nfprintf('sv  init solution\\n')\r\nfprintf('%i ',sv);fprintf('\\n') \r\n\r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n%Author Note: I found creating the complete set was time consuming\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge  add by raz as a Gimme\r\n\r\n [nr,nc]=size(s);\r\n %Example Zero processing\r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  %enter setting of p for 1s in corners\r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  %enter setting of p for 1s in corners\r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n %enter setting of p for 1s in corners \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n %setting p for 03 adjacent cases\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n %setting p for 33 adjacent\r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  %setting p for 03 diagonal\r\n \r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); \r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n  %setting p for 33 diagonal\r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); \r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i); \r\n  %3-0 adjacent set segs to 0/5\r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n %Slithering Starting Techniques misses the 13 edge Gimme     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 2;2 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5;5 0 5;5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 3 2;5 0 5 0 5;5 3 5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 5 3 5;5 0 5 5 0 5;5 3 5 5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T17:23:06.000Z","updated_at":"2025-05-02T19:04:22.000Z","published_at":"2020-11-12T23:27:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink II: Gimmes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":57810,"title":"List the two-bit primes","description":"Each year at Christmas, my father-in-law and his partner send me the Puzzle Mania section from the New York Times. This year’s edition had a puzzle by Mike Shenk called “Two Bits”, which asked solvers to find words whose first two letters could be shifted as a unit elsewhere in the word to form a new word. For example, the clue “??BID” had the answer RABID, with the accompanying word BRAID. The clue “??PREME” had the answer SUPREME, with the accompanying word PRESUME.\r\nIn a similar way, one can define two-bit primes: prime numbers whose first two digits can be shifted, as a unit, elsewhere in the number to form a new prime number. The first in the sequence is 113, with accompanying prime 311, followed by 131, with accompanying prime 113. Some two-bit primes can have multiple accompanying primes. For example, the two-bit prime 99611 has two accompanying primes: 69911 and 61991. Leading zeros are allowed: 1103 is a two-bit prime because 311 is also prime. \r\nRemember that the two-bit prime and its accompanying prime must differ. For example, the first two digits of 99991 can be moved one or two places to the right, but the resulting number (99991) is the same. Because 99199 is not prime, 99991 is not a two-bit prime. \r\nWrite a function to list the th two-bit and all its accompanying primes. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 321px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 160.5px; transform-origin: 407px 160.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 313.108px 8px; transform-origin: 313.108px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach year at Christmas, my father-in-law and his partner send me the Puzzle Mania section from the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52.8417px 8px; transform-origin: 52.8417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eNew York Times.\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 17.1083px 8px; transform-origin: 17.1083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e This year’s edition had a puzzle by Mike Shenk called “Two Bits”, which asked solvers to find words whose first two letters could be shifted as a unit elsewhere in the word to form a new word. For example, the clue “??BID” had the answer RABID, with the accompanying word BRAID. The clue “??PREME” had the answer SUPREME, with the accompanying word PRESUME.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.058px 8px; transform-origin: 381.058px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn a similar way, one can define two-bit primes: prime numbers whose first two digits can be shifted, as a unit, elsewhere in the number to form a new prime number. The first in the sequence is 113, with accompanying prime 311, followed by 131, with accompanying prime 113. Some two-bit primes can have multiple accompanying primes. For example, the two-bit prime 99611 has two accompanying primes: 69911 and 61991. Leading zeros are allowed: 1103 is a two-bit prime because 311 is also prime. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.875px 8px; transform-origin: 379.875px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRemember that the two-bit prime and its accompanying prime must differ. For example, the first two digits of 99991 can be moved one or two places to the right, but the resulting number (99991) is the same. Because 99199 is not prime, 99991 is not a two-bit prime. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.375px 8px; transform-origin: 80.375px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to list the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 133.808px 8px; transform-origin: 133.808px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth two-bit and all its accompanying primes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,a] = twobitPrime(n)\r\n  y = isprime(n); a = primes(n);\r\nend","test_suite":"%%\r\nn = 1;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 113;\r\na_correct = 311;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 5;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 199;\r\na_correct = 919;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 31;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 977;\r\na_correct = 797;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 311;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 5119;\r\na_correct = 1951;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 500;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 7963;\r\na_correct = [6379 6793];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),sort(a_correct)))\r\n\r\n%%\r\nn = 873;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 13591;\r\na_correct = 59113;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 873;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 13591;\r\na_correct = 59113;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 1876;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 29009;\r\na_correct = [929 2909];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 5277;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 86069;\r\na_correct = [6869 8669];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 8153;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 134921;\r\na_correct = 492113;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 10003;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 168617;\r\na_correct = [861167 861617];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 25000;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 433261;\r\na_correct = [324361 326143 343261];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 39000;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 697877;\r\na_correct = [787697 787769];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\n[y,a] = twobitPrime(twobitPrime(54));\r\ny_correct = 19853;\r\na_correct = [81953 85193];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nfiletext = fileread('twobitPrime.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-03-19T00:22:09.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-03-19T00:21:05.000Z","updated_at":"2025-01-02T08:51:14.000Z","published_at":"2023-03-19T00:22:09.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach year at Christmas, my father-in-law and his partner send me the Puzzle Mania section from the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNew York Times.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e This year’s edition had a puzzle by Mike Shenk called “Two Bits”, which asked solvers to find words whose first two letters could be shifted as a unit elsewhere in the word to form a new word. For example, the clue “??BID” had the answer RABID, with the accompanying word BRAID. The clue “??PREME” had the answer SUPREME, with the accompanying word PRESUME.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a similar way, one can define two-bit primes: prime numbers whose first two digits can be shifted, as a unit, elsewhere in the number to form a new prime number. The first in the sequence is 113, with accompanying prime 311, followed by 131, with accompanying prime 113. Some two-bit primes can have multiple accompanying primes. For example, the two-bit prime 99611 has two accompanying primes: 69911 and 61991. Leading zeros are allowed: 1103 is a two-bit prime because 311 is also prime. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRemember that the two-bit prime and its accompanying prime must differ. For example, the first two digits of 99991 can be moved one or two places to the right, but the resulting number (99991) is the same. Because 99199 is not prime, 99991 is not a two-bit prime. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to list the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eth two-bit and all its accompanying primes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44752,"title":"Lights Out 2 - 5x5, 4 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require four moves to solve. For example, if\r\n\r\n board = [1 0 1 1 1  \r\n          1 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 1 1  \r\n          1 1 0 0 1]\r\n\r\nthe answer is:\r\n\r\n moves = [2 5 16 24]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves 5x5, 3 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves 5x5, 6 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require four moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 1 1 1  \r\n          1 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 1 1  \r\n          1 1 0 0 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [2 5 16 24]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003e5x5, 3 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\"\u003e5x5, 6 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_2(board) % 5x5 board, 4 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 1 1 1  \r\n          1 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 1 1  \r\n          1 1 0 0 1];\r\nmoves = lights_out_2(board); % [2 5 16 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 1 0 0 0  \r\n          1 1 0 0 0  \r\n          0 1 0 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_2(board); % [1 2 3 4]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_2(board); % [7 9 17 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 0 1 1 0  \r\n          0 1 1 1 1  \r\n          0 1 1 1 1  \r\n          0 0 1 1 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_2(board); % [12 13 17 18]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_2(board); % [8 12 14 18]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 0 1  \r\n          0 0 1 1 1  \r\n          0 0 0 1 1  \r\n          0 1 0 0 1  \r\n          1 1 1 0 0];\r\nmoves = lights_out_2(board); % on your own now\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 0 0  \r\n          1 0 0 1 0  \r\n          0 1 1 1 1  \r\n          0 0 1 0 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 0 0  \r\n          0 0 0 1 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          0 1 0 1 0  \r\n          0 1 1 0 0  \r\n          1 0 1 0 0  \r\n          1 0 1 0 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 0 0 1 0  \r\n          0 1 0 0 0  \r\n          0 0 0 1 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":24,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T14:43:57.000Z","updated_at":"2025-11-29T15:31:35.000Z","published_at":"2018-10-31T12:25:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require four moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 1 1 1  \\n          1 1 0 1 0  \\n          1 0 0 0 1  \\n          1 0 0 1 1  \\n          1 1 0 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [2 5 16 24]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 3 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44761,"title":"Lights Out 9 - 5x5, light-only solution? II","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state contains a potential answer—i.e., if toggling any given subset of the starting lights is sufficient to solve the board.\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44760 5x5, light-only solution? I\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44764 5x5, with wrapping, 6 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state contains a potential answer—i.e., if toggling any given subset of the starting lights is sufficient to solve the board.\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44760\"\u003e5x5, light-only solution? I\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44764\"\u003e5x5, with wrapping, 6 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function tf = lights_out_9(board) % 5x5 board, lights-only subset solution\r\n tf = 0;\r\nend","test_suite":"%% all true cases first\r\n board = [0 1 0 0 0\r\n          1 1 1 0 1\r\n          0 1 0 1 1\r\n          1 0 0 0 1\r\n          1 1 0 0 0];\r\nassert(lights_out_9(board)); % [5 7 23]\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 1];\r\nassert(lights_out_9(board)); % [1 13 25]\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 1 1 1 1  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board)); % [9 15 19]\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 0];\r\nassert(lights_out_9(board)); % [7 9 17 19]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 0 0 0 1];\r\nassert(lights_out_9(board)); % [1 5 7 9 17 19 21 25]\r\n\r\n%% \r\n board = [0 0 1 1 0  \r\n          0 1 1 1 1  \r\n          0 1 1 1 1  \r\n          0 0 1 1 0  \r\n          0 0 0 0 0];\r\nassert(lights_out_9(board)); % [12 13 17 18]\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board)); % [8 12 14 18]\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 0 0  \r\n          1 0 0 1 0  \r\n          0 1 1 1 1  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board)); % [3 9 15 19]\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1];\r\nassert(lights_out_9(board)); % [1 5 11 15 21 25]\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 1 0];\r\nassert(lights_out_9(board)); % [7 8 9 12 14 17 18 19]\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nassert(lights_out_9(board)); % [2 6 8 12 14 18 20 24]\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 0 1 0 0  \r\n          1 1 1 0 0  \r\n          1 0 1 1 0  \r\n          0 1 0 0 0];\r\nassert(lights_out_9(board)); % on your own\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1];\r\nassert(lights_out_9(board));\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1  \r\n          0 1 1 1 0];\r\nassert(lights_out_9(board));\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board));\r\n\r\n\r\n%% false cases start here\r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nassert(~lights_out_9(board)); % [7 8 9 17 18 19]\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 1 0 1 1  \r\n          1 0 0 0 1  \r\n          1 1 0 1 0  \r\n          0 0 1 1 0];\r\nassert(~lights_out_9(board)); % [1 2 5 10 16 21 24 25]\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          1 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 0 1  \r\n          0 1 0 0 0];\r\nassert(~lights_out_9(board)); % [2 5:6 8:11 17:24]\r\n\r\n%% \r\n board = [1 0 1 0 0  \r\n          0 1 1 0 0  \r\n          1 0 1 0 0  \r\n          0 1 1 0 0  \r\n          0 0 0 0 0];\r\nassert(~lights_out_9(board)); % on your own\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          1 0 1 0 0  \r\n          1 1 1 1 0  \r\n          0 1 1 0 1  \r\n          0 1 0 1 0];\r\nassert(~lights_out_9(board));\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          0 0 0 0 0];\r\nassert(lights_out_9(board));\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          1 0 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 0 1  \r\n          0 1 1 0 1];\r\nassert(~lights_out_9(board));\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2019-05-02T12:12:00.000Z","rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-30T14:14:44.000Z","updated_at":"2025-11-29T15:23:35.000Z","published_at":"2019-04-22T15:59:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state contains a potential answer—i.e., if toggling any given subset of the starting lights is sufficient to solve the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44760\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44764\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, with wrapping, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44764,"title":"Lights Out 10 - 5x5, with wrapping, 6 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require six moves to solve. However, now wrapping of the lights occurs. For example, if \r\n\r\n board = [1 0 0 0 1\r\n          1 0 1 0 1\r\n          0 0 0 0 0\r\n          1 0 1 0 1\r\n          1 0 0 0 1]\r\n\r\nthe answer is:\r\n\r\n moves = [1 5 11 15 21 25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44761 5x5, light-only solution? II\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44765 5x5, wrapping, x moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require six moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 0 0 1\r\n          1 0 1 0 1\r\n          0 0 0 0 0\r\n          1 0 1 0 1\r\n          1 0 0 0 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 5 11 15 21 25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44761\"\u003e5x5, light-only solution? II\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44765\"\u003e5x5, wrapping, x moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_10(board) % 5x5 board, with wrapping, 6 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1];\r\nmoves = lights_out_10(board); % [1 5 11 15 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          1 0 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 0 1  \r\n          0 0 1 1 0];\r\nmoves = lights_out_10(board); % [4 9 10 16 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_10(board); % [7 8 9 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 1  \r\n          1 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 1 0 0];\r\nmoves = lights_out_10(board); % [4 8 11 13 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 0  \r\n          0 0 0 1 0  \r\n          0 1 0 1 1];\r\nmoves = lights_out_10(board); % [7 8 12 14 15 21]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 0 1  \r\n          0 1 1 1 1  \r\n          0 1 0 0 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_10(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 1 0 1  \r\n          1 1 0 0 0  \r\n          1 0 0 1 1  \r\n          1 1 0 1 0];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [1 0 0 0 0  \r\n          1 0 0 1 1  \r\n          0 0 0 0 1  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 1 0  \r\n          0 0 0 0 1  \r\n          1 0 1 1 0  \r\n          1 1 1 1 0];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [1 0 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 0 0 1  \r\n          1 0 0 0 1  \r\n          1 1 1 0 1];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 1 1  \r\n          0 0 0 0 1  \r\n          0 0 0 1 1  \r\n          0 1 1 0 0  \r\n          1 1 0 0 0];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2019-04-30T11:31:05.000Z","rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-31T16:24:02.000Z","updated_at":"2025-11-29T15:08:07.000Z","published_at":"2019-04-22T16:00:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require six moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 0 0 1\\n          1 0 1 0 1\\n          0 0 0 0 0\\n          1 0 1 0 1\\n          1 0 0 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 5 11 15 21 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44761\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44765\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, wrapping, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44766,"title":"Lights Out 12 - 5x5, three stages, \u003c7 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem is back to no wrapping and contains boards that each require six or fewer moves to solve. However, now lights are activated through three stages, rather than two, cycling from on1 (1) to on2 (2) to off (0). For example, if \r\n\r\n board = [1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 2 0  \r\n          0 0 2 2 2  \r\n          0 0 0 2 0];\r\n\r\nthe answer is:\r\n\r\n moves = [1 1 19]\r\n\r\nsince the first \"1\" will change the 1's in (1,1), (1,2), and (2,1) to 2's. The second \"1\" will then change those three values to zero, while the \"19\" will bump the five 2's to zero. Therefore, up to two moves are possible for each button (index).\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44765 5x5, wrapping, any number of moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44767 5x5, 3 stages, x moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem is back to no wrapping and contains boards that each require six or fewer moves to solve. However, now lights are activated through three stages, rather than two, cycling from on1 (1) to on2 (2) to off (0). For example, if\u003c/p\u003e\u003cpre\u003e board = [1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 2 0  \r\n          0 0 2 2 2  \r\n          0 0 0 2 0];\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 1 19]\u003c/pre\u003e\u003cp\u003esince the first \"1\" will change the 1's in (1,1), (1,2), and (2,1) to 2's. The second \"1\" will then change those three values to zero, while the \"19\" will bump the five 2's to zero. Therefore, up to two moves are possible for each button (index).\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44765\"\u003e5x5, wrapping, any number of moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44767\"\u003e5x5, 3 stages, x moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_12(board) % 5x5 board, three stages, \u003c7 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 2 0  \r\n          0 0 2 2 2  \r\n          0 0 0 2 0];\r\nmoves = lights_out_12(board); % [1 1 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [2 2 0 2 2  \r\n          2 0 2 0 2  \r\n          0 2 2 2 0  \r\n          2 0 2 0 2  \r\n          2 2 0 2 2];\r\nmoves = lights_out_12(board); % [1 5 13 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 1 1 0 1  \r\n          0 1 0 1 1  \r\n          1 0 0 0 1  \r\n          1 1 0 0 0];\r\nmoves = lights_out_12(board); % [5 5 7 7 23 23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [2 1 2 0 0  \r\n          2 0 2 0 0  \r\n          2 0 2 0 0  \r\n          2 0 2 0 0  \r\n          2 1 2 0 0];\r\nmoves = lights_out_12(board); % [6:10]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          2 0 0 0 0  \r\n          1 1 0 0 0  \r\n          2 0 0 0 0  \r\n          1 1 0 0 0];\r\nmoves = lights_out_12(board); % [1 1 3 3 5 5]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 2 0 2 0  \r\n          2 2 0 2 2  \r\n          0 0 2 0 0  \r\n          2 2 0 2 2  \r\n          0 2 0 2 0];\r\nmoves = lights_out_12(board); % [7 9 13 17 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 2 0 2 0  \r\n          2 1 1 1 2  \r\n          2 0 1 0 2  \r\n          2 1 1 1 2  \r\n          0 2 0 2 0];\r\nmoves = lights_out_12(board); % [7:9 17:19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 2 1 2 0  \r\n          2 0 2 0 2  \r\n          0 2 1 2 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_12(board); % [8 13 13 18]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 2 0 2 1  \r\n          0 1 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_12(board); % [7 7 12 12 17 17]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 1 2  \r\n          0 0 2 2 1  \r\n          0 1 0 2 2  \r\n          0 1 1 2 2  \r\n          2 0 0 0 2];\r\nmoves = lights_out_12(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 2 1 0 0  \r\n          2 2 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_12(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 0 0 0  \r\n          2 1 1 0 0  \r\n          2 0 0 2 0  \r\n          0 1 1 2 0];\r\nmoves = lights_out_12(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-31T17:08:51.000Z","updated_at":"2025-11-04T21:21:50.000Z","published_at":"2019-05-02T12:25:05.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is back to no wrapping and contains boards that each require six or fewer moves to solve. However, now lights are activated through three stages, rather than two, cycling from on1 (1) to on2 (2) to off (0). For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 1 0 0 0  \\n          1 0 0 0 0  \\n          0 0 0 2 0  \\n          0 0 2 2 2  \\n          0 0 0 2 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 1 19]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003esince the first \\\"1\\\" will change the 1's in (1,1), (1,2), and (2,1) to 2's. The second \\\"1\\\" will then change those three values to zero, while the \\\"19\\\" will bump the five 2's to zero. Therefore, up to two moves are possible for each button (index).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44765\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, wrapping, any number of moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44767\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 3 stages, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44776,"title":"Lights Out 15 - 5x5, broken buttons I","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains 5x5 boards that require any number of moves to solve. However, the game has a glitch now—each time you press a button, it doesn't toggle itself, only those lights that are adjacent. That is to say, all buttons toggle two, three, or four lights (indices), rather than the normal three, four, or five, respectively.\r\n\r\nFor example, if:\r\n\r\n board = [0 1 0 0 0  \r\n          1 0 0 1 0  \r\n          0 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 0]\r\n\r\nthe answer is:\r\n\r\n moves = [1 10 18]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44769 5x5, four stages, x moves\u003e — \r\nNext: [Check back later for new problems in the series.]","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains 5x5 boards that require any number of moves to solve. However, the game has a glitch now—each time you press a button, it doesn't toggle itself, only those lights that are adjacent. That is to say, all buttons toggle two, three, or four lights (indices), rather than the normal three, four, or five, respectively.\u003c/p\u003e\u003cp\u003eFor example, if:\u003c/p\u003e\u003cpre\u003e board = [0 1 0 0 0  \r\n          1 0 0 1 0  \r\n          0 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 0]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 10 18]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44769\"\u003e5x5, four stages, x moves\u003c/a\u003e — \r\nNext: [Check back later for new problems in the series.]\u003c/p\u003e","function_template":"function moves = lights_out_15(board) % 5x5 board, any number of moves, broken buttons I\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 0 0 0  \r\n          1 0 0 1 0  \r\n          0 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 0];\r\nmoves = lights_out_15(board); % [1 10 18]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_15(board); % [7 19]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_15(board); % [1 5 13 21 25]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          1 0 1 0 0  \r\n          1 0 1 0 0  \r\n          1 0 1 0 0  \r\n          1 1 1 0 0];\r\nmoves = lights_out_15(board); % [6:10]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 1 0 0  \r\n          1 0 1 1 1  \r\n          1 0 1 0 1  \r\n          1 0 0 1 1  \r\n          0 1 0 1 1];\r\nmoves = lights_out_15(board); % [2 5 7 11:13 19 21 24]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_15(board); % [2:4 6 10:11 15:16 20 22:24]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_15(board); % on your own\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 1 0  \r\n          1 1 0 1 0  \r\n          0 1 0 1 0  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 0 1  \r\n          1 0 0 0 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 1 0 0 0  \r\n          0 1 0 0 0  \r\n          0 0 1 1 1  \r\n          0 0 0 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          1 0 1 0 0  \r\n          1 1 0 0 0  \r\n          0 0 1 0 1  \r\n          0 1 0 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 0  \r\n          1 0 0 1 1  \r\n          1 0 0 0 0  \r\n          0 1 1 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 1 0 1  \r\n          0 1 1 1 1  \r\n          1 1 0 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 1 0  \r\n          1 0 0 1 0  \r\n          0 1 0 1 0  \r\n          0 1 0 0 1  \r\n          0 1 0 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 0 0 1  \r\n          0 0 0 0 0  \r\n          0 0 1 0 1  \r\n          0 0 1 1 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 1 1 1 1  \r\n          0 0 1 0 0  \r\n          1 0 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 1 0  \r\n          1 1 0 1 0  \r\n          1 1 1 1 1  \r\n          0 0 0 1 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-11-06T13:17:12.000Z","updated_at":"2025-11-04T21:41:48.000Z","published_at":"2019-09-16T10:59:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains 5x5 boards that require any number of moves to solve. However, the game has a glitch now—each time you press a button, it doesn't toggle itself, only those lights that are adjacent. That is to say, all buttons toggle two, three, or four lights (indices), rather than the normal three, four, or five, respectively.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 0 0  \\n          1 0 0 1 0  \\n          0 0 1 0 1  \\n          0 1 0 1 0  \\n          1 0 1 0 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 10 18]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44769\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, four stages, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next: [Check back later for new problems in the series.]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2036,"title":"Santa's Cards","description":"This Challenge is inspired by the \u003chttp://www.kaggle.com/c/packing-santas-sleigh Packing Santa's Sleigh\u003e contest at kaggle that runs until January 26, 2014. Will Elfonso win another kaggle contest? The kaggle contest is a 3-D fitting/optimization.\r\n\r\nThis Challenge is to place Santa Cards in a minimum area given a board width(columns) and a set of N cards of (rows,columns). Cards can not be rotated.\r\n\r\n*Input*: Card_Array, W ; Card_Array(N,2) and W=width of M array\r\n\r\n*Output*: M ; An X by W array of values 0:N, 0 is unused space\r\n\r\n*Scoring*: Number of rows required to place all cards\r\n\r\n*Example*:\r\n\r\n[2 2;3 3;1 2], 5\r\n\r\nM\r\n\r\n  1 1 2 2 2\r\n  1 1 2 2 2\r\n  3 3 2 2 2\r\n\r\nScores a 3, number of rows\r\n\r\n*Contest Results:*\r\nAlfonso put in a tremendous 40,000 point reduction in the last days and appeared unsurpassable. However, a pair of Polish Professors in Mathematics and Computer Science, Marek and Cygan, submitted an astounding further 34,000 point improvement for the win. Alfonso won the Matlab category prize.","description_html":"\u003cp\u003eThis Challenge is inspired by the \u003ca href = \"http://www.kaggle.com/c/packing-santas-sleigh\"\u003ePacking Santa's Sleigh\u003c/a\u003e contest at kaggle that runs until January 26, 2014. Will Elfonso win another kaggle contest? The kaggle contest is a 3-D fitting/optimization.\u003c/p\u003e\u003cp\u003eThis Challenge is to place Santa Cards in a minimum area given a board width(columns) and a set of N cards of (rows,columns). Cards can not be rotated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput\u003c/b\u003e: Card_Array, W ; Card_Array(N,2) and W=width of M array\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput\u003c/b\u003e: M ; An X by W array of values 0:N, 0 is unused space\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring\u003c/b\u003e: Number of rows required to place all cards\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e[2 2;3 3;1 2], 5\u003c/p\u003e\u003cp\u003eM\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1 1 2 2 2\r\n1 1 2 2 2\r\n3 3 2 2 2\r\n\u003c/pre\u003e\u003cp\u003eScores a 3, number of rows\u003c/p\u003e\u003cp\u003e\u003cb\u003eContest Results:\u003c/b\u003e\r\nAlfonso put in a tremendous 40,000 point reduction in the last days and appeared unsurpassable. However, a pair of Polish Professors in Mathematics and Computer Science, Marek and Cygan, submitted an astounding further 34,000 point improvement for the win. Alfonso won the Matlab category prize.\u003c/p\u003e","function_template":"function m=cards(c,nc)\r\n % cards array row=card; [h,w]\r\n % fill array with values 1:size(c,1)\r\n % m is nc wide\r\n m=zeros(sum(c(:,1)),nc);\r\nend","test_suite":"assignin('caller','score',30);\r\n%%\r\nnc=8;\r\nc=[2 2;4 4;2 2;3 3;2 2;1 2;1 8;1 2;4 4;3 3;3 2];\r\nm=cards(c,nc)\r\n\r\nassert(size(m,2)==nc)\r\n\r\nfor i=1:size(c,1)\r\n mt=double(m==i);\r\n mtc=conv2(mt,ones(c(i,:)),'same');\r\n assert(nnz(mtc==c(i,1)*c(i,2))==1)\r\nend\r\n\r\n\r\nassignin('caller','score',size(m,1));\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-06T02:03:07.000Z","updated_at":"2014-01-29T00:17:00.000Z","published_at":"2013-12-06T02:19:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is inspired by the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/packing-santas-sleigh\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePacking Santa's Sleigh\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest at kaggle that runs until January 26, 2014. Will Elfonso win another kaggle contest? The kaggle contest is a 3-D fitting/optimization.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to place Santa Cards in a minimum area given a board width(columns) and a set of N cards of (rows,columns). Cards can not be rotated.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Card_Array, W ; Card_Array(N,2) and W=width of M array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: M ; An X by W array of values 0:N, 0 is unused space\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Number of rows required to place all cards\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[2 2;3 3;1 2], 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1 1 2 2 2\\n1 1 2 2 2\\n3 3 2 2 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eScores a 3, number of rows\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eContest Results:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Alfonso put in a tremendous 40,000 point reduction in the last days and appeared unsurpassable. However, a pair of Polish Professors in Mathematics and Computer Science, Marek and Cygan, submitted an astounding further 34,000 point improvement for the win. Alfonso won the Matlab category prize.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2026,"title":"Skyscrapers - Puzzle","description":"The Skyscraper puzzle challenge comes from \u003chttp://logicmastersindia.com/home/ Logic Masters India\u003e and \u003chttp://www.conceptispuzzles.com/ Games' Concept is Puzzles\u003e. \r\n\r\nCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\r\n\r\n*Input:* [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\r\n\r\n*Output:* M  an NxN matrix\r\n\r\n*Example:*\r\n\r\n  vr=[0 0 3 0 0]';\r\n  vL=[3 0 0 1 0]';\r\n  vd=[0 0 0 0 0];\r\n  vu=[5 2 0 0 0];\r\n\r\n  M\r\n         5     4     2     1     3\r\n         4     5     1     3     2\r\n         3     2     4     5     1\r\n         2     1     3     4     5\r\n         1     3     5     2     4\r\n\r\n*Algorithm Discussion:*\r\n\r\n  1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n  2) Calc Skyscraper count from Left and Right\r\n  3) Determine subset of SkyVectors possible for each Row and Column\r\n  4) Sort the Qty of 2*N possible solutions\r\n  5) Recursion from least to most valid SkyVectors\r\n  6) In recursion verify valid overlay or return\r\n","description_html":"\u003cp\u003eThe Skyscraper puzzle challenge comes from \u003ca href = \"http://logicmastersindia.com/home/\"\u003eLogic Masters India\u003c/a\u003e and \u003ca href = \"http://www.conceptispuzzles.com/\"\u003eGames' Concept is Puzzles\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e M  an NxN matrix\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003evr=[0 0 3 0 0]';\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eM\r\n       5     4     2     1     3\r\n       4     5     1     3     2\r\n       3     2     4     5     1\r\n       2     1     3     4     5\r\n       1     3     5     2     4\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm Discussion:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n2) Calc Skyscraper count from Left and Right\r\n3) Determine subset of SkyVectors possible for each Row and Column\r\n4) Sort the Qty of 2*N possible solutions\r\n5) Recursion from least to most valid SkyVectors\r\n6) In recursion verify valid overlay or return\r\n\u003c/pre\u003e","function_template":"function m=solve_skyscrapers(vr,vL,vd,vu)\r\n m=[];\r\nend","test_suite":"%%\r\n%Games Feb 2014 #1\r\nvr=[0 0 1 0 5]'; %1\r\nvL=[0 4 4 0 0]';\r\nvd=[2 2 0 1 3];\r\nvu=[3 0 0 2 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd; % view down check\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view Left check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m); % view Up check\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #2\r\nvr=[0 4 0 2 0]'; %2\r\nvL=[5 1 0 0 0]';\r\nvd=[0 0 3 0 0];\r\nvu=[4 1 2 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #3\r\nvr=[5 2 2 0 0]'; %3\r\nvL=[0 3 0 3 4]';\r\nvd=[5 0 0 0 0];\r\nvu=[0 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #4\r\nvr=[0 0 4 5 0]'; %4\r\nvL=[0 0 0 0 0]';\r\nvd=[2 0 2 3 0];\r\nvu=[0 0 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #5\r\nvr=[3 5 0 0 0]'; %5\r\nvL=[0 0 4 0 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[2 0 1 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\n\r\n%%\r\nvr=[0 0 3 0 0]'; %Games Feb 2014 #6\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-29T19:42:36.000Z","updated_at":"2026-01-08T14:21:06.000Z","published_at":"2013-11-29T22:09:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Skyscraper puzzle challenge comes from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://logicmastersindia.com/home/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLogic Masters India\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames' Concept is Puzzles\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e M an NxN matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[vr=[0 0 3 0 0]';\\nvL=[3 0 0 1 0]';\\nvd=[0 0 0 0 0];\\nvu=[5 2 0 0 0];\\n\\nM\\n       5     4     2     1     3\\n       4     5     1     3     2\\n       3     2     4     5     1\\n       2     1     3     4     5\\n       1     3     5     2     4]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm Discussion:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\\n2) Calc Skyscraper count from Left and Right\\n3) Determine subset of SkyVectors possible for each Row and Column\\n4) Sort the Qty of 2*N possible solutions\\n5) Recursion from least to most valid SkyVectors\\n6) In recursion verify valid overlay or return]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2072,"title":"Packing Santa's Sleigh: First Layer","description":"This Challenge is inspired by the \u003chttp://www.kaggle.com/c/packing-santas-sleigh Packing Santa's Sleigh\u003e contest at kaggle that runs until January 26, 2014.\r\n\r\nThe Bag has a 1000 x 1000 base with the contest having 1,000,000 packages.\r\n\r\nThis Challenge is to place as many of the first N Santa Presents onto the 1000 x 1000 grid. The packages have been presorted to have Z as max, not provided, thus minimizing the X*Y cross section. Only the first 240 packages are provided as package 241 busts the bag bottom making an area greater than 1,000,000. Packages out of sequence cause a severe penalty thus all packages less than the highest package used must fit on the board.\r\n\r\n*Input*: Presents ; Presents(240,2)\r\n\r\n*Output*: L, xyTL; L(1000,1000) of values 0:n\u003c=240, 0 is unused space\r\n\r\n*Scoring*: Unused Area\r\n\r\n*Example*:\r\n\r\n[2 2;3 3;1 2] is Presents\r\n\r\nL\r\n\r\n  1 1 2 2 2 0  thru column 1000\r\n  1 1 2 2 2\r\n  3 3 2 2 2\r\n  0 0 0 0 0 0 rows 4 thru 1000 are zeros\r\n\r\nxyTL\r\n[1 1;1 3;3 1]\r\n\r\nScores 1,000,000 - 15= 999985\r\n\r\nBoxes 1:236 are possible, 97.5719% efficient pack in \u003c 1sec.\r\n\r\n*TestSuite Sample Code:*\r\n\r\nIn the TestSuite at the end is wrapper code for entering the kaggle contest.\r\nUpdate your SantaPack routine and execute the wrapper to see your contest score. The official presents.mat file needs to be downloaded. The wrapper has further discussion on efficient methods and current scores. \r\n","description_html":"\u003cp\u003eThis Challenge is inspired by the \u003ca href = \"http://www.kaggle.com/c/packing-santas-sleigh\"\u003ePacking Santa's Sleigh\u003c/a\u003e contest at kaggle that runs until January 26, 2014.\u003c/p\u003e\u003cp\u003eThe Bag has a 1000 x 1000 base with the contest having 1,000,000 packages.\u003c/p\u003e\u003cp\u003eThis Challenge is to place as many of the first N Santa Presents onto the 1000 x 1000 grid. The packages have been presorted to have Z as max, not provided, thus minimizing the X*Y cross section. Only the first 240 packages are provided as package 241 busts the bag bottom making an area greater than 1,000,000. Packages out of sequence cause a severe penalty thus all packages less than the highest package used must fit on the board.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput\u003c/b\u003e: Presents ; Presents(240,2)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput\u003c/b\u003e: L, xyTL; L(1000,1000) of values 0:n\u0026lt;=240, 0 is unused space\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring\u003c/b\u003e: Unused Area\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e[2 2;3 3;1 2] is Presents\u003c/p\u003e\u003cp\u003eL\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1 1 2 2 2 0  thru column 1000\r\n1 1 2 2 2\r\n3 3 2 2 2\r\n0 0 0 0 0 0 rows 4 thru 1000 are zeros\r\n\u003c/pre\u003e\u003cp\u003exyTL\r\n[1 1;1 3;3 1]\u003c/p\u003e\u003cp\u003eScores 1,000,000 - 15= 999985\u003c/p\u003e\u003cp\u003eBoxes 1:236 are possible, 97.5719% efficient pack in \u0026lt; 1sec.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTestSuite Sample Code:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the TestSuite at the end is wrapper code for entering the kaggle contest.\r\nUpdate your SantaPack routine and execute the wrapper to see your contest score. The official presents.mat file needs to be downloaded. The wrapper has further discussion on efficient methods and current scores.\u003c/p\u003e","function_template":"function  [L,xyTL]=SantaPack(p)\r\n% p is an Nx2\r\n% xyTL is Top Left position of pieces 1:n.\r\n% xyTL created for Speed vs find in a 1Kx1K array\r\n% Place 1:n of p onto L, a 1000x1000 array\r\n% Put p row onto L array. \r\n% If p(1,:) is [2 3]\r\n%[2 3] may be placed as [1 1 1;1 1 1] or [1 1; 1 1;1 1] for box 1\r\n% Note: big boxes typically pack to 95% and small boxes to \u003e98%\r\n\r\n L=zeros(1000);\r\n xyTL=p*0;\r\n\r\n% Placing first 16 pieces\r\n% No piece exceeds 250x250\r\n pxy=[1 251 501 751];\r\n piece=1;\r\n for i=1:4\r\n  for j=1:4\r\n% putting piece on layer\r\n   L(pxy(i):pxy(i)+p(piece,1)-1,pxy(j):pxy(j)+p(piece,2)-1)=piece; \r\n% TL Location of piece\r\n   xyTL(piece,:)=[pxy(i) pxy(j)]; \r\n   piece=piece+1;\r\n  end\r\n end\r\n %figure(3);imagesc(L)\r\n \r\nend % SantaPack","test_suite":"assignin('caller','score',100000);\r\n%%\r\nSanta_L1=[3     2;207    73;160    78;8     3;\r\n     9     9;\r\n   170   120;116    91;\r\n   206   142;28     8;\r\n     3     2;41    22;\r\n    31    11;28    20;\r\n    29    13;98    96;\r\n    26     4;34     9;\r\n     4     3;84    78;\r\n   219   114;28    22;\r\n   195   185;3     2;\r\n    31     9;104   101;\r\n    32    31;188   142;\r\n    45    18;8     2;\r\n    13    10;49    22;\r\n   172    72;28    17;\r\n    90    87;33     5;\r\n    32    23;16    14;\r\n    42    18;32     8;\r\n     7     5;6     6;\r\n   201    69;20    11;\r\n    30    18;211   120;\r\n   206    97;3     2;\r\n   124    92;48    43;\r\n     2     2;173   103;\r\n    26    12;8     7;\r\n     8     8;57    33;\r\n    21    20;24    15;\r\n    26    12;44    24;\r\n    30     8;\r\n    43    26;\r\n    23    23;\r\n     9     4;\r\n    16    13;\r\n    58    29;\r\n   133   125;\r\n     5     2;\r\n   197   117;\r\n    39    10;\r\n    31    11;\r\n    41    18;\r\n     6     3;\r\n    31     8;\r\n    40    32;\r\n    41    39;\r\n    36    30;\r\n     2     2;\r\n    25    24;\r\n     6     2;\r\n     3     2;\r\n     2     2;\r\n    85    70;\r\n    37    25;\r\n    24    20;\r\n    60    26;\r\n    29    14;\r\n    49    30;\r\n   153    75;\r\n     6     3;\r\n     7     3;\r\n   185   162;\r\n     4     2;\r\n     6     5;\r\n   176    99;\r\n     4     2;\r\n   219   154;\r\n    24    22;\r\n   148    87;\r\n    32     7;\r\n   143    98;\r\n    23    13;\r\n   150    65;\r\n     5     2;\r\n    53    41;\r\n    25    12;\r\n    36     6;\r\n    21    10;\r\n   211    79;\r\n   183   130;\r\n     6     3;\r\n    36    28;\r\n    32    16;\r\n    21    15;\r\n    27    26;\r\n    39    14;\r\n    36     7;\r\n    57    17;\r\n   214    90;\r\n    36     5;\r\n    27    16;\r\n    52    15;\r\n     8     6;\r\n     5     4;\r\n    52    37;\r\n     7     2;\r\n    92    79;\r\n    37    35;\r\n    33     5;\r\n     5     2;\r\n    52    10;\r\n    29     5;\r\n    44    18;\r\n     8     5;\r\n    16     8;\r\n   137   105;\r\n    78    74;\r\n     9     5;\r\n    39    29;\r\n    43    31;\r\n     6     3;\r\n     8     4;\r\n    26    19;\r\n    22     7;\r\n    30    15;\r\n   199   195;\r\n     7     7;\r\n     7     5;\r\n   134    81;\r\n   206   108;\r\n    54    29;\r\n    16     7;\r\n   116    99;\r\n    35    23;\r\n    31    17;\r\n    56    11;\r\n     7     3;\r\n    52     5;\r\n   102    99;\r\n     5     5;\r\n    35    17;\r\n     8     6;\r\n    51     7;\r\n    28    16;\r\n   107    83;\r\n    26     8;\r\n     8     6;\r\n   149    83;\r\n    45    29;\r\n    55    52;\r\n    27     6;\r\n    82    81;\r\n     9     5;\r\n    27    21;\r\n    19    10;\r\n    56    26;\r\n    19    14;\r\n    11     8;\r\n    47     7;\r\n    26    13;\r\n    36    19;\r\n    87    73;\r\n    14    10;223   100;2     2;33     5;198   135;38    15;19     8;211    95;9     6;21     7;175   145;22    16;7     5;7     4;9     8;42     5;3     3;2     2;3     2;5     2;30    24;29    29;27     9;168    72;6     4;22     7;9     6;10     6;19    16;7     2;43    14;138   115;138   130;39    20;9     4;27     7;26    22;169   144;8     8;41     9;50    26;62    10;33    19;7     2;121   112;102    93;109    88;9     8;40    40;25    19;31     8;55    23;41    11;6     2;8     3;128   114;40    16;7     6;5     2];\r\n[L,xyTL]=SantaPack(Santa_L1);\r\nptrxy=find(xyTL(:,1)\u003e0,1,'last');\r\n\r\npresents=Santa_L1;\r\nfor k=1:ptrxy\r\n  ptrxmin=xyTL(k,1);\r\n  ptrymin=xyTL(k,2);\r\n  assert(isequal(L(ptrxmin,ptrymin),k)) % Verify TL corner\r\n\r\n  if ptrxmin+presents(k,1)-1\u003e1000 || ptrymin+presents(k,2)-1\u003e1000\r\n% BR Corner verify for rotated only fit case\r\n    assert(isequal(L(ptrxmin+presents(k,2)-1,ptrymin+presents(k,1)-1),k))\r\n  elseif ptrxmin+presents(k,2)-1\u003e1000 || ptrymin+presents(k,1)-1\u003e1000\r\n% BR corner verify for non-rotated only fit case\r\n    assert(isequal(L(ptrxmin+presents(k,1)-1,ptrymin+presents(k,2)-1),k))\r\n  else % rot or non-rot case\r\n   v1=L(ptrxmin+presents(k,2)-1,ptrymin+presents(k,1)-1)==k;\r\n   v2=L(ptrxmin+presents(k,1)-1,ptrymin+presents(k,2)-1)==k;\r\n   assert(v1 || v2); % simple corner check\r\n  end\r\n% More robust checks may be implemented if needed\r\nend\r\n   \r\n\r\nA=Santa_L1(:,1).*Santa_L1(:,2);\r\nAs=sum(A(1:ptrxy))\r\nassignin('caller','score',min(100000,1000000-As));\r\n%%\r\n%{\r\nfunction SantaPack_Cody\r\n% www.kaggle.com Santa Packing Contest \r\n% 11/2013 thru January 2014\r\n% Given 1 Million Rectangularoid packages\r\n% Fit Packages into a Minimum Heigth Box with a base of 1000 x 1000\r\n% Rules allow presents out of order but this is virtually non-optimiziable\r\n% Presents out of order incur a penalty\r\n% Packing Construction Here:\r\n% All boxes dimension sorted [Mid, Min, Max]\r\n% Boxes 1:236 all have their tops on the same plane (97.5719% efficient pack)\r\n% Boxes 237:423 have their tops 250 lower in Z. Max Z of 1:236 is 250.\r\n% The very bottom layer, with box 1000000 has bottom box at Z=1\r\n% Note: Max dimension after box 700,000 is 70\r\n% This construction has min cross area per present, max dimension is placed on Z\r\n% Input is presents that have cumulative area \u003c= 1000000\r\n% The optimal score with perfect layer packing is \r\n% Layers 4098  zsum 996483  Score 1,992,966\r\n% Layers 4210, Score of 2,047,696 is possible with sequence layer packing\r\n% Kaggle Lead as of 12/21/2013 is 1,999,256. Unknown method.\r\n% Pack routine returns a 1000x1000 array with values 1:n, n\u003c=N\r\n% N is the Nth  box that fits in the 1,000,000 area limit\r\n% Next call uses n+1:N\r\n\r\nload presents  % available at kaggle site as a Mat file\r\nnumPresents = size(presents,1);\r\n\r\npresents(:,2:4)=sort(presents(:,2:4),2);\r\npresents(:,2:3)=fliplr(sort(presents(:,2:3),2)); % x\u003ey, z\u003ex\r\npresents=[presents presents(:,2).*presents(:,3)]; % Area of box tops\r\npresents=[presents cumsum(presents(:,end))]; % [presID x y z A Asum]\r\n\r\npe=0;\r\nLayer=0;\r\nzsum=0;\r\nz=-1;\r\npresentCoords=zeros(1000000,25);\r\n\r\ntic\r\nwhile pe\u003c1000000\r\n ps=pe+1; \r\n Asum=presents(ps:min(ps+5000,1000000),end); % valid for layer 1\r\n if pe\u003e0, Asum=Asum-presents(pe,end); end% remove prior layers sum\r\n ptr1M=find(Asum\u003c=1000000,1,'last');\r\n pe=ps+ptr1M-1;\r\n \r\n [L,xyTL]=SantaPack(presents(ps:pe,2:3)); % L has values 1 thru n, being ps thru ps+n-1\r\n % xyTL is Top Left position of pieces 1:n\r\n %figure(3);imagesc(L);\r\n\r\n pe=ps-1+find(xyTL(:,1)\u003e0,1,'last'); % find number of boxes placed\r\n zmax=max(presents(ps:pe,4));\r\n \r\n % Convert Layers to coordinates\r\n % Locate pieces in Layer and assign coordinate values\r\n % z axis values fixed in post processing to positives\r\n % Valid placement and sizes assumed\r\n for k=1:pe-ps+1\r\n  idx=k+ps-1; \r\n  ptrxmin=xyTL(k,1);\r\n  ptrymin=xyTL(k,2);\r\n  if ptrxmin+presents(idx,2)-1\u003c=1000 \u0026\u0026 ptrymin+presents(idx,3)-1\u003c=1000\r\n   if L(ptrxmin+presents(idx,2)-1,ptrymin+presents(idx,3)-1)==k\r\n    ptrxmax=ptrxmin+presents(idx,2)-1;\r\n    ptrymax=ptrymin+presents(idx,3)-1;\r\n   else\r\n    ptrxmax=ptrxmin+presents(idx,3)-1;\r\n    ptrymax=ptrymin+presents(idx,2)-1;\r\n   end\r\n  else % assumed placement if xmax(1)\u003e1000\r\n   ptrxmax=ptrxmin+presents(idx,3)-1;\r\n   ptrymax=ptrymin+presents(idx,2)-1;\r\n  end % if\r\n  \r\n % place this section inside SantaPack and output presentCoords vs L\r\n    presentCoords(idx,1) = idx;\r\n    presentCoords(idx,[2 8 14 20]) = ptrxmin;\r\n    presentCoords(idx,[5 11 17 23]) = ptrxmax;\r\n    presentCoords(idx,[3 6 15 18]) = ptrymin;\r\n    presentCoords(idx,[9 12 21 24]) = ptrymax;\r\n    presentCoords(idx,[4 7 10 13]) = z;\r\n    presentCoords(idx,[16 19 22 25]) = z - presents(idx,4) + 1;\r\n end % k\r\n \r\n z=z-zmax;\r\n Layer=Layer+1;\r\n zsum=zsum+zmax;\r\n fprintf('Layer %i Start %i  Final %i Zsum %i  Time %.2f\\n',Layer,ps,pe,zsum,toc) % Processing Status\r\n % Deep routine to 2M takes 30 minutes\r\n % Fast Placement takes 187 sec\r\n \r\nend  % pe\r\n\r\n% Offset Z coordinates \r\n% Bottom is 1 and very top is Positive\r\nzCoords = presentCoords(:,4:3:end);\r\nminZ = min(zCoords(:));\r\npresentCoords(:,4:3:end) = zCoords - minZ + 1;\r\n\r\n% Scoring function\r\n% Ideal order is the original order\r\npresentIDs = presents(:,1); %z\r\nidealOrder = presentIDs; \r\n\r\n% Determine the max z-coordinate; this is the max height of the box\r\nmaxZ = max(max(presentCoords(:,4:3:end)));\r\n\r\n% Go down the layers from top to bottom, reorder presents in numeric order\r\n% for each layer\r\nmaxZCoord = zeros(numPresents,2);\r\nfor i = 1:numPresents\r\n    maxZCoord(i,1) = presentCoords(i);\r\n    maxZCoord(i,2) = max(presentCoords(i,4:3:end));\r\nend\r\nmaxzCoordSorted = sortrows(maxZCoord,[-2 1]); %sort max z-coord for each present\r\nreOrder = maxzCoordSorted(:,1);\r\n\r\n% Compare the new order to the ideal order\r\norder = sum(abs(idealOrder - reOrder));\r\n\r\n% Compute metric\r\nfprintf('Metric %i MaxZ %i  Order Penalty %i\\n',2*maxZ + order,maxZ,order);\r\n\r\n% Creating a Submission File\r\nsubfile = 'submissionfile_SantaPack_Cody.csv';\r\nfileID = fopen(subfile, 'w');\r\nheaders = {'PresentId','x1','y1','z1','x2','y2','z2','x3','y3','z3','x4','y4','z4','x5','y5','z5','x6','y6','z6','x7','y7','z7','x8','y8','z8'};\r\nfprintf(fileID,'%s,',headers{1,1:end-1});\r\nfprintf(fileID,'%s\\n',headers{1,end});\r\nfprintf(fileID,'%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d\\n',presentCoords');\r\nfclose(fileID);\r\n\r\nend % SantaPack_Cody\r\n\r\nfunction  [L,xyTL]=SantaPack(p)\r\n% p is an Nx2\r\n% xyTL is Top Left position of pieces 1:n\r\n% Place 1:n of p onto L, a 1000x1000 array\r\n% Put p row onto L array. [2 3] may be placed [1 1 1;1 1 1] or [1 1; 1 1;1 1] for box 1\r\n% Note: big boxes typically pack to 95% and small boxes to \u003e98%\r\n\r\n L=zeros(1000);\r\n xyTL=p*0;\r\n% L(1:p(1,1),1:p(1,2))=1; % putting one piece per layer\r\n% return\r\n\r\n% Placing first 16 pieces\r\n% No piece exceeds 250x250\r\n pxy=[1 251 501 751];\r\n piece=1;\r\n for i=1:4\r\n  for j=1:4\r\n   L(pxy(i):pxy(i)+p(piece,1)-1,pxy(j):pxy(j)+p(piece,2)-1)=piece; % putting piece on layer\r\n   xyTL(piece,:)=[pxy(i) pxy(j)]; % Location of piece\r\n   piece=piece+1;\r\n  end\r\n end\r\n %figure(3);imagesc(L)\r\n \r\nend % SantaPack\r\n%}\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-22T04:12:10.000Z","updated_at":"2013-12-22T05:37:27.000Z","published_at":"2013-12-22T05:37:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is inspired by the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/packing-santas-sleigh\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePacking Santa's Sleigh\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest at kaggle that runs until January 26, 2014.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Bag has a 1000 x 1000 base with the contest having 1,000,000 packages.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to place as many of the first N Santa Presents onto the 1000 x 1000 grid. The packages have been presorted to have Z as max, not provided, thus minimizing the X*Y cross section. Only the first 240 packages are provided as package 241 busts the bag bottom making an area greater than 1,000,000. Packages out of sequence cause a severe penalty thus all packages less than the highest package used must fit on the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Presents ; Presents(240,2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: L, xyTL; L(1000,1000) of values 0:n\u0026lt;=240, 0 is unused space\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Unused Area\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[2 2;3 3;1 2] is Presents\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1 1 2 2 2 0  thru column 1000\\n1 1 2 2 2\\n3 3 2 2 2\\n0 0 0 0 0 0 rows 4 thru 1000 are zeros]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003exyTL [1 1;1 3;3 1]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eScores 1,000,000 - 15= 999985\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBoxes 1:236 are possible, 97.5719% efficient pack in \u0026lt; 1sec.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTestSuite Sample Code:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the TestSuite at the end is wrapper code for entering the kaggle contest. Update your SantaPack routine and execute the wrapper to see your contest score. The official presents.mat file needs to be downloaded. The wrapper has further discussion on efficient methods and current scores.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60785,"title":"Solve an easy binary puzzle","description":"A binary puzzle presents a square grid (or matrix) of cells in which each cell must be 0 or 1. The finished puzzle must follow these rules:\r\nIt may not have more than two 0s or 1s next to each other in any row or column. \r\nEach row and column must have an equal number of 0s and 1s. \r\nNo two rows and no two columns can be the same. \r\nWrite a function to solve easy binary puzzles—i.e., those that can be solved by applying only the first rule. Empty cells in the initial board will be marked by 9s. \r\nThe examples in the test suite and the one below come from binarypuzzle.com.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 487.112px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 243.55px; transform-origin: 407px 243.556px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA binary puzzle presents a square grid (or matrix) of cells in which each cell must be 0 or 1. The finished puzzle must follow these rules:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 61.3125px; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.6562px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIt may not have more than two 0s or 1s next to each other in any row or column. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach row and column must have an equal number of 0s and 1s. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNo two rows and no two columns can be the same. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve easy binary puzzles—i.e., those that can be solved by applying only the first rule. Empty cells in the initial board will be marked by 9s. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe examples in the test suite and the one below come from binarypuzzle.com.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 272.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 136.4px; text-align: left; transform-origin: 384px 136.4px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = binary1(b)\r\n  y = bitxor(b);\r\nend","test_suite":"%%\r\nb = [1 9 9 0 9 9; 9 9 0 0 9 1; 9 0 0 9 9 1; 9 9 9 9 9 9; 0 0 9 1 9 9; 9 1 9 9 0 0];\r\ny = binary1(b);\r\ny_correct = [1 0 1 0 1 0; 0 1 0 0 1 1; 1 0 0 1 0 1; 0 1 1 0 1 0; 0 0 1 1 0 1; 1 1 0 1 0 0];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [9 9 0 9 9 1; 1 9 0 0 9 9; 9 1 9 9 9 9; 9 9 9 9 1 9; 9 9 1 9 9 0; 0 0 9 1 9 9];\r\ny = binary1(b);\r\ny_correct = [1 0 0 1 0 1; 1 1 0 0 1 0; 0 1 1 0 0 1; 1 0 0 1 1 0; 0 1 1 0 1 0; 0 0 1 1 0 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [9 9 9 9 9 9 9 0; 9 0 0 9 9 1 9 9; 9 0 9 9 9 1 9 0; 9 9 1 9 9 9 9 9; 0 0 9 1 9 9 1 9; 9 9 9 9 1 9 9 9; 1 1 9 9 9 0 9 1; 9 1 9 9 9 9 9 1];\r\ny = binary1(b);\r\ny_correct = [0 1 1 0 1 0 1 0; 1 0 0 1 0 1 0 1; 1 0 0 1 0 1 1 0; 0 1 1 0 1 0 0 1; 0 0 1 1 0 1 1 0; 1 0 0 1 1 0 1 0; 1 1 0 0 1 0 0 1; 0 1 1 0 0 1 0 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [1 9 1 9 9 1 9 0; 1 9 0 9 0 9 9 9; 9 9 9 1 9 9 9 1; 0 9 9 9 9 9 1 1; 9 9 1 9 9 9 9 9; 9 9 9 0 9 9 9 1; 0 9 9 9 1 1 9 1; 0 9 9 9 1 9 9 9];\r\ny = binary1(b);\r\ny_correct = [1 0 1 0 1 1 0 0; 1 1 0 1 0 0 1 0; 0 0 1 1 0 1 0 1; 0 1 0 0 1 0 1 1; 1 0 1 1 0 0 1 0; 1 0 1 0 0 1 0 1; 0 1 0 0 1 1 0 1; 0 1 0 1 1 0 1 0];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [9 9 1 9 9 9 9 0 9 9 0 9; 9 9 9 1 9 0 9 9 1 9 9 1; 9 9 0 9 0 9 9 9 9 0 9 1; 1 9 0 9 9 9 0 0 9 9 9 9; 9 1 9 9 9 9 9 9 9 9 9 1; 9 9 9 9 1 9 9 9 9 0 9 9; 9 9 9 9 9 9 1 9 0 0 9 9; 9 9 0 9 9 9 9 1 9 9 1 9; 1 9 0 0 9 9 9 9 9 0 9 9; 9 9 9 9 9 1 9 9 9 0 9 0; 9 0 9 9 9 9 0 9 9 9 1 9; 0 0 9 1 9 1 9 1 9 1 9 9];\r\ny = binary1(b);\r\ny_correct = [0 1 1 0 1 1 0 0 1 1 0 0; 0 0 1 1 0 0 1 0 1 1 0 1; 1 0 0 1 0 0 1 1 0 0 1 1; 1 1 0 0 1 1 0 0 1 0 1 0; 0 1 1 0 0 1 0 1 0 1 0 1; 1 0 0 1 1 0 1 0 1 0 0 1; 0 1 1 0 1 0 1 1 0 0 1 0; 1 0 0 1 0 1 0 1 0 1 1 0; 1 1 0 0 1 0 1 0 1 0 0 1; 0 1 1 0 0 1 1 0 1 0 1 0; 1 0 0 1 1 0 0 1 0 1 1 0; 0 0 1 1 0 1 0 1 0 1 0 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [0 9 0 9 1 1 9 1 9 9 0 9; 0 9 9 1 1 9 9 9 0 1 9 9; 9 9 9 9 9 9 9 9 9 1 9 0; 9 9 9 9 1 9 9 0 9 9 9 0; 9 1 1 9 9 9 1 9 9 0 9 9; 0 9 9 1 9 1 9 9 9 9 9 9; 9 9 1 9 9 9 9 9 9 1 9 0; 9 9 9 0 9 1 9 9 0 9 9 9; 1 9 9 0 9 9 1 9 9 1 9 9; 1 9 1 9 9 0 9 9 0 9 9 1; 9 9 9 9 9 0 9 9 9 9 9 1; 1 9 1 1 9 9 9 9 0 0 9 9];\r\ny = binary1(b);\r\ny_correct = [0 1 0 0 1 1 0 1 1 0 0 1;0 1 0 1 1 0 1 0 0 1 0 1;1 0 1 0 0 1 0 1 0 1 1 0;1 0 0 1 1 0 1 0 1 0 1 0;0 1 1 0 1 0 1 0 1 0 0 1;0 1 0 1 0 1 0 1 0 1 1 0;1 0 1 1 0 0 1 0 1 1 0 0;0 1 0 0 1 1 0 1 0 0 1 1;1 0 1 0 0 1 1 0 1 1 0 0;1 0 1 1 0 0 1 0 0 1 0 1;0 1 0 0 1 0 0 1 1 0 1 1;1 0 1 1 0 1 0 1 0 0 1 0];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('binary1.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext, 'regexp') || contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2025-01-14T12:13:56.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-01-14T12:13:49.000Z","updated_at":"2026-01-25T13:59:44.000Z","published_at":"2025-01-14T12:13:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA binary puzzle presents a square grid (or matrix) of cells in which each cell must be 0 or 1. The finished puzzle must follow these rules:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt may not have more than two 0s or 1s next to each other in any row or column. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach row and column must have an equal number of 0s and 1s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNo two rows and no two columns can be the same. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve easy binary puzzles—i.e., those that can be solved by applying only the first rule. Empty cells in the initial board will be marked by 9s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe examples in the test suite and the one below come from binarypuzzle.com.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"267\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"617\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1978,"title":"Sokoban: Puzzle 10.45","description":"The \u003chttp://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 Sokoban Site\u003e has many puzzles to solve.  This Challenge is to solve puzzle 10.45.  The link may place the Cody enthusiast at 10.55. \u003chttp://en.wikipedia.org/wiki/Sokoban wiki Sokoban reference\u003e. \r\n\r\nThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\r\n\r\nSokoban can not jump blocks or move diagonally.\r\n\r\nThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).  \r\n\r\nSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\r\n\r\n*Input:* Map, [nr,nc] of  Sokoban characters [0,1,2,3,4,5,7]\r\n\r\n*Output:* Moves, Vector of [-1 +1 -nr +nr] values\r\n\r\n*Scoring:* Sum of Moves and Pushes\r\n\r\n*Examples:* \r\n\r\nMap\r\n\r\n  11111111\r\n  11111111 Moves=[5]  push right for a 5 row array\r\n  11042311\r\n  11111111\r\n  11111111\r\n\r\n*Test Suite Visualization:* A visualization option is provided.\r\n\r\n*Algorithms:* Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid. \r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138\"\u003eSokoban Site\u003c/a\u003e has many puzzles to solve.  This Challenge is to solve puzzle 10.45.  The link may place the Cody enthusiast at 10.55. \u003ca href = \"http://en.wikipedia.org/wiki/Sokoban\"\u003ewiki Sokoban reference\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/p\u003e\u003cp\u003eSokoban can not jump blocks or move diagonally.\u003c/p\u003e\u003cp\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/p\u003e\u003cp\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Map, [nr,nc] of  Sokoban characters [0,1,2,3,4,5,7]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Sum of Moves and Pushes\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMap\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e11111111\r\n11111111 Moves=[5]  push right for a 5 row array\r\n11042311\r\n11111111\r\n11111111\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eTest Suite Visualization:\u003c/b\u003e A visualization option is provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithms:\u003c/b\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/p\u003e","function_template":"function moves=solve_Sokoban(m)\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n\r\n moves=[];\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\nvisualize=0;\r\nif visualize\r\n figure(1); % Start\r\n map=[.5 .5 .5;0 0 0;.5 .5 .5;0 1 0;0 0 1;\r\n    1 0 0;1 1 0;0 0 0;1 0 1;.5 .5 .5];\r\n colormap(map);\r\n figure(2); % Move map\r\n% -1 0 1 2 3 4 5 6 7 8\r\n% -1 color limit, 8 color limit\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n colormap(map)\r\nend\r\n\r\n%Sokoban map http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 \r\n%Puzzle 45 \r\nsmap=[0 0 0 0 0 0;0 3 2 2 4 0;3 3 2 0 2 0;3 5 0 0 1 1];\r\n[nr,nc]=size(smap);\r\nm=ones(nr+4,nc+4);\r\nm(3:end-2,3:end-2)=smap;\r\n\r\nif visualize\r\n im=m;\r\n mend=size(map,1)-2;\r\n im(1)=-1;im(end)=mend;\r\n figure(1);imagesc(im)\r\n m\r\nend\r\n\r\ntic\r\nmoves=solve_Sokoban(m);\r\ntoc\r\n\r\n% Check Solution\r\n valid=1;\r\n ptr=find(m==4);\r\n pushes=0;\r\n if isempty(ptr),ptr=find(m==7);end\r\n for i=1:length(moves)\r\n  mv=moves(i);\r\n  mvptr=m(ptr+mv);\r\n  mvptr2=m(ptr+2*mv);\r\n  if mvptr==1 % Illegal run into wall\r\n   valid=0;\r\n   break;\r\n  end\r\n  if (mvptr2==5 || mvptr2==2 || mvptr2==1) \u0026\u0026 (mvptr==5 || mvptr==2) % Illegal double block push\r\n   valid=0;\r\n   break;\r\n  end\r\n  if mvptr==0 || mvptr==3\r\n   m(ptr)=m(ptr)-4;\r\n   m(ptr+mv)=m(ptr+mv)+4;\r\n   ptr=ptr+mv;\r\n  elseif mvptr==2 || mvptr==5\r\n   m(ptr)=m(ptr)-4;\r\n   m(ptr+2*mv)=m(ptr+2*mv)+2;\r\n   m(ptr+mv)=m(ptr+mv)-2+4;\r\n   ptr=ptr+mv;\r\n   pushes=pushes+1;\r\n  end\r\n end\r\n \r\n fprintf('Moves %i  Pushes %i\\n',length(moves),pushes)\r\n valid=valid \u0026\u0026  nnz(m==3)==0 \u0026\u0026 nnz(m==7)==0;\r\n assert(valid)\r\n\r\nif visualize \u0026\u0026 valid\r\n % display moves\r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n ptr=find(im==4);\r\n if isempty(ptr),ptr=find(im==7);end\r\n for i=1:length(moves)\r\n  mv=moves(i);\r\n  mvptr=im(ptr+mv);\r\n  if mvptr==0 || mvptr==3\r\n   im(ptr)=im(ptr)-4;\r\n   im(ptr+mv)=im(ptr+mv)+4;\r\n   ptr=ptr+mv;\r\n  elseif mvptr==2 || mvptr==5\r\n   im(ptr)=im(ptr)-4;\r\n   im(ptr+2*mv)=im(ptr+2*mv)+2;\r\n   im(ptr+mv)=im(ptr+mv)-2+4;\r\n   ptr=ptr+mv;\r\n  end\r\n  \r\n  figure(2);imagesc(im)\r\n  pause(0.2)\r\n end\r\n \r\nend % vis and valid\r\n\r\n\r\nmovs=length(moves);\r\nassignin('caller','score',min(200,max(0,movs+pushes)));","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2013-11-11T01:51:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-10T23:21:50.000Z","updated_at":"2025-12-03T12:16:08.000Z","published_at":"2013-11-11T01:51:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.game-sokoban.com/index.php?mode=level\u0026amp;lid=16138\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sokoban\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ewiki Sokoban reference\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban can not jump blocks or move diagonally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sum of Moves and Pushes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[11111111\\n11111111 Moves=[5]  push right for a 5 row array\\n11042311\\n11111111\\n11111111]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTest Suite Visualization:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A visualization option is provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithms:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2004,"title":"BattleShip - Petty Officer (Level 2)","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships Games Magazine Battleships\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\r\n\r\nThe Seaman Level is the simplest and can be solved by direct evolution of current condition. The Petty Officer Level requires just a pinch more effort to solve.\r\n\r\nMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\r\n\r\nShips have no diagonal or UDLR adjacency.  The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\r\n\r\nThe map is ringed by zeros to make m a 12x12 array.\r\n\r\n*Input:* m,r,c;  m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\r\n\r\n*Output:* b; A binary 12x12 array\r\n\r\n*Example:*\r\n\r\n  r=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\n  c=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n  \r\n  m              b\r\n  000000000000  000000000000\r\n  077757777770  000011000000\r\n  077777777770  000000000000\r\n  077777777770  000100010000\r\n  077777777770  000100010000\r\n  077777777770  010000010000\r\n  077777777770  010000010010\r\n  027777777760  010000000010\r\n  077777777770  000101000010\r\n  077777777770  000000000000\r\n  077777477770  010001100100\r\n  000000000000  000000000000\r\n\r\n*Algorithm:* \r\n\r\n  1) Initialize processing array based upon input matrix.\r\n  2) Implement a cycling check of driven array changes\r\n  3) Quick Test of Change every single Unknown serially\r\n  4) Evolve and check if complete solution created","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 795.667px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 397.833px; transform-origin: 407px 397.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGames Magazine Battleships\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 291.35px 7.91667px; transform-origin: 291.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 360.45px 7.91667px; transform-origin: 360.45px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Seaman Level is the simplest and can be solved by direct evolution of current condition. The Petty Officer Level requires just a pinch more effort to solve.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.1px 7.91667px; transform-origin: 382.1px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.267px 7.91667px; transform-origin: 382.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 165.183px 7.91667px; transform-origin: 165.183px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 220.5px 7.91667px; transform-origin: 220.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 74.3px 7.91667px; transform-origin: 74.3px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e b; A binary 12x12 array\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 326.933px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 163.467px; transform-origin: 404px 163.467px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 111.65px 7.91667px; transform-origin: 111.65px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003er=[0 2 0 2 2 2 3 2 3 0 4 0]';\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 7.91667px; transform-origin: 107.8px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ec=[0 4 0 3 1 3 1 4 0 1 3 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 7.91667px; transform-origin: 0px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 61.6px 7.91667px; transform-origin: 61.6px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 57.75px 7.91667px; transform-origin: 57.75px 7.91667px; \"\u003em              \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 3.85px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 3.85px 7.91667px; \"\u003eb\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e000000000000  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077757777770  000011000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000100010000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000100010000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  010000010000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  010000010010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e027777777760  010000000010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000101000010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777477770  010001100100\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e000000000000  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 35.3833px 7.91667px; transform-origin: 35.3833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAlgorithm:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 211.75px 7.91667px; transform-origin: 211.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 53.9px 7.91667px; transform-origin: 53.9px 7.91667px; \"\u003e1) Initialize \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 157.85px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 157.85px 7.91667px; \"\u003eprocessing array based upon input matrix.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 200.2px 7.91667px; transform-origin: 200.2px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 50.05px 7.91667px; transform-origin: 50.05px 7.91667px; \"\u003e2) Implement \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 150.15px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 150.15px 7.91667px; \"\u003ea cycling check of driven array changes\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 204.05px 7.91667px; transform-origin: 204.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 34.65px 7.91667px; transform-origin: 34.65px 7.91667px; \"\u003e3) Quick \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 169.4px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 169.4px 7.91667px; \"\u003eTest of Change every single Unknown serially\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 184.8px 7.91667px; transform-origin: 184.8px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 38.5px 7.91667px; transform-origin: 38.5px 7.91667px; \"\u003e4) Evolve \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 146.3px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 146.3px 7.91667px; \"\u003eand check if complete solution created\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function b=solve_battleship(m,r,c)\r\n% WSUDLRMX 0W 1S 2U 3D 4L 5R 6M 7X\r\n% Surround 10x10 with ring of zeros\r\n% r : RowSum Vector [12,1]\r\n% c : ColSum Vector [1,12]\r\n b=zeros(12);\r\nend","test_suite":"%%\r\nglobal valid\r\nfiletext = fileread('solve_battleship.m');\r\nvalid=isempty(strfind(filetext, '(exist(fullfile(cd'));\r\nassert(valid,'overwrite assert forbidden')\r\n%%\r\nglobal valid\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n% Games August 2013 2-Petty Officer %\r\nr=[0 0 1 4 1 3 3 3 3 2 0 0]';\r\nc=[0 2 3 2 0 5 0 4 0 2 2 0];\r\nm(5,4)=3;\r\nm(6,11)=3;\r\nm(9,8)=0;\r\n\r\ntic\r\nb=0;\r\nif valid\r\n b=solve_battleship(m,r,c);\r\nend\r\ntoc\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n\r\n%%\r\nglobal valid\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 2-Petty %\r\nr=[0 2 2 2 3 2 0 0 7 0 2 0]';\r\nc=[0 2 5 1 4 1 4 0 2 1 0 0];\r\nm(3,3)=3;\r\nm(5,7)=1;\r\nm(9,4)=0;\r\n\r\ntic\r\nb=0;\r\nif valid\r\n b=solve_battleship(m,r,c);\r\nend\r\ntoc\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n\r\n%%\r\nglobal valid\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 2-Petty\r\nr=[0 5 1 4 1 0 5 1 2 1 0 0]';\r\nc=[0 2 3 3 2 0 5 0 3 1 1 0];\r\nm(9,2)=1;\r\nm(2,7)=0;\r\nm(3,9)=1;\r\n\r\n\r\ntic\r\nb=0;\r\nif valid\r\n b=solve_battleship(m,r,c);\r\nend\r\ntoc\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2020-10-01T19:08:44.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2013-11-17T22:45:53.000Z","updated_at":"2025-12-10T03:22:09.000Z","published_at":"2013-11-17T23:06:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames Magazine Battleships\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Seaman Level is the simplest and can be solved by direct evolution of current condition. The Petty Officer Level requires just a pinch more effort to solve.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e b; A binary 12x12 array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[r=[0 2 0 2 2 2 3 2 3 0 4 0]';\\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\\n\\nm              b\\n000000000000  000000000000\\n077757777770  000011000000\\n077777777770  000000000000\\n077777777770  000100010000\\n077777777770  000100010000\\n077777777770  010000010000\\n077777777770  010000010010\\n027777777760  010000000010\\n077777777770  000101000010\\n077777777770  000000000000\\n077777477770  010001100100\\n000000000000  000000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Initialize processing array based upon input matrix.\\n2) Implement a cycling check of driven array changes\\n3) Quick Test of Change every single Unknown serially\\n4) Evolve and check if complete solution created]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2005,"title":"BattleShip - Seaman (1) thru Admiral(6) :  CPU Time Scoring(msec)","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships Games Magazine Battleships\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\r\n\r\nThis Challenge is to complete three full sets of Battleship in minimal time.\r\n\r\nMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\r\n\r\nShips have no diagonal or UDLR adjacency.  The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\r\n\r\nThe map is ringed by zeros to make m a 12x12 array.\r\n\r\n*Input:* m,r,c;  m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\r\n\r\n*Output:* b; A binary 12x12 array\r\n\r\n*Scoring:* Total Time (msec)\r\n\r\n*Example:*\r\n\r\n  r=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\n  c=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n  \r\n  m              b\r\n  000000000000  000000000000\r\n  077757777770  000011000000\r\n  077777777770  000000000000\r\n  077777777770  000100010000\r\n  077777777770  000100010000\r\n  077777777770  010000010000\r\n  077777777770  010000010010\r\n  027777777760  010000000010\r\n  077777777770  000101000010\r\n  077777777770  000000000000\r\n  077777477770  010001100100\r\n  000000000000  000000000000\r\n\r\n*Algorithm:* \r\n\r\n  1) Initialize processing array based upon input matrix.\r\n  2) Implement a cycling check of driven array changes\r\n  3) Quick Test of Change every single Unknown serially\r\n  4) Evolve and check if complete solution created\r\n  5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\"\u003eGames Magazine Battleships\u003c/a\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/p\u003e\u003cp\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/p\u003e\u003cp\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/p\u003e\u003cp\u003eShips have no diagonal or UDLR adjacency.  The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/p\u003e\u003cp\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m,r,c;  m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e b; A binary 12x12 array\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Total Time (msec)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003er=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003em              b\r\n000000000000  000000000000\r\n077757777770  000011000000\r\n077777777770  000000000000\r\n077777777770  000100010000\r\n077777777770  000100010000\r\n077777777770  010000010000\r\n077777777770  010000010010\r\n027777777760  010000000010\r\n077777777770  000101000010\r\n077777777770  000000000000\r\n077777477770  010001100100\r\n000000000000  000000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Initialize processing array based upon input matrix.\r\n2) Implement a cycling check of driven array changes\r\n3) Quick Test of Change every single Unknown serially\r\n4) Evolve and check if complete solution created\r\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs\r\n\u003c/pre\u003e","function_template":"function b=solve_battleship(m,r,c)\r\n% WSUDLRMX 0W 1S 2U 3D 4L 5R 6M 7X\r\n% Surround 10x10 with ring of zeros\r\n% r : RowSum Vector [12,1]\r\n% c : ColSum Vector [1,12]\r\n b=zeros(12);\r\nend","test_suite":"assignin('caller','score',2000);\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 1-Seaman\r\nr=[0 2 2 3 1 1 1 1 2 2 5 0]';\r\nc=[0 1 0 1 1 2 6 0 5 0 4 0];\r\nm(2,2)=1;\r\nm(2,6)=1;\r\nm(4,9)=3;\r\n\r\n%tz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\n%tt=tz+cputime-time0\r\ntt=cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 2-Petty Officer\r\nr=[0 0 1 4 1 3 3 3 3 2 0 0]';\r\nc=[0 2 3 2 0 5 0 4 0 2 2 0];\r\nm(5,4)=3;\r\nm(6,11)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 3-Ensign\r\nr=[0 3 0 4 1 0 0 1 2 1 8 0]';\r\nc=[0 5 1 1 3 1 1 1 1 3 3 0];\r\nm(4,7)=1;\r\nm(4,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 4-Captain\r\nr=[0 1 2 2 2 2 5 0 5 0 1 0]';\r\nc=[0 5 0 0 0 2 1 4 2 1 5 0];\r\nm(4,8)=0;\r\nm(7,10)=4;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 5-Commodore\r\nr=[0 1 1 5 0 3 1 3 2 1 3 0]';\r\nc=[0 2 2 1 0 2 1 6 0 5 1 0];\r\nm(6,4)=1;\r\nm(6,8)=0;\r\nm(7,10)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 6-Admiral\r\nr=[0 5 1 4 2 3 1 1 0 3 0 0]';\r\nc=[0 4 0 1 2 4 2 1 1 5 0 0];\r\nm(5,2)=1;\r\nm(10,7)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 1-Seaman\r\nr=[0 1 1 1 1 2 3 3 3 1 4 0]';\r\nc=[0 3 2 0 1 6 0 3 1 4 0 0];\r\nm(2,3)=1;\r\nm(8,5)=1;\r\nm(7,8)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 2-Petty\r\nr=[0 2 2 2 3 2 0 0 7 0 2 0]';\r\nc=[0 2 5 1 4 1 4 0 2 1 0 0];\r\nm(3,3)=3;\r\nm(5,7)=1;\r\nm(9,4)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 3-Ensign\r\nr=[0 3 0 0 2 4 3 2 1 4 1 0]';\r\nc=[0 2 2 5 2 3 0 3 0 2 1 0];\r\nm(7,2)=1;\r\nm(7,4)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 4-Captain\r\nr=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\nm(8,2)=2;\r\nm(2,5)=5;\r\nm(11,7)=4;\r\nm(8,11)=6;\r\n\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 5-Commodore\r\nr=[0 3 2 3 1 1 1 3 3 2 1 0]';\r\nc=[0 1 2 4 1 4 1 1 0 5 1 0];\r\nm(2,10)=5;\r\nm(8,4)=6;\r\nm(8,6)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 6-Admiral\r\nr=[0 5 1 0 3 0 1 5 2 3 0 0]';\r\nc=[0 0 4 2 5 2 1 2 1 1 2 0];\r\nm(2,10)=0;\r\nm(8,7)=0;\r\nm(10,5)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 1-Seaman\r\nr=[0 1 1 2 4 1 0 2 2 5 2 0]';\r\nc=[0 1 1 1 1 4 0 7 0 2 3 0];\r\nm(2,8)=0;\r\nm(8,3)=1;\r\nm(9,6)=0;\r\nm(5,11)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 2-Petty\r\nr=[0 5 1 4 1 0 5 1 2 1 0 0]';\r\nc=[0 2 3 3 2 0 5 0 3 1 1 0];\r\nm(9,2)=1;\r\nm(2,7)=0;\r\nm(3,9)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 3-Ensign\r\nr=[0 3 0 2 3 1 1 2 2 2 4 0]';\r\nc=[0 1 1 0 6 1 4 0 3 1 3 0];\r\nm(4,3)=0;\r\nm(5,6)=4;\r\nm(7,9)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 4-Captain\r\nr=[0 0 6 0 2 2 4 1 3 2 0 0]';\r\nc=[0 3 1 3 1 2 2 2 2 0 4 0];\r\nm(5,2)=0;\r\nm(9,4)=0;\r\nm(3,5)=4;\r\nm(6,11)=2;\r\nm(8,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 5-Commodore %\r\nr=[0 5 2 1 1 7 1 2 0 0 1 0]';\r\nc=[0 2 3 1 2 1 3 1 2 0 5 0];\r\nm(8,2)=1;\r\nm(5,11)=2;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 6-Admiral % Solved with with Bship HV .10 \r\n% solved recur .023\r\nr=[0 0 2 4 1 4 1 0 2 0 6 0]';\r\nc=[0 3 1 3 1 3 2 1 2 1 3 0];\r\nm(3,2)=0;\r\nm(4,5)=4;\r\nm(9,9)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\ntt\r\nassignin('caller','score',min(2000,floor(1000*tt)));","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-17T23:26:01.000Z","updated_at":"2013-11-18T00:27:11.000Z","published_at":"2013-11-18T00:27:11.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames Magazine Battleships\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e b; A binary 12x12 array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Total Time (msec)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[r=[0 2 0 2 2 2 3 2 3 0 4 0]';\\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\\n\\nm              b\\n000000000000  000000000000\\n077757777770  000011000000\\n077777777770  000000000000\\n077777777770  000100010000\\n077777777770  000100010000\\n077777777770  010000010000\\n077777777770  010000010010\\n027777777760  010000000010\\n077777777770  000101000010\\n077777777770  000000000000\\n077777477770  010001100100\\n000000000000  000000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Initialize processing array based upon input matrix.\\n2) Implement a cycling check of driven array changes\\n3) Quick Test of Change every single Unknown serially\\n4) Evolve and check if complete solution created\\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":2052,"title":"provide the numerical answer to these number questions...","description":"return a row matrix containing the sorted numbers which answer the following questions:\r\n\r\n a) what is the first Knuth number to repeat 3 times?\r\n b) how many Platonic solids are there?\r\n c) what base is the decimal number system in?\r\n d) what is the additive identity?\r\n e) what is the multiplicative identity?\r\n f) what is the smallest perfect number?\r\n g) in the Fibonacci sequence, what is the larget cube number?\r\n h) what is the number of spatial dimension we live in?\r\n i) what is the square of the only even Prime?\r\n j) what is the only even Prime?\r\n k) any number can be divided by this digit if the repeated sum of the digits is this digit\r\n","description_html":"\u003cp\u003ereturn a row matrix containing the sorted numbers which answer the following questions:\u003c/p\u003e\u003cpre\u003e a) what is the first Knuth number to repeat 3 times?\r\n b) how many Platonic solids are there?\r\n c) what base is the decimal number system in?\r\n d) what is the additive identity?\r\n e) what is the multiplicative identity?\r\n f) what is the smallest perfect number?\r\n g) in the Fibonacci sequence, what is the larget cube number?\r\n h) what is the number of spatial dimension we live in?\r\n i) what is the square of the only even Prime?\r\n j) what is the only even Prime?\r\n k) any number can be divided by this digit if the repeated sum of the digits is this digit\u003c/pre\u003e","function_template":"function amat = numtest()\r\namat(1)=100;\r\namat(2)=2;\r\n% etc\r\nend","test_suite":"%% intentionally obfuscated\r\ny_correct = -4.6:.1:-3.6;\r\nassert(isequal(numtest(),int32(10*y_correct+'0'-log10(100)) ))","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":17471,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":59,"test_suite_updated_at":"2017-08-18T16:07:56.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-15T06:20:43.000Z","updated_at":"2026-04-07T13:37:29.000Z","published_at":"2013-12-15T06:20:43.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ereturn a row matrix containing the sorted numbers which answer the following questions:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ a) what is the first Knuth number to repeat 3 times?\\n b) how many Platonic solids are there?\\n c) what base is the decimal number system in?\\n d) what is the additive identity?\\n e) what is the multiplicative identity?\\n f) what is the smallest perfect number?\\n g) in the Fibonacci sequence, what is the larget cube number?\\n h) what is the number of spatial dimension we live in?\\n i) what is the square of the only even Prime?\\n j) what is the only even Prime?\\n k) any number can be divided by this digit if the repeated sum of the digits is this digit]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50018,"title":"Number Puzzles - 010","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 21px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 10.5px; transform-origin: 407px 10.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of fifteen (15) consecutive five-digit numbers whose sum is a palindrome.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_010()\r\n  y = 1:15;\r\nend","test_suite":"%%\r\ny=puzzle_010();\r\nassert(isequal(length(num2str(y(1))),5))\r\nassert(isequal(y(15)-y(1)+1,15))\r\nassert(isequal(size(unique(y),2),15))\r\naa=num2str(sum(y));\r\nassert(strcmp(aa,fliplr(aa)))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-28T01:33:03.000Z","updated_at":"2026-02-25T10:48:18.000Z","published_at":"2021-01-28T01:33:03.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of fifteen (15) consecutive five-digit numbers whose sum is a palindrome.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2027,"title":"Consecutive Powers","description":"Return 2 numbers and 2 powers such that their difference is 1\r\n\r\nA 4 element row vector is expected: x where \r\n\r\n x(1)^x(2) - x(3)^x(4) = 1;\r\n","description_html":"\u003cp\u003eReturn 2 numbers and 2 powers such that their difference is 1\u003c/p\u003e\u003cp\u003eA 4 element row vector is expected: x where\u003c/p\u003e\u003cpre\u003e x(1)^x(2) - x(3)^x(4) = 1;\u003c/pre\u003e","function_template":"function [x,y,p1,p2] = conpow()\r\n% your code here\r\nend","test_suite":"%%\r\nx=conpow();\r\nd = x(1)^x(2)-x(3)^x(4);\r\ny_correct = 1;\r\nassert(isequal(d,y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":17471,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":98,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-30T12:48:23.000Z","updated_at":"2026-02-25T10:57:25.000Z","published_at":"2013-11-30T12:57:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn 2 numbers and 2 powers such that their difference is 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA 4 element row vector is expected: x where\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x(1)^x(2) - x(3)^x(4) = 1;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50392,"title":"Number Puzzle - 073","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 42px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 21px; transform-origin: 407px 21px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of a pair of four-digits prime numbers which has the following form ##XX and YY## (where # could be any single digit number) and whose sum is a square. Your answer should be y=[##XX YY##].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_073()\r\n  y = [1234 5678];\r\nend","test_suite":"%%\r\ny=puzzle_073();\r\na=num2str(y(1));\r\nassert(isequal(a(3),a(4)))\r\nb=num2str(y(2));\r\nassert(isequal(b(1),b(2)))\r\nc=y(1)+y(2);\r\nassert(isequal(sqrt(c),floor(sqrt(c))))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-02-16T18:48:55.000Z","updated_at":"2026-01-30T15:49:42.000Z","published_at":"2021-02-16T18:48:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of a pair of four-digits prime numbers which has the following form ##XX and YY## (where # could be any single digit number) and whose sum is a square. Your answer should be y=[##XX YY##].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":50008,"title":"Number Puzzles - 008","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 21px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 10.5px; transform-origin: 407px 10.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of five prime numbers whose product is a palindrome.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_008()\r\n  y = [1 2 3 4 5];\r\nend","test_suite":"%%\r\ny=puzzle_008();\r\nassert(isequal(size(unique(y),2),5))\r\nn=num2str(prod(y))\r\nassert(isequal(n,fliplr(n)))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-28T01:02:08.000Z","updated_at":"2026-03-17T07:45:10.000Z","published_at":"2021-01-28T01:02:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of five prime numbers whose product is a palindrome.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2770,"title":"Probability of Choosing a Red Ball","description":"Given two jars of red and blue balls, find the probability of choosing a red ball from Jar 1 after going through the steps. \r\n\r\n  Step 1: Choose a random ball from Jar 2 and add it to Jar 1\r\n\r\n  Step 2: Choose a random ball from Jar 1\r\n\r\n  Step 3: Calculate the probability of the final ball being red\r\n\r\n*Example:* \r\n\r\nGiven inputs for Jar 1 and Jar 2\r\n\r\nJar 1: (r1,b1) = (2,7)\r\n\r\nJar 2: (r2,b2) = (5,5)\r\n\r\nChoose a ball from Jar 2 and add it to Jar 1. \r\n  \r\n   _Note: Jar 1 could now have either 3 blue and 7 red or 2 blue and 8 red._ \r\n\r\nTaking into consideration both possibilities, the likelihood of the final ball being red is *0.25* . ","description_html":"\u003cp\u003eGiven two jars of red and blue balls, find the probability of choosing a red ball from Jar 1 after going through the steps.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eStep 1: Choose a random ball from Jar 2 and add it to Jar 1\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eStep 2: Choose a random ball from Jar 1\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eStep 3: Calculate the probability of the final ball being red\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven inputs for Jar 1 and Jar 2\u003c/p\u003e\u003cp\u003eJar 1: (r1,b1) = (2,7)\u003c/p\u003e\u003cp\u003eJar 2: (r2,b2) = (5,5)\u003c/p\u003e\u003cp\u003eChoose a ball from Jar 2 and add it to Jar 1.\u003c/p\u003e\u003cpre\u003e   _Note: Jar 1 could now have either 3 blue and 7 red or 2 blue and 8 red._ \u003c/pre\u003e\u003cp\u003eTaking into consideration both possibilities, the likelihood of the final ball being red is \u003cb\u003e0.25\u003c/b\u003e .\u003c/p\u003e","function_template":"function prob = probRedBall(r1,b1,r2,b2)\r\n  prob = r1/(r1+b1);\r\nend","test_suite":"%%\r\nr1 = 2; b1 = 7; r2 = 5; b2 = 5; \r\nprob_correct = 0.2500;\r\nassert(isequal(probRedBall(r1,b1,r2,b2),prob_correct))\r\n\r\n%%\r\nr1 = 0; b1 = 5; r2 = 0; b2 = 5; \r\nprob_correct = 0.0000;\r\nassert(isequal(probRedBall(r1,b1,r2,b2),prob_correct))\r\n\r\n%%\r\nr1 = 0; b1 = 3; r2 = 1; b2 = 3; \r\nprob_correct = 0.0625;\r\nassert(isequal(probRedBall(r1,b1,r2,b2),prob_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":32736,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":66,"test_suite_updated_at":"2014-12-10T17:44:29.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2014-12-10T17:13:21.000Z","updated_at":"2026-03-05T15:56:58.000Z","published_at":"2014-12-10T17:35:42.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two jars of red and blue balls, find the probability of choosing a red ball from Jar 1 after going through the steps.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Step 1: Choose a random ball from Jar 2 and add it to Jar 1\\n\\nStep 2: Choose a random ball from Jar 1\\n\\nStep 3: Calculate the probability of the final ball being red]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven inputs for Jar 1 and Jar 2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJar 1: (r1,b1) = (2,7)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJar 2: (r2,b2) = (5,5)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChoose a ball from Jar 2 and add it to Jar 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   _Note: Jar 1 could now have either 3 blue and 7 red or 2 blue and 8 red._]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTaking into consideration both possibilities, the likelihood of the final ball being red is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0.25\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50232,"title":"Number Puzzle - 044","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 136px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 68px; transform-origin: 407px 68px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA four-digit number N can be written as abcd where a, b, c, and d do not have to be unique. Find all four-digit numbers that satisfy the following condition:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 55px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 27.5px; text-align: left; transform-origin: 384px 27.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzY1AACSkgACAAAAAzY1AADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAyOjEwIDE3OjE3OjE3ADIwMjE6MDI6MTAgMTc6MTc6MTcAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAyLTEwVDE3OjE3OjE3LjY0OTwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIADEBGwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiivH9W+LGv6B8bIfDGow6ZNoEl3DbPeRQSJLC86MYkZjIVJ4BJ24Iz0oA9gorifif4l8QeF9J0u48L/2bJc3uoxWAgv4ZH8xpThdpR1xjBJznI9Mc9haLcrZwrfSRS3IQCV4YyiM2OSqksQM9iT9TQBNRRRQAUUUUAFFFFABRRRQAUVi2vimyu/Gl/4ZjiuBe2NrHdSSMq+WVckAA5znjngVtUAFFFeb+ENY8QX3xn8XaVca5Nf6FpEUKxwy28K7JpQH270RSQoDDk9+cnmgD0aWVIYXlmdUjRSzMxwFA5JNctF8TvCMslqF1VlhvJfJtruS0mS2mfptWdkEZPB6NXUyRpNE0cqK8bqVZGGQwPUEdxXnniXS4fHjW3hDRYI4PD+mXMbajdwqFSMxEFbWAAY3ZA3EcIOOpwAD0WiiigAorEv/ABVZad4w0nw3NFcNearFNLA6KpjURAFtxzkHnjAP4Vt0AFFFFAEV1dQWNnNd3k0cFvBG0kssjBVjRRksSegAGc1zlp8R/C17fWVrDqMiNqDbbKWezmhhuiegjldAjk9grHNdJc20F5ay2t5DHPbzIY5YpUDJIpGCrA8EEHBBryf4hvdxeI/DS+MtPtrbwja6zEbNtMmMkn2gblgMwZU2R4JJVA2OmT3APXKKKKACisSXxVZQ+OYPCrRXBvp7Fr5ZAq+UI1fYQTnO7J9Me9bdABRRRQAVm+IPEWleFdFm1bX7xLKxhxvlcFuScAAAEk57AGuH0PWtfuvj7r2h/wBuTXmhabYJPJbSQQjyZ5SpSMOqBioUkjJJ9SetcX8W9ZOueHfE93q2n6tbxadEbXSba40m6ERcsFkumk8vywSpKpluBk9WxQB75HIssSSRnKOoZTjqDTqxvC2sx61oyyw2d/axwlYVN7avbmbCKd6q4DbcsRkgZKntgnZoAK8A1Xw2/jf4T+OvEVuGe8uNbm1HTpE67LT90m31JRJAP96vaPFNzqtr4Yvn8PafJqOpmFktoI5Uj+cjAYs7KAAeTznjisj4YaRd6H8MtH0fVtMfTrqzg8me3kkjk3NklmBRmBDEk9c89KAOMsvEq/EfxD8MnQo6i2n1q+ReiSxJ5K4+krv+Vew15F8Ivhlqngjxp4luNSjxpy4tdFfzFbNu0ryMMA5Xkp1xk5r12gDE8V6Xrur6VHB4Y8R/8I9drMHe6+wpdb02sCmxyAMkqc9flx3rkf8AhCfid/0Vz/y2rb/4quu8V+DtC8b6VHpviex+3WkUwnSPznjw4VlByjA9GbjOOa5H/hnz4Y/9Cz/5P3P/AMcoAvaL4T8f2OtW1zrHxL/tSyjfM1n/AGDbw+cv93epyv1Fd3XCaL8FfAHh7WrbVtH0D7Pe2r74Zftlw+1umcM5B69xXd0AeaeNdUu/EfxO0f4e2F1NaWb2ralrEtu5SR4AdqQqw5UMww2MHBHPUHnPGPhPRrf4zeANC8NWMOmo0kt9qMFkoiWaOEo8TSBfvfMjDJ5561teI/D3i/RfjSnjPwlo0GuW97pn9n3UEt4tsYTvDb9zZyPlU8AnqMdKzZfBvj+w+Lkfie0hsdRuNR0k2k95LcbYdMlMm7KRH5nVVAAUY3EkkqSaAPZq4/xR8Qf+EX1cWH/CI+KtYzEJPtGk6Z58IySNu7cPmGOR7iusgR47eNJpTNIqAPIVALnHJwOBn2qSgDwLS/iZ5Pxn13Vv+EJ8YSfaNMtofsUek5uYtrH53Tdwpzwc816v4S8a/wDCWS3Sf8I14i0T7OqndrNh9nEuc8J8xyRjn6isjSNOvYvjx4i1CSzuEsptJtY47lomEbsGbKhsYJHcV3tACMyopZyFVRkknAArwj4X+FB8SNH8Q614kkuDoetavcXMVpDM0JuhnapkZSG2JghUzjO4nOFx694yg1O68EazbaDF5upXFnLDbKXC/OylQckgDGc/hXDeGLDxr4c+Gdn4P0zw4ttqdvA9uNVe8iNpGWYnzQAxlJ+Ytt2DnvQBX+ATXd18OdZ0yS8uPs1nq11ZWNx5m6SOIKmNpII4LEg8jPbip1+AmmLpjaafGnjRtOcMr2Z1ZfJcMSWBTy8YJJz65Ndt4J8J2fgjwfY6DYMZEtU/eTMMGWQnLufqSeOwwO1b1ABXN+LvGX/CJfY/+Kc8Qa39q3/8gWx+0+Tt2/f+Ybc7uPXB9K6SigDwHXviZ9q+MHhPVf8AhCvGEP2O1vE+xzaVtuJ96qMxpu+YLj5jnivTPDPxE/4SXWRp/wDwh/izSMoz/adV0zyIRjtu3Hk9hVPxHp19P8cPBl/DZ3ElnbWd8s9wkTGOIsi7QzYwCe2etd9QAE4GTwK8p8CxRfFWbVPFfiaJb/R2u3tdH024G+3WGM4MrRn5Wdj3YHGMCvU5olngkifO2RSpx6EYrxzwNpPxM8A6GfB2neGtMvbaGaU2uuz6iEiVXYsC8IBkJBPQY9M96AO70v4caDpHg/VfDNqk/wDZmqSzyTRGQAoJeCiYA2qAAAOwHeoYvhvZSf2bHrOtavrdppcqTWlnfyRGON0GEYlI1aQrnjeze+a6nT4bm3023hv7r7ZdJGqzXHlhPNbHLbRwMntVigArl/Fvjj/hE7q3h/4RjxJrXnoX8zRtP+0LHg4wx3DBrqKKAPAbr4mb/jlYaz/whXjBfL0OS3+wtpWLpszBvMEe7lB0LZ616j4U8f8A/CVarJY/8In4o0by4TN9o1jTfs8TYZRsDbjlvmzj0B9KzrzTr5v2hNO1FbO4Ninh6WFroRN5SyGcEIXxgNjnGc131AEdxcR2trLcTtsihQu7HsoGSa8s+HumwfE7S5/Gvja1j1GG/uJV0zTrtA8FnbIxQYjOVLkgkuRnpjAr0vWNPGraFf6cX8sXltJAXA+7vUrn9a8n8FeGfiFB4CHgXVtPtNHsbVJYTq8V6JJLqNmYhY0UZTOcFyQQvQBuQAP/AGetMgXTPE+vWu4wanq8kdqzMW3W0WRHyew3MB9K9F8Y+Fbbxr4ZuNCv7y7tLS5K+c1oYw7qDnbl0YAZAPAB469a474OeH/F3hzwzY6P4hsbXSbLTYpYxDFMs8l7I8hfzWYcIoBwFBJJOTjAFem0AVdNsjp2l21m11PdmCMR+fcbfMcDjLbQoz9AKtUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q==\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = puzzle_044(x)\r\n  y = [1234 5678];\r\nend","test_suite":"%%\r\na=puzzle_044();\r\nassert(length(a)\u003e=3)\r\nassert(length(unique(a))\u003e=3)\r\nassert(sum(a)==5409)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-02-10T22:20:17.000Z","updated_at":"2026-01-30T15:01:34.000Z","published_at":"2021-02-10T22:20:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA four-digit number N can be written as abcd where a, b, c, and d do not have to be unique. Find all four-digit numbers that satisfy the following condition:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"49\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"283\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.jpeg\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.jpeg\",\"contentType\":\"image/jpeg\",\"content\":\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzY1AACSkgACAAAAAzY1AADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAyOjEwIDE3OjE3OjE3ADIwMjE6MDI6MTAgMTc6MTc6MTcAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAyLTEwVDE3OjE3OjE3LjY0OTwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIADEBGwMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiivH9W+LGv6B8bIfDGow6ZNoEl3DbPeRQSJLC86MYkZjIVJ4BJ24Iz0oA9gorifif4l8QeF9J0u48L/2bJc3uoxWAgv4ZH8xpThdpR1xjBJznI9Mc9haLcrZwrfSRS3IQCV4YyiM2OSqksQM9iT9TQBNRRRQAUUUUAFFFFABRRRQAUVi2vimyu/Gl/4ZjiuBe2NrHdSSMq+WVckAA5znjngVtUAFFFeb+ENY8QX3xn8XaVca5Nf6FpEUKxwy28K7JpQH270RSQoDDk9+cnmgD0aWVIYXlmdUjRSzMxwFA5JNctF8TvCMslqF1VlhvJfJtruS0mS2mfptWdkEZPB6NXUyRpNE0cqK8bqVZGGQwPUEdxXnniXS4fHjW3hDRYI4PD+mXMbajdwqFSMxEFbWAAY3ZA3EcIOOpwAD0WiiigAorEv/ABVZad4w0nw3NFcNearFNLA6KpjURAFtxzkHnjAP4Vt0AFFFFAEV1dQWNnNd3k0cFvBG0kssjBVjRRksSegAGc1zlp8R/C17fWVrDqMiNqDbbKWezmhhuiegjldAjk9grHNdJc20F5ay2t5DHPbzIY5YpUDJIpGCrA8EEHBBryf4hvdxeI/DS+MtPtrbwja6zEbNtMmMkn2gblgMwZU2R4JJVA2OmT3APXKKKKACisSXxVZQ+OYPCrRXBvp7Fr5ZAq+UI1fYQTnO7J9Me9bdABRRRQAVm+IPEWleFdFm1bX7xLKxhxvlcFuScAAAEk57AGuH0PWtfuvj7r2h/wBuTXmhabYJPJbSQQjyZ5SpSMOqBioUkjJJ9SetcX8W9ZOueHfE93q2n6tbxadEbXSba40m6ERcsFkumk8vywSpKpluBk9WxQB75HIssSSRnKOoZTjqDTqxvC2sx61oyyw2d/axwlYVN7avbmbCKd6q4DbcsRkgZKntgnZoAK8A1Xw2/jf4T+OvEVuGe8uNbm1HTpE67LT90m31JRJAP96vaPFNzqtr4Yvn8PafJqOpmFktoI5Uj+cjAYs7KAAeTznjisj4YaRd6H8MtH0fVtMfTrqzg8me3kkjk3NklmBRmBDEk9c89KAOMsvEq/EfxD8MnQo6i2n1q+ReiSxJ5K4+krv+Vew15F8Ivhlqngjxp4luNSjxpy4tdFfzFbNu0ryMMA5Xkp1xk5r12gDE8V6Xrur6VHB4Y8R/8I9drMHe6+wpdb02sCmxyAMkqc9flx3rkf8AhCfid/0Vz/y2rb/4quu8V+DtC8b6VHpviex+3WkUwnSPznjw4VlByjA9GbjOOa5H/hnz4Y/9Cz/5P3P/AMcoAvaL4T8f2OtW1zrHxL/tSyjfM1n/AGDbw+cv93epyv1Fd3XCaL8FfAHh7WrbVtH0D7Pe2r74Zftlw+1umcM5B69xXd0AeaeNdUu/EfxO0f4e2F1NaWb2ralrEtu5SR4AdqQqw5UMww2MHBHPUHnPGPhPRrf4zeANC8NWMOmo0kt9qMFkoiWaOEo8TSBfvfMjDJ5561teI/D3i/RfjSnjPwlo0GuW97pn9n3UEt4tsYTvDb9zZyPlU8AnqMdKzZfBvj+w+Lkfie0hsdRuNR0k2k95LcbYdMlMm7KRH5nVVAAUY3EkkqSaAPZq4/xR8Qf+EX1cWH/CI+KtYzEJPtGk6Z58IySNu7cPmGOR7iusgR47eNJpTNIqAPIVALnHJwOBn2qSgDwLS/iZ5Pxn13Vv+EJ8YSfaNMtofsUek5uYtrH53Tdwpzwc816v4S8a/wDCWS3Sf8I14i0T7OqndrNh9nEuc8J8xyRjn6isjSNOvYvjx4i1CSzuEsptJtY47lomEbsGbKhsYJHcV3tACMyopZyFVRkknAArwj4X+FB8SNH8Q614kkuDoetavcXMVpDM0JuhnapkZSG2JghUzjO4nOFx694yg1O68EazbaDF5upXFnLDbKXC/OylQckgDGc/hXDeGLDxr4c+Gdn4P0zw4ttqdvA9uNVe8iNpGWYnzQAxlJ+Ytt2DnvQBX+ATXd18OdZ0yS8uPs1nq11ZWNx5m6SOIKmNpII4LEg8jPbip1+AmmLpjaafGnjRtOcMr2Z1ZfJcMSWBTy8YJJz65Ndt4J8J2fgjwfY6DYMZEtU/eTMMGWQnLufqSeOwwO1b1ABXN+LvGX/CJfY/+Kc8Qa39q3/8gWx+0+Tt2/f+Ybc7uPXB9K6SigDwHXviZ9q+MHhPVf8AhCvGEP2O1vE+xzaVtuJ96qMxpu+YLj5jnivTPDPxE/4SXWRp/wDwh/izSMoz/adV0zyIRjtu3Hk9hVPxHp19P8cPBl/DZ3ElnbWd8s9wkTGOIsi7QzYwCe2etd9QAE4GTwK8p8CxRfFWbVPFfiaJb/R2u3tdH024G+3WGM4MrRn5Wdj3YHGMCvU5olngkifO2RSpx6EYrxzwNpPxM8A6GfB2neGtMvbaGaU2uuz6iEiVXYsC8IBkJBPQY9M96AO70v4caDpHg/VfDNqk/wDZmqSzyTRGQAoJeCiYA2qAAAOwHeoYvhvZSf2bHrOtavrdppcqTWlnfyRGON0GEYlI1aQrnjeze+a6nT4bm3023hv7r7ZdJGqzXHlhPNbHLbRwMntVigArl/Fvjj/hE7q3h/4RjxJrXnoX8zRtP+0LHg4wx3DBrqKKAPAbr4mb/jlYaz/whXjBfL0OS3+wtpWLpszBvMEe7lB0LZ616j4U8f8A/CVarJY/8In4o0by4TN9o1jTfs8TYZRsDbjlvmzj0B9KzrzTr5v2hNO1FbO4Ninh6WFroRN5SyGcEIXxgNjnGc131AEdxcR2trLcTtsihQu7HsoGSa8s+HumwfE7S5/Gvja1j1GG/uJV0zTrtA8FnbIxQYjOVLkgkuRnpjAr0vWNPGraFf6cX8sXltJAXA+7vUrn9a8n8FeGfiFB4CHgXVtPtNHsbVJYTq8V6JJLqNmYhY0UZTOcFyQQvQBuQAP/AGetMgXTPE+vWu4wanq8kdqzMW3W0WRHyew3MB9K9F8Y+Fbbxr4ZuNCv7y7tLS5K+c1oYw7qDnbl0YAZAPAB469a474OeH/F3hzwzY6P4hsbXSbLTYpYxDFMs8l7I8hfzWYcIoBwFBJJOTjAFem0AVdNsjp2l21m11PdmCMR+fcbfMcDjLbQoz9AKtUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/2Q==\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1154,"title":"Identify the heavier bag","description":"There are N=2^n bags of rice looking alike, N-1 of which have equal weight and one is slightly heavier. The weighing balance is of unlimited capacity. Using the balance, the minimum number of weighing required to identify the heavier bag is?","description_html":"\u003cp\u003eThere are N=2^n bags of rice looking alike, N-1 of which have equal weight and one is slightly heavier. The weighing balance is of unlimited capacity. Using the balance, the minimum number of weighing required to identify the heavier bag is?\u003c/p\u003e","function_template":"function y = heavier(N)\r\n  y = N;\r\nend","test_suite":"%%\r\nN = 2;\r\ny_correct = 1;\r\nassert(isequal(heavier(N),y_correct))\r\n%%\r\nN = 4;\r\ny_correct = 2;\r\nassert(isequal(heavier(N),y_correct))\r\n%%\r\nN = 8;\r\ny_correct = 2;\r\nassert(isequal(heavier(N),y_correct))\r\n%%\r\nN = 128;\r\ny_correct = 6;\r\nassert(isequal(heavier(N),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":4,"created_by":9317,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2012-12-30T18:11:08.000Z","updated_at":"2024-11-12T06:50:37.000Z","published_at":"2012-12-30T18:11:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are N=2^n bags of rice looking alike, N-1 of which have equal weight and one is slightly heavier. The weighing balance is of unlimited capacity. Using the balance, the minimum number of weighing required to identify the heavier bag is?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2359,"title":"Integer Sequence - 1","description":"Check the test suite to determine the relationship between input integer scalar and output integer scalar.","description_html":"\u003cp\u003eCheck the test suite to determine the relationship between input integer scalar and output integer scalar.\u003c/p\u003e","function_template":"function y = your_fcn_name(x)\r\n  \r\nend","test_suite":"%%\r\nx = 7;\r\ny_correct = 354;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 12;\r\ny_correct = 1739;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n%%\r\nx = 21;\r\ny_correct = 9272;\r\nassert(isequal(your_fcn_name(x),y_correct))\r\n\r\n\r\n%%\r\nx = 35;\r\ny_correct = 42886;\r\nassert(isequal(your_fcn_name(x),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":68,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-06-11T18:12:27.000Z","updated_at":"2026-04-02T10:34:45.000Z","published_at":"2014-06-11T18:13:19.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck the test suite to determine the relationship between input integer scalar and output integer scalar.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":50078,"title":"Number Puzzle - 022","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 135px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 67.5px; transform-origin: 407px 67.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGive an example of m and n that satisfy the following conditions:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzkyAACSkgACAAAAAzkyAADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAxOjMxIDE3OjU4OjI1ADIwMjE6MDE6MzEgMTc6NTg6MjUAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAxLTMxVDE3OjU4OjI1LjkxNzwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIAGMBJAMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiiigAorltT+I3hvS/F+n+F5L3z9YvpvKW2twHMPGcyHOFHt156V0tzcQ2drLc3UqwwQoZJJHOFRQMkk9gAKAJKK4IfF/Q1trXULvTtYs9EvJRFBrNxahbVyThSfm3qp7MyAe9dZruvab4a0K51jWrpbaxtU3ySnn2AAHJJJAAHUmgDRorjbL4l6bNren6Xqulavoc2qZ+wPqduqJckfwgqzbWx/C+08jjJArZ8TeKtN8J6fDc6o0rvczLb2ttbxmSa5lb7qIo6k/gPegDZorl9D8fadrHiKTQLmy1DR9YSD7QtjqUSo8sWcb0ZGZWGfRs9eODjqKACiiigAoqOC4huoRNazRzREkB42DKSDg8j0II/CpKACiiigAooooAKKKKACiiigAoqNriBLhLd5o1mkUskZYBmA6kDqQMipKACiuW8WfEbw34MubW01i9zfXcqRw2UADzNubAYrn5V9zgccZPFdTQAUVwdx8XdEhtbrUIdN1i80WzlMVxrNtah7ZCDtYj5t7KD1ZVI967A6vp40T+2DeQ/2d5H2n7Vu+Tytu7fn0xzQBcorg4/i5onl2F3e6brGn6TqUqxWmrXdqEtpS33TkMXUN2LKoPXpzXVeIfEOm+FtDuNX1u4FvZ24G5sFiSTgKAOSSSAAKANKiuQ0/4j6dc+ILLRdV0vVtCvdRVmsV1S3VFudoyQrK7ANj+FsHpxkitTxP4s03wpa20moCeae8mEFpaWsfmTXMh/hRf6kgDuaANuiuY0Dx5p2ua9caFNaX2k6zbxCdrDUYlSRoicb1KsysueOGOKl8Q+NbDQNVtNJFre6nq14jSw6fp8SvKYwcFzuZVVc8ZZhk9KAOiorn/C3jPS/FovY7Bbi2vdPl8m9sbyPy57du25ckYODggkH1roKACiiigApGUMpVuhGDS0UAeL+PNF03Qviz8KrXSLOK0hF5eZEa8sdsRyx6sSSSSSSSTW/+0DfTWPwS1s2zFGm8mFmH91pVDD8RkfjXN/ETWZtU+KPgbUtL8PeJLuy0O5uHvZ49DugFD+WBtDIC2NhJwPpmu78daGnxJ+FepaZp4mhe9i3W32y2kt3Ekbhl3JIoZQWTGSOhyODQBm/EfSbc/s96rYCNfJtdHUxqRwvlKrL+W0Vw3inULjVvhz8HrO8kZ01PUtOF0Sc+bhVHPrndn61saxqXiTxZ8KI/BkPhjVrXxDdQRWN5Ld2pS1gClRJL5x+RlIUkBSTz04rW+I/w/vbj4d6BbeFIxcah4VntriyhYhTOsKhSmT0JAB+oxQBT/aQDW/w1tNVgwt1pmrW9zA/dWG4cH8R+VSeLHa9/aY8D2U5JgtLC6u407eYyuuf/AB0H8Kj8XC/+LH/CP6JZ6Dq2nWEV/HfarPqdm1uIkjB/dLvxvZi2MpkDGc4rQ+I+g6pbeO/C3jvQ7CbUm0ZpYL60tlBme3kUjcg/iK7m+XqcjHegDP8AimGsvjD8MdStsJM19PaSMOrI4QY+mGf/AL6r1uvL5bC9+IPxY8O65/Zd/p+h+G4ppUk1C2a3kubiQAbVjcBgq7VO4jBxgeteoUAcRrHxHC67deH/AAho134i1q1IW4SL9zb2pPTzZm4HHOFDE4NUT4A8Q+LiZPiP4hZrRuuh6KWgtsekkn+sl+nyjitnxF8ONC1/UP7VjWfSdaA+XVdMlME//AiOHHA4YHjisY6l8Q/BZI1axj8a6Sv/AC96cghv4x6tD92Tt9wgmgDutL0qx0TS4NN0m1jtLO3XZFDGMKgrmPidqWsaL4J1XVtK1RdMSxsZJllSJJJHmGPLXDqVCE8HjJyMEY56PQ9ZtvEGi22qWKzJBcKSqTxGORSCQVZTyCCCPwriPjfp+pa74FtvD+k2l3cNq+p21tO1tCz+RDv3NI5AO1QVXJPHNAHK+LPGvxAtPhfZeO7PUrLTLaGK1b+z3sQ76hvKqzsxP7tSWJVV528lgTgaHijxR490rU/C2ti7trHT9Z1e3sB4ea0V5RFLk75JTyHwOVXAUkDLY52PijpN5rN34K8O6dp9xJp76xFcXskMLNFDBAM7XYDCg7hjPUr7VH8QLXUJ/ip4Hun02+u9G05rm4lNnbNN/pHl4iDbR8nOMM2F5OSOoAPTKwfF82oWvh+6urHUV02G1tpri4uhGskiBELDYGBXqMksDwMAc5GZdeOrmy8f+GvC11ou2fWrSW4mmS53C0MaFiuAvzjI25yOtQfGT+05PhNrVpoVjdX99exrbRw2sLSMQ7gOcAHjZu5oA4N/GnxC1b4Kx+L7PVrTSF0+yE0s01iryalIpwwAPyxp0AIXLMDjauCfYvDl9c6n4W0q/wBQiEN3dWUM08Y6I7ICw/AkivPvif4avYPgrp3hnR7O5vLaGSytbtLOJpJDbRldzBVG48qp4BNejaTdyXtl50lhJYx7ysMUvDmMcBiv8GeoU8gYzg5AAKXirxbpPgzR11HXZnjiklWCFIomkkmlYEqiqBkscH8q5Vrv4h+MwV062j8FaU//AC8XirPqDr7RA7Is8j5iSOtdrrOh6X4i0x9P12wgv7STloZ0DDPYj0I9RyK4pvBHifwmDJ8O/EDS2icjRNcZp4Mekc3+sj9gSwyaANvwv8PNE8LX0upwm61DWJ02T6pqM5muJBnOMnhRwOFA6V1Ncj4Y8b3Wrau2ieIfDmoaDq6RtJslXzbeZQQCY51+Vuo4ODXXUAeL/HTQ9M0nw74el0+yiglufFltLPKBl5WZZSSzHk9u/AAA4Ar0P4jX02m/DHxJd2jFJ4tMuDG46q3lnBH061578c9QuNasdG0zRNB1/UbjTtchvLhrfR7gxhI1cHa5QK+SwxtJB9a9HuTZ+O/Bmp2KRXttDfW8to4vbGW2kQumMhJVUnG4cjIyODxQBzfgLSbeb9nfTNOMamG60Rg6sOD5iEtn8WNeT3Os3n/DFFsDK255/shbPJjFyxA/IBfpXbaBqniXw98J5fBVz4W1afxFa28thayQ2pa0nDbhHL5/3FUAjIYg8dOa0tS+E80n7O6eBbWSNtQgt1lR+itcB/NYZPQFiy59DQBofGbSbdvgTrlkqL5dpaRtFkfd8t1Ix6fdxXH+Lb+fWtI+C8F85dNSu7K7uRn/AFjrFG3Pr99vzrR8S6h4h8ffDSHwfb+GtWsNavhDb6jNe2jR29oqMpkkEp+WQHbwFJJz2Na3xN8EX0/hjw1c+FLf7VfeE7y3uba2yA08UeAUBPf5VPvjHpQBR/aGDW3hbw7q1thLrT/EFtLFJ3HD8D8Qp/CpNdc3n7VHhm0n+aKw0Ka7hU9BJI0kbH8lH5UniiK9+Kuq+GdOtdD1XTtK0/UU1PUrjU7NrbaY1IWFQ+C5O5gSuQOuTVvx/oeqab8S/DXj7R9OuNTj0+KSy1K1tE3TGBw210Xq+0ux2jnp70AUfiIGsPj78Nb+3wsl0bu0kI6sgVeD6j94SPepPBcjX/7R3j+5uDueytbO1g/2IygYgfVlz+NWI9OvfHPxg0jxK+l3un6J4etZRbvf27QSXVxKMHEbYYKowckDJHFRXmnaj4H+Neo+KotKvtR0PxBZxxXR063aeS1njChWMa5YqQvUA8sc+4BBYbtP/az1OKDCx6l4eSadR/E6uqqx9wFx9DXrdeb+C9D1DVPihr/j3VNPn02G6to9P022uk2TGFdpeR16rllGAecdff0igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDKi8OafH4on8Qujy6jLAtsskrZEMQ52IP4QTye5P0AGrRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFUrjWdLtNSt9OutSs4L65/wBRayTqssv+6pOW/Ckj1vSptWk0qHU7OTUYl3yWa3CGZF9Smcge+KAL1czqPxE8L6VPdR3movizk8q7mhtJpobZ/wC7LKiFIzz0Yitiy1zSdRvbiz0/U7O7urU4uIILhHeH/eUHK/jXM+J7SDWbXUvCGgwwWv8AaCs2s3kcSqtvHIPnJ7NM69M9B8zcbQwB2MM0dxBHNA6yRSKHR0OQykZBB9KfVPR4rKDQ7GLSpFlsY7aNbaRJN6vGFAUhh94EY571coAKKKKACiiqNtrelXl7c2dpqdnPdWYzcwRXCM8H++oOV/GgC9XLH4leExIg/tQ+TJN5CXgtZjamTONn2jZ5Wc8Y3VuaZrOl61FJLo+pWeoRxPske1nWUI390lScH2ry/wCKEJ0vwnDqqtpl74Isbu3ll0XT7fyXkBlUZEqsyviVt21VTPQk85APXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPHvixYC78deCNA0TZa6jqesPqUt0F3OnkRAeZznJC52g8ZQdqzPEngbSf+F4eEdD8LxjSJYLC5vNSu7UkXE8D/ALv5pPvM7Heu8ncN5OcgV6XceDDdfFO08Y3F/uSz01rK3sfI+47OS0u/d1IJXG38aWw8GG1+Juq+MJ7/AM972yisre28nb9mjXBYb9x3bmGegx70AcR4Y8P6RY/tJamnhmwt9Ps9I8PxW11HbRhVaeWTeC2OpKY5PJ2iui1f4JfD/XdYutU1bQWub27kMs0rX9wCzH2EmAPYcAcCrOnfD+bTfG2ua3b63KttrVxBc3FskO2XdCPlTzt3+rOeV25xxnBIPaUAVtN0+10jSrTTdPi8m0s4Uggj3FtiIoVRkkk4AHJOa5v4l/8ACH/8IZL/AMLF/wCQJ50e/wD1338/L/qvm6/hXW0UAfNv/GMX+f7Trqvhx/wo/wD4Ta2/4V7/AMh3y5PJ/wCP37u07/8AW/J93PX8K9oooAa6CSNkbO1gQcEg/mK8UsvDln4p+Pmv6dFEkOg6HpFrp1zbxDaJwT5qwkj+DIO4d9gB4Jr22uW8HeDD4V1DxDfT3/2+61zUnvXfyPL8pD9yL7xyF55469KAOQ+Eel2MPj/4h6joVtDZ6SdRisILe2QJErwIfMIUcfefPHqfWuxtvhz4XtZo3h06Ty4pvPjtXvJnto5M7t6wM5jDZ5yF61S8F+AJ/B890q65Jc2Mt/PfR2yQeUd8vBErbj5gUdOFGTkgkLjtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/2Q==\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [m n] = puzzle_022()\r\n  [m n] = [1 1];\r\nend","test_suite":"%%\r\ny = puzzle_022()\r\nassert(min(y)\u003e1000)\r\nassert(max(y)\u003c10^5)\r\nassert(isequal(y(1)^2,y(2)^3))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":180632,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":29,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-01-31T23:08:58.000Z","updated_at":"2026-01-29T21:56:32.000Z","published_at":"2021-01-31T23:08:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGive an example of m and n that satisfy the following conditions:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"99\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"292\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.jpeg\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.jpeg\",\"contentType\":\"image/jpeg\",\"content\":\"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RE6RXhpZgAATU0AKgAAAAgABAE7AAIAAAAlAAAISodpAAQAAAABAAAIcJydAAEAAABKAAAQ6OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEtBU1RBTllBIERvZGR5IC0gKE5TJkwpIC0gS0lORUNUUklDUwAAAAWQAwACAAAAFAAAEL6QBAACAAAAFAAAENKSkQACAAAAAzkyAACSkgACAAAAAzkyAADqHAAHAAAIDAAACLIAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAyMDIxOjAxOjMxIDE3OjU4OjI1ADIwMjE6MDE6MzEgMTc6NTg6MjUAAABLAEEAUwBUAEEATgBZAEEAIABEAG8AZABkAHkAIAAtACAAKABOAFMAJgBMACkAIAAtACAASwBJAE4ARQBDAFQAUgBJAEMAUwAAAP/hCztodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvADw/eHBhY2tldCBiZWdpbj0n77u/JyBpZD0nVzVNME1wQ2VoaUh6cmVTek5UY3prYzlkJz8+DQo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIj48cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPjxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSJ1dWlkOmZhZjViZGQ1LWJhM2QtMTFkYS1hZDMxLWQzM2Q3NTE4MmYxYiIgeG1sbnM6ZGM9Imh0dHA6Ly9wdXJsLm9yZy9kYy9lbGVtZW50cy8xLjEvIi8+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczp4bXA9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC8iPjx4bXA6Q3JlYXRlRGF0ZT4yMDIxLTAxLTMxVDE3OjU4OjI1LjkxNzwveG1wOkNyZWF0ZURhdGU+PC9yZGY6RGVzY3JpcHRpb24+PHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9InV1aWQ6ZmFmNWJkZDUtYmEzZC0xMWRhLWFkMzEtZDMzZDc1MTgyZjFiIiB4bWxuczpkYz0iaHR0cDovL3B1cmwub3JnL2RjL2VsZW1lbnRzLzEuMS8iPjxkYzpjcmVhdG9yPjxyZGY6U2VxIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+PHJkZjpsaT5LQVNUQU5ZQSBEb2RkeSAtIChOUyZhbXA7TCkgLSBLSU5FQ1RSSUNTPC9yZGY6bGk+PC9yZGY6U2VxPg0KCQkJPC9kYzpjcmVhdG9yPjwvcmRmOkRlc2NyaXB0aW9uPjwvcmRmOlJERj48L3g6eG1wbWV0YT4NCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgPD94cGFja2V0IGVuZD0ndyc/Pv/bAEMABwUFBgUEBwYFBggHBwgKEQsKCQkKFQ8QDBEYFRoZGBUYFxseJyEbHSUdFxgiLiIlKCkrLCsaIC8zLyoyJyorKv/bAEMBBwgICgkKFAsLFCocGBwqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKioqKv/AABEIAGMBJAMBIgACEQEDEQH/xAAfAAABBQEBAQEBAQAAAAAAAAAAAQIDBAUGBwgJCgv/xAC1EAACAQMDAgQDBQUEBAAAAX0BAgMABBEFEiExQQYTUWEHInEUMoGRoQgjQrHBFVLR8CQzYnKCCQoWFxgZGiUmJygpKjQ1Njc4OTpDREVGR0hJSlNUVVZXWFlaY2RlZmdoaWpzdHV2d3h5eoOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4eLj5OXm5+jp6vHy8/T19vf4+fr/xAAfAQADAQEBAQEBAQEBAAAAAAAAAQIDBAUGBwgJCgv/xAC1EQACAQIEBAMEBwUEBAABAncAAQIDEQQFITEGEkFRB2FxEyIygQgUQpGhscEJIzNS8BVictEKFiQ04SXxFxgZGiYnKCkqNTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqCg4SFhoeIiYqSk5SVlpeYmZqio6Slpqeoqaqys7S1tre4ubrCw8TFxsfIycrS09TV1tfY2dri4+Tl5ufo6ery8/T19vf4+fr/2gAMAwEAAhEDEQA/APpGiiigAorltT+I3hvS/F+n+F5L3z9YvpvKW2twHMPGcyHOFHt156V0tzcQ2drLc3UqwwQoZJJHOFRQMkk9gAKAJKK4IfF/Q1trXULvTtYs9EvJRFBrNxahbVyThSfm3qp7MyAe9dZruvab4a0K51jWrpbaxtU3ySnn2AAHJJJAAHUmgDRorjbL4l6bNren6Xqulavoc2qZ+wPqduqJckfwgqzbWx/C+08jjJArZ8TeKtN8J6fDc6o0rvczLb2ttbxmSa5lb7qIo6k/gPegDZorl9D8fadrHiKTQLmy1DR9YSD7QtjqUSo8sWcb0ZGZWGfRs9eODjqKACiiigAoqOC4huoRNazRzREkB42DKSDg8j0II/CpKACiiigAooooAKKKKACiiigAoqNriBLhLd5o1mkUskZYBmA6kDqQMipKACiuW8WfEbw34MubW01i9zfXcqRw2UADzNubAYrn5V9zgccZPFdTQAUVwdx8XdEhtbrUIdN1i80WzlMVxrNtah7ZCDtYj5t7KD1ZVI967A6vp40T+2DeQ/2d5H2n7Vu+Tytu7fn0xzQBcorg4/i5onl2F3e6brGn6TqUqxWmrXdqEtpS33TkMXUN2LKoPXpzXVeIfEOm+FtDuNX1u4FvZ24G5sFiSTgKAOSSSAAKANKiuQ0/4j6dc+ILLRdV0vVtCvdRVmsV1S3VFudoyQrK7ANj+FsHpxkitTxP4s03wpa20moCeae8mEFpaWsfmTXMh/hRf6kgDuaANuiuY0Dx5p2ua9caFNaX2k6zbxCdrDUYlSRoicb1KsysueOGOKl8Q+NbDQNVtNJFre6nq14jSw6fp8SvKYwcFzuZVVc8ZZhk9KAOiorn/C3jPS/FovY7Bbi2vdPl8m9sbyPy57du25ckYODggkH1roKACiiigApGUMpVuhGDS0UAeL+PNF03Qviz8KrXSLOK0hF5eZEa8sdsRyx6sSSSSSSSTW/+0DfTWPwS1s2zFGm8mFmH91pVDD8RkfjXN/ETWZtU+KPgbUtL8PeJLuy0O5uHvZ49DugFD+WBtDIC2NhJwPpmu78daGnxJ+FepaZp4mhe9i3W32y2kt3Ekbhl3JIoZQWTGSOhyODQBm/EfSbc/s96rYCNfJtdHUxqRwvlKrL+W0Vw3inULjVvhz8HrO8kZ01PUtOF0Sc+bhVHPrndn61saxqXiTxZ8KI/BkPhjVrXxDdQRWN5Ld2pS1gClRJL5x+RlIUkBSTz04rW+I/w/vbj4d6BbeFIxcah4VntriyhYhTOsKhSmT0JAB+oxQBT/aQDW/w1tNVgwt1pmrW9zA/dWG4cH8R+VSeLHa9/aY8D2U5JgtLC6u407eYyuuf/AB0H8Kj8XC/+LH/CP6JZ6Dq2nWEV/HfarPqdm1uIkjB/dLvxvZi2MpkDGc4rQ+I+g6pbeO/C3jvQ7CbUm0ZpYL60tlBme3kUjcg/iK7m+XqcjHegDP8AimGsvjD8MdStsJM19PaSMOrI4QY+mGf/AL6r1uvL5bC9+IPxY8O65/Zd/p+h+G4ppUk1C2a3kubiQAbVjcBgq7VO4jBxgeteoUAcRrHxHC67deH/AAho134i1q1IW4SL9zb2pPTzZm4HHOFDE4NUT4A8Q+LiZPiP4hZrRuuh6KWgtsekkn+sl+nyjitnxF8ONC1/UP7VjWfSdaA+XVdMlME//AiOHHA4YHjisY6l8Q/BZI1axj8a6Sv/AC96cghv4x6tD92Tt9wgmgDutL0qx0TS4NN0m1jtLO3XZFDGMKgrmPidqWsaL4J1XVtK1RdMSxsZJllSJJJHmGPLXDqVCE8HjJyMEY56PQ9ZtvEGi22qWKzJBcKSqTxGORSCQVZTyCCCPwriPjfp+pa74FtvD+k2l3cNq+p21tO1tCz+RDv3NI5AO1QVXJPHNAHK+LPGvxAtPhfZeO7PUrLTLaGK1b+z3sQ76hvKqzsxP7tSWJVV528lgTgaHijxR490rU/C2ti7trHT9Z1e3sB4ea0V5RFLk75JTyHwOVXAUkDLY52PijpN5rN34K8O6dp9xJp76xFcXskMLNFDBAM7XYDCg7hjPUr7VH8QLXUJ/ip4Hun02+u9G05rm4lNnbNN/pHl4iDbR8nOMM2F5OSOoAPTKwfF82oWvh+6urHUV02G1tpri4uhGskiBELDYGBXqMksDwMAc5GZdeOrmy8f+GvC11ou2fWrSW4mmS53C0MaFiuAvzjI25yOtQfGT+05PhNrVpoVjdX99exrbRw2sLSMQ7gOcAHjZu5oA4N/GnxC1b4Kx+L7PVrTSF0+yE0s01iryalIpwwAPyxp0AIXLMDjauCfYvDl9c6n4W0q/wBQiEN3dWUM08Y6I7ICw/AkivPvif4avYPgrp3hnR7O5vLaGSytbtLOJpJDbRldzBVG48qp4BNejaTdyXtl50lhJYx7ysMUvDmMcBiv8GeoU8gYzg5AAKXirxbpPgzR11HXZnjiklWCFIomkkmlYEqiqBkscH8q5Vrv4h+MwV062j8FaU//AC8XirPqDr7RA7Is8j5iSOtdrrOh6X4i0x9P12wgv7STloZ0DDPYj0I9RyK4pvBHifwmDJ8O/EDS2icjRNcZp4Mekc3+sj9gSwyaANvwv8PNE8LX0upwm61DWJ02T6pqM5muJBnOMnhRwOFA6V1Ncj4Y8b3Wrau2ieIfDmoaDq6RtJslXzbeZQQCY51+Vuo4ODXXUAeL/HTQ9M0nw74el0+yiglufFltLPKBl5WZZSSzHk9u/AAA4Ar0P4jX02m/DHxJd2jFJ4tMuDG46q3lnBH061578c9QuNasdG0zRNB1/UbjTtchvLhrfR7gxhI1cHa5QK+SwxtJB9a9HuTZ+O/Bmp2KRXttDfW8to4vbGW2kQumMhJVUnG4cjIyODxQBzfgLSbeb9nfTNOMamG60Rg6sOD5iEtn8WNeT3Os3n/DFFsDK255/shbPJjFyxA/IBfpXbaBqniXw98J5fBVz4W1afxFa28thayQ2pa0nDbhHL5/3FUAjIYg8dOa0tS+E80n7O6eBbWSNtQgt1lR+itcB/NYZPQFiy59DQBofGbSbdvgTrlkqL5dpaRtFkfd8t1Ix6fdxXH+Lb+fWtI+C8F85dNSu7K7uRn/AFjrFG3Pr99vzrR8S6h4h8ffDSHwfb+GtWsNavhDb6jNe2jR29oqMpkkEp+WQHbwFJJz2Na3xN8EX0/hjw1c+FLf7VfeE7y3uba2yA08UeAUBPf5VPvjHpQBR/aGDW3hbw7q1thLrT/EFtLFJ3HD8D8Qp/CpNdc3n7VHhm0n+aKw0Ka7hU9BJI0kbH8lH5UniiK9+Kuq+GdOtdD1XTtK0/UU1PUrjU7NrbaY1IWFQ+C5O5gSuQOuTVvx/oeqab8S/DXj7R9OuNTj0+KSy1K1tE3TGBw210Xq+0ux2jnp70AUfiIGsPj78Nb+3wsl0bu0kI6sgVeD6j94SPepPBcjX/7R3j+5uDueytbO1g/2IygYgfVlz+NWI9OvfHPxg0jxK+l3un6J4etZRbvf27QSXVxKMHEbYYKowckDJHFRXmnaj4H+Neo+KotKvtR0PxBZxxXR063aeS1njChWMa5YqQvUA8sc+4BBYbtP/az1OKDCx6l4eSadR/E6uqqx9wFx9DXrdeb+C9D1DVPihr/j3VNPn02G6to9P022uk2TGFdpeR16rllGAecdff0igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDKi8OafH4on8Qujy6jLAtsskrZEMQ52IP4QTye5P0AGrRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFUrjWdLtNSt9OutSs4L65/wBRayTqssv+6pOW/Ckj1vSptWk0qHU7OTUYl3yWa3CGZF9Smcge+KAL1czqPxE8L6VPdR3movizk8q7mhtJpobZ/wC7LKiFIzz0Yitiy1zSdRvbiz0/U7O7urU4uIILhHeH/eUHK/jXM+J7SDWbXUvCGgwwWv8AaCs2s3kcSqtvHIPnJ7NM69M9B8zcbQwB2MM0dxBHNA6yRSKHR0OQykZBB9KfVPR4rKDQ7GLSpFlsY7aNbaRJN6vGFAUhh94EY571coAKKKKACiiqNtrelXl7c2dpqdnPdWYzcwRXCM8H++oOV/GgC9XLH4leExIg/tQ+TJN5CXgtZjamTONn2jZ5Wc8Y3VuaZrOl61FJLo+pWeoRxPske1nWUI390lScH2ry/wCKEJ0vwnDqqtpl74Isbu3ll0XT7fyXkBlUZEqsyviVt21VTPQk85APXaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPHvixYC78deCNA0TZa6jqesPqUt0F3OnkRAeZznJC52g8ZQdqzPEngbSf+F4eEdD8LxjSJYLC5vNSu7UkXE8D/ALv5pPvM7Heu8ncN5OcgV6XceDDdfFO08Y3F/uSz01rK3sfI+47OS0u/d1IJXG38aWw8GG1+Juq+MJ7/AM972yisre28nb9mjXBYb9x3bmGegx70AcR4Y8P6RY/tJamnhmwt9Ps9I8PxW11HbRhVaeWTeC2OpKY5PJ2iui1f4JfD/XdYutU1bQWub27kMs0rX9wCzH2EmAPYcAcCrOnfD+bTfG2ua3b63KttrVxBc3FskO2XdCPlTzt3+rOeV25xxnBIPaUAVtN0+10jSrTTdPi8m0s4Uggj3FtiIoVRkkk4AHJOa5v4l/8ACH/8IZL/AMLF/wCQJ50e/wD1338/L/qvm6/hXW0UAfNv/GMX+f7Trqvhx/wo/wD4Ta2/4V7/AMh3y5PJ/wCP37u07/8AW/J93PX8K9oooAa6CSNkbO1gQcEg/mK8UsvDln4p+Pmv6dFEkOg6HpFrp1zbxDaJwT5qwkj+DIO4d9gB4Jr22uW8HeDD4V1DxDfT3/2+61zUnvXfyPL8pD9yL7xyF55469KAOQ+Eel2MPj/4h6joVtDZ6SdRisILe2QJErwIfMIUcfefPHqfWuxtvhz4XtZo3h06Ty4pvPjtXvJnto5M7t6wM5jDZ5yF61S8F+AJ/B890q65Jc2Mt/PfR2yQeUd8vBErbj5gUdOFGTkgkLjtKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/2Q==\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60536,"title":"Jigsaw 001: Intro 2x2 square. Pieces 128x128","description":"This challenge is to re-assemble camerman.tif in grayscale from four 128x128 pieces into a 256x256 image.\r\n\r\n\r\nThe pointer layout of the image is [1 3; 2 4].\r\nReturn a four value vector that remaps the scrambled image into an original form.\r\nThe displayed scramble is [2 4 1 3] making the solution [3 1 4 2].\r\nThe four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\r\n\r\n\r\nThis series will explore various puzzle pieces, orientations, sizes,double sided, and ultimately DARPA shredder data.\r\nMultiple methods are provided in the template to achieve re-mapping. Which will work and which will fail?","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 522.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 261.25px; transform-origin: 407px 261.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 338px 8px; transform-origin: 338px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to re-assemble camerman.tif in grayscale from four 128x128 pieces into a 256x256 image.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 222.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 111.25px; text-align: left; transform-origin: 384px 111.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkEAAAGxCAIAAADqHPV+AAAAB3RJTUUH6AYOFhcTwMqrVgAAIABJREFUeJzsvWm0pGV5NXxV1Rn6NN3MdDcyNQiiiASIJEg0gIhDEqPEOCRGzbAUWcYsnBYSB4gkEmApoEEgGhWnOGEiDgQVDGpsBAVR04oQRZuhERDoBvpMVfX92N+z1372dZ+j7+v7fZ56z339OKtO1TPc4zXsa7g7w+EwKlWqVKlSpRGk7q+7AZUqVapUqdL/JlUZVqlSpUqVRpWqDKtUqVKlSqNKVYZVqlSpUqVRpSrDKlWqVKnSqFKVYZUqVapUaVSpyrBKlSpVqjSqVGVYpUqVKlUaVaoyrFKlSpUqjSpVGVapUqVKlUaVqgyrVKlSpUqjSlWGVapUqVKlUaUqwypVqlSp0qhSlWGVKlWqVGlUqcqwSpUqVao0qlRlWKVKlSpVGlWqMqxSpUqVKo0qVRlWqVKlSpVGlaoMq1SpUqVKo0pVhlWqVKlSpVGlKsMqVapUqdKoUpVhlSpVqlRpVKnKsEqVKlWqNKpUZVilSpUqVRpVqjKsUqVKlSqNKlUZVqlSpUqVRpWqDKtUqVKlSqNKVYZVqlSpUqVRpSrDKlWqVKnSqFKVYZUqVapUaVSpyrBKlSpVqjSqNPbrbsD/53TggQf+uptQqVKlSiNJN91006+7Cb+A/u+XYRFx6qmnzszMjI2NRUSn04mI4XAYEd1uFx+Uut3u3Nwcr+z1eri+2+1GRL/fj4gVK1ZExOzsrH6J5+vDI2J+fh4Pwb/4CU/udDq4Ba/jXbgF1+Nvr9fDr4PBgFcOh8OJiQlrPJ5sL3rTm950xhlnsIV4/mAwQNdAfHJu53A41HaOjY3hGr5LP+OZeFqn09HhHR8f55M5aCAOnTa+1+vhOfqKwWCAi3Uo+v2+DhevxzV40Wtf+9pzzz13bm4Oc4efcP3Y2Jg2hs/UvnAZaJPYTl1UNgW4EWM+NjaGX/k6DCnmEZ+73a6+l0uLk64d5JjoZOEWjDZ/mp2dRWNe9apXnXvuuRExMzMzOTkZskTxWbcJiF/OzMzwJ7YTX7LNOhQcB30arpybm9Mr0WYuGN1BReIz83Qs9F798rTTTvv7v/973jI3N4ehwNN0QOyxnDjdSv1+X3kFhjoSB8CkcPdFmxflL3UeuX5wpTIorkk8B2uJTUIbMEe8HV+effbZr3vd67hNtA3dbhdvx1JZ4lSxxEqVKlWqNKq0LOyw+fn5iYkJ1figFlHXVu2PBgqtpRC1VLW/breryg7VLlWBabLoe6mMq3pFrUrtFTapaPTgAx5illDWSaOtMlP/Um2Xpo9aYyQ2DH2hjYgWZkuIirCaWZ1Oh/Ycp4OGlNLs7Cy0YzWS2ELVYWnfqOnDxlOh7na7K1asUB3fhleNJE6uWav6JUaeT1OLjYqwGmf9fh/XcEAwpBgQatyquePJtFqs1woA4K7hcMgG5EHQkSQIobdzFekUc8ap40fbANLZ0ckFDYdDDlSIZWlDAdK+sw16O8cz21jRnsFer2ed0gagndwLah5x69nc6aMMO1HDCD0iSKMdnJuby5uOc8S5xr+4Ev9y4nSFcwoUF+H1OllGvBEWtvYan4uTu2SpY3zq/z468MADlzmWSFrOWCIhrGWOJfL25YwlRlvRWZ5YIpfr4lji0veHVSyxUqVKlSqNKi0XLJHaiqqZ8/PzRbURHwhzRcT4+LiaXNPT0yEQhOo1VPSojoUo9YoI9Xq9hx56KNqo0czMDDQpVSFnZ2dVV8IFvV4vozpUHg1lUpWWbdbG4/OKFSugsoEMSiJiZrZsCKqmZgfxW4UNOREGx6lFQlBFlXSz1XSy+CLiMCGomo781NSUPg2fbXyg+RL30ykzUNd0XjOy9Us8s9vtYlXwGqj/+rTBYKAGHNpJXEt7XTRTqEHbDOLhahNMTEygGWqCTExMZDCZ5pE1CS/C7TQitS9YS/xSZ5NfGjhveJr2vWi76CDMzMxMTU3pK3RGuP0zMjk+Pq6bnWT4od6iLeSLDCfEBx3e2dlZXY18C4bUQF1lOCDwMf5Ls093Gcdch4soBRiXeTR0OSkgOSq0LGQY2Vm0MaixsTFMqq4zSibd4f1+X4F+LlnFtShLcCPYFl+qng8uFGx1XUMTExMK+LD9Rc+QCiGuy7wiya30J+5bEDcAuICu9eysUqZApE63HNkxrlffTLfbVZiLYkZBNnJ8bZthLOwaHoWn6SgRF9XxpLxRSUa1wzwKGXwmqKvXk4XZ68y7pn1h383rGcJ9FAIl59L3khfrM0NUDftsQ2ELoOhPpYNH/6X3TrUQDgXdcpyd+fl5dfixSYvoXtoGTpn5ApVHU5rqQjU3Z9GLzCsNhY6kr3AodNDITJSrsJ2qyxZBfg6dbmS7XWeQ+D+eaSqsAYwmJrWb5s9+8MEH2TAg7Z1OJ3OhJUvLQoZhlag/hjstTyolk64zevhVl+Q0G5fEB2LlETE2NqaKqjmNbcfqM6k8qqqFR5lOZ84A82eobWf6nZmGNKFCWK3Jm6whElU3CWrOZ+2LdpOuQfVjW6QGuYza0xxJ/ZfzqPu/yOjtc/FLC7FRA46Ea7Zt2xZJkqk/g93kv+bfsrGKX4L/RttcMAOFnBdfmsjUWzh04I+6AMwKJGvOgQlUd8y/aM4nfXsONjGiuFJTicLS5KIFcehgko+r8CNpszksupxsexoAY+E8kUJm2AbrRYjJZYIwu6nYJJ24iYkJVa8pMvGranV8oG4WRn/oBrE4miVO1R9WqVKlSpVGlZaFHdbr9eg90oBXWhhqeDFiSkPdiPupOdVpIuYVNhwbG4MqhC8JSNL1EhHA66i+qelDfMnsDFXK+OQcdmXwJmjlypXZSFJ8NZJGaYGIZg2wpyGKvz7cFEy4/VavXo2GAbLfbrvtQqxVnRczO9R24VBrbDR9SEUvjqr/xdhRQjQ6StFWvTnR2S9FLEjDRC3FgisKa4Naf7bgiVbpaBvAqONPYrwf/Xl5WrWb8/PzajewhYpvs/E6rRxPA1S1F/q6SDp+JEyYL9J/eX2eHTbJ3Gm6bomdGOqIDmIi2DCN27QFYDOudipBHV2iNqFKxGN0TU5NTanjEJ+J/+sozc7O4kXqNZyZmdEAURhe27ZtwzKAU8Acddp3Qi/Z2B0VWhYyDBuGmzwEwsJMa+zpQrif3YgnKw8iSAIRpT4AW3wG+OjOoawFcU/qe3WjxgKOaG0eUUdtZ9H5b9fQ4YH3rly5MppdEW0my88K5nB76IDwyq1bt0bDCww5AQ0GA9UbLNQCRNaTo6LJo43zZtiHLD7/FG0OODc3p7CPOTn0RjZJe20OQgo/fYh5VXm9wdchkhu3MPA9g9707akiNT4+nh1v27Zt0xgNhjwAJoWzhD/l2zudDvQVPIRcWB2i5uxUCUGAUVcR8VvFHvM0hWzSIpjMKdCoJTbMvJ6RiNHt6junQFXvLK4sqggPPvjgqlWroi1UKALxJS4wqBY/TU1N6YyTt2TQks5OQ8L1FRgHjr+qpNTOR4KWhQwD42MsQyS1UVXmSIEbETExMaH7kJszx3T0ej1NozGBYWqmqregYTsIkJtZlxQZk6rANCx0e5DF6962vuMhbLPxlBB9VnU6HYSQoAzTtZUZFQX/IpEIRZcAW6isc3Z2VqW7SSZbCWofcySLSr2GJ7BHVJNzk+xKVe05m+ZyM2bNNWCDRs+ittDcVJTx2hjyfR0K8/dYKGmOLxgMBpBeumCKMEA05rV2hF5nlU9FscEHqmun2+1Cgqo1RreWGYj2zBx5S3esxRmpMkErpyPBWWaimVtU32uxOZprPDU1lYe3285SRWejLZ65O3RVcIFlbbLb7WqvKU3R7IcfflifqS5bDkjOPV2yVP1hlSpVqlRpVGlZ2GH0EkWKS9SkCmpMRZhClR3qTapr2+2qDtvtVI4s8BpXmlEYooab60KVMgI7atCY8mgAiLrxTKdTfN/sNn2gvl2NQgPitEfDdtAwb9e2UZ/NhhSVehtJfS/7bkZziEkKInqce2RuUQMPtbX0YBWDzfTtg8FAzUfaTBbkls3rQan+xezsrIIKhLIVsqNrNkfoEewyhxZIgXeDnc0LqBAfl5+OPMdHDSkaH2oFmttPLWabgm63mzFPmoa8RRNa2FnFtxnFp1A/109efmy2uRt1drjaFbmls9PM6xCURYOHi25mOjsVXec2KUbMKqTEt+vKJBdSdIQbaiRouciw6elpxX/IW4u+bl03xcpMZJ05asCQAcOXzSuWC8nMzMwohG2JwNp4IgPKL4hP4kuyMHWhsSWQ3MoHTX4TssAHdYdYC8mO1TFAzmuxEiDbxllFIBioA0LZACJkSgd1fpHJUXUbWOi/sjDL9iXPUpZNdqYcn/xIn2b+c3ZBn0OWTWkUohhps/GQyclJXT8Ef3TM6ZfSV7ANWTybO5ZrQ6/hcs2CrdtOnEebp6endaEWq24aym2uQZ0d7gidHQsh4XozpS3EyaohQixHoN2cmJjQMgWcYkvDD+H4CvHZklYvWu5aTkExNsIXaU/JMTRFGjihqeycXJXfdB+wcl7IeqPbe+nTyAjbSpUqVapUyWhZ2GFIA1Q7WtX2aCMDjGpVj24khTpEOzYN0azyEMepXkkQQDGT8fFxBUlAY+0i32Y3qKEwOTmpz6TOa0WGQvBJbacVpdXWhlif2hiQhVqQLN6EF9uXpoCzndpsBsdrJDHawGr0Fs+iKjO7qeotMSsFIanVamyOGcQWN5Ej7ojp6dBRMbeqsjqStBQNZtQYBAOHdVQ7TXlfG1i1V6h3644g6mCIX6ToGK6QbDoToLZqHRkoHrbLCxgirUSDxiATgxZDwBJ+qWYuu5ARDqv6QXNKEXXG5ijCgZEcNvGQIIbd22RprxmLhIdYrRNtp06EVWTmXssjSVIYnzzQajeruWkgxEjQspBhSKHIW86WtQUU6Z4hiGQLRb9k/JLuBHPDmGfIdnUIxmJBbspZiHXoOlscRlMuwJ9yOztNKKD5FXR3GdxEBNU8YflGDnX2XZE1aO7OsJ3BZoFSRW1AG2+JVrxSu2a8T8UG0VFlImPt+vF8XfaAmlA3WWIgpAI+nXa1MB0lXknPh462sWNzp6kMI8/SXrANukgAKLEcpT6Zvhn1YNneMSau3ey2A2ht2WgbuE1sKIowtQ2dzUgIVGtNwgcAcYDaBk3VD3uFTiiEgaH6XBUZa+U3mHeGkppLOxZYMNzIIJYY1ZExGaaeftvOeObMzIy+QiX0qNCykGE0rSJl5pofPkRVL3q5FQfvNjmVIKowKoTIpGBdqbfJCtbxpdn5RFZe5GvFxEzlAnQeKPumOWUsrKjNqSVksQDmhLCgYX0IxXlmW/SjmL2iN1IDNUeRfmkBBdoMTrTuVTZJB6HYIyZI5SyFYRNGbzUtdbSzSh4pqIe8VeUNH5WtB2s2e5RvHysd3cIsbP4bsviLYQJ4OwQbhaX+tfdy+rKqx4LONiC6QSx5y7x3qmJy2ViR0uzt7pcO1uKStkWVc6T4pTZ+8a2kQ1eM7DdnJ1+kU2bVBlS36zcZk1paod8uMq5SPJJlqeW2+fZsEC9Zqv6wSpUqVao0qrQs7LBOQ5H0L4WwQBYZSGNCrR+qe6aOhYCBGgVLpVUfRYcNGxkCRVrQcE4Wpo9EjSQCL+qWoOmjRUksqJJxU6rJsiV4EdL7x8bGmIYZYspkjG7YLnFk3kd9OyfCQEtcb2W37L0hucPaBirCCtHQSlYbkVgihovhmrpgMFPbtm3LmC0NhaLTy9J1eUuIz0wtjCLIRlXdHIQZ2ORxjlYmRh1Utn5UDWfRB3UGs86LGWdqsrPXiziBFH8j+KxjNWhqPgHNQ8kP8wWy71ZWSt/Oz2qvcF+YBy7EFrFSZyAdeaupoVYO/6U/TC0hlmuxJapXFp+pe6ffPkGC3AM9Qi037E1GXBuHwWNRcIdrSYeCOHw2DZcsjUxDfxXSna+rx+KnyXCVGfEC/dcKo9miV/NfP0dbLnKD6e0GVhRvNx+SlkvnflAQctgOMrZ24u0AiLrtrB3Ke82ie/jhh3NMdr99QIa5dkAcXpUKlhikLpZiAtOw8Z/r7YP20Tb8nHURi+koyhtz2Oj1Fr9uEI3OI5ukfM2m1SSTCQOTZMq8zN+jKDfhJiWyTquhrmNOFq8rB58ZKGT+15yGMWinCRbHxxBm7aaNueUVqPbJbmpYueGTFFcqBqgt2XrLLJuP0lSHYiohi7GpuDIPOsfKRFQkIW1xWwa54wO0SZyZQgXOarnp8Nq/9tdcDPhyhI4QWxYyDCyg04QbRTKkbLPpv5b/pIcWDkphVIN2hpa9SBf0sHTyiyVacZMocyf7VuuBkUuZWxm6zfMJdbdwn2N87EV8TqTaNuydNrjoE9L22F/ebumohvvjy2K0mJYxtcxcNQKYq6TPpE1QHF7zL2YXi5kUlCvqxtOOxwKCirLWbIsQ81qH15gsyNTq4pcMKNAxB1mEXjEIsNgk3q7qne0dfSYXv9lt9gob0lggdIVbQCer1+upeGYX7HA7JTU0aSVr7HGn7XnVz9HWlmx8TH7bHOVeU//TTcrdDQuVC6xowGlf+MxcfDmE8/CnYbsmwxKn6g+rVKlSpUqjSsvCDut2u4YQgui7KoZ7qZba7/dhgRksqf9mvCUas48eFw24Im5TjHU0uy27dqipmf6lFgkVw2zQWIQesSNFwKjAqtY5aJ9eaDqdFcrqNqlakYLj9XriIVbHXZElvkKRJWrcOdhsrH2EpgW5Kf7WaZ+hYw4bU8bV9UXPh6GIeKYixoaj2ntBvBIrTSeXZymoYRFtC4N6tyrgnLgc5sq3W9Em+KKKwe46dJZepv5XfoknP/TQQzoUFoWvlgGj6ZhcoSOjC4YuOkU77HbrAldREXpRq9dcvBZtmAfEXJgcc8MkImEDBGkUj6WH2IZLB8RWkb5dwVV+aVmGChSTC+mNRSN1ydKykGHghoqAc60rr8fuJT+1UmPFHCAtIcOFpXubTBzXKHsaDod6ChHZU4YpOu3QatCgfeoVfTm69Nl483WH8Ggrwa7Agrm46aVQoasuq2iX8KHHRXtNsrxOc5OEJHhZAoPuZx7OYqIikhzFiwgF5wjpEN1Cb9eRn52d1VNFyBqyKsMBsbB7vTFKIBKFkPldMlxJ0pVmZN5cCz3PzvxhU5hfc/lnZmZUjTCwmjy3+G8kLxfO3FmxYoWec8a1ZLh6JCiSA4JXqGwmDGtanfmGdVFRX8mvGC6QqaliwNRWFRj9UtnVaK8cCjnd1yTVd1m5X1ejLVGdTf5kAff4UmeHUTzW+NyeJUsVS6xUqVKlSqNKy8IOA0CnBrKpTiBL7y36SPXLbrsqgcUcm66Uq9ETIDKdTl9EBTZr2ab+0zOfsbIQHT/aFl4ki0S1P1qiqoAPmvoF+mW0jQN7pqG4qvepxp17bXEf+nBFjRhKYE1SiNgASTUlzaTglaqu8iF6cqDF0ag5TpMC80492lBH7ZqtNGu8JfOGBCvaGKotQpteU8j5FkV3ebuWwbVtMmjC92MBUI7RQ2qNWbwGYuqibTHY2jBgQ1cjN6nOINtgtlrOo+i3z/xjs7WD2rxo79xiBKM12C6w0BVjCyHWlSGEOiAMwzEIPWT5abwux1z7Zd0k/KtvZ+PVZF/itFxkGOEmXROTk5PKjLg9ipzFgsciQXxcKLrZLIC+GOmnXICsU4vvddvHrNBbUIwdKgIvCs2zszluindZgXPcyHLpeqOd7qGbhKFZCkwRqsVPLPKN9yqnth6Zr8VEpklZHUm93qBIRvnrv0V405aBCT/lLNY8bYOFVhtya5UyzI+iVxKfVGCKyztD2Vz82raVK1faOIcEpuoCGB8fz5I72mLARJF9qeKKm8WeluedXc6rots+DoZzpH230eZWVTHJidAJNX+zVheM9sIrDgjTVHS0+Xzz+MYCstb6a8esWN6b+nHZWd3yltSoXaCjwdzbI1RualnIMAs+DqkYq9Npzljlv9FG1Q0cN83L5Id+oyybeZogi/PWjcftqtuPZpzeTh+SuUyy3yVfE7JJLG7CJKhC52yD7hOOpD4NfTcZxntzTp5VSKK80aAVNszUajTMHNo6ZXgm3FQopxltGWYqgoWJ6/WDUqqyGR/G4lnFTrUBzRmIJBH1SovsMEmm4S3U99W8phOXzlpdANpgEPOmjf+qnKNqr1KE5RY1xdtiOkCMcVB5zInWoSD71r3AAVEWT9+whsPQR4vb4U6bnp5Wx7blpVl+mNVr1mHRsAiaRyA8k9a54SI6aHxmDlNitoB2k9sE7nw86uGHH9bidqaWWVKNrg2iOCPkEqv+sEqVKlWqNKq0LOww6NQ5dK3fPr2QQWuqFhEEUOvByrSwCmpIxJRFoKmXwkKk9HW0cgZNVmNIhSSzAnOPqL5ZMJXik9Rq89ujrd7yszabZHGeVLpDHCF4gtamovpvSJ1ORDFei4OmKjN7jX+hVtMyyO4i64I5/IquCLXtuu06JlwtOoY2aBYbjRtR7KfTLhJvbjldBnyvtnBYOkay0460tokzbxw+qOdj2JxsroVL+v2+BiuyYTo+dtimro1B+/xYjpUGxHL21WTn2/W9NOkyemzBeHawwyJOVqu+zestdFanVbvJTae7htCCTtz09LTa3FbRQ92NNEnNNaA9QqYzG6+Lf2pqqmjiq9+OyKca9xzJop9iadKykGEQNoucfmKpSwphGcPVjC7CYiDiRUXgricFb4wP6tInWKHX9JqCh+YEUk7BhukqJ4xmOFUItwKZ2DCcR0E5NtuaVPQC5oQEQh82SllgcHNaER2FFs0Vr7gWn2kgp0JDbKSyJPNBGpybA+g7pbPTTOoYLq1jHu25NsnNxmSI2OILzKdiCGr24piHnxOnK5wdVCliyLblVmp/+ZNOrnmdtbX8xh6iw6uTkodXGz9IeY06rfpTr0kJtaFTQc43mqAKyTYxyNQUjhC3sc4m1Q5bBqYShWgtDPHQhukJzkV/gWX4FDkbIdMRwhKXhQxTRUNXcKd9wAEtJz07UQ2vaKui8+1Ss2Qoumq5IrOTjIqephxF+wAIrnVlH2SgarvQW8DdEo2B2Guflb6IQsp9q8YcnUCWSWpKrp22HsI6zceWQ576TQq59tqkSFFkGtfTiTB+wXmHuspmhxwwr1d22inkoF77/M9iO9lavZFP1tvNQOGCybzPFA4jNWV6vZ5G0OiTo72YLSwCw9JvH4XF8QR3Vm3APK9FaKEYWGiWpWpX3VKdrUjyJuQ4Rx0BC9iJtjXJL1Xn4wiY1Rvi8NMvWf/ahjeH83DKdBFaNUvuryzD2Be6gSN50Ck4M1wxMzODF2n+3Pj4+FCiVNiGPOMsbTwSVP1hlSpVqlRpVGlkhO2vQlNTU3Nzc4aZRLJFCCsrDmP2jWorK1euhKeHQUchJpfCTXSnmXLNs8x5O1V1tUu67YB7M4/UIrFIS7PwVI+mogei/oVbNMCal9GcLfoJrBqCjr+2sNcu5UVvikbHscHqU6RHYbvttou20cP3wu40D5+6yobNeZWqI1M7Rn8ZqazOJPO4gKh365UWWKguTPN8hGj3OiAWR6eDZnaGOfDwk5oyXD86I5wytSZpVKnVy3Zmg5jzZfB4jtcnYXa4ehWgNjBWcVFD7LkBbQHo0BFFyFhip9PRZnBULQUiBJZfZMw5aNkQZ1CuDtp8+zRakJnjPPpHVwXPplDTuWgQM7RSe827bAHnZ7JhtW790iIsHQWaNDY62vDCsJ1lQvglf8nobeVExIJ0A/RL50fYORfGbvRKQ/a5VTIoRyeHSRFjnSEhyyoXiQWpj5qb2TwZum877XN1ucNzXANJBTCL79mGx5Uaac3sNEvJAmPSJ4fIdb0+axidJgyHYjKES6oXsN8+Jdl8JMZfsufM8gIZFqHch79qGoY5tCyTrOj6suu1AQYFW1iENob+RQ2jp8DWK9lO3SaW46xrw1yYFNh6IxdMDmSn1FGdkmNO4G4o3u6iV5WNMYZgn3Xo8nstYt7AQB3kfvsUaT4kR1eZY5vNU7WSoVI5oIkjyWJaIVCtLgA6ERd5+xKniiVWqlSpUqVRpWVhh0FnzPY+NUTDwdTPTJ1FAz2oo2XrqtMu3lHEJfhS/dcKchdJdViL/TNTQHGtblO3XhEMGnxahmBQSnGllmogpCp6eqxaiALelZxKKptFaEgHgf/qwJqTWVVXttYyQNWqs14bqTFhCKFewPZYhI5ZA7nxRoo9RsLTmLsayUTTkTeTi1dq20CEIi1tNsclDpoK9xoN0SmdZRVtU4/rXJ9pQUAGmeYYXVo5aEPeMjosOe3aAP8Q41JnR7FQO96vGL+jk8Vmm12rK83erpgwc8ANBlAzFx2cnp62qlEhq3coMVadJmpUr6R1pTZoNPtU4XQ2WzepsaYlTstChsGy1tQT80hpTDZJ95gVWOIFwJ2Ug3Mf6g7stMOvKd4s5Ckkmk7bwKcVEUIVQlzl6CZ6NzMzowlwVoJP0RtCgprmRaiNLEzDzChB9b2UIhnCmp+f13+5exX3J1+jMzKE82qMnAkVBfHZJKO84elZNLjJQh9DcFSV3xasSL6pN5KRKQvjAlBslp3SUeL6sQpAmcsbVsYHZtEyPT2tb8fnqakpraxoQYCqN5B0HjnmhqOq1mU+SFuTWRvgKtIn80pdBsN2Yc9hOzKQ46P+Zv6EqdeYVfJx01AtVSMSgN9pAgL1CBiuTxUYpu7oeuOcqqI5MTEBhqOHb/B205nwHFxPnUDRdYu17ksZsxUrVmRNaMnSspBhFkcbskTIhkLWpYWwhyxTVZnpFc91+aLNGsj7QKa0qlbVbacekyHqluMr8lFhhOZ1b1MY9KXUkPnPudvtmIYQ1sDxUWuAw6L7iqOqZeiUZ9krGBGuriA223SCnPnAmGMdOioTJopMCwk5Nc3OTjPRovNIjh9J9TEFVrk/tX42rDhZpv3kp5F5FTl+VndMf1er6WnbAAAgAElEQVQvUchqDIEB1Eu6YsUKbSc3y6DJTNAmZaKdoYvBHEvm7zGdoGgvKq83yW3RVVrFmGRhIwgUspWmU8lRNWtbh0Kv7LRPITfNRoU6T4DKi4EvQlL89PS0usCLqQvqIo2kTOiO4E43Jz0+aF2CJU4jYzBWqlSpUqVKRsvCDoPBVHRaqK5NbUhBNqquOQJ10C72an4UC7RTDZF6ZVHTz6e0GLG1OQRxWMqG7rbzSYtY0CLQATVKK6agje+2Dzhm3/PZzTMzMxr0CLIgLrMFzdBUVI1VVtX4MJugaEgptNtpn6dMHFVVZi0OFMn0wZcWbIYvdTpoGdhSMXQ0q/+ddig5F1WG+IrLxqwHwgbFyDd8qcNroees9YxpMr9dboYFdvNK7YsV/dLWDptKabah8uxYsjAbw0LyIUCF9oVJ3EpEmPM8Rtp0ukEW9xRYvLHNciS/hnpABoNB0a2u42z4bcbY2XgGvupI0hwv4vBLk5aFDMsbg6a9HltAX3rGoCyuwSSTMsQQts4GdNrHK3P/5JJx5Oa6+CYnJy2tLVI1Gn5eJENL+z5sZzURHNMXESyy7DQLIogUtELsyIYrpFi7AZK52b1eL3/JY0Qs50wbYw4tWwPW7EhYK53hOdKawtJgooxPmuvLvDhsgzqorJSXIXXK64uCistbB8FCiozzqrjlX50dwnfKowl9q8wuDgXHXFPWsOYJWqonst/va5kYtjkvGHLezH9zr9nfkCwO3aSUNxailcE9PlN1mmG7Todpydpr+sN0diIJyBAhBKLPW4MytHm2DKKtLoOYaWcMJ49SUftcslSxxEqVKlWqNKq0XOww6trqgqbJpagIfblmTqmlz7tUcaPrWKObLC9YLYOJiQm1hBZBCFk+A0+jgzdbJAanWGy06rO9dnlW3p4R18nJSVXtu+0sbKp7dFBHO8iFVERO+ExDbkM0Sm0MLSFVhwkG5irG9nazFwkbqn1syrhVyMwz3imdYViM4hlLRyAqasop0JExpFdjUBlqYQCa1XKMVOoiz4i+SK+kfaDRHOyRTVNIWrGGVlovuGzM6uWYRBtP46AZXKzBeBwxjbGanZ21IB19hX7momKNDO2+NpvxF7qVDGGmeaompi5aDgjLpVoyeJ4jHnRn51brixRgYDEdnU1LZ2abtS/F0NMlTstChmFr6a42d4XunLH20YtEEnSmiarlxTdoKvna7so+Ngu4omjRvcp2Ks6AkKFBuyqovUg3CeUid0IkzApEpE5hIquvGu2oehAj5jWejXtbZYMVSuZnhc4UMOEzWXEA/E6xVgoMlRCG3/J1FvMWiXETacnldiwTSBkKB4RiRnFCjqe1U5cK26CjV4z3WyTknetEg/GIF2mvifRq0GCUeJmtNI4AnmMQqJJNrq4024AcEFOzQma8GDuuAOOgyfrQESDxJxWo7IKqmPxJ9yBflAWbqbm20hSWNxCYyq4+s+jm4I6jWhmy/PRGhgdbAH1IeLOKN64NvX0RZ/wSpGUhw+bn54t5iBaUYTsWRNaZTRlzv5uGaOaUaqZc69m9HG0zxUBt3VS99lmrFhGgYoMrsui60H5Zdho3oXJA1nxTDsgnm4DMG54eO30FXfQqLAftZCOq6nTShBheGrdtM6I8yFwCNFmymUJvnHbE+Bp5gX5JdmMcP4+kaeW07UwSh4QUmedMNSoq7CoXObym6GinbKayddVtn0xvjiX9MpLupQ8vxhnpBcUzwS24xmZKzTg6rYu2SPZS64DopivOi2UUWO9yAIWdXoafWF4u60zRXtJMF7E4LHMfxgK5/MRjNFucjTe4QttpytZIUPWHVapUqVKlUaVlYYd1GooEcykwZRCf5fmqYUTFR/VxPiRXgjA92mAfXhPJgrHgb9OjVW20qF+1S6hnmQNMAXSQAUogGh8Wm6cGDTtrQ2EDq2+xOE8dLiKEebiG7VOd+BDVsvk6w3ZCADS1sYpoDOMngdyyjoPiqLSS1WFDm0DL/PB1uorMj2KNKeY8GGygA4srZ2ZmMhzUa5dN4ks1uI6fNSCNk6vgHsPE1UxhCLguOdq+GfcbtuMni9ACpzh7sGhZqu85UgVhxdMMWjRQV00Tg1607+y+Lv5oLyfrtS6qmZkZYyCRfLREC/OSZhUV3fJFT2e0ARJz7upIcpRsWIrOuaVJy0KGYdkpzMVoCBZxCVltuqst0Fa3HIEpBStmZ2f17Aw6jXWV25nL9kxlXmyt5uvYlQb0aQeLh7KT8emVlCW6JczPT+xCmRefnIvom+QGmceOr8NEoPIFvf06FBxP/Kqsttvt2itCguN1wzMMB/1lyQ+whjvuuIODtnHjxo985CMR8cADD+g8ooWPfvSjI+LQQw+NiD333HPnnXeOiL322iuaiBseBq/DS9+D5QzYMtDxIXfL80Jpp5yaATiGE+rTOCy5hePj41wzOpXqrSRX1cr6FAam+Wk3TaHJCKHFnhSzRCxAQ1UEHlDJvZM9mpb1xXaaTzpEQ7X4lEWiY6zxOtoG91llH50dNsn88ZFqZ1gWh+1K1VeoZ+AJ3CAhu0z1IQP8lzgtCxmWYxA433rCFmcRs6ulxkxtJAe0lI6IWLlypRULDlm7Gg7Xbw5iANE8yu5iW2fkcer00ueTGM1oOx+3q6JHQWj1QJXMmFDBT61TLaeZmRkdXjOklMENm8KVGjfF1ykXGLbTYCmZFnHDqBu80+lA3kAy4RW33XbbF7/4xYj4xje+gX/R+Byw0+/30cJrrrmGf/lSPG3XXXeNiEc96lFPeMITIuKJT3xiROy4444h6i3XklqxtGCyG8Z8tCDzzWTwgLcznNJOJsv5T5xW1d877ZgXkk4TmCP9qcXrzQGWQ+C46nTkh+0zrIuyZCHDXR9umd06XP12jSvQoH0MjekNKh25UHURUljqSFJIK3MYDod2Qkok3c4kioXYZF+7hZmwI2olk01phCfHQZ2yS5yqP6xSpUqVKo0qLQs7DOqhRdBFigwEdduHJlM1szyhEE1W1SJaJCAquap50ZBSrJyoiOqJVHVVp6NGmZ0HVJlV/7LashZeaOllCkxRazMrp+gJ0/5SxVO8iCCePtwis22Usg/SgClOR3Yz0F0EItS2devWiLjrrrsi4j3veU9EXHvttRp3p4HLRsN2yTF+qbr5nXfeib9XX311RKxevToi3vKWt0TEEUccoY0xNx7nSF/NedSVo4Ypr6HirFPPfvEE0ZA6Ujlc0M7/LU6ZQoghtlrImlTLwA7pfvjhhyNiampKwWSLWdV9QdtX4/TMYWNm3yKQRtGiZa/1SvPD8accWGheriL8SzAguzDNB8mYw7zsaS+qyUX4V+e905T3Na9qzj1gwRRzTMbo0LKQYbDrFZGwdWa4n+4upvtkhIebxCIglAvgSjrALTgiJ8xafLDBKbbTrJBgCLsxn4qub24nZV68gN7jSPg7I7n1zLAiv2BrdbiY6aJ7xsQkiI/KbK7XJL5o3gz3tjGRPGX9fv/mm2+ORqhAkg3blXUeeuihkN1urCGDlp12lqiJlgcffDAiXve610XEH/3RH73gBS/g0NE5p2yRuDGIPcrMa9g+6M5uUWg32hoDNSFFg7mWMtSmt+iYK0O0edQZZyAMbt9+++0jHS0N6na7Cp1RsOk6t0AY1R7M61wcFmoh6hDtl/K1mfmuCzvaMDtds1r6wBI2dMtzSeszTdli3zNYSle97p3Z2VnVKak1FjVp1QMw1AQY9ckmVpc4VSyxUqVKlSqNKi0LOywENlRIsNM+mtICH3I0vP4ayb7hBapqUZnKCCF1XjWPiGRaeIIlWobU4MGViEBh/SpVnA2CMOVdrcZomzJ8lEKCMzMz+FetNJaxwcPxEy1FanwhCjhANpgpvXZ1EpqwCkwRNlT9moaFFmHiX8Y3sg3XXHPNe9/73oi477779EV6dCd7rdYA59HmWr8xUzLDvx//+Mcvu+yyiHjjG98YEXvssccuu+xi00oUSNXwTlM218yFHNfa6XQUuONDcnTcYDBQDIrDqyAkV7JWG7GAe7ZQlwr+si6MrklLF1HLaWZmRnNyrTCSgYf6NEICOuYWasHb1ZCiVZdjOoooi8UScxY0Vsu+NDaCZmg4NK1Ai3HXyWJRNF2THMmMOnbbRQPIfPABcC5fZ0BOJBx1idOykGEQNhYdF2kfMiJIF6hiVtFeKJZ2w796jQk5fV0R64gk/CJtTjbMRFruuMXyFnmu4TbZncYeEXpSEImDpjyFCG2WxzYR5Boq4y2qSpvUaWL5zJ2mMdaG/uNKBCLedttt8IdpO+nF0dOrKZUVEZqYmFC+T9SoOON6I3+CqnHmmWdGxAtf+MKjjz46Iu69995ogvLvvvvuAw44ICJWrVqlw6IPJ4vX7Eb6kKyyQ6Q8CpPKGqXWbZ9pwuvRa5sIVTjMbWz+IWXHXHtZ/5uYmNAx5+yraGGTMucdtA+XiLaMNy3EykqpCkWQ0xA/3K7NLmo2XJ95/fCDQt9kI0pU4PR1jL/VMe+0Tw7i67Kzk1uvGMmsM84rR4KWhQxD1Rn1EJATKUOkfmoyCQ+xCnsh+1bxaBNsXG1QMNVZxZWnDJTqmzJli7fmMs0bj9LOOFHukbk62GaVrOTpFsmiUQ900akQUuPDvuR4wu1EyZdjXiz/lDfmukfFV3Sa8Bbcjuj2PffcM+vRND4gYGz3qhCyU6Y4mzqGpgirncExR98//OEPQ0M69thj+eXGjRtvvfXWiDjhhBN4Ix+igzw1NZVD800t44BkdkwhZF5VXY3spvJc2vTG9SItKuOYOq3GeW2dm4TQ7Um9xLzI2nheaaZbpLARkooKvl37S0NfFR0bZ3umenO5frKuaWgQz6bRdcikPTTDxLB2kyOg1rxNmTbPhKXxlpGg6g+rVKlSpUqjSsvCDpuenmYYnto3/SbLOHsLog1k03kAE4RKiio7VItU36R1ZQ6GkAA/RYQs9GjYxBDmF7FsEsjCYdUOoxpexB4ZVI1/ixqllk0atisPsUdqYtINll0C1PvUrrXGoLUTExMZ2BwOh4iZBPH5aqvZPCo+uWbNGn2FZTFnwDaS+ZijRi2UtAjCsElqfDz00EMf+MAHokER99lnn4jYYYcdvvzlL0fEk570pGjMR5odIH5WTZ8GsTbYkKLs1oq2tWRKPWYczdP3Rsq4oIGo7yWYVtxfGpTLkVSLnwtVzRTu3GJdmOwaiEX3ApOL9UuDVXX1ElY1z6JOPR+S3VR2zAoXoc6gWYEWs2qpCJHwGMtqt5hDnRcuVONUeSSXOC0LGYZIB0XqLWnfeJ8u0CKaR96hIpALUW/kOlPpRfaBjaEr2H7iYs2+KwpLIzh+dthhhwjHT1Q+WUgIv9RSQ4ZLsGG5sDpBNryRMR16DYgefqVhO1OKk6L7X0cg2kLI4HtCT8pTtmzZEhFve9vb1B9ulD00/JKPsqDzEBamEpe+QHuUvQIfLrrooog4+OCDI2JychIu96985SvRlPkw0UupnFnSWPuIMsZ85xqe1hjj5poiSdZJMakjox0hJmwhSFkyhYi9kHlU5xxu59tBXGnZB2nKRHErMSZIG99vn7qHh1jMun6O9lLhe63GTd5f09PTisqyDTq5DDPRebQdYYswo9ZkYop+00tiqqFmDbJJtguWMi11GXbggQfaNzfddBM/X3HFFeedd96mTZv22muvV7/61ccff3zxIWD3ecMzcM6WKf6FB8vWpca/UcE0H6nqPlxz5m0O2du6pCgC1XsXbS2JndI+ctsj/4avCGENugGGpdrEneawcxBNNN2N1Iu1MTaSHJAszsnmQJbcah47lfGsdprDPk0RppRSP8ob3vCGiNi8eXMR6M8M0YIOzPGW0/V0fCIJdb6U3o5IbOK73/1uRExOTiKaY8OGDdFEb8JhZuPD4BoLKMguXtYv1tjRhVyn2Ulmmj5VKA125UrW4eXnbHkzM9f6pWep8EUKFRQPzaLsMb9vNpp5je3EvD27TRFOWzDqBTTRojHMrMVqEIi20LyVfG9IRMlQwqnM68yxNVM4xOTSrdprn9Ji7dR+UT0aCVrqMizaQkvphhtuOO20084666zDDz/8+uuvP+WUU3bbbTeUYa1UqVKlSsuBRkCGLUSXXHLJSSedhOjko48++sQTT7zkkkuKMgw+GMPTI9nm/KyKP4jmEXwDNM4MasfFikiY9qc63VhziEkRB+d7I8E+VlIBRIeWApt8KTRE5pBFUvRwPfETC0vDB8KGeg1vVz3OvjTgTh1ptPByoWT6vdSVWAw2i7a5w1gsfEBl3ltuuSXEdF4ENrTP9qWaPrSSdQzZIw0JY/6Zrg1OnzYDSnFE/OxnP4uI66+/PiKe+MQnqmlSLM9B80vBUlt+al1x8Vt1iexk7bfrHnFp2bzoRJilPpB8L1zAbtreUVDOmoHVyxnPbmNabBbGaeChugMJM6p5xIFVeI3mlM4dWYoa64QNtQ2G6hfBUjWL6TbWF3Fq1PZdsWJFDuIftsuDmVsUhBcZts+hG6GavyMgw4466qgtW7asXbv2cY973Mte9rKDDjoI399www0nn3wyLzv22GPhIc+E+j05dZRrV5lsp11ykJOqNzK4XFmnof/YAFYjXOEXe69JUPOxaeNNloCUoUSbX5DdKD81YWmokRbSps/ZOIVGohuwyS2nY0h3SOZ95l7Gi7Zt25bd/tHmegw11sJX5gi59tpr9Urt7y9PpqCoD4kahrIbjhJcgzYvRRCJTcI18Iohdezmm2+Gt0yXVrddTIuCTeFKy2u0BaNIL0de1xjIeDTXJ16EDkK3m56eXiS6wVxQmB2dYsp4RR3JjjHF3DsK3LFherYIP1hFULL+kJj13EELPqI7TceHf3OewHCB88oXwW+VLGiFaS3qfeBK0+1J6ahvNxVBmR6bZA6RIuS+NGmpg57HHnvs29/+9g0bNnz0ox895phjTjzxxCuvvBI/3XPPPWvWrOGVa9asufvuu39NzaxUqVKlSr8GWup2GEK2ImL16tXPfvazd91117e+9a3HHXfc/9JDzj77bHw49dRTTeXJ4QmddjgciBiUuTqzj3QwGFj1THxWV79FIoDMm2qIkMZWEaZQPZrX419V4ugKNmspW0I8EU01NfrGLSTXFHYLtQ+x6jT+wjrIdupQcKgNGrJfo53iGikPHddcd911sQBwtwgVL2MchI2AriKaIDpcZkpygWWlntfADtu0aVNEfOYzn9l7772jKZjLxaDWObVpjelgkxRIVxjN+tJr1/dia9WgMWhOzRTG+1iQQobce+3iFByQHN0waIrSmi/A8rXRMKtDjefYYtbYLh06UrHkjXkfdEn326c92Cyrv6DbTq5gB/NiYwyhGu69UkUPM14tUEtXyKBds98ARs7RO9/5ztz9pUxLXYYZHXrooT/96U/xedddd/3Zz362fv16/Puzn/1st912K971hje8YdAu5Awalo7SiDYjtkCgInyv8VrFFxEuUD5VjHfii9Rx0mln7fBky3y4szneLEDLRJFuY/NqmJvQwi8zK7RhZAd1R3GfZyEdae8pGa5VRC9B2s5ut4uyUrDO/7fBQ+1RtJkmv8mMm9l7+adI4spcUMp3UNfxuuuuu//++6MJU+w0MavqtOAMWnxabjzFm4YpghiFrw+hXDTgV8+IoWDIfl8T1ebi5XsjOZhtQxW9lTqAhtTZ8NqXJlBz+uawXS2MQsvCcXXMDdpVYc9HFSWojg8nJU+uNYaOCd1QlpOnzyQX0nk3kDwiXvnKV1KZGAl5NmIybOPGjbvvvjs+H3bYYVdffTVl2NVXX71QUCJwZMycJimbSaHOrUgyRsvXcvFlzkhsna8OcV2oUkZF2FQzZR9c3Cp+uGM19NzUcBBfmkUgzw3SqH0ufeYjh0R/MC3UtnqIdWVcModacCOpHWBmrt2uL6ILU3vNfAAb3o0bN0YyHzNHMxWhSEXrgQxXWWrR5F3kmdGWYf32oRugqakp1fFtsuDaoamnxZAYQFEMtdBBoDammWHUM0yNwAeVYWS4qs9R9ui8kLJFy43Qk0x5vsW8cTm9gTYW/hpcwYdk1+BwgTyTHE/B23W5mrBkC7Mbj5IJU8YoFS28wEdlvy9lvLbBLH6QxYtxWLQ+HM9z0V5wtIvK/dKkpe4Pe8lLXvK1r33t3nvv3bp165VXXnnKKae89KUv5U8XXnjh1VdfvXXr1quvvvrCCy98yUte8uttbaVKlSpV+v+TlroddtJJJ7373e++8cYbx8bGDjjggNNPPx3B9BFx2GGHnX766WeeeeamTZv23nvvv/u7v1vIDoOGAt1HFRnD36lGWbpfiI6jxT6K+DI1RJC5dlTnjbYWyQT+DLUZFAmaLx1ezAjGottPa3DQdaEhy2Y9QCmzmr9mi5gqmpO+o602RhtyUV+FzouOjyrXtGjVs8hB09mZmpr67//+bxsKm/FfxlpSMpPL3H6/DFy5EES50Df4d/369Xoq6XbbbRcRc3NzRTcMJlQNKervmnVrLzLjjKcJR8K3aR8YXh0p2dyeadkU+EkRMyYGZDg02tbDcDhUTIKmUvaxRbL/ir3WdlqzdXINrqQXmXGD2mAFIczKybagNWbYzoa2huntxG+LfMBy1dW0pZcxl92JhluOBC11GXbkkUceeeSRC/369Kc//elPf/ovfIh6DiwKVncLfmKZPhUw0Q4fMGe+rp6xsbEcvtFvH+pqnFqFZb99eDE3jNYv4OrM/mHifrr/7ZnkMgZ9hLjf8RNOt7r//vsZWKwNLqINygHp8zCPQtGLk+E4eikMjcnBNUR4NO1mZmYGvD43z8jcRUVRZFNWfE6eYnsvB4QeO8ibRd6IvjzxiU/EILB0YQjrLIJsCvT1SjUqO6XD8zpN9EdeRSQ+SpeTVeQz/5CuMQ5yMQhIr2Tzcmh+t10ljvrfIioCm2SSJkSfswiIrKvZjZzrjJkP25VEOPImzCIpfGxejmQpamDUq7S+/rB9NDkbZhq8Nl6/LDr1lywtdRn2f4Ry4TIQtxz/DbFvVJPl9FsWhQoq0GAwUJ8Zl4KyYy6XYi2cDLV32nWE+aKszZlrh54nYx/5SloweAX2GJKTbHx6pUqAFgtgObxKg3aFJFMtdd8WtzEFp415dpJ1Oh3EQWjji9Ki6Jda6JoslaMkt4rXj4+PQwg98pGPjIiVK1d+4xvfiHYurRkBe+21V0TMzMzg8Eycz4Jurly5EmWCH/3oR0ejcERbU2ESla4xWwBmbbBGoo55VuD6pRO8bAC5kovpUwNJQDbHrQmPojtNmaypNUX0wkx2bfDs7Kw6gNlNvZ2lznJP++0Uabx9cnJSp9XGXOMppqen9V+2Vn1X1EF177DIQF6TZrzysxrEIKIs/DfEcB8JWur+sEqVKlWqVGkhWhZ2GAAotd9BxKA0DmrQVJVV58GgnaRCXdVSlyJFypqpZFqnOqiIjWgIoiq50dY3Tf+ieyAHvhMVwY0PPvhgRKxevZqBgpHUTG0D+2Kgh1YA4gmzGDS8l0f2GenDLcROL+AbDXBT5xxtjqEEKOLzi1/84ttuuy2SiZAVzCLuNxwO4RJQV2J+2kLUaUriAs9cu3ZtRGy//fZ77rlnRPzGb/xGRKxevRoVsO66665oK9cchHvuuScibrnllic/+cnRlJ5CwP3uu+8OBzCSxr72ta9FxOMf/3j6Ozk+s7OzGo5LMDAbyjwmUUNeDbUmspe/HLSPdyCpja6DbC/ihjJEOj+kVzq5m6uIy0CXCm9UF5Gde6CLnylo1sJssdHgU7yHvEXNXFpXWr+Ykcx6Bgobo2wn2mYWS8eZfyuSDcp+KXrMidPRtgjGkaBlIcNUchjgo+KK53jlbWxlyI2fgiiuinG3Gm7A5ZXPQCKWCLJNkoM47EUGMBIg0i2HY1mI/inToYNQEQxKFDZMhS49NPgX7INwk540wVHClVpucTAY5GJR5JI6EZ12ui6Zl/772c9+NiI2bdpUlDe/UPyADj300EMOOSQakX/VVVdFc66NPcqEIv6dmppat25dROAshcc97nEYK7QTgzY/P/+sZz0rIj70oQ9FxCMe8YiI2GOPPf7nf/4nIu68886IwEO+/e1vf+c73+GgQbjefffdOJ8FXz7zmc+MiOuuuw5VqXbeeecQuagcnxNqmkqkHAlTYrTXBgLbSuPTQtakDZQKVHXeRFtTpItX9TAC/vbAYuSUaWBad43OqrwqzHvEKzNwZwKGXTBgPGSdW8BFp4kN4e2my1rden0dEycW8piQDEdlN7NbfYSAxKhYYqVKlSpVGl1aFnYYNB2Nv7DQI4vNw12qcJn+RZQjwwW8xqAArTtOHMyCayOlHrNJ+UC8KLnTF4pAU1+uhaWp55nt1GAzgk6GSGgybLTtuaLpw5HPWOv4+Li+UaHdhchiNFST/f73v6+DtkgURpFgOU1MTCA0/0c/+lE0hyx/97vfzRBNpx1lvscee0TEc57znN/8zd+MJtSCc4rSIQjN6HQ6Rx11VETceOONEbHrrrtGxD333HPHHXewL8ASe72eQlj9puSYhhtceumlEbHnnnsCYsLhmajrYVgiy7tk457XaDRdpzlVQK1zw8po6+gmIv6h7eRL1XSmsZIjkmIBo6cItmtjLITB4E3Fou0MB75UpxVEgJHrFp+ZL8zricoqwxm2o/851DkAkjCJhthMTEwors7e6bTSvtRdVmRQvEu3G4dlhEyx5SLDGHBVjMm2YB5Fjbn5dX0vcqIBt7GWqB825Q9wC7AgW1KEPtTe54o0mRRyNInmprCboCLLNsGptR7m5uaUB5lfgbdrpD7boNFfFpVnmIxuIUNTFeEh02FtLb1G22lAP8BSRmMqGbMzSQbf1R/+4R9GxKpVq3Bw3T/90z9FxM0331x8jj5tv/32i4jTTjstItasWZPH/MEHH4TrC8dd7rWibIAAACAASURBVLbbbpBwkDe33nprRHzrW9+iF0rHU1cOuaT6MvGoO++8E2wOR7f82Z/9WUgQP/2UIWvSItcz5E42p5PLibOySdkNbNzQdER9HQP8DARWdZCiS2Fq7mKVncyoUeHXax/yaUqewaTqOaOU1UVFSDAfUTszM6MijcprFtJU4CwuUTNEi3vHAkSLAZA6AtRF7EWql3NOM1S7ZGlZyLCcs2LwvaXIFOcvg/KdTgdOdajqcJxwP+ji6/V66sUt1jSjvqYijeaXFUUMWWfqiLJkYbZWN4lFrqsNaiVqihp6p0lW07Eln7LxySoC/QTKGvrtM+z5NDwH/NeqAZmHRn0eOYFMJ9EcovorDg3HBTvvvDPKbz7mMY/hlXfccQcMIzuIGa6sU089NRpZsm3bNswd8hMgDjdt2rRly5ZoFsx9990HW+2YY46JiBtuuCEi9t9/fzRDRcXExITFyuss59iciIAzD3dtt912xWwhXZMoMUx+qrPTa85AV5Od6r+BAbkMMVNQdBnYISZ8nTlEQ3I5MmQSbevKYk/Mh22bWiWE7UQ2SRcS5VNWsyiZdD9OTEwox+DnLNcHg4FOBH/SJlkapfZodnZWt4b5IBXMoIzXfhWjb/rtxOclTtUfVqlSpUqVRpWWhR0WYl3ZqbW5zgpVdT0lzzRf6qo410Mh71677jhRZq0xT/09g3LDdmVrXqbIAFurmhczAXJRA1as0IYZsg+iSqhaKq+xqF8NBeZDTK1TXY/qbTExXKfALD8QjVcLa9ZeI5bvS1/6UsgJpRlRtF5TP4WzCs277777PvOZz0QEzqg74ogjImK//fZDDCHCFHk7wg532mmnEHsIjUE0PD7fddddKKKP4Pi5uTmEIMKMQxv+6q/+CiGI6CCsuqc85Slf/epX+RyL2zTbFx++/e1v8+/4+PiOO+4YEfiLh/R6PdjuwBLQza1bt2awy9KZDck0q1eNexpnGfez+FvuneyCIliiDhsD3i1Gl1iiWpOWvVsMMM5YS7Qdb2bKkGMUzbj8Ipq5atsNmqoIllRjY4gu5EBfumNBnA69UYeFVAw6XcRVv2RpWcgweHd14ummMnQuxPoGukIerbY5YWUVQrbO1E01Njamx6tTgmb0vxhRQohGZQkRDN32dqU5LXSxcjvp7URjTJZo9SyCpdrrXlMXynLdNA6eX2ZvcwhaEm3xZld22iUnyBHw6+WXXx4NqGvSzlwsRZc1RAsYyv33379q1apooip+53d+JyIeeOABoH9f//rXeeVgMICKYMMLyaSVNW699Vb8i8D3I4888vGPfzxv/MIXvoDrr7jiCjYVeOZPfvIT1Bz5wQ9+oBNh+GGIZqMo7mAweMpTnsJl8NGPfjRkSUOS/eu//msIgFYc8yLsrOzYwC4TSOb1segqXJMdNrYe+Nl0r5CVSV+XvtGCHRaB+PjenJNjAoPPzBkFvdIRBwTwbejg21adkhqYtpaSyRzwxdgT60uIeq0BPlZArhiUv8RpWciwWGA/MHZIrxm0T71itE+xMiauUYcNlVYQdUD1RXHNqezEfpidnVXxY7i/mmjGzYvOA+uRXs8mGWqvYY38yZzVsFDNR8L2R1KTtYN8Wo7aiiTtcuia5TZxWCA7YcFwny9yJpmROiTYBYgNnFf3ve99D1/ShGI7JycnwYM0MGxychKCDTIPMYfbtm1D2vVf//VfR8Qee+xBcRtNqvJ1112HxuCMIRT5veqqq/AKU3RA6vMYtIsh4a6ddtoJz4QVyHnHUsEr0BKLCQJ1Oh3N9+cFquiQ1WZWSG+TKXx6JZeBuU5DZtxiZLK6020iUS0zTJe92X/skeqv1hcLa8x2Kq0WDY7glzRbI0luqsVqh5GyBsZeF2MI1WPKetAmk9QgM91CB4RRkSNBI2MwVqpUqVKlSkYjI2x/RbI4Xeie5uABjbXPU6c6YzpaCBpj8XtmGOHLHLJMi0TNBdMTaYiodqyx+yHaXKTqG7QMDFLAX1Wu2QZVltk8BYhMDddstmjr2p12xRPqp+pCU/dktNEbthm9YPQ/y6nwvePj45/73Oeigdo4vEXAUAcNxBLsaCEsrXvuueeVr3xlRJx88skRAftp9erViIAHoUlr166FJwx2G5o0OTmpI7P//vvjM7DEvffeOyKmpqaA6uCaV7ziFRFx3nnnIbj/UY96FLt53333KSqA69evX49cNLW5u90uiokgABLlgHfffXc05pOf/GRE7LvvvhiBn/zkJ9Fkp8HQvPnmm3EQBIoRY4W86lWvesMb3sCnoVAWNX2LjVRDoXj6KI9zVDOLvdOoP96lIBtNtBx3Z7CKjglvH7YPJSckkF2ntOrUaqGnwAAGvabXlP7RvhQHwZBwRVDGxsb0+BverhZq9pFzhdDTr7ZysboYR9LgihGKS1wuMizadjRwHq5d9f1ymvV6O9nImKPuQ2Z0gszFavJJ324ITxE1Kv6rC3qsfYQ5PcC2/7WbigHSh2RuCd0J5sEyIW1Av44Mc5uswfpMrThl7kZcOTk5qTeCF7zvfe+DO0fPlMqNiUTkcTpl//7v/x4R55xzDoC4l7/85dEItmhiHw4//PBoYkAe8YhHoLo84Dh40cwtypYAIWSOs/YXWWI333wzspU1aIWDjBQ0tOGxj30s38Xrd955Z+hneDs+X3HFFShA9b73vS8aRHE4HP7pn/5pNIWvIGXXrFnz27/929EUeMR7d9ppp9e85jVsJyQfxZUFFOSQB4bR63QM2+eVgLqlukdWuM80RQs+0nk0FZNN0haaQ9Giqyz+K0RcKR8oJrRY7gE3VDGgSZU2e6b2i8yBox2y5S1AzHarDp36KSjULUciRocqllipUqVKlUaVlosdZmFCVDTU4KC7NevvpiFSvyvmXVo8RSQNiMqUGlu8XsNnaQCpikcMQcE9SxbOKEokI9J+DQm418M2o9GFGbmgii2hS6JJ+nbFTBDnyWtwIxFaXKOldAbtIuiID7z33nuBfaHX//mf/xkRt9xyS84TIJJZDLjSoSMehWajlMbHPvax5zznOdGc9YWq8Pfccw/C05/xjGdEY1l2u119DoNcNEIE8RosHYLE55mZGYwzfsXTjjrqqE9/+tORQroB8W3evDkao3C//fb74Q9/yEHD33322UcDTABa7rHHHgjNxxhyKPBevAgG4h577IEmwQ4jmAawFFYdY3kUtbZ4dAsUwpdqFUXbVuNSVNOnCAwuBJNEimcZtuvWg2jVqXk0bOKT7ViJXBEmEhYSUoq3uNJsTeZc/iJr4tsZ8BlpdxsaZDCjYie5wfpTxqJCpmnp07KQYVgl6tDiQsz5FgaOGxJt1yscT1teizhYFFb2dYWA8iGONws2053DHVv09+j65g63eK2QCEaLiTKcAX+VO/d6vYwQbtmyBYlQKDaBn+6//37kP4G9su86BSjoNzU1hWb8/Oc/16GwpIUQca7Uax/Oa1NmvkDAyGDu8GA98MAD2mu8/fLLLwc3R4g8+j47O6th9HQl5joLXAYofAW8rt/vQ/wg1nH//fcHlgjpjs/0PioL23HHHSF+MLwHHXRQRGy//fZnnXVWNBIRUOQ//uM/vuhFL+Irnvvc50bE8573PCQygrgY1GOHJ59//vkXXXRRRODJEFoXXnjhSSedFM2BnH/8x38cgpkr77MDkUF0Nyo2SHQdxLBAFVQcT4yMnuPTbZeeKaLHJjs5Wbq2TaDy4SF1cIqeMwvtM29CpMBLfqmaH9ebih+oIDMzM3ojpZ0OGhcMVlox0NfSAzJz4AhoAmskAbmUaVnIsI6c12ziCh9UaFlqBfW17CgyTJ/bT/cMLafs+hq0azByseqNthu12WyS/tppp2RxF+EaLW1n2iI3TIb7O03KEZ+m1UgRzv6Od7xj06ZN2owQrdPUTB0KiLeFVNfsWSzqleSnZvXqfobraPfdd0ccBBoG4+Pmm29GBISakv1+H/YQnE8IZNh55521YiFXiCbCg7Mw/U79H1u3bkXUCV63Zs0aMCy9cbvttsOX8JnhIWvXroWggryBHL3jjjtwC16BTLKNGzf+3u/9XkScf/750QS5vPe9712/fn20mSw1fRCy0+6///5rrrnG5vHGG29UFYFYgmIDln1lZ1lpcqTlUdjq1Q+MGcEiUX/PfPvccN6ogt8EW5GP217QxCnKY3PjmcUZqTInfpqZmcmCjc22kCLdJuysqrYcluzYpg9StxUxDB0QiqtiEp4KbCIEI0HVH1apUqVKlUaVloUdFoJBqWVDvcag+RxQRH+PGuC99mGyIBpSqtRbZCBJoXYLAtQLQkw3Xs9IWQMBVPtjvwy7CCkjq0+mHq1aGFVXvh2vQCn3008/PSK2bNliuL8Oi/kA9EqCTjn00WBDg4byMGbClTChUMd9u+22AyQFjRtI5uMe9zhAbZroOmiOK8TDEcvHkdGzkufn52E5KaI4Pj6uVbuAkd52220w43Dj+eefj8hGXAN4c9dddwW0qJO1efNmxA3CH4ZDYc4++2wtu4BjXKampj7/+c9zzAHwbtmyBbcoEk57GlfCNNx1110RZA+zFV3YsGEDjDzE1nPNZ4uk0666xC1jIFsIUmcrTefarHkQ92YuAWPvNUDFlo3aWINSEQCuN8uJzr22t1soMoh8I48Pn1k8p9ReodnNXK45WyCSMRoRs7Ozymp0BELMx2gDV0ufloUMGwwGxNN0pq2gC6gY7jEYDNTbQUdrlnbmKjMPLdsTKZOf3iYLF8ZDcn5YMTSDK3IRuH+R6hUL+e3wL/jvcDiEm+Qf/uEfookvt0Vv7ndFjQxL5Ocs423DG0Jo7EZvtK6BHaPg4YYNG/QacP8dd9wRv37zm9+MBsyZnJxERSiEeLB+PDxb6tpZtWoVGDFLXUTEDjvsgF8BCcIN1u12ESuBKXje856HtDaUW0R5RvYXzQZPmZ6ePuaYY6KRhXwaBl9TwXq9HsDPW265hY054YQTnvzkJ0cEXofTZJ773OfiVyCT6ODuu++OzDDFtZ73vOdBD4DI5/rUNcZlY8gbHgUZn+ul2ZcG+Ju4MugyKz2sjQLfYSQ9MlKwAxWFrC0RozNBmHnFsJQnwNt1QIglqoJCpTnLJ76XHEblHI9wolrAJxPeLPoLVUASHdUDoQz+XeI0MsK2UqVKlSpVMloWdpjCEQyIiIjJycmsNk5MTGjhO5BBkZanybdE0oCK+CQjJFWLNKxMYweireIZ1Kbq28TEhGIsVOLMdANpxQEz4NRaZcQEHrJq1aqLL744GnTOiJppJKiWL9Jr2DtVGBnblqP/i4hQiJ6rv+KZiNA7++yzI+IHP/gBJheWAUZp1apVOG0LYYrI4d1rr70A3EG9hTXGFAtFcS2QFU366U9/iohEVM1gTB3OaIb9d/fddx977LHRROojT2CXXXZBXXkNwHnlK1+JJO6Xvexl0RSeHxsbw1ma6OaHP/zhiDj00ENRj1hX5qZNm5Ck/LSnPY3t3GuvvdQiR5z9y1/+8h//+MfRGHwwvPbcc89eU3sixOhByRLFJ5ktjutZR1TrX+vU2IwzdlwDi4wWQcy2bdsGhIBrKdclIMCoyybaW4O4nz6ck6stpKGWl/TExERe/L2mOI7uMjIH7aAl6hiAaQFfOrAM4tAeaaEcvp3Dogk/GrI4KrQsZNhwOLQ6KywPkcOTuJGyuyjaW27YPl6BDNdQxJD9YEh09hN024Vn8BMxT13fRQTDoBLDEEDmCzTQoBhJqIGIN998MyApQztNEuuX2YVgA1uMGjUZbz0y7LEYaY2+IKIPTPmUU0555zvfGQ2WRUUBV6KKPIIV77vvPkiajoR03nXXXdoMoHbD5jQZPaxnampKMWHIpwceeACgHILUV6xYoR4mDMKKFSsw2mDHhx12WESsW7cOQvctb3lLNBWE+V4U70Cu2Pj4eM7s+fjHP37KKadEA2wi422XXXYZSN4eZO3WrVvf/e53s0mQeatWrYJ0x3vRkptuuglPg6OOI89NFO08jWhz8yKX7LTrUNjk5hjgSKvI0ht0D1KKqGDjVjUQO7+dmqKieaZjmbKVE1ooAm1AcmBhtBczluv09LSm3DArRvF/iiLVOzlo2he+TrNryK+KvoalSctChkHTyTJs0M7MAJmxwuk3P7NerHyNK1IZU8jiiMTNQcXoW/zlSUggk3bakmLMMVekNT6j8L12+StuLc0Pe9vb3gb2arvRLLBI+qkJVNMesuPdBtmUiTz+mdBgiCIYVYcffvjxxx8fjeuLNgQcS+gX7pqZmcn6zczMDGwgbHv8nZ+fhxjAWV+08HDLxo0bo0kZHgwGX/ziF6MxUA466CD44SwyW3koJNNnP/tZTV2AVCaPxr94xde+9rW8UDudDtxjcOaBfvzjH++7774cBPzdb7/9EL2C4TrjjDNCxCquQWd32GEHmG74l5IJH/AiunhNNYkEbFDYZL+mrQ32KLt4mX3P9VZUQ/UVxrKLV6qE6LYP9KGhpi1kj7JrmXtWcRFmixfP0FEVajAYWOJdHh/epatXO8tnskd6ODhHYIRiOkamoZUqVapUqZLRsrDDoGepxmcqEl1Z+qXFoWajh3qfgYdFBVNBgKKCyZdaJBKfH23LiYqSlcsyP1wkH4ApjxpXZvmnbBK+/8QnPhERt912W67l0W2f52TKoPbaYo6zI0F/KhpnGWCMthZpD4e1BPNienoaEXq33357NDbEww8/DIsB5hFrRGlhCBJGG6YPY5phgQ0l1nFubg62HZBJFKH/3d/9XTjbEL/+spe9TLtvKK7WYr766qvVOKMRAMMRvUA84dTUlF5D3xVsJlTPuuyyyyLi0ksv/du//VteAyzx/e9/P1xuKKnFSsEYmWc961kR8ZWvfCUitm7deu2110YEqliRYNshZtVS7LWDXKhau3nYrgjDC/JZlMPhUE9bZXCjbhB7I71Tef0M2seeFevgcPVmYLPbVLhXc4qLX60c8hbdQYay8BBqerj1S1yDJQpjl4CBQaA5qNIcIgsBTiGo7EjQspBhOO9R90DxSDrD6K1OGkidVdyH5vXVZ3Ld2CqMlALC1WbwvT5En0l0VBtv2DpfqsHx4Ed2LDpJBTAlNNgW6qnPzc0VgyzMF53JPBn6IttOBkUaZWUiEg6jr1CpEw3W98xnPjOaEvXkQQiOBze3ZBp7JrqJGPfx8XHVKtgShObDh4SQkA0bNkCGoTa86VUcQH0jWXauTU7ABy/iOOBKdPOpT31qRBxyyCEIOoe8wQJYu3atJqu94AUviIiDDz4YDYYMw13Pf/7zEdyBt8OZ1+/3UbxfD4Bdt27dPvvsEw17hXAtKhb0OlsAlJbrNHeRElMzdaHaZBGj0/QpjvkiEB+nQ71rfIXuQUpclUnFlDX+pP5COh3oMtB513RD0xTpJo+kxtnnIhPjGOrtptZDFRsJWhYyDCh2BuVJ6qyi/qXXmNVC7sZslUgWm4HOyhCNcWdPkn3JaEP9yUwuxiPZaSYhXi6LtyymjurhXrjg61//+gUXXBANtyLZKOV8lKKRZBZt0UvBnzI31w+k7bffHuLEflJ5g6iKpz71qQgX3HPPPaNJzLrpppvyjh0fH0fX4BkqtgHSYvPmzWDWSFKGX2rPPfdEnVw8GbGR119/PaIkEIhoi0rDRKNtYUTb+Ga0COQNjBiIDVYgQ3wK6iP/6Ec/QhwHpA5MpeOOOw5vRFAG2rl27VoEv6hYnZmZyehFv9/HeoC5yXRpDVO088OUilLHApo4fdmMK2IYlklmpp7ZTLa7NXSCwRGKr1CH0KEAFZkJyU5LoeWnvdbtSQeY5ZuGSGW1LIftyCkOS06qo2mo0Ub0dtvrFvIxL0Gq/rBKlSpVqjSqtCzssEglOOlsMAgbF6sSSrXIjK0QZVDTWaiFFS02VYvMocXXLRIxr80zVZTX5/wwNt5Af3UXMY4ct+Nf1IG96KKLLO1Gm2EtXMRZRbUxw/FWE8HMOP23aPZZsW1ThHEjAhE3b97MY1CiKeiwfv16GB+AzoCbbd68GR8QiAg7r1PKBOr3+4AfEUOIhq1cuRIGH14Ex9Luu++OqEjqyDpZLOCriwrEck0a7t/pdGDz4e2IdOevMJLQ+B122AGlmTGPmPEPf/jDr3/966NdXuQVr3gFeo1RwvHNb33rW/HMZz/72RHxrW99KyLuvfde+BSRo8YBAT75B3/wByF2xiKYsJna5vvUtaFB7YQiNfSUVg5vt1I7+lg1p3hgadGO14UHr4RdEO3FbKmoIFu3+plQvyKftJmyx5RENmJB+SELVW3ETrsSGB2Kut4WKeKzZGlZyDAALOoooh2twIJBfCppiCxZvcQcic6MYAVSDGBcRFjaobcUsdo2SkcVBhRaJo8jYmJiwpqtt4PpUMBjZQN5e9e73hUR09PTRSewdiESM9Kfip4wg+8z87JtTE5kLpCQ2kKL7D0wu29+85vg9Zdffnk055Xsvffe8PEgvZdlkJAUBW8T6jnRm1LsJmQDotjJGvA6pqNZtHdmiBMTE/oK8zJqDtDs7OwiTlb0BWDprbfeiuNacMAKZE+320WS8ic/+cloznPZvHkzegqhjuiPW2+9FW372Mc+xp/m5+chO3XKSJZ9lYUQkTrDG4v+UV0AdIMp8sbTjnRGhu3zw+iyyq6vaCNptopUwNiN5qay2JMc1268BcSgDJMfGXUsbhPW99K/3Dsmv7PbwsJMQHNzcxqDtsSpYomVKlWqVGlUaWSE7a9CCAdSY4vaXDEwTP+l4pNjFqjzauwAy0jrwX0WwWggiZpx1N3UOKPNpFZjUV8bDAYac2xBxopcRVspI9gI/PDCCy8MqS20CBZkPxmokk0uRif/Qtuu+N5ihFWnnfG6CH3kIx9BpQzEXyAWY25uznTSiNhpp53QTgTawWQZNtHbBlcqAcS76aabYO4g7B7G7gknnMD1gNdZuEEI8Kt9pwFnJinKfWl4Ol4UDcCIWJLdd9/9pptuiiY+hYY+AFIYmrjxyCOPxDVow5e//OWI2HfffbHw8Doq7PiAuBKq/3iaLtF+v681f2nxGFARCXXkUC9yVJghYEVbxKKcdAzN4i+C/Goa9vv9fFwqTS5LzskrfNgkPpuJpjEd3aYij0ZgceiyQyTa7IhMLEcdsyUa1mi8BdyDQcsjQctChsHJhKnShUJSFm+4ue0cQ8wMx8M1eI4WRjMJWvT06PND0M4QhBArjPiJrl3Kpwy89NoHVVAu4kX6zE996lMf/OAHo3GcgCyyv9su4WOX/UIk3XLyLFnNkJN8u6GybMkvfC+RPRxDjPIZAMc6nQ6KxOPUaXBzyhJ4hkDz8/O2SPTh1i+cRQmvGAThwQcfjFVBfUhHEpO1detWBUvpwtQ3cgQgL9FCVKXaa6+9EHIJfBWr/corr8SEas5Zv99H5D0cYEhZO/nkk/XsTa46/IvXYXz6/b6iecbNQZTKEKj2k8GqIWvDNMvcd063FbWx0n86vEVly+LsFd40gcopy1jisCn6pfg2w+i1R51OR/mPQYvaeKt4x6HIIHZngYxJPf2SP6mfgnxDC7XQtV/cfUuTloUMU5XE5kaFEHesrl3D1nXRc/XouVO2+LgbM4+maFFiVoqV6VSZxDBr1acoitQfxk2lTeJhtRpJ/P73vz8iLrvsslzIzuT3sFSnsRgib6KaHTRXxEKTMizVYIz2XjW4v3i7jsBwOIR5BPYNdn/77bfjvGN6eiJiZmYG3bztttuiYd8WNVAkXoDhPeGEE0IcNurf6pYSZn/605/CRkRKGQdNhwIzvttuu+FKNfguvfRSVAd+85vfzBcdfvjhV199dTQrh9wN6XHwh0G4Ikss2uyYw6vqztjYmCEHIDNQQjzEZlgYxw+pC6rv7bZrqnE/qunMZaCyxBzGJpmsPKDKbDZMBWQxH4at1Rmk5NZbuCaZAs+HjI+Pa0IxsROrXaDDq36+Ybtqq+FGyq/I2ZRvcB4tqdniR5YyVX9YpUqVKlUaVVoWdhgClrIdzUAmVd/m5uZyvj21GzUCaEUpmmfGRzEAsui8sdizReKR+KjsojPQkqTuMWqLcJYgfxnn/FqRCO2I3phtkWIgWZF+mWMdDMo3xDVjib3SmcJWT4TGB8wUeGjoBQSeBmPr+9//fkTMzMwceOCB0T74caGuFb+84oorIuLFL34xe8QjAmj3q3tj3bp1ePuTnvSkaI7Jpq9CLU6M4QEHHADcD7AhHnXUUUfBgFu/fj1HCc68aFsPtAJxO9eP5fnGAiVxebtuKN6YK6tF25ShcaaTS2jBNlR2Qg8GA6u+oW2w/BZFL6K9o4vJv7xSPXbcyIadhpSqMh+bPoRDoSNpm9R4i/Irvj2/iL9atomyEfojc8grncFFl/lI0LKQYSF7BmQc3xZfEeYqohzqXKWBr5zC/LrG6w3QCIk5XgRA4zfabD5En8mcNm0nOOlnPvOZf/u3f4sm80nbY1QMo8hN0i3HhmXPsIkifln0cmWPdBGmZ2xOZnbR3sbT09MoAI/qgr/1W7+FYUFAPIYCgR7HH388Yuvxk3LDhcjahuAIdYsa+GyF8o466qiI+I//+A+ETqDMB2cnM2umdoA+//nPR8Tq1auBIsJrBTfYgw8+qCPDBQY3lTb7oYceQpUpi0dX1kYmqwg8v8yYOUlv58KwYxN0fIxMY8s8d5COY9bETcP9bGPqBiHqqA83z7SJ86yhcnwUqeu0K95hJxbBw16vlw9dy4MQsk2w0qiCWIYobs8Hx/BFRUByJGhZyDDoL8pPNZQrEv+1oL5I3lfOtzlX9UuVTPTJK+DeadKZjb9kbm7CkpvEiv8qGcvDvygLdN5550XEDTfckJ3qpOIiNqGlHKoopIel1Owi/S/ZdsZ9InEiPlP9f7yABaKimY4777wTIR7I9v393//9iLj88suRIKwWyeJ7OzcsIq6//vpo/ekc5AAAIABJREFUKj9ZzAJTazE7yNDaeeedcT7L6173umhiT+bn53X9QMo+9NBDCEV5zGMeE6JI6dQzgsA89hGxevVqnJYJpxp431e/+lV0H0Q2qmuMa96SjkNWkWr6FC0WQ2j2TYi7UUW1KUncXLhd+a/pTMUlXfQeGdJg+IpeY+mblE865uwR/gWToTapMkm7YM3ul87qZAdVklExYl6j9teuVEca25AnN7dqKVP1h1WqVKlSpVGlZWGHqWWj4bMTExN6vga1P01Zp2ICpUy1quEC5WtzsBkhPiWC4yBz/xh0ZrFVIQqUanbj4+OqZuLvxMTEHXfcERFnnnlmNBUciHkWrYfckhA7LGOtnXYxrUWeaV0zy2ahNxafk28EmVJvId0YLthYiOW7/vrrMbD4F3F6dKcV+7J4B7XxH/nIRyICZ11GcnYC61Pg98UvfvE73vGOiDjnnHOiOSqa+juuRA7WjjvuiOpWCkn12xVycRehJF2T9913Hz7AD4fPV111FWw7VDBRIDTaNtP09LSi1ja8HQlEjPY80pIwyDeSE4iDnEPsbElYBXeCFjpNnM0cam/mEa0rbZvF6yt6wb5YkKReSfRYoUWDNOzJGUi3LBfatWrm8q6cptLv93ENkAlaWjnrdDBSZ2AuCxnGjL9Ip1doFoVxARUDDNs1rmrYYKSIXn6pS0r3pF1JYWBNyqVfbONR7urTAC9s3Ljxne98Z0Tg4A/bw9llZQ8Zto8IyU8IEagGzigZmKPXLIQTLiIqdI6sC2webkekhnFMuIKQn/uqV70K0CJqKlIk2AziUZZws1DzxsbG8C6MOZxw69atsxN7ldfjFUcffTQ8cKiGBX8YA6DRF2CJq1atgmqiyRUUG0cffXTE/1ui/vbbb7/qqqsiAUR4u2aSjY2NffrTn47GObdQWI02yeRERuq4pIveShBfpKKXERb5DC0GuSg3N6HVbx/vwiZpX4xT6088LEIRRe5u/Ykx69pr5l2phBg0+dqK+3HKNO/CwpSMt4BU1Y5oRWxZJhkFsBZA4OAob+GYj5BLbGSEbaVKlSpVqmS0LOwwBL9mzZ3YhapanXZ1Z4uwMl1S9RqQmRRsgMKAvCDjb2Zv0c4rvkItACpQGtb4pS99KSLe8573aHlWkNmLbKdeY3r0IvAC+5JHyWgh/C2bZWYE8DM6iLC99evXR8TGjRuBeukg20QwAEGDI/bYY4+IuOCCCxB5oUHYFiwOWr16NctTLdIX/AT9Gle+5jWviYizzjrrEY94RAhapU9Ak1asWHHiiSdGk3+N3OROU0kET0NZ3ocffhjlP/BMoH+XX345TLenP/3p0SRKX3HFFWqBUWFH1/RYuNnZWZiMOPYa4Cojkgw2zPEXxKAs1K2IEOrsWK4xiIi94ofcERqfQpPCAi50IRXRYPYrW4eMjlErma+wcMoMRRKZUN5C207XZKfTKYaZ5NXVaYebWaa8YsicMm0SyWpNmdcjZG2MBC0LGaagkE6VbaSc2hKCS+SdYFFYxeILXB85xok5HP3mCIZIUJt5vKzZ+iL+hC8/9alPRcRHP/rRSJlSJo9tP2TBkzMQdCQ5CEVwRl+0CDoxLEUwFr+Mdngb9iGLBOYiEdGeXEvCe/vb3x4R1113nbIwc/OoTjA7O4sYfVyjKGWktaH/Ikz//PPPP+OMM6Kp/fG5z30Oh5sgEQ2pDmvWrMERMIceemg0MYcEvS2gHB/Q/QMOOCAijjvuODQYBagwPj/84Q8RkqpHt9x7773FecE1WmErUmpXJK2FkklFFBe/oeV6SzEVTIeOPjbLOctBuZ3SgSO5g6oscgTUQWX4v+5BYnQG9GmaAZtdVJrZ1JA4eN1KfFQOcx82GQX65UI15jPcTeagheuYDstmh8jvkaCRaeivQqpzgcjsVNJQFOF6LY1DGF3XIg/+KXpHTNXKi49pN+ah1ZZ3muhbq4YVAvdbWugll1wSEZ/97GdD9kw2uTrto7CMnZnMUxW46CiiJlu02BZxkhWvKbq1yZ40KAN/x8fHd9ttt2gfhVXkXBwQBFOguNQFF1xw8sknR+O74pJggSjOC79Uq8icW/jpkY98JI5Oxqku+LLX68FehHl0++23w5rEhCLBa/PmzSixiLLLsLE4sLgRURidTge9hrUN6218fByJz/CE0WZ98pOfHE2yMwZh9erVOVto06ZNp59+erQTEqjAZXMq2nqDhRSBaF0VnVU6dJ12cDwXvy5OLjP9kgJJtUnaanZ+jY4nyRocSUMlH8jex6KyxdWrxv38/LzWmuJm0eEtnmXIhilT0okLUUMj+bA5LDo7FumjsnnYpKCMBFV/WKVKlSpVGlVaFnaYGjeKXXRKkbKEaCxnUGmRSDnqL/qisbGxnAzPyKVF3Glm+oD4KFXE8KILLrjgK1/5Sgg4E6Kfgtj3rFwbtk7N14DNrEEbLWTPLXRl0VdR9IdxrFT9n5+fR4SeAoyRjMsQi3blypUR8aY3vSkibrzxRh4LqY2xjPKQJWFZokDenvWsZ0XEc5/73IjYYYcdYCPCd2XRaBpCFk0IO4zCww8/HO994xvfGE0I4n333YdOmVIPFHG//faLxiaYnp4GwolrAEvus88+SBjA09hsXZMoqfWYxzwGLkbtID1SFt2uyDZXsi2nWMCHZCuNY54D6IueM5tcQzsMhVukmq2Be+ZD0n/p+sKVPJYoEoZpUfg2SpgdbTyBH9t0ivF0m0ocymTs7coxut2uGtm8wLhfLFBvJRIgtJRpZBr6qxDmSefYEI8isGBQcr6y364uSHerIicqJzJlDt5tZ9hYwSSFDaNZZ8COED1/7bXXZjRmoddl3M/EBvmR9siyFMwRslC/Fvqy+O8iqKMFN/N6YlmR9ra1AZ0Cc4eAOeecc4rOf/UXcijAvDSv69GPfjS8XOAaKAb/ne9850c/+lG0md3ExAQ+IF5jy5YtcIzhcC+I4dNPPz3HOh9xxBGICkEhKLbTANIQNwzadu+990bEIYccctZZZ/EaMvqB1DHBgKxdu1b1AA5yxsq4oWxVqMBgO/Np6RwTO6MrQ5GWFMU2FNebxtpQpOlIWvyFKXkmF4vJA3mpzM3NaaiFPjlEVEQKuFcgNNqS27yAFEg65mxenp1uqdh/pLy0SHEinL4RwhKXhQxDAofqsNznuozMsWxrN3utTImzg/h0y83NzUEHz0vQXkTvvS7oYePBVsFG2+7cc8+NJrepSBSrZvBlydppF56xL827ps0u2kzRFlH8qeis1jE3TN8Emw5C8XW0corinHwnIlD/94EHHsgBO8O2/7zfVJxScY4E5AsuuAAeKcTRIK6P602tjcMOOwwvOumkkyLisssug3/rLW95SzQnTN5+++3KvDDFW7ZsgV/thS98YTTeNRMtNpIwy1DQ+cgjj1ROqvZBNE5ElBhet26d+WNCZIMOoA1vcXbM42L2tKpl/EmfyXm0xkRSH9kk/ddyyDiPReecbclYwDlX1K46Td6VxTPn2bG4RBDbCeK8KMMhT9BmqMEdbYPPFE0qUsXZ0ZE39WgkqPrDKlWqVKnSqNKysMNUZywihGqNLRQxr8Y1dVV9Dq/MamOnXaqAGpaqTmxDPgG9iG5t27YNWvYNN9wQbcXZ3l5EY6KtDPL2X5hCYNqu3W5f6isWstX0ykVu571F40ObqkO90DUa0VckImCWc4bHwgJD6OAXvvCFf/mXf4mmpLL5AvEXevRBBx2E5wBmXLduHQqFAM07/PDDI2J2dhYtRO4aWnjMMcf8xV/8Ba+kLo+VoOvWUqxe+9rXRsTDDz+MyHsAhpqMEU2wIlDHF7zgBYoNEv2z85FD7FTD4Ysmhf5rjiWDQNT6tFMrF0G3zAoE2Wo3wEAXycTEBItlaN/zXohkbIXUy1ALb3x8HNaPOQjzM2dnZxXnMKzVtoDOC6GdYviuttasK+Mw2TQ0qHaJ03KRYcP2ScqMLlUnJ2shmlkdCRlQ9hRt1jloysnYlsvwfbedgMIXaQ1sXo8rFbJ417vetWHDhmjvnOLrFgedbKCKrjsTRbkvi/9bFCe6RX+Zu/ivMU22KlLqWHEfYvRQNJIjr0EHTJfWsBF6xZD2C+3h29/+dkR88IMfhABQjaTT9rTjmRdffDF+Rfz6unXrvvOd70QEDgx7xjOeEelgLSRfv+hFL9LCQvCf7bPPPlgPGmnNaf2bv/mbiPiTP/mTiDjkkEOAO+lSWbduHXoNOYog/l122cWiOUI8QwrK0WGjPLrTLpvJiTMlL1J8ATdCVjFNPvHt5ocDmRKjsDwvUK2ieBSy9UV7TYGh8tvawIbplJno0vFhik5RJwCZP0yfaaiswbbZ+Rdt6d7pdFQHWkjfXco0Mg2tVKlSpUqVjJaFHYYa7aoKWfyr6tq0o1VtZDiTKWX6FuqJ+awpCy80YEptrE670hWbpMdXXnTRRRGxYcMGC7FdqPuGExYjSnhljtcgqZYabYWx+PZhu+zC4i3MvxpUu4hJF+l4xhBQ1yZLn0PLCbcgwlMtA16Jf1evXo2i8j/4wQ8iAhDiPffcY0WfdbisnSCEFx5wwAF4F4pxEBvANTDdXv/610fEYx/7WH3af/3Xf+GZj3rUo6J9VhxB7y9/+cvRVP5dv349AkawxhCRz0pXiOnAKWKWj8yx1SXNQc6zw1qxtoosmmOhUbKg3GJUJMdT54Wzr4dY2lbiK9AX3Tv99omRtiq0g73m3FEdJZ4D9wvDKfvtU8FsSRe3XhGPtT2rTSIIoSBtcetx6Ow0DD4237I0aVnIMAAXGXQeNkkYBt8V/T3GoUC6IbmGMoQ1KBVksxKOlKa6kfDMFStWaJjZNddcE5J2o20eLlCtpzgsGT8pYuuGOnZL9agiSbhIjJstzNGGxSuLWCIBWNwCdkwNw0KtirwPfFxbS9iw2BgQSkyde+65iID/53/+52iqZtDJob0uokYklDScnJxEjL5VRkcvkLL2l3/5lyHyGFc+//nPxyvQKgsBRw1+PPmxj31sRGzYsIHHZurg4EYF3HgijzZ+MBhg0PSkYOLwqmwZKfQd7fXWb8ql68gP2iXZzD9dlBCaxEn1kbtMNxFfoVPPPatsgbg0VpeCq4N2PRpu6uyI7bQPoKCemh1v5EL2OuUtVgtGyZQtrmG8qBi+q5t0ZmYmK/cjBCTG0pFh11xzzdvf/vYbb7wRWL/SFVdccd55523atGmvvfZ69atfffzxxy/+fSZogjzCJ0QH0QXN+TZLJVJUK2jYdmsrvq+vxgcVk8bgdD8M26mj8OEPh0OED0B6Uc00dUzbbGs920xFVzz7bn+zlyLaOzbfkt+1iFpdJLvSDCk8DTYESd1a+WnaWo0aMC5gte+Q/IvKihFx8cUXRxNATy6j3JYSJavV7AIegiq9IWEOeAiSxuDKsvbjFShGRXVH+e/s7CxOidt///2jORv6zDPPtGWpw4u/ZruAqKErazNFwf7V1rJarm4QfEkvjkmdRZQJE0UqVjl0NhEmfvAhP7yYNFIEVGxhs7VFzS8Py/z8vMbK25bXoaAyURwlfZHtJu6ynBvHHBu9nS23sRohMbZUZNgFF1zw2te+9kUvepF9f8MNN5x22mlnnXXW4Ycffv31159yyim77bbboYceutD3v5bGV6pUqVKlXwstFRn2wQ9+sPj9JZdcctJJJwHWP/roo0888cRLLrnk0EMPXej74kMs3p1UBA+7pXrV0VavDLgzMAfXW1VQtR4MJFHliGcn6jPf8573IASxeIYh+xiiXJsHoojCq6JnOLg9xIZikcE0OAU/WcBVVlTN5CqiiOyLvogPVDOFp5XmZxJ1tCgstaFZ7AMz+Na3vjWa2lRnn302coFtGeQAtqJfgZ+ZzowYfS2K8fOf/xyXAVnauHFjRExNTaGgFML3AQmuWLECNqJq3B/60IeuvPLKaDYUDnq+5pprFvGIYLhwVou1v3hOAs0pnYJiRLh5zn6hv8dMbTMU1A7rtmspWQtBNGWsoIw2hssGNwL24BouYtH6Iu4LfZpl41idl0WgBSMNVjSMx/z3OggGRSoVAUn23WCnYpOWJi0VGbYQ3XDDDagpDjr22GM/8IEPLPJ9kebn53nsrC59ki5rzr0CylGCCrvtuj58lC4mcnNlCoRo9EsuGvwLFobSD1/60pcyrGG70Rxa+fpI/PSX307GsosgxiKLvjg+BtFk4Ve8ssiLFwIntZ1kN9l1YWAgZmf16tWnnnpqRKxduzYizjvvvIj43ve+lxtDLqBfciJyiAS/vPTSS1GwQ6Gku+66C1lcWGmIif/Qhz70hCc8ISIOPvjgaJxzc3NzKvxQc+S0005DyA8SwgBIqhsst/C4446LiCOOOCIEGLRq9LpriAybbIhmo+X+6jO5XHXQLDHAIqdMeuXhNaUQRD+lCVS9hRcUE1q0sg8nTpcoV5Hqplxp2kJKCL2GzdaDynSsoi3JQmQhW2txNEUBzDbrWWicTZX0lLsZfF6ytNRBz3vuuQe6KmjNmjU4nW+h7ytVqlSp0vKhpW6H/R+hc845Bx9OOeWUYrKhgQCqmJDyYUtFi5tQpGXIZ13SgKxOE9+FX7/1rW9FxCc+8YkQEEAbb8/kX9N2tW1FjG4REE+HKP9q2ZTFpxVNrvxwUxvZ7EWiM8x4LeJUej0nRaeAP+kz8dI///M/hynDYsp4mnrjO000hFp1XDwa510c0uuuu+4Vr3gFG4OzxJ7znOegNscVV1wREU972tMiYt26dYAZjj322BDzSHuB5OuLL774qKOOiog3v/nN0QQBmYnPYh/77rtvRLz0pS+NdBiCDu/s7GwGAxkPpT1ifQCbTR3eYviuhYCb8bpI8q+NeUdiSRiyqLZdpx2Jrj9ZBw1lKd5Owrv0eL9oLzy73v7V6HZaQpm3cKWBGNip40MbC7ebAZ3Pnbcx7Ha7sONHiJa6DNt1113/H/a+PMquqkp/v1dVr+rVlIHEJBIyEQwkBk0AI4kEmWzQbod27LWaRpxQe9mmccBxiS4ap162M62t3QLdju2AOAACUoAQAQnRMCUSIDFUJjJUKlVvqPfe749v3W99d5/zXkIPv65adfcfteq9d++555x7ztl7f3vavXv3ggUL8HH37t3IEtTs+yh98IMfHBsb03XGFRyGTPFLhxO6oxDfuOw7eqXb4VH3a/qSmWRbAPLzb//2b5Z4SLuWHcYStQzp6Z9Lm754QITwXSOWvMNd48gB/W4qQuTN7UzHCN3jwugW/u9uDG1s7honMTicR18BHIvWrl0LX0RyLwvEGmdZdPkgQsaWS7uS7t69e9u2bZZ4wIOHjY6OAtb7/ve/b0nF5xe84AVwtcftABvbkoqaGMuaNWvMbOfOnW9+85vN7LbbbuNPhUIhRNX6+/tRRdp13kF21uTlOssQpcDwSr4yPamdbZXX6Ec6EofwLwFt5yiPX13yJ10M9cR933EyB8TpvW5/hangaPrSbvNB+nRdHjrqMGVJLh2pycGGgQHskqYFd3mEObdqYnATwpPtLW95C6WBCcHPxjsPW7FixcDAAHnVwMAAHDeafR8liCrhmWLp1+mqMzh0G9dHt5wuwVw63TVvD4H+XLqCF3Pfffvb37akEkczrcUEyAZFPSacgKl9ppipa53WAr0ypKP0JM7HIsmOxqAVZagtFL4o63VCfVTi5pX4uGrVKjN7+ctfbmZXXHEFElGqI4Ol1etQ59AuhWnoLL38arUa7J3goPC/YMAsjptf/vKXZnb99dcjigAOJtCxisUiuoGfkIPxPe95D9rBcoJgNzQ0BLuaduaNb3wjHFXACDkW1cBoccGVkKjo5x3unVw6L6ibHydM6O3ONkOtzgIO6kIXnErhwA/FV9iI8myqMjozrgRg2FsSBxiWim4m4OqNHKZawhhUp9ew+nNoROQ36n9PNMhZCt1JZcF55fI8TAga7/awiy666KqrrhoYGDh06NDAwMBVV1110UUXtfg+o4wyyiijyUPjpU7MkiVL9KNGOt9www2IZZ43b966detgIWjxfdjyhz70oXoStRfqEO7LfLrKIoXB0J3JufS4GF4Vo2g8UCCCWjyuh/w1ODiI9EJh6lULsI6oz1tIDn+j1ceBJBYIemELzQboDFpR7NGhskqtFSmn6kWtFGG3czEbZK5JrU7k24UXH1wQH3jggdBy5kbt6IiKl/uYy+VgxAKccPDgQTPr7OxEVWjU58TTly9fDh0IyhZQx7Vr1yLY+cCBA5bkcKHLu64fSPGWwFyISHn729/OIASOixiUusPx7egA3YTQsy6aDMkZ0vC94lrORV4f4eA7B2zoMqArILUx3ZhuAeiVVDtUs3Hr3C0w18NQ4aMi7vKQQSF2ToDh4WDplcanh/B4I+3MSVtJaCVxb4d9CM2N/PLrX/96mHRivNF44WH/e7RkyZL3v//95CLKn3IxA69LHM4t53C8ZreTNCC/Wq0qQMTsarrCANdcccUVyIYePbgdRB7ieK4Pjm0ra6nX6+GRFGVgjsHkmuQHiZpAnunqaoZealPu+NCf3DHXoh1t5Iwzznj/+99vZp/61KfM7Le//a3JKeBAy3BE7unRqIZmgw3HQkJAGDIiwr8jvD3qX65BTvQowSNgePvQhz5kcp46gSM8st1L4QHqUGjtkh6y+VihA37pRu2qCVuw3siW1B89Kii4oTERie5ETloIy0flqnw6ZYkblMvwEh44+SSKAzPvtqc6vrN0i1orXGlpLp5aUiRa21SGqpze0sySjF8DCegnMiF42Hi3h/2PEMzCihdTOAqBex5e0XSZTj514WIWSExOOWvhaAcx/NFHHw33ttvnziSgX0YNS9HHsUsqxJEVqeeL60brzkSP4+goov08+hvd2I/Iid1duBIayZVXXvkP//APlrhvuA2vN9abxKgeMd1i6864L/FoxC/DynvzzTeHSnMj7ZXj/IxcVJMabBBeViqVNFMiGwnld9pmoi55brr0FTgB3zHLMF+ik+p4VziitnQObj7IIQRhTGcunUyLr9X5TIatsUvhFm6kq5CDOGkuik677UhnfmxsTD8yU6Vyd16AR+AAgWpu6QOhxdPpKKARcpzeCUHj3R6WUUYZZZRRRs1oUuhhkFhVXHXaQxhgYU3i86OmLyc2qqDqPHRdBIkKbiinOzo62gzQM3E60i/d9SrtOklfr8+la9+5/mhmZKduUo52o45aucKORSEaa6IyOmQybJNf6ouIusO5p0C/QeDgZz7zGeRnclJqmPXcoX9srUUA09HQ7NmzzWxwcFDb3LhxoyW5pqKv2Om+oGaLAfI7SsY8+OCDZrZkyRKAV6oPRXUsi2lg9HyLIo06Pw7ZIxwSVg7KxUyYFigTJp6Eqj00ghzc+kKJlbkMHSZpKRzu57B3MysUCnqNWxWqeLmC7ExgFs55Lp3mwyGZOslMg+KmDh9R0CdKTmNzW14holpS/yGKGI1PmjAd/e8QTuHQ2lyr1RRO4WtTwDAaRRE1fYGawWKuXpEFoOXevXtNlpc7eR370ZaPyK5MNrm7IJyoMIEQvaij0GIL9313ZWum0qJXDiAKfyJFnQ5cy0jo/uUvf9mSEio/+9nPdAG4g8nx7/A85STo23QmFtdb/gSfDnjMI67LRXHwmNM3yIWKRaXOR2NjY+FE0f0H1//whz80sw9/+MPhiUbrr0psbFOL2DkLKPlE6L3diOWFysWsqg6Ui8oibk2C+N5hZ2K1B30vPPHRQwCqjCvQ50Y5k+Pi4YLR2TNBeh27Cv31+caVvbmZYSO6wl0ye30R9XSJGRCNee4oU9MpGXA0Nn98UoYlZpRRRhllNFFpsuhh7e3toYjn/MspcOmVlFxC+3lHR4dDS0zkGv0yny6hxCv5qyUO0xaoOyaCnjOAh9fwyuhHd33o09GMXGvR+ISwnahqaGmBOqozsUGWuLQkkjfaphPqo97JoDlz5nzhC1+wJJny9773PTMrlUqa4wDULKpdQd2oE4eDzqKEnzo6OuAPCfcNN4H6MZdOWQI9I5/PY050TRLCikY+4OMf/vAHM9u+ffuxxx5raT3D0iCEAwxCxcLS75HbxM1ViAZHnUfq6WgTvT6cbfdcE3WKsGGI/7e3tyPuG3OOK59++mkkx1GNrVarUZ+zQClUH9QolshrtMIZHQuV3LaiH7w+kVBEaPugkh3VFB1IE0IL9E/WB9GjZELQZOFhXHxOAQ/Pcb5vtzLcTjA5WXQfOuSkhRGI9gz8XbBggZl1dXWh5G7UuKKomrsgevZxazkuG14ZBSR5pXNgc7E1emXUuuZG3eJwV+rp6QG6gvOFb8oBPmGbUQQVIsK//uu/3nvvvWb2rW99y4Qv4ixzMXlRo07Yspsuni+he7rr59KlS0899VQzQ+Xl6HOZ4E6xaDqUh+BwLjFzOhd/fb8Y7LXXXovE/NFFhWu4sPXsc0Fj7uAOvTpz6YrGjlVH8166uK5om5q5yoGWXPZoBwAjeEl3dze4F9YDjVWwRmsdACc3OIxOO++Ia0NhQAof6gdPa0WYsb4tXZ7XcRS3W9WQ5l6Ekwacyc2CYAyd/4lCk4WHcX27Fxb1JNbDyEmdUfHEWc6c0cKCIAwKXCp5IbL11FNPvf3225sNpIU9jPsNz9VVWE8HPHJO9Pbo6HhIOW6n3Xa6zhH9GnJpt4jwVzYyNDQUNZk4iSGcH3cLJhaFsLdu3frVr37VzJ5++mkdUYvwZJCbLseqdUT8Gw7QfdPd3Y0OfPGLX9Q2dflBEy2VSjpeV7Re74pmNXOEBz3wwAOoRgbLkHu6rgRGvOrRST8IXZPObAxytqtmko32zUkkoV2KsdhRsCQaZwJO5kaRT0IzNZkqZ0ADtthPnXY3WOX0vEtfRKVSYS5HnV46jLA1JwO1eIR741zJodhKE51+SSxKJzmcvfFME0ZhzCijjDLKKCNHk0IPU7uXEzcjF9T0AAAgAElEQVRUZKMMqGI1xSJVyOiPpLq5wxJdm6rpMzDT6X9mduGFF6LqCmCuoyEn2CrY7XzxnWwViupEjaKiGa908QkmCQiYtDvat2g39EqH/ofXN6vOF2ps+XweOvfHP/5xS0Tsz372s6ihrJ3PxRLdOkUqqvtGr4wivVF69NFHYZqCyzswru7ubhTGQ+ehPdRqNYW5QPv378egFDakTqk1Yuqx0OxqtXr99deb2Wtf+1oLXll0VehCtZjOxKlQrKxarep0uSURRf/ci2gREazbqpF41QKNr1arvb29vBJfbt++HV9i0pAc59ChQzq93N0hWNpIu6e7SVMF2r0sh4Q7vDfqMd8CqNAXQXd/bZkd036yRrzLeKfv0QHFE4ImBQ8LsQWXnjncePwyWtubd+kpz0IYehBzeYX70NVHwIOmTJnyhje8wRKDjdsqUatM+L+ljVX5WIp6Z1jSPpCiz22kHaAdCqQwYyMJLDuaAz0cRbSHTpjgXfgSsBjt85dddpklzvSoM7J9+/aoOU2f7g6R6Mmr08LJccMM2aqlZ7JcLsPBBKk+ly9fbmZLly7FeTowMGBJGbl169b98Y9/tKSOM2qvbNmyZcuWLZYUmaNRzcFrFpxTJDT++te//ohDC+eHjes2cbIal18oEhFI15bz6QRUDr+NZiNUyufzePWoaDM8PIykFQicAjC4Y8cOSIcQFGAV6+joCJk0d020h85IptyOMoT6dFBsVWcQgIfValWfzj5ojCYnUBeVs1q1WKjR+EW3JNxyzXjY+CJwi/BIcnyFJ2/U5uEERlwfNSyHHNGFW7IRXaYUWs8++2xLQp5vvfXWI47OrVr9SBbSgoe5e8N966bO0gdc9LDjl87Ry8zGxsaOXjnT5zrDG2xCaHPt2rX4eMcdd/Derq4uJBtEHinoOvUmYVvhwdSMotam8PjIxRwu+A+FZRxw8A/Eedrb2/uRj3zEzDZt2sRhfuc738GpB072ute9zsw6OjoQHD1//nwz27VrlwXLD/93dHSEtbsajcb+/fstvRecTsCf9HY3Fn1Q6xxFzmwcbqho3qNGOnmuexHubaLbKOk+PDy8b98+MzvuuOMsCSEvl8tIpowcynhQT08P2InjoKqaOAbgEtGpUsg6KTQ4WaAeqdMKw72Vh1laoHRJFXT+6+nEnpxJnic6Y+GadPJNPQkEPHq58/+cMntYRhlllFFGE5Umix7GdJkQuGBmIJTsYOJQY3OZY6jgh+mI8uks3ZRx9BGU5UOMhaLoxRdfbIn7HOuAtNBgnPDoBK4WWg6vMZHUnC7iNIyoKQK/OodgFdUdfhtF3pydSefHQXaAiYCDdXZ2wrUPrxXabaFQeOMb38jrqViESK+JGqEzGaKOubTlLDo/bs5d591gw8S1xWIRtVSAfUFRO/fcc2G1Qpqo9evXm9n06dPDrO1uRKyTECZrz+fz8+bNs/S75ppUZJhRTU7nVj2DbyoEGAkbKrDsNDbqjgq1sWM6Sw60ZLf1dqyNRqMBFHHOnDmcXmirlui+uNKB7XyQuhbzlakHYxSFpiqps80RueWkw3eApF5JwCbEWmlW12VQq9VcYn59EIgnki5pmkVYr2f806TgYcViketb9X0moHLHsQsCM1lSUSOHhk+1tbUBpoBthms9zJpfrVYJO5gsQT0vLr30UjP7/Oc/f//997tBuXMzeii7Pea6HdpIoidvI52N0AKebbJJoilqHLoVQh/sXtQ5HsSnAzo744wzLKmT8sADDyg0BBoeHg7doKOz197ezhI54bS0gEx5dIYQTa5lCAHc/efPn48EY+p/UalUcLBqjAQKTJvZ3Llzzez3v/+9ma1duzZ8hDNvkJfkk0Aodru7u/ttb3ubJczSLRV9R+3t7SrqgdzecfC7m65QGqinw3UdLOaMnbhFkyE1EjfxfBIRjLuAx5522mkmpzPwWDCtSqWC1rD12Bk0Dr8PtMlRu40fBnjRuz26B1VsZS4u/dJEJubTo6i+Y2xu+Sk2y2MEMpArgqjzmU9XkyGHzrDEjDLKKKOMMvpfp0mhh9XrdephLqdtiPsRWHA+HYoJUPEK3dNrSXW+qCCj2JqlQRL+rz3Eg9atW4cM63DFbg1khTJvtA/NKMQlQlLkjQ4CDhG1QCB16WsVJBkbG2sRZax/jznmGKhc3//+900gGtVa1D3MJDNTOEyKpcz9amlFhP/wRUdl82bTGH6JUSC4mLXivvGNb1ji510sFuGJgG5jfj73uc/B7RCyM8IDFixYAOd4XO9yv7rVi4/QV5Bo+N3vfvf06dMt7Z4QVdzDKdVrXDKLcA6d/61TzhSfyCUp2J12ru/OrRO98vDhw7NmzbIEAiHp7fV0el+3enWhWhpjYLd1z/JICYFNF9BCgFG1T7xNOlCoJlQqlVRbcrnQtEt09FB1iiNyx51zlkFrrkCHiXI2IWhS8DByIEsr7MyopshALlatnCtSPQn5q+b+oJHM+Rrpl25BazEIPl2P4+7u7nXr1pnZJz/5SUuc046eHCz2TG/kfgBFY8g6Ojqiyft1A3N3hU7VLMfuUhxhZnC4g3UdPHhQk7WDWFAjejABIIpaILi33aGp/XQQTRSVjVqkms0ne8hVBP9AmMF4mR5z69evh0c46hzijB4eHoYDHhztOGlYouDKrjMIQUOYwdy5cxVFdFOh74ignONhepJySUQhYiX2U8UdNyHOMBk+yFmdsSTK5bLbztqO40xOyHNMV39yJnD9yLtCc5qz7dE8oXPIw0TDRVjiWV00o4lIOD9qoXDzo41YIAFYEJPHCcxqr4wv0qXjnA5Cl3dLL2i3UHTDU4ByJ69qGNw/zqRkss6ijh66+EqlEs5xBDzBWfyJJ55ooWyB/gtMKzyI3bTwnILAiAlcvnw5gnJg4HEHott42m3+1X2I62fPnq1JV3HgVioVtIPjO+o84kwCbiocPzY5Op35LWTVTrN0M3NEHhYSFgmivmDlaqQTAqGH0NtI4E9btmxBukVEFHCpuEgP3AKb0Ec/+lFLfM3ps+D85t0Bp13SHRGdH0eMgtJHsCntJ2VKPeJ5QaiLOAZDKRDdAI93dkqFYSwtKLD/kJOcZdrxeDUKckRalYZX6jqn775Kxk6vck78OlJCO2p652W6bsnvdWZopQ4PBLd39KVMFMrsYRlllFFGGU1UmhR6WLlcdtp01I2K8ojifkzootIxPVAV9+NPKp9SmArjNF3eUgpo+iUFKPwKfzYU7PjsZz+7detWS4vAToCKKmothKyoFST8Xj9iYu+7777Q5kE/KB21ezrVzVAAR0QEv6TruXO8BkULjmhvo1Ohb8rSki81Egeghfo033g0vLe1SHvCCSeY2SWXXGKJD2qpVFLlFQTFi4QHPfDAA5/4xCdMklSZ2U9/+lP8Cr95RMq3t7e/973v5eOoLmiHXWhHdCz6U6FQUK2XW0bBdi7pEJgivK/vxUGRDtdyYJfzSGSzFmg50RBp5+XIAHyTV+w+6iSoilar1fTL6FhALqLAdUznkwqfhk/UY0VwnLchexuGLuRjFQB44Ci5olTjnCYFD1OERI0cjotETSbcKgpMc8OHMfxMEhO2bEFSg5Avun5SwVeDwYwZM8zsAx/4AHKxP/DAA+Ht2kg4Gxb4s+hPYTtRclMRMgkCsEoO+uDYQ5NSqVRSX3A8qFAo4EXAwx7eDQcPHgxxvGYDdD20lrNEci4erS/WK1tw0Fwut3r1agu8Y6LWtXA5HThwABk9YA8D8XBfsGAB52dsbGzhwoWW9uF2llcGgSgs5gA33SDMt+JkFz2X3ZqMen84i6ne7qpzuU0aTXyj/ayn6y2AeOLri+NzHb6t88M+6Hij8o0LM3XQrhoaWPFH28TMs0qLngNtsSqGJntQe6JjCaUinfNQEGcMwISgScHDkA9NlxR3bAvXI93A9Xoda6tNCrLkYr4AXD1u40XlcY1DBLEplRNpeNO9PWXKlPe85z2WeLXBKBJNeBjVw6KqlfvSbTxe6WRnk7MvmhCoxSPc6aNMhe4JelJUq1VMF37CSV2v13fs2GGJ+wYPHTVyOPZ8NIh/aBtwuh27HRVaW4gIPF9OOeUUS6LcolpvtJ+ceQ1oo00IRkQEFCLId/fu3ddee62Zvetd7zI503VH8O2ooyBfh2ok5LWqcnFWddIcM3CPi3oNOJNb2Fue5moocnPFmVTu5YquuYUaOnrkknBmZ54MdX3HMHhiON5poofhdhrJVMGlh22o5pK1OL8SdQlxcI7OOQeiSIbzo+YQjmaDjBPK7GEZZZRRRhlNVJoUehhkIic7mzhHqQzCJDGaVMbZCShhOUuYSYEDEMVbFSrZB3iLOcIt6nDfSFfeo5wOifud73ynJUE/v/jFL6CLhA02I6eXRJVI/SnXxHdR9VQK4y7hgmvHRE4M4VxmSAI5ARNEneykk06yJFIKitfY2Bi81bWISXTUzTrWQhSNTunRiK6u80BEEevGC1TDePazn21m5XIZXvWqU+bSzpyctJkzZ5oZwEPcNTQ0dM8991g6FxcVBfX6c6g1B6sahlvDUcOkag9UzlwSW6fH6+ToVuVU647gr2q0jipJJIeIOsUrRN7cSiPypjAp/+qV7KG6KztAVeeQoQuq+zJOQAEYlzqEq9claNapUGUuH4vw0fGyTfoETAiaFDwsVJad2UmXC5ejFghvNBrqSu5wdl0ZFoMina3bLWiQYxXupFDiT4yRNLO//uu/NrMTTzwRRVv+9Kc/hTe6XR09iKOHu7sgikE1xJzuoPaobSk8INwFZGC4En4NnZ2dGvyLv+RPmBBc2dXV5YqZNZsKHrIhHw2vD0fUzPNFb4yyt0ajcffdd1tSLKaW1OzAwjvnnHPMDHGBo6Oj11xzjZn9/Oc/N+EQWJNY3uDfY2NjePVwx4e7R29vL2IefvWrX5nZy172MhNXFHdwhzuCVyqi6FwJOCJ1Yedq1+PVOY/oLDXS2Sxdgk2dXpf7DVPHPFJ8nHIat6F0JxYKBcykw+gcemli5VLKp7MiRNcbj6CoLKvdpjd8CJa6lcZxhXKnpWULnX+T9aPT6+ojZlhiRhlllFFGGf2v06TQw+j8SqIc5KJi9eIwfN1dSalTQQPnKUuxVAVGwmJh3DQBIpUT6+kE+W4UKh2ffvrpSHv6gQ98wIIMvC1AxairReu7nEdvKAxSywzNy2Ejeg0g1nw+D43KQVi4BagaBtjZ2Qk9A18iZ+6ePXtUk476FziKwpv6E4XW6I1HQ3plb28vXAoBGIJQm9GSwgXIVHv48GGk/YWnxvbt2zEVgAod7of1gIoHyAACzw4ze/jhh83s5S9/uQWuBJzeEOKztM7t1BS9oJ5O2svEbKoJcZuEEBbBZ+gEzFfrPCdNlA9F4eh7gvdeKBTCON+oL0m5XA7TmjhPVO28pfP15HI5PFFVmeiOqKerprkN4r4MXUJy6Wz9iiFb+hxwL8t1Xm+sp/31eWWGJY4vyufzDkrGC+PucrXAXTkJk70d5rl3Hx304aA2/Uvbg+ry+XTqEHd0hqeqG1GlUokWNgznJNfS9bwFzMh/9ErXmsNF9f/WjwAhz0K5XNaKIayHG+b3GxkZQSomXAkwjYFWznUtfC47GXrDkxwsFkVv3C1ufsKpmzFjBrq6ceNGfjl79mxcgwgK/D1w4ACOYFg9kRIl6iZO3qB/e3p6sDgfe+wxE6tYiAlbDN92Yge3gLJ85/xNjM6Ebbj6DOHTnUGLYLKTlkwOWWWElrxrQqwhMukcdzlp+BW2ZETaWZNNFM4PcT9dAI43OBHKMVc9QHAlM8dry4VCQfvP+XQgpMnR5BhwaPYjMewPP2X5EscXIQLDiYEmhhDVV9yK5GqLelXolQ7d1r3NI0ydxamHKSdjrSYXix2mBeJprkmtCoXCQw89ZIEcHSofzVSKKJp/RIoe363Nb64z+pEiM+YHJwu3tBbGdVYKiMMwL1maezlF1rGikNlHOVMjnQgqyu2i441es2TJkscff9wSpssRoatq9uvq6gKTdtlstVno3xdeeOE3v/lNM8P1GOCuXbuWL19uSW3oO++808zOOuusUA9zvCHqvhEVy7jaQ8Agl87B6I57ZzbGl6GljcSW1UzFWHgXAxAqcCxD494jPoJ78blhSevoKJwfPNERx9Lwj4plaKSzs1MFDjcJus6ZGlvTbjkRQaEd95GyCLaJq0mmcxU1+41byuxhGWWUUUYZTVSaFHqYpk6JqvYKa9RjSWIsLZO6FDWqdlDqdPKs6moEN9yNJrKSKlIU9Bw8RdjBElG0ra3tkUcesSYqgn6kic6pC/81044DD930Rudcn8v5cR6ksG9BbMTMdHV1hfG29SQCHWoKGhkdHY0KwqGOGEU+nZ7aWuU6IjrqZhVjWb58+e9+9ztLqzIdHR3qI4cv+/r6YPpyNThA0B6Qgex5z3seMpCpB2OlUtH5ufHGG83s7LPPVkMjw+1V0o9aNCmqh27ibkOxzVC5d5CyQ8B0zvP5fKhhOGc8Xq+dIfKm7w7pDtwjLL2vqZ4q7EGdzC0S/UdHzS9d/Qq1ndOiEeYcYT8dxqPYoHPi173jrLl8xeo1yr3jsGKdyQlBk4KH5XI5HtkOD9G1y5WkVxKycEq6yebUZc2wEocisicWMCFlV85g4wqwah+4rN1JhEJTLc5T9lO7xL/RLdpsVpt9bGHzcCASH6QzQ18A2MBqUuCmXC7D40OvHBkZUe7V09NjZkuWLMFprqYyN0DHvRzk0ppvWfrkekazhEccf/zx6JtSe3s7ynrpI8bGxnClpoBqNBpw1vj4xz9uZnPmzDGzUqn0ute9zhI3ep6YsIRpgvwnn3xy0aJFJoZGnR+1SNE1xllqHZ5mwXvkysRHd7szKpsgmS6Ro24TJz4qlphL/KG4F7Qz/KuvwBnSlOfl0nZxsna1O/DlqsDhkstol6rVqiL/eq+lmZDLI+U4iqsDEHXKcIeSdl7xWyfLOqB4QtCEYbYZZZRRRhll5GhS6GHwTVelPprymcJOKKk5jY1SlebycLc7oV7J+d+r0GppxYj6PsQ3dFuxNUeHDh3Sko9RpSdKUZUi+ginpkTbcQ89oh7msCZ8LJfLGD70KnzZ2dmpc06IDI+AN4frGFQ09cgPe6gfncDuxhXFmUNqPeEYwubNm9/xjnd84QtfUMW6VqthaC4DBW7UBPYnnnji+973Ps4PDfWAFlFa7K677kLnsSrUT+Tqq69G2nsdEXeE05ych5GJotACrwbV07l3eYEufuc7rluPT1cPHecPxez1+AeaOluLJvhwkLJqdRwaIzf0QWGV9lw6JMCpUDrAtrY2rTHGaqXuqDFRc1u0CcrHsic7UJcYqWLRfKgeNaHDy/inScHDLEB4eU6FyaIIteu6dGgeE1CFuZTc4eVWg57jzdB/RdWZY0YfxBWpNiTQE088oZvENX40s4R/3A7Xj8QZosd0lHe6Da/Xu6OQEQL4Esexnua5pCIirqHvOMKh4DWO+RkdHdVDEycRzzUH6uqIjgZOccMMr2wtPSAdFAqjOPzNmQYpcnFQZnbGGWeY2cUXX6w1QjEupoX9y7/8SzO79957MVe4BrZSPH3Lli2YNBT04etQD2+upRD0Jgal482lfcfdr87hNkx17ZJ3ONAy9CvWblsQiMYbnbSkX3IxRCE+tRSSGWhneDtz8mo/dVE56F6rydAeBiKqGXrVutAxDjZ0laTpK3oc4SfdQY4ye9i4I5wLYbhG1NWika5ayy2n9l5eqQuFKziEsN0pyf0TtbhEqxaBnJajFlocZ5s2bQrP01wsKKo1tdCZnEnJkdtO0c6oJzHnU+eQdgVoDyq0Wjq6GRHBIyMjGhgETuZOK44odKN3o24xIWa2ePFiPvc3v/mNNSm1Fb2d84biXog4bjQaUKQYEhAGube3t2v6xDe96U24TJcKG8eNeAR6+/DDD+NL5KPCT4ODg9/97nctybdJDTWUyi1tiG2hibqcatQhQrOxWwY8uPXtsA/hl86dnT/pNW4jO+923VnsofLafFIBwBnnwlHX0/kSnSLlGL+uRrdy9D3m0mE/3Fa4BYommVCIc5D/6Zf1JFWCE+B0aA5MmhA0YZhtRhlllFFGGTmaFHqYBeXj8P/w8DBj8k3ktTBmkKq9VvzKpxNtUIBSYIFQZOh0xMBM/dLBd3SCco6z1qTwz6OPPhqO/RkpYSGqZoHI1oKisKGTUlX3dV+qKFqpVEKQpFQqQfxEQimkmOrp6QEgBj89plmCQuaMjiHY5fTU1ljieeedZ2Znnnkmv7nrrrv0EdE23VSsWbPGzBCM3Gg0IFbTkEkAmbfn83kX023iw+1S8eo1F1xwgZlt3ryZEJMl6YCnTJmCumUXXXSRCQKmK7yF7YozqWuSaF4UQNMrWQdO2y8UCpoezLnYOUVBUX1WnVWzcb1e1wJgUVSf9qFQmXY557hJdYk6DS8K3zmXRaf/mVmpVMIjsFwZJKNGAU6FHk1Uc9UeT9Qx3LPOks0hhMa8Zwrb/N/SpOBhjUaDOa0Zk2GBxu3en/O+VR7DK9UUTLwltJzlk0xuiuPzjNZ16exDLuuB28xqsMX127ZtC/HJo+E9R2M/Y8uhmcE9KwqBsvPRfRJiLGxfd1e5XIbYAV9zTBqxRARR4cbu7m7wORzZ0dor/D+ctKitq1gsPu95z+OXf/d3f2dmXV1dAwMDlna4aDaB6BuyZrCQCiA+zhJNd9qOfumwaBf1oWNBjc1Zs2ahRii+RMjBKaecsmHDBjO75ZZbLOF2rAOiL6KWJLN37D+6DELEnqSLn1tPd5ljJFz8Do7TYeraIMDogmSUGTg7HFeaYxUW+EqEQ9CfopKfEiIfXHQj2t+5c6fWbmWXcmIXjx5KUUcYChYqBDeDFvFl6MUTDekbtzQpeJgJvhza+fUak3WpHKKeTmbKpRxueHfEc8NHLdKOI5qUCHI7R491PtStWkvOa0tvp1ysmvvRU2gVO6KY5vah7lhKiM2eoqPGP2p8ZtAYxFUyfj28eCO4HVIOMkYYU3TEdHDuXMPTFy9ejOAttdu99a1vhdnp3//93y15BY2YW2MuKQ2MhIeUbxxCgIIpjmbNmmVmTz75pM6VHqk8uPXswwo/99xzUboFRG0MHpuIJHvJS15istL0qHVOB5yQ8JjL5XLuzNVbdD4ZvKVqCneZmzpdzE6e034y8SC3qr5lcgi9pZGYWnUnunXufDI1URMvC11R6vU61GvVqpm/WPdjf3+/iqqMyVMHJVLoPej8LV1MnivdotZK96Uqc41nkuvg/5wye1hGGWWUUUYTlSaFHtYM+6IjslMXQhdEerdH4QIViyi0hmI4r3EhJtF0tEczEL1yz549FhRb4WVH1MCi43JqXNhs2BkHqkQnQZ/oftJuUFR3bwezDc9AzOT27duBy6llkaYd1UhmzZqF3LhPPfWUJQBj1HZFpE7pz//8zxXG4WBRrxLa2Fe/+lUzQzJfS8u8ljgWPvjgg/yJ4KozsipAbYkrGrTAKODMRajqDq4899xzUSoa48UFjz322POf/3xLsubDSXLp0qUhGpxLByS4aEj34kIfOYciUF0IVQpnftOWLTDRhepUiGCrhsEv8Sw1lY2NjanxG4o+U8FpXRXXjahCjDaLxSJiGLRYa61W27VrlyWrEQjB9OnTsSa1pO3o6Ojg4KAFlXjDACEivW7sobWSZj+dbRpEHOrg9L/xTJOChwEdVl2bNuSoY3Ro+so3KZaqL56LG6tQE6NxP+gGcCZWF9SiwALdi3UIvAX7AaE/zVDso0QGml12NLdHrwktTPmkILpjV7jm9NNPN7P77rvPhB877og3cuDAAX45depUwGKwK9BNHM3i4MbpXyqVUJvG9SHsJ18EPuLIW7ZsWTQyFwcNfNavuOIKM/vmN78Jz3u1w+Xz+dNOO82SMmBRItbKuF0T4E6XK2O5cNgxQ5WKEYzShRcJTF+gcrlMgMvMfvKTn+gAYW5kERNMr+NPKiLw3Fcpzckizt8HX7JIGL6Pmr7CYAxLe3PUk9g48iQTI7Q6qfN0xiKBZNDe3q7uP2TVrB1jAoFGY5zDw6G9vR23K+pYKpW2bNliZrNnz7akkk6hUNDlxBHpiYHF0JYubcNJds4yFvguuWPByej60dnYJgRlWGJGGWWUUUYTlSaFHkaJ1dIOFA67IGalUjkd5VXio5ASesxTPnVYmUrHuIAO96FPlLvdefRSNocGhh7Cx8zR/5Jh9uib1VHT9ze8nXO+fv16S2tvjqiIwIGeQi4mAboI3PzK5bK2gy/p/dHChSwKqCI1Rj6dd5yjUx9UvJR3v/vdSMOBZLskuE7cfPPNvH3u3LmrV682sx/96EcWaOfOxU5/IgoEcd4h4SAiim94wxssqRxGRBGQ5sKFC80M+sHBgwcxhxo4XywW1TGBS1H9aNDm6Ogo9CqdpehsExZziW+cgouLQ48Joh16JT3XHSKCazAWKlJOEad3vj4ijMax9JLm6BT+xU+lUmn37t28HopsrVbDy8KVqBvnYICaZLjmR2duUHKgt1MT1Ucmn84Zxs7rSgMxfH5C0KTgYQhbiXr96obn5gxB53y6KBwXSmiwaST5q5wjoq4JB9Orw6Qjdwpob7lj0Q6sGs1sTkfkOjwWW/APd3EL45z7RgcYvZ7mRkVvnO+vM5mAcFYeOHBA4/zg15fP5/U8JTPA6aCHrJscd+Di48tf/nITC58iPK62PSFQcE2ltrY2nGW0w5nZunXrYCQDmkcZiLeYrDR3xIemr1ws5WCj0YDFBXnrAdU2Gg34asIqA1774x//GDVcHNtwp56ZdXV1KSyPOa9UKoBzgfTSSKnzzO6F5747eXmlhk/p5rL0osol8WEaoOkad1YAkHO8dIh9KIeF14SCUa1WA8ZL85iZFQoFfMTK0YoKlix+TDj+2lkAACAASURBVBrMvXwErWJ6ShC2VV9Q7jU1ffF67bbbStoyQywmBE0KHgYGFq6zeizCptFoYMVgSfGu0GnYocaO27mjxOlVFpRu4S4KA3ujwYncsddff72ZwczjYnecI7Ijx3pNeJjTU6M3triGj1NZu3Uj4SkZDWDgK9PXUavVcBzgmHNMxW17NVA5e5h2iS8XgjMMGK4KMOV9ZcBoZOfOneiSzk+xWARjQ+dhjJk5c6YGlrn+c/mFR5Kl1wPnKlyTY2NjWNKvfOUrLRF3KpUK2oS+CE52++23I/UUeJILo1bfk1Kp5NQyzABEBAwNM7Bnzx5d51zeur+iI3ILTMuqsdy5Soq5pOAcO+8c903c6F1cSignUaNVuYFrSePSOBY9TLq6uvBaaaszUdnVZN7Z2UlrookPiHI7Pk7nhENQXZbvKIxnaCQh0iqHRXU7teGNf8rsYRlllFFGGU1UmhR6GNLGqE5AgUt1IApoKlU5coCkisCU0UJPYktL7vhLxzAVhShHuwfpl5QuIb799Kc/1UZCi4JTwpyeEXWS1qeHXuZOXwlnidJfqIHl0gHXbCScbafmsmONtFOW9lBhw0KhoMOPvjIOJDTY8NFIYEFQLtQ+Xc/RsS1btqjlFe3Pnz9/06ZNbBy+1xTDadRRYZmKpuqmblYVICIS7sAAtAPQ8thjj7VE/bIk4HrlypVmtnPnzv/8z/80s4svvpjtRxMXWUxnIrTQ29trsvwQ983ob4xal2g0kpdqkOoNtXSKeqc56RunOuuWdJii3kHuUfSCzvHOGBFOiFOP1NuQcI4rUK5x5SwtFOrTztjplEh97y57Mp0Vdbq4tNQk4Qp4TgiaFDyM3hMWJJTSA5Fv3QWUmHgiaByJc7XQ8A5LswE6x6v5NJcOu3HmNFzJpa/X4IJisYi848gZ6PBG/d9hZfwbIjZRrswT0DGMKIN0/Qx5g7smel5wLGFSg2ZMyMkBzdpsxBIQ5GNZhRqNBk6TF7zgBSYvrgVf1EZ+//vfhwx45cqVf/jDH/jlggULLLBBRuWqtrY2NT7x+A7R4KjNo1qtIiwJXgYnnniimW3btk1hLtjGenp6kLYDPiBM6QlSbwiLnePVajX8csqUKYAWsa1gJZo5cyYygTkvfNziKuQpb0D7hw8fDqeX/2CWmF4unCVLi258rfqlsz445xpn4QvNVPV0mKkOxNJvJ5+uc43yBX19fSG7yqfrbPBo0s64tFsOcQ29VNpiiWS5IyYETQoepixKX6018RRoIde4TKx6I9t35nTtA4i2nBCap3HFHUxq+sb1w8PD0MCcOBwe8ZbeSO6bcJ+7Nt2vbhvr9eEjwhMtyvNc91rrTOGDLNir4QVuaC2IFzznOc+xJMmTJpdyjbCfehxv3rxZn4u/S5cuvf3223kNsibyjYNcRHBUNHHKveP02g1YpLZv3w7OgZ/w3PXr18PzAgS17MQTT0TPb731VjNbu3atydmnWiDXJBUjnR/tElUK+JW4Q9YpUqGo5ySwqGcQV5EzvEVvcYxKXxM+OsFRH+QypTlvLLcjQjzGvWvYDuvpDFsu6kvnx600/hQ6kbk2ebveGM3Fys5HXczGJ02YjmaUUUYZZZSRo0mhh5ngWipwReGCXMz45Ny9iPCEJoFcugK6c0HUOBtKZIoQUpRzYrh2BuLbtddeOzQ0xC45dNQ1osCm07GcUhXiNlEoMhx1eA01NqdOhZqQk3lbXNmszRDNc5PQok1LC8t8EGLCQnWB17NMiYrAwMHoWK+C85w5c+A+ii8XLlxoQcVeS08syL0d9jaMRLQk8wW6gYQgf/rTn7C64HwInXLp0qV3330328Rd3d3d6MzPfvYzM0Myqnq6YjiWX2dnJ4w6oeZkaR9C/oo+wAq4a9cupPsKB26iTFiQbgON9PT0hKUsw6lw+pwFURzOJOzMRdq4W+eq+rhsTw780A3l/JN5GqjNjBlD9FhQaMfElmGBb72bARfAEIYQ8EsHh4b7cdzSpOBhWHl6QvGt68bgScG0NBakftHzhVFfzjVDDzjaS0P8zTnvcnnpvtINwH7ifLzhhhtCtuH4TRRYCCcn/DJ6ZIOixi3nUcJGwsajvDbKV0JDUdhbzmoLRLRZByzgFvpTsVhE4RL3IL0RZh63NnA0uwhrhK9Vq1WwCuUojSSWA8SSzY71gnMoTkhoyOUKworFg3ggoqvoBqqsvexlL/vd735n6SSKmzZtQq/g6AEocvHixWoGdo7XinIzhNzFaYVQZKPRQA/xCBrAdNJYwli3kstb6FJV0RKGLulZD3KVpjm9YbaBXKzaQ9SrwuV+c47sGsvBA0d/YlYqZVph4yYbWWUXJnBwC9tBi9YEnLe0cBDys/FPE6mvGWWUUUYZZaQ0KfQweGSE4ka1Wg2Va8ogWj4un875C6KBV62vzkpMyU6lKhVLLY2A5dM5EQhLqrX529/+tpmVSiUnIeqQnQoSeqlYWrdwWk6IooRttkAbWuB1TpWhwheKfs2eG/0yVDQbsaz5Dt1iI6HPy9KlS+Egrt4cjbTHBMEclV5///vfW1r2tyRE+tFHH8WD4JLHEmiKJebSdZyJZIYVCQgtOAE8zNbf09ODsF9Uu4ZO1tvbi7QdwBvRyOHDh+G1CA/G73znO2a2bt06XT/cQap2cP5xpaah2r9/P7qBp2Nc/f39S5YsscQND9k9xsbGNFDXua7oK67VatTSTLYJ/sHjGMQdxcCj0IKqsI10xAU3iL7ZaLVr176LlNCVw6UV3W4g995Dc4BbBg4hVHIJi51ztSKumV/iuKNDhw7V63WkewHRBqARRTRdaPE63SrudoduO97gnHej+n64zuqx/FVMoY0kgXfccYc1Qeqi1IhVc4+ataLnfmheCnFRi+EPUZenZnhmeLJYMF3RoemzorcfERhppB1EQSyzQmDKmpQRoc8qjk4AdO7pqP78yCOP4BZYwjg/Losdm7X0meUGGAWfK5UKKhigcDMLW8N2BQ97fFksFrX2Ch+HKs/gsshKtWXLFiw/x1zRGTV95XI57J2enh5LWMLUqVOdUz4IwCb+kucBWtSsKw5yB7W3t2tef064IqisrIQb0bFogBeNAlHEWNdG1GO+ra2NeeX5pVvSHKCuMVaTVyGYL1efy45p5ykDOelZm1LskfZ7zeth6QOhhUw5bmm88LD169d/7nOf27hx46OPPqrfQ1hT4gU33njj5z//+e3btx933HGXXnrpeeed16zxvXv31ut13Wx4f3z9IBo5VCRBYOb06dOZtdNk8ek64/8K3zuMO4SzLdjbYXxYW1sbduB//Md/WCK6NmJu9LkmRRNClYuKqaOQzzlToqX3dgshlHsmSlF7mG7RXNr/ooVa5rQrp006oTXUp93YMdWLFy/WMEF2SYtK48p8EhSPkxqllt1UwD3k85//PH5CiRleFg3Y0OMm6pHkwu84/xB0tm/fzisLhQJWr05vo9GAdjhnzhxL8s/mcjlwOLA3WF7vuuuu17zmNZbOZkkTXV2CzMjOdT65vB2wEUqHbW1tNBxaYlkcHh7WYeL23t5ePF1froveY3Cbvms3aVznocpl6X3q7GEN8d9xPlNsSuVjcr7QTMW8WRgajyldaW4m8QgwbE6ge1DomGbBBtH/nRrXYueONxovPOwrX/nKe9/73gsvvDD8yXE10IYNGz72sY99+tOfXrly5f3333/ZZZfNnDkTGy+jjDLKKKNJQuOFh1177bXP6Pqrr776He94x5lnnmlmZ5555iWXXHL11Vc342GweylGT3Ej1FoqlQqAF4WSKpUKJFkoRoBKCoWCpkun4BPq5jSZaNbUKGxYjyX5bm9vh3SMGoYODImaqaLGJ6VGEm7poMWoYSmqselznSjKCYlaHUIxsBGr/xt2o9mo3e3RmWxh+nL04he/GP8ovqSpbNkIRObOzk78CqMO/dD0RSxYsMDE4R42J6r7rs5FGA5PN3oMkxqMAgb0zUOSYsQsY9H29/cjD7papPL5PAx+5557rpldc801JiZehG3ggoGBgbe85S2W9kjs6OhAN4D7YeydnZ0h0sAkIxpzQsuQ010wkxgCOr9jxw5AoC46JXTfZeM0QekbxP+VSkWVZi5sBzxYAFQ4vM6Fe6tWx1WHj5gZJjgOdzeRTH25zhbI60N9sVKp6IJx+Lm+CLdJ2Vttk1dmetj/JK1evXpoaGjWrFnLly9/29vetnTpUjPbsGHDunXreM1ZZ52FTRilSqVCiE/haYKBLq2AmvEJmGhYCdNK6T506d1CbxG26Tz7o7eDmAP7xz/+sSWWDFJolwptVxYgme7XFmyjWbMtnqhnQS6XU7zIsR/XSPhls12km82hnVHO7YYZxRJ156PMCl8ZLEMQVgqFgp59nF6sCjhHuPoG6npeLpdxI85omuvdpCl8xG4rYEgCU8Ttc+fONbORkRH0EAwATGjatGnw6cCXWNK00Z566qmWZN18+umn8SBAi8uWLTOzjRs3YmhI24F+Hj58GOVawO3otQE27+LDdCq4DcNVFIVqZ86cCTwfnAwjasQcLurpMhSWZroMYNDEgFxLoTSZS6eC4+HgHHCsiZCXS9flcjxJxQ7CqrqVeDS5N64L1SGELpW+4+6YFpcqz5qk3bHg3Y1nGu887KyzznrjG9+4bNmyUqn0m9/85pJLLrn88svPOeecvXv3ssSOmT3rWc/as2dPs0ZqtRrLW7RJLaJisairFkTMGtfwfAnz8zohzn0f5Q1OFHUfTfB6PftyudzPf/5za3Luu0M5ZANO4WMnQ9mzGWnno4s7yslowdZfWUJJHa54iCi1ZqvumlAPcyyzWQ9NNvyiRYssSYk0Ojqq/YRqXi6X1QjEMw4cDloyn45/oPrA0aNer4N7tTC/t7W1OfOYBd6q6NKhQ4eQLApPZ+JgNa7gro6ODhyX1CPRCGYGXGHNmjVm9rOf/QxfaoWzQqEAB0UN+n744YfB2DBdqJxZq9XQGpgln44n6tZz2jBXu5bWw+3t7e3QYvET1D7nz6Jzpa1F/TbVM8UpcC72S7eGCxPW3LiMMtYNwtzN6g7Dl4iPWFSNtLMriI6X7LY1EWVodXbytFPITM4BXWDOREfNssWmG2803nnYP//zP+Ofvr6+V77ylTNmzLjyyivPOeecZ9TIDTfcgH8Am2SUUUYZZRSlL33pS//XXXhmNN55mKPnP//5yCAwY8aM3bt3Q0Azs927dyOHTZTOO++8Wq2GSoCKhjswB0TvWwcC4EbAGsBw+vv7IVZDCKXw6IxeJlK5E7gUqXcqi0a63HTTTUARnUoRqkSNmHN89CMNhA4Wc8Y5fUpU4YvCd+6jagZz5sxB7JHiJ1E9zJF2KRxaOOpGLEV9eCMIzZ599tm8YHh4GG/ZSfEqtLJoITJfIDLMyf5I9oF09bVaDStWZflCoaBmLUrlanEhCIkR0a2caZ/0uaFFqpH4yKloz3UOgw1wwl/96leqJ23ZssXM5syZg00H7yoMYdq0aatXr7ZEjcNf9tOVc8QAteCICZZlooPqbKO3dKpE8mUGHmiyKG4rdQXkYKNvUDcdzVTqW+vSy/FMcCnn8RMLplgaErC0zpRLh4Kx22hT7RQ4r/RGk7MF+jRmMooBckNpb+tJ+Q5db+5F1Ov1v/3bv2VrX/nKV2zc0wTjYQ899BBcgVesWDEwMEAeNjAw0Nopkfiynqr5dC0DoDHU4hVpcWAOVluxWFTrCDV0XcTcJFociKQLmsALzhQsPsBE3/ve93TZRVEy96W26ciBnLrHXJY2tuxuaWG7cixQWRqmd+fOnYwhNTlktc0WbNJ5avChIWrUrIcgPZ4ajQYAMZQKwwExZcqUefPmWTqK1iG9RI1wWGsaeNJrX/taS7zqG40GkADYrjhLelrVY7XFLWFUIJzpjSQcCssVXw4PD4NVKBchu9KC0QwJwCiAzC9evPjBBx/kcyE5nXHGGYg2g1H2sssuM7PjjjsOsB4TGJrZyMiIMhUcsmRsWNjcFzmxDdPjCdsNjaD9YrGo/YS4MDIyorNE5qFvuVAoaOMUIlU44DngXCcsiAh2wXy6CNvb2/XEwEO7u7vdazVh0viIIJl6vY5trvDvyMgI7R0m0qROrCa6s/QKZ+C89pPD1LxlpVJJWS+bOqLr0/ih8W64u+iii+68886nn3760KFDt9xyy2WXXfbWt74V31911VUDAwOHDh0aGBi46qqrLrroov/rzmaUUUYZZfT/lcaLHsZYZvzDmLB3vOMd//Iv/7Jx48b29vYTTjjh8ssvhz/9ihUrLr/88k9+8pPbt2+fN2/exz/+8RZ6GFAXjQGkeKJeZJSwVPSG6NrV1aUVkphBVZ2G2SakJAg7QDjzSUVjSF74O2PGDCaNtURFa29vB4QFoXXDhg2WeGSFFHpqWFpriephVBND1YdfqljKzjs/KP0bdTCJApuHDx9WgZFCcagzRR/HBlu4ovD20ODvRs1GXvrSl1qiTICouIe9ddTW1vb4449bWs/gE+Er8YEPfABfoiYZVh1jVKOjcNlXIeljwTBrLW5k7igzGxoaUsgOj+jp6UE7XMwmAf7QQfHlK17xioceeoijwAUbNmwAWr5+/XpLgPS2tjYsY1UQqXlohvvh4WH9yDeuESzU1HGNetzU0iW7oIdNmzZNc3lgXJ2dnQ5wdoE0JlHYDqmj572+AnUYpiYUamyMwlYbxOjoKKZUFWI+HX/pIIp/mDnBzEZGRvJJfDrfDudBQR16R7vkZGFefxcDzjiE0JYxNjaW+XQ8Y4oGMpvZC1/4whe+8IXRn84///zzzz//KNsnoKEeRB0dHdiH2P/UvpmWxpocmjhQyuWygjlov1gsquMTPLP7+vpwO1yWYRDq7+93lQPNrK2tDecF/Nk+9alPmZxWLShqH2rNyRTz5NLXdoii6HnK5I0t3Nn5UwiWOmMecze4PD06qKiFLwqrugnRAbqgOm2zs7PzVa96FT/SzKBdIs6jWCKbQnonXTA0a+Ej06sfe+yxlj7N6SnH3uqBxeNJ1xiupzlW2cDUqVOf/exncxQAqfr7+7GctNR4rVaDlQusF/Sc5zwHyTu0MMpTTz2FmLnf/OY3lpTHfPWrX60D5PS6POsmHnqEXi2Qb3BlX18fRuHOX+UN9A7HlTjH8Xfq1KlgadjUbqVxMSvAyM7gGuxWeqKGqdyr1aoeILiyVCop2smUJToWx93xioETMmDDpQXBu8PRRKaly55DCC18buxcIeGGyufzKhhRpp9AWOJ44WH/q9RoNCjN6carVqssTmHieazoNs8sF4hqZk8//bSW/OHqgWCLJQgfkJGREZwp0Khwatx8881YxIw9MrPp06efcMIJlpgiVCh2FA32spZ8y5Ee6zxGWyhnTk91/CnUWvKxOtdR25Xb4dHOR5ll1GLX2jSoBlHQ9OnTwVo0LMmEqZic+3pGUFKGy4brLYqYwLyEFTVr1iycku6AUFEmlwTVqf8FK4bQaGrC7bByeORh+QGWcBXDtQ5ItVpFMTOEeWEBvPjFL37d615nZl/84hdNmAe4HfRUWMVe+9rXhpkD+cZdqkldabS/qpWUR62DNEzKQHMq0FvVXdyRzU2NOcSVXOe6VMgX2SwfweAKh0noq+fpT+8VE2VOlS38X6lUHFCB3oaVX9gl/EXg0IwZM9AaYvK4hpXL0iaH9aCdr1arKglRVgvZFbf8hKDxbg/LKKOMMsooo2Y0KfSw9vb2PXv2QKLRdDujo6NMJWWJtDJz5kxI0MidSosCwWtL5DWadiDyUNiBmoUrkXp1aGgI+CF+gtTPyGuF3Q8ePAj04LrrrjNJ76vDITjmfPPCK90t+n80VNliOhC/oVjqcDkTjymVo+uxjOPU2NQtjbm4QHQu1/lpMdJGLFUVTRc6XtclXLBq1SoFUqgTaD8p6jqPTfzdvHmze3oul0NCKQSn40GLFi1yiI01yWlEciiQXjk2NgaFQ8ss5JJUvBpai6gSTizfIzR+rF4Ybmu12qpVqyxRuVgoHNAiRrRp0yYzu/XWW08++WRLOxZySSi8SVRNcS2uIo3fqNfr6KeW0CyXy5qnjelyHACLB2F/MekXGseWpz6kuj4TebtamjrV2plcOkEzcVS9UYFiS0ML7ktQI0k2phPCxPxAbgjnaDENF7fjstHrG6HNUl0ZiTY1xMMTV5bL5QxLHF80e/bsRqMB12c1gJdKJUUGsKXXrl2LxDzwp4BX8THHHMNbLMkVdOjQoZ07d5rZcccdZ8lGmjJlCh6E2BoswR07doAbgfBlV1dXyBfL5TLYHuKyo8d31JUjyr143IecqRHLk9bMqKazlIulk68nBa4Uaos6ekR7aOmDmEeSMjbWutXWXFCUY1fKVFxgmY7opS99qRugmXV0dIRYq8P0aOvCEe+YOiKugLyBTjrpJLpTW8I8GLXj4vBA7LZ+SSuXGufd7S6MCbAhFjNPXrAonPhAq4aHh+ErgZQliHhrNBq4RhNPXH311ZdeeqklUV84HIvFotquXJ4kECtZq583zcz6Cng7GsfU4XYmtaIRGj9B7oS8ODw8jMaxZ4k9qkWKoZz6Wp23uj6ira0tGjmj64dWNL2GvCQUYhhehmsYagYxgglQMNV79+61dP052qddPAPeuLrRd3d3cxQmcobGZqDNUqnk3FvGM00KHlYqlV70ohfhnUFkxgumKqPyWj6fBxfBToAj2eLFi7F58CV4WKFQwEfsfxobVNhBm21tbYODg5b4dGEhNssqhL2KoySqgUUtQ46cTnCU6LZrk404rumiOK2JUtiiY+GINKWTi96jgqtPd5MQWivDb3CXJsrDez/ppJPCOD8XiOYYm7onbN68Oaouo8IkEs3gp5NPPhnHh+YY3Ldvn1rjKOmrZxBPc3QDi4rJXlXQIY933nc4m9Txkj/hSx52WHjISgpzbKVSQf//+Mc/WuLHuHv3bjxi4cKFJl4YqkxwIzDjrYm2oU4ZaGp0dFTXDEeNf9R03dvbq84jeDpZIF5HqVTC0LBbMWmcXp269vZ2XQAu8ZUatBrpvFA8PfBR+TElMJfFCnOCjvFLZfbOKUMdJunFA5ECw9y/fz92DQhXMuoLkBLOrsHBQaw0uO3gLNq3bx+ehS/R+d27dzfzhR6HlNnDMsooo4wymqg0KfSwJ554Yv78+QyLsQRLrNfrkGUgDEKE2bZtG7ywFFDeunUrFClcg1q31WoVAjXAw1pSeR2yD3RztD979uz58+db4gMGSGf//v0q1PMvGv+v4dFRD73WRrIW10TVuPCjib+WgxlDLyzXOCgal+Pi0gjf63txGqGTjrWHvECfy9Srmh7C5RhzramSjWVzzz33qM5HP0YsA6g1oN7eXgi/gLygWAwODmraLXZPFRp+iYkCgs0IJKw0+iiqGx7nR9EnKnwQ59FD3DVlyhQk7ICydffdd5vZk08+iQ6g8/Dd37NnD2DS973vfXzQ8PCwFroEJl+v1+Fkqyk8+vr6sDWAZGACp06dim4AJsHffD4P7QGoGk10muEeE7Jnzx78g5anTp2K9FTQwIgWKnDnwkVAzAGmYW3RRcVXFlqX80mhDI0T4O265YlPOgMh/tGEUtVqFZ3HxDI3Fd4g0B2aytB5HCZYb3wiJgTVeR566CFoaStXrrSkIOqWLVtwRk0ImhQ8zMw2bNiQl4Q32PCHDx/GKmR8qJlNnToViw921AceeMCk6ImzqaqFn372uBFwAZqaP38+DhEsJmLWOMWcwQZb1x3HIToX5SXuIxH5kIvwend0HhF1JBtwvCF8+hHbMTnx8aUe2VGzhPPXZ+fVoO0OGj1fGrEqLddddx1N2byS+STVTZxcGY/DsrnlllvC6Z0+fTrQGPUTueGGG9QaD95TKpWUg3J6Hb6kCw+WWrr/YKIgSBUKBbAfHN8492fMmIFug2GgY5VKBWsSt+PMIioFkxuSb33ta1/TJQphrlgs3nvvvWaGv0AUf/GLX8A0iKcjHUGpVEJIGSB3WJ3POOMMhITefvvtHNeqVatWrFhhZvfcc48lyf6LxeLrX/96SzxKHKYHdoXje8eOHWhnwYIFZjZt2jRlV8zcr7kNoyH2tAyFqOzY2Bh+VeGJ0eK60oig6tTV09nX3HJVHubMHFwSOL6U502bNg0vC8IHDrH9+/cztRvfOIQqEyHGJMYZoas46FiCZ0JQhiVmlFFGGWU0UWlS6GGHDh1ilDHI2ZC1tuTmzZud/m6S5sNp/SorEfhSEQ+0detWiG8QRZ2greLbzp07XRZzeyZqDU367qfol/jniKiIBbpaqBQ2u+so/RKdr4T+ZGk9LKpuWqCrWeBm4vxTGuJPPG/ePJWIXRAuiG5+GlYMSOcHP/hB2KWFCxfCBx2NQAS+4IIL8CvAH1jR9+7dC7/266+/3kSnBFFkxnPxRNTxOnToEBpHa1CP9u7di+fed9997PayZcte8IIXcGagtezduxf4NlQop8ogKzHuuuaaazSNFhDFhQsXQnK/6aabzOzNb36zmT322GNAy5988klLHEPy+Ty6hH4CpXjuc58LTAK4PYa5ZcsW9ezn1sMbga6MmeRChSYKbaO3txc3Mq07OowvWXJdc/5qsjcLsmEpXEl9SOOm+cZVY3N6FYg5NUKn03w6fxU1vPC8ou0DOhaUZmalCntraY/5YrGIa/Cu8U6pnGFcdCg7mjNnnNCk4GFDQ0P1JGESiG8IX+J1hseoNeEihBdCH7l8uiorAUw9ZHmAKs6AdYbdaAFr0Y/OP1CpGUIYpXCZNgsaa9Gm4yKun25mmt3uzFSuY0eENdyDoo24fuLvhRdeaGbnnXeeShXO+IRTgNYUfeM4lKPp6leuXIkSkXi5yJpx4oknYm2ADQD0y+fzOIg5aSoSMe5CrYD4WywWsWZwO8xUxFrBKmjrAmyo7nM9PT2wi0C6GnBokgAAIABJREFUYoln5gK15IhftmwZAEMQujc6OopbHn74YUu8vadMmaLGSwxh1qxZaE3jJsfGxuiyy45Vq1WMhUzazDZt2uSwZcwATnCwQPDIwcFBh5KB5aNvtPPh1MZHTrW6R3J3sx22yUI5ahblrnGGbXWg56IKt5hzoOVKhqEBbxD2hZGRETB+zAzPFrod8svu7m4klsTMQ1rq6upSgJH1DbAeNBd+d3d3Fh82vghnXGjwjx6OjPYAubAbFdWd8zeontRFPaIgw1pEIEhA6iYb9jB6uEcPaxeGHI70aOSso3lcGO98RIq6eISPcJy7GfOz4B05fdG9a20T5/69997LilyWCLnValV5GJtSWRtnAeVlbfnkk0/+9a9/zeeCz33jG9+AuUITLDHVkGY4s3QIkSWaH84yxE0fPHgQBzFOajxi1qxZsEWpW7klhlicfRj1s571LOXcMObPmTMH61AZ8/Of/3wkhETncdfevXuhwCHfMbzwp02bFooRxWJRyzqDLzJcFx9xUnOT4vyFetrd3Z2T4Ar8/+STTw4MDJgZBAUGt6jpK5/Pqz8FHjR16lSomDDLYSrmzp2LHqo6nku88DXiwgJlC6T2Lc6ARkfQoUxv5+pVlQtrY9euXXhlGBqEnq6uLuU0DGPHR7wdhmnCBgY5CeJCrVbDy2UyLZPVq21OIGOYZfawjDLKKKOMJi5NCj2sUqnk00UsSaoTtDA+5ZIKqsz9agLKH1H1aaQTSThIELIPoABnsOHTW4zOdTscRT6ddamFchZ9otNgmulPR+w2JzmcbYclcibDK50Nki2H3oYWvAITaxM+fu1rX7Mgaz490FSsdqqhitj1dLJ/rJDFixfD6oCfYDrauXMnFClIx0x+wXQPzeZ5bGxMwwwgm2/evBkoGRQv4GlLly5dvHgx5wdSdj6f1wywWnGRrdERHL1SF82uri6Ae7/4xS/4ZaVSYdonM7v22mvNbNWqVWFqpY6ODlW5aKzCWKAv0gKNbkALZAQLzNW4Bh7hP/zhD5FDRBPj5tJ1EupJinp0BrcPDQ3Bc3Ljxo2WqHpLly5FQDpUNIKcutIcBqjOisyUpna7XCzTcS6J5VdzbL1eh36sPoS7du1SjIEvK8zoRp9VhUM5doKQWHVYeIrNEop0UO0EUsUmBQ8L+QeNDXqi8f+oZ4HWR3BZzDW8n/tQT3Aekc54y4K8lsAFUYqywGZXOgZpzXlVeM1/mVmSCR3RlNWsMzpABzY2kyr0yih/bYG1YsOzSAdOUla2NXGYdlxZ+Y2GRpAYFKU1TYDpVSoVJojR7rnsghpHRSQTz9WEF2NjYzgQNdtnW1sbegUkCqd/o9E4/vjjeSVOyba2NnQVZy6jPrB6AS3i+tHRUTh3wEUeGFSj0cCZC/MSnDhWrVqlhiIitJheIIT4n/ZpsDTmClHegNsLhQJGAaZ188034381I3F/KdfhoayWaYo+mCVk5HnqqafuuusuS4oXIlJq4cKFOPE1XyLfDvM86RvUAmAOIdTEJSY2RUwdogjwXpgDSEFs13k9tVg/zBX500WFdXLo0CENLGMjyqTJI6N5wsYnZVhiRhlllFFGE5UmhR7W399fqVTUbMsYZ3WjgjA4PDwMaQVCOr50yhlFPKY6tURh7+vrg3gFxIb5RmG11kyjXV1deBBQRFzZTBNqoSGppEadIOpq0cJHI+oNEXW+cEgd2wx76DwgeH1UFQsRm+hl0Rjn6NMb6Xrc7hb3V/Nu4L13dXVBbwDSAnTLmeuZg0Nbgw/Y1q1bdW2cddZZuP7GG2+0dLVDCrzPe97zzGz+/PnqwoBVsWPHDnwJZf2kk04ys7/4i7+AV56qgwyDhV4FO39XV5dqNqw+hdYwTDQyOjqKK7EyafbHYj7llFMsqYTJiQL+hu7RfREfqYmqskXvBuckiSupOJr4YmAscIBEvtOOjg7c6GpnO+8qxcf44kJIo5EkBMfQADOecMIJUMiWL19uiXrERaV4TL1eD11JG7Ei5sysj4hyjOihhx7Cl1h+eFBnZ6eq1xyRtsY37haSBblY6cShXjkMIdDiouiJq9k2zmlS8LDjjz++0WjAiQvQM1AChqPj1fIC/MNqy2Y2OjqKPaNpPcvlMrYxsiFg+82ZMwcLFBgLWu7r68MmwXmBs6O3txdHA9IuuGARJfhV8mOzHWtNzvRcy4we+mUzUC5K7hHRxsOxRO1h/Od/ZOew5ZCR5/N5MAAFaixZD+A3yGzb398P/A2vGKBTPp9XCyiOPDdkHHybNm3C0QD/N2RLsiT3BBYAh7xs2TIzO/vss81s2bJlwLWAzmEIt912G2ojYE2+6EUvMrPVq1fjll/+8peWMNR9+/Zp8UzmUNbXCjyTBSe15CZ2hyUnKUFLcLg/+7M/M7P169ebMCF0CQv7ySefhHO8MqFisRjmWaaHHjYUffHVb5OcHpZF2LG4QsJQGVqworYryhmK//MV6ILHYDds2IBYN/hkrlmzxswWLFigUQquxKgWoKhWq2ovpPs7HDhhvMRB0dbWpimAiYvqKFyIhQ6BnVHv1lqtpqmNnXyjYXBcBoSITczGE4ImBQ+r1Wq0imO1YfFVq1VKUvxLIxmNAWZWKpW4DtjsgQMHtIIzDrtKpYKDAPwJt7tyR2wZV6JKS9i+ycY7IlNpbcpSamH6at1IM0vYEVtwHDTU/1o4pDTrRgtbl/uo2lixWHznO99p4syNpjTWFWVHDhw4gJMFhzK0DZbLgageFSbOOeccM7vmmmvwK5wF8Fq7u7uhpTmDK77EYvjTn/6E8w5C+q9+9SszW716tYYEMVkUOAdCib/73e+a6ARohOEB4MH4krqLapP4qVwuYyxaUaWR1F4B+4dwRqcDiG5Q0TAtJrzBzEqlEraJ+l+MjIxoDBlmtZ6Ug9Fw3VwuB5avPzn7q5OEaKYKWRqlELdO9EYQ61xDpEAIwZo1axCFDWcQarfoqlZzZg4nNAKN+Xe/+x30SJwVtBrS8mdiOcM/WvKpVCo5pxW8Ka0Oz1MuzNZYqVTUgAqpur29XbNZwj8ol8tpye9xTpk9LKOMMsooo4lKk0IPGxwcpBeWAuiHDx+GAKgluw4ePAj8EJIvy9Rq/lBQPp+n9MoraQTClwQBINTjL7DKzs5OSMfqwBZVuRxo4KTOqIoWeidaoD9FLWF6e2tqgU+2AAajD6JYHb3yiL6O7qeoURDy6fLly9UFkW9HAR/gflOmTNGgWmg8fX19UK/hZe5Mg3hNp512mpl95CMfwZennnoqH1StVqHwafDs8PAw0KrXvOY1ZvbII4/A9Q6rEfrfbbfdhjVJ66yZDQ4OAscD5olUIBs2bNC0L9CZaHmFdkVoQQ0hIKZQwk+MjFYdCCP6+c9/rngaHPS7u7s1Cz41Qi0OR9969TKnxqax1aznBwdIrV57+PBh7BpdGx0dHQpaclG5ZYDYA+B4WpnWAhRE8TToOtdddx1eFrRt4tK61InNQgMDePjggw+a2VNPPYU3CEASus7Q0JCOxeXXx6hZOVMrzYL27NmDsagumEuKn+G9M9M0HoFlg+5NmTJFA5/RMf46IWhS8LAFCxZ0dHQgOxyWC3YCVrMlkRkwa1UqFWb7Zgu1Wk3tYQytwLZEa6xTh2uUZU6fPh3rFUchNmd/f3+0WHNoLsrn81FX16jPRXhBLlZ5OXp7s6Za8Dl2Pmqxa/HR8WP90nW7Batu7ScS0po1axB1pLYK2khcEha8JqwNHBMwblkSuuS4rNYCpmEJywAnYD6fX7BggSVnClbR8PAwwEC4s3/rW99CMJmaTg8ePAjwChY7MLZjjz0WCTuARaOq8jXXXINDFmsY/LirqwtHJw4v4FrDw8NYnFpQ0QVFMQcbhgPnI0xCoVAAy8ckoM1FixbBjIdR88WpswNxRXVa4ZwD11IUt5GkKMSIcMgODg6Gq6KtrQ1bj9JkdIVjz6rrObFEdXagz7oyjFKphEIWWA8wkq1Zs0bHywAG2LnBw9AUKymr7YpJFLVCQkdHBz5i+bFjIZa4b98+rRzLFDAam8HsHviI14qZLxQK6hMEEWRsbEwDGcc5TQoetnfvXjoyqdTZ39+PrUsDg8nu0oCtkZERLD6tRXTgwAG1PTClKaBzXIk9OWXKFNaf5ZXd3d20H1hLdSrKdZq5UThNsVnLjqIuIc08LLRXfMR/Ry2LMsJmFruoxhZe7PxT8PqWLFmC00ef6yq/cCrUmsJ6HDgvGFim/QSbwdsfGxuDBA1/BKyl9vZ2aDY4RLDeDh48CK9FiOqPPPIIjmkUEMeNhw8fxo3gH2AYP/zhD9GN6667jj288MILv/nNb1pi7cBpPjw8DPajxcymTp0KNslywOihGtLAqnfu3Il1q+luFy1aBGapdtxyuYxRM+2nCV9UCIQ1ULTqVS5JbYyjE+2Xy2Vd8Ex6q9uZh7gKB1wqykHHxsYQJR0yLfdCqV1pWHGhUFC9E/mOH3/8cTCzefPmcexPPPEEJAa8FwaQgT2roFMoFDC9aBnzT8amI6LDl1YDZ5kVRkybWOM0Tr+9vR2vQI2d5XJZ3YbJwyaQX2JmD8soo4wyymii0qTQw0ZGRgqFAiQaSB9Qj+j/qqL92NiYquFa4plfOu9tdYrt6OiA5BW6QVqih0G037lzp2b0ATnXcxdr4pCEUB+ytJYWdd6LqmVOYzt6w5teYGlJ1uGE7hGa3CTa1agy18xuF/VSU1q9erWZLVq0CJCytlav12Hl0pfV3d2tfmJ4U729vahpotV5+HZgI4HD/djYGHJbABiEX3ij0cBHrXOxePHik08+2cy+9KUv4XFIRHv66adbEg51yimn4EZoY+jD0NCQYm6f/exnzaynp+eSSy4xsw9+8IOWZLVgMiQoXlAXxsbG1OQGo9r06dMxUiBg8I08ePAgPPsVe1yxYoX609JEB2UU2hWUv2XLlkFngr6LPsydO5e5lS3RDI499lgNxsLaeOqpp6DQMH8K/qrJjZoEFhUe8ZznPAfdgGbMuC7NpkHlzOEBurrUeFkqlbRwKLr9xBNPQC1DhB9mdXh4GD5+eDvQp9lDjSswwbRNdoTmByEAq4AhRjc0NKTJqYmHa8AlsQSsbbRGG6QOWQ/JiUKTgofBHoCVjWXHCAm8XfVAnTp1KtM8W7KwqtUqvgRhzfX29hK8tgTELxaLcNlQ4JsptLVs/K233qp7hidvNDxZr+TprxuPvdXAl9Z2qfBLZxhwfOVokMmo28gRn+u8MGidOqJrSdSoxv/16W9729vMbOXKlaGNhOFTanQsFAr6Ijj/P/nJTyzgyrgG4VNf//rX8eWb3vQmSxbAT3/6UzPbvn27+vSDh51zzjk4fRggrDk5wTz6+vrA2IA+wRVobGyMECXbvOKKKy6//HIz++hHP2pm//iP/2hmw8PDOMcBMHLBYBkT7sajsU2AruPpxxxzDOxqsPrggDvuuOPAC4E3ghj1hRH99re/xVRoDBlCF+r1OtqEgRD7aPr06Rpmji6Njo6CLwI6A02bNk3TWTE7ASYBbR5//PEwMQKZxKiJ/ys6un//fvX+x06nfzns4vRxV2M5ujQ0NASmggGiS729vTgHcCPgX9ZgwoOYvdAdIGgTpwTYOdjVoUOHsCowP3j7pVLJYeAmFnRd57VaTb3PQGNjYxrWhs5Xq9UwPHzcUoYlZpRRRhllNFFpwjDb/w51dnb29fWpwk45GmIjhB0IPpbo15B5mVcbIowKO6VSCV9CYITAZYnrPB7B9JqKSaJlFWAtUCZU0o+qR9QMXLbcMH9Ba0jQoY5RENJBfCFw14jlGraYskWw9Oi1urAPrttRCJRfAicESEVnM6fXhp13fiKEnhDxqjo6CXG+RNjg9Qoth2WZVNrFqlu5ciWSd9A5VktiQvWZNWuW4lqUo7XeFe7q6+u74oorzOwjH/mImb33ve81s5tuugnPgj8LQ2LRuHrYDw4Oai4lBGiPjo5CUSBKZmbFYvGCCy6wRO8kuIq1DY0E+uKLXvQi+P1js+B1HDx4UEthYZbGxsaoxHBWh4eHMXtQqjhkzYIPzWnbtm3qo1Eul6FH4kZoQnR2wIio60DRgX6D1np7e/VK9KG7u1vd9zGBXD9QzuiXqEEOGBHLAuB2rJBt27apzzNxUcwkverxF3MI/U89dEguglvNInSucSXx3FGDp08gn45JwcPq9fqBAwewQDU7wIoVK7AKkSgB73toaOjRRx+1ZKtDtT98+DAgb8Ue6cGIBQqQgeY0EJeL2lrAvdwSdMdxCxiNyzSaWD083Ju1GXowWowNHBHQOyI53qD/OLfGZr6I2pkWyGTUnAbICx+ffvppyh+8khV7QfRc1W2MNz40NARXUtdPei2aOJthGeBwZCIGjBd2posuusjMpk+ffs011/C5+XweaxLHK0ws8+bNAxyng3UHDbq0cOFCdPUzn/mMJYji+eefD3MafB1x1D7++ONamhLAVK1W0wLHtLiExpVCoYArsXfArhqNBo5XfIk2t23bhvxbmBm6lYNv6ZlerVYZjmlim8GIwPyY1o+pdjiErq4uTX86MjICR3/9kuYDTcXU0dEBuBLgHo3WeIRmZqpUKuiMxmn19/fjI6YLnZk5cybMFjCFMp2/2rkh7pAF4hruCKwZtEkZSDOYoM99fX1q4SOWqIlheYGa1Qkpgy+6nzBevNZxTpOCh/X39/f09GD1gBXB3Dpz5kzNfsYkm1ya/ImmL9VyGkm9cA2RbiThLJpHuLOzE2sCqxbWEVcvim0ecTg8r8OsOY10JS2X9MzFxETbDNkADdFO+YgqeW4U4bNaM9Qo041auVxr0Y8gbNFPfOITJl486kHe2dkJ24zmD3zqqaegu+DoxF379u2DV4WTGyDpw2mbBx/cIsA8eKCoZQhOH9u2bWMxFDM75phj4HWCoDEw4M7OTp1JnGiWFqix0nbt2gX+AU+Nj33sY2Z25ZVX/s3f/I2ZXXXVVZYs1J6eHtyIScCEzJ07F6IYzcA6k2rNrVarsK5BwYWGR1cCnLy4/r777oPTiia8bm9vx8EKtkHPBa1wDV7e29urXhV0lEcP1Vvk2GOPZZgESLMLojOzZs1S5RXU3t6uxbdA/F/XG70/VGtx5U7IhHQnkjfj1atFirdrDEAuqTSmcsPMmTND2bSjo0NVZ/UycyPiouUbNBHg3NbDTCKB2TinzB6WUUYZZZTRRKVJoYeNjIywfoRG8m/dulXlOAaEQoaFLAOJsqOjg2qWJajI/v37NXsmBOH+/n6AJABSIODv3bsXgh6ABcjdTlkB1Wq10AgUvbKRdriPeug5zSkqlDn7WajbRbU9/h8Njna4n8MnQ22ptTrlJuSIt3Na0DeoNYT7FEqihA6vP5XNn/WsZwFTgtCKNn/729+G5kZLUs4PDAxYMp/Tp0+HnAtlDoliq9XqV7/6VTN79atfbUnFxZtuugnZmwAwOq2XqaJVG0BEbbFYDMfS1taGxawmlo9+9KOf/vSnzeztb3+7mf3gBz8ws5GREdWkaVTTmFxM7PTp07XGI3uCxgFpwEx44MAB3IItAHvPE088gWWPYTKxur4yPi5ERzl2qGguGxbIKWcg+rXqX4YZ6Jp0QDqVJMXcqA+p16gzV+v1jUaDAGCz98ghKILKEuE62y4EhQPElYy/NlGn9LXmErdb9WEGFQqF0HDQyPLWjzcaGRkplUoLFy605EQD79mzZw93qSXb49ChQ9hmipwUi0VAi2o/z+fzmgURbRaLRWw2TXdmgj5bSxSR5RWiB3cUf9M91ohFklnacuZgQ2fLVZcHHqauUkm4Yx1pxyzN2JqBpeFUNHMe0WuiWULcNsbRSTOPWrkZn6c7FhfMnj1b61ZAANq+fbuOl30AKvid73yHo8YyI2HxrF+/Hg8CS4PpaGRkRMM2TE5bE9gQJxrWGBpvpKt78y9tS5YgivV6/X3ve5+ZgZO96lWvMrPvf//7oT2Vae/dyw1NpzSuoIcvfOELzeymm25i2nVLQMuOjg442YP10idedwQXWLj8HKbHrBm6Jh2qxttDrxwH8VHIC9dYPqlU50xubruZLB5dRRShHC9R5seOaWwPtz+OIzfzLaJxGuJExso4epiQf+vTXYgOH/Tft4L/f6NJwcNmzJiRz+cha8MABvje0nZ4mDq4ztTUeejQIfUoo8eRGp8Z8Ig1ARYIgbS9vR1bCN4c4dlhAX86IuXS9bFaezo425UF574LL9NQbkvvVTLgqAam//O5oWYZdqbFSKOs17UctZxBgICxSo8eS294d1Iz7lVt7PgJEcduLPl8HhoVyk2h5QULFqgBFV/++te/RukvFAxDNDTtPexYqPhaOoLVWX1c5LtyBczAc5/7XPzz4Q9/2My++MUvmtkb3vAGJKlyXERHjS/7+/vVR46vQ82HZ5xxBgaoGwSelgsWLIBREIXZYK5zDJjzqeIRM9XiVx014580vUAjCbRy2cKUtXD18ljX6XViVvgi3OqlBqzCFtU4FbPYvuavYmthNtRcLqc5FsjvlXM7BqwrLZ/P40VA9uJUO4sdWlYmTT4aXYTjkyZMRzPKKKOMMsrI0aTQw6ZNm9bW1qY+hMxeGkUbVHyjVKWlIpyHjxokcrkcNDDniIwvkdfVAYOhkmQBkKLklI8owOigtihSpx8p5qsJTRuxZ1JkOapXOVUp6gPmrmwhDLZ4BIeGOde6f/TCUh2ip6fHQS4WTDKrkOiXoK6uLsY88cspU6bwYjP7wx/+YGYjIyPIIAWC5zdjodh59WslOKb6jS5FS+sEDg0G1et1aIrYApdddpmZ/dM//RMSi6DMJoghREpI1WZNzKtQfQCHrlixArAhiBotVtett95qZn//939vZocPH9YMSRy7AowE03LigshdFqZ5o5qC24mLEl7DjToWTqCqMlHUEedGZ2cntryma6J+o4cDlT9Wo7bAtscL6HlvwabT9+7idvi/K9asnUc/acMLocgoYp876kIQ44EmBQ/r6+vjgmZEiEkUha4bZwqmQcshWibrzK0h3Ve4pru7G3GswBtbkIPvWpzU7hZ3fCvDcCsyiuaBiIq49vXw4oaP9twxqtBMRQrtCmFT0TajIGRoGbLEOZuZzk22vW7marVKJ3t+yWhofKku8m6ws2fPBmimjSxYsEBXxd13321mJ5988ooVK8wMAYiI72EqdIRIDwwMYLYROAXf+scee+zGG2+0hCsTINLj0tmN9OisVqs4uPF0VA9Zt27dF77wBTND1nw6pKgrAQsT65lLPEqtOPh7wQUX3H///ZY+eQ8cOAB7M7BWZg7U1HwOamONMZOCfAoJchGqoEATnS4JSxsFaP5xi0pXOP9Xhsrshbr3eb1CkWQYKkywY5ppAWPv6elhOlaTfaEZpHhqac0gkgYGMM+9iuBcDDpAN6sqM+USe8qEoAxLzCijjDLKaKLSpNDDarUaHXVUYqpWq85d2Mw6OztZwkcbgVauwiClcvXF4o3aSK1Wg/3c4XigFiqUu+bo3UCicGV4gbXM+RTtWOtrnJDr1EFrYmnnl+7KUH1sNgPRiYVXHqJK+fpC/5TOzk4VhCHzMukDnE5d4WYFOZ/73OdCyUZrcKbv6+vDNffcc48l0RdnnnkmOoN08vBKbzQa0KugcjUaDXQbGTqQIdcSFQoR+iACvxoX7PwL+L/WpkL0dKVSec973mNmX/7yly1xLPz1r3+tE0uEWRVch1Wqs8ns2bPRYSia+HJoaAgRJuj89ddfb2Z/9Vd/hTlXz5dc4lLkKgYQGzTBCfF0BSSdmxJ1EV2izK2jVcE6OjoAkFDv1ElwuXQ1vQ5Pj9C7iumPtRGXH464H0ahGm2j0VDkQBsxAcZN3DfcMFVf5CsLfYmZ3wvXa0GGiUKTgofpJsf/LF0KN0WsdSzucrmMzaa1WQuFgrrFo6lSqYQDTr0T6eCrcTD79u2L1vYODVoOqYtyO/fR4XVHydjc6X80FEUm3XMdmqdX0nigh6DjoC2wR/dlFB3VAdbrdRzWOAWIEwKnUoimntRewapgqW5FmNWQ6Z5+6qmnIiUH2kSOPsZIQHYBf1q+fDlCA/EImov0PM0lVeQVkW5vb9e4Rh7NUYkqxNNyiY0NjaDzp59++vr16y0xUKHyy9q1a8F0dZj0UlN7YaVSQWyAdr6trQ0GP+SjIdeBNRFAKPj9K17xCk0CQhlCEUJasDBMrTvKt6OrjoETXIQK3bPbiuPx6boeXBlo7WepVNIncuMrFEkWqICqY2wahOfGwgyrugwoJeuVTOfo4r1M4HHFHhtJ1Jced/xH90W5XJ5AfomTgod1dnbSDx5ch6R6GO20dP81OQV0MXG1qbjKxceH8ss777zTyY8WnLz82+IcP3o9zFHYpjMv8cuotck9LqoShVdGx9JIZwdWgTRsJLy9WR/CLnV2djLPL78ka9FtTPlUx16pVPDutFaT42Fo5Mwzz4RugS9RarlWq4EZ4MZzzz3XzBYtWgQNTN0K6vU6TF88sqMignpzoEv80lWq0ynltOA01zJppVIJXUU1sg996ENm9pnPfAZnHwrB0Eaiugs9SnTXULYDt4YBjDkeIRTiJ4S13H777UiiqFQqlZSL8H/1YcFDu7u7VRjli1N3qmq1ihsxauouaEerJnHO1aeDV1I2tcA2TP1PzepkgeQHFrh4gHgBY3VMVHydBPKwMMKaNZedQKMGURrqVNmiypsTNxNyPmdyG880YZhtRhlllFFGGTmaFHoYKqdAxIDKDNGsra1NnaMo6GmNV1ZnVjceCD7FYlFlOuemiCshGG7durVFPgtVjGgSiGpgUSXJ/RRiLM6KQ1UyNHK45zqlJ/oIh9S3MOO5n3SYzUxuLRS+1hqqmb3yla/Ee9FoCl7psDV8qQHvuVwOKjvyJLnstyBcP23aNHgtohEk0RgaGrrhhhssSRJ/2mkiCOw0AAAgAElEQVSnmVmlUgGqpvmrGkkIAX3/wvdC8wYxcBPYWXPwO4uL66rGL7MdBFzDWfGyyy5DPWiM97bbbjPBtfAg3q5gF3uLRyDJL6xrtVpNrXEY+3333QeDnwYSWLLp1EJj4mRvoiioSopdRuWDGKaGVbhsGor/016oQdy0LKqtNJfLOQXO5BhxSiFI4d+2trbwQU4LJOkS5fVqt6OKpv0kmuoKXZqozjq95XJZYw9oftNRj3OaFDwM+jIWuiPdgeQ9uugJp4QIGP/R+DCXDwbG/F27dkUzM7n8LvgnZFRRdpVrmYDKfePYZLOJag1OOuBOyXU+yrTYbZ3JKAN2T4yy3mjf0M75559vZqeeeqqej84HQc/0QqHgUoabpFbR7Btu7HDfOHz4MNmeJR7zDz74IAA02IeQXGrjxo2aBd9ZmxzX0WHSUVv/EvDRpztkCSdgpVLRk5dyGK4Ekgnb4f33349cHldeeaUlEW933HGHHojE6/RLnukYxUknnWRmd955p4mIAFgVHiuDg4OwmWnFAMJ3Lj2jphPkBei2SyjjmBbeIOaH8K8ak3hkI5kLYiQorebEE53LD1xTUcdauj4yV4gyVK5kdFhrUteTWmgqH7OMuHI7S9u3XKY0ZYHOKs8oCE1gxnJlyq4osk8g3/pJwcNgNXXmX3yvVl8uPvUzZJCg1g1S2Yo30ukD/2BdYqNWKpVQPWrGe1poQq6RI7oU8v9QZ3KmLyebt24temV4I/lNaEoMrw8ZmxtgVCl0zBIPQoJEiuEa7En7uaYfZESgS1+EtwxvjnAeLPEVfPDBBxnow5m85ZZbEPaL5FI493fs2KGTQFu6e4966uFvV1cXOasJP9bD3UU3Kn9yM8nlrU6AaHnVqlV33HGHmX3yk5+0xEg2MjKCJFsuKtmp4CbVuXA+wt728MMPo5/Omw5x39DG8HZ4mOpJnc/nsUkxXextKD46Vu3shbwyTK2Uy+Uga+IjHseoL3259JVQWxc9A5WhlstlNVDxoapCUZxS9kyGpMwS/mWNdDZUriVNzUqdXovDAYqgzKEwAIema5KraEJQZg/LKKOMMspootKk0MMAm6gxgBCwS5Rpiehqgg1akGPUpV+CVEVvMRWZ4ZzmFHOni7QAkaJmKgejOwqVPCebt36Q6jctOuaoteCmz23EvP+jw7RAzQp76AhIXXR+mOnA5QAzSTkBcvUDgS9xCNr5c845x8w2bNiAB8HvDiVU9uzZg6LJSIaL8piuz3T9Qt+QdIpitfazs7NTk2IQEsCSVpCcoLfChuVyWZPhYoVTkVJXN0sCxZBYBNmBL7300p/85CeWgArEo6CvqG7HNkFr1661JFaMVwJinTJlyuOPP25JpWCqSiHOwfeI/YWH5vN5Z5wzKbLM/ciLTeIZ1BrEN4KVo0WlifGqOkUAVnUm6jfOZVHNeLS8qq6mR4qlbWxjY2OK+HEjqB7PIfDI4vWjo6NqfWQcoebb46xqtxnHNoH8EicFD2s0GrQoaJgXE4jRS9XMqtWqMirnCwAiGq5bjnmMWMPFkkT12hMLIKnQ1BFSeHA7dJSXhaavZtwlhDejF7gW+GULM5VrU4+5Iz4rfITrZwu0E3W5AOLVajWGBJm8HZVaorAqRwT7jdaN42Xo2Ete8hIzu/jii9EmnMVvuukmMysUCmAGMP+gZFd7e7tG7UTrADQaDXRVYUMeXowa1rFjEXLRasYsyiKKN/J40qVCv3ncDggUqRRzudy73vUuM/vRj35kCT8ulUouOxHaVJ8FtDlz5szBwUFL42/d3d0YBYQ8wL/OxoYLopAgu60gHsN1eQ16hXP8/7F35rGR3+X9f3yMPTO+r/Wund317ubahJzQEK4maYBypIJSKCpHoa3aCNSqEFUtLUIEQUFVoY1AaiiFqEFAL0Rpi2ig4UiAkIQNSSAk5Ngkuxvb6/saz3g8Y8/vj5fmrfd+Z9aEtj/97N/O5w9rPPM9Psfz+Tz3+1FYSm3mkzii+5laWloEZVlLAL6zstmsh/zwugShJridL73cGZ4UIWeeZ3brjY6epS4lwtPchC5pI5H/w08uGIlt7yB/WMOW2GiN1miN1mg7tZ0RephDZiCJSBxGbHGtP2G7kFjkEp9HAOtKPYRfMZIo7rmunvEzfzpdBMTPvNG/rKvW1DXfJfSMreM73N5YqVc3tq61s26kRl1Nse7tinhOqJsIrRS6JH5d8QUeRa1ABuxv9HZ1dZWcXF/WdDqNVdBDANR4XV9fX0QcO3aM5yDtgsRx3nnnYWlEHeev8utdHJZ9SWqNhxQp2MTlcRkGfc4hv1Kp5NnQim3z6I9EyIOHt1SqpRf58uqrr46IL33pS6i2v/3bvx0Rn/70pyNicnKy1qomRcqxUa688koHWuOupaUl9BUmmRcl0NrA5ZJi6jFWLS0tniSjaBHX1WRP84zylpYWn20thGMH6yFEUrgqowAcV/iampqE7aKHaHV81JunwoN5iGxi0nS7m0yVpOxUVCgUvD6DxuVkL5XOY15ENj5psktv4TjYbu2M4GGVSqVUKnnsmb53CpMLwcOTtHMSJTn8s1tOZLRUreewzekbXmS6BVbFFlxkayKra/pIcK+6FsK6RsvET24+0ouce+lFtcwp0aUthlm3n6d7Wm2ZlaZqnLfHgClgmmvkGfLwdBmKqY3ifRMHhXsRzJbP57mRmpacQZdccsmhQ4eiigcvMnAzjmjP+ZMctzT5KtyIpBPN05L4Wy6XPerMk6ji1NItctgknEAOisGjrrvuun/8x3+MKvrGW9/61oj47Gc/i2eL3kLt+XzejWbM/MGDB0F0A26NJy8tLRHOjm8GT9t5553nh7uG4PwmkcXhjK2pmtUkdp5Iq+BKx3lKCFte1GKzWhzSX6cTg4eIwXBWuH9RO8L3RTqd9vBmWeMdGjERK8uL5Nf0vSBMejlBfFrc7ScEEF4hUuE5XpRA4bs7yJZ4RvAwHN0uzSXYBnTJT6qWRBO/cV1Np4CTKU9bW1uDTEluJT3ou9/9bkKsDjsQfTup1VWPtvjy2Rz0dT1nPzOItpYRuhNRrTYBbmslsu5bEmpc3cTwuk87++yz49RIa13mMnIifYqTN+E+UW6pe3ESrB3/DdHhOgs4owEqu+KKK7iR9Gdesbq66gHQkpQ54hOtFsg1TiVUjUUOmLAkEE/bl9jhnjbVfGF+OCuLxaL4ehjLhIz/9m//Nqpn9Fvf+lYYG14r+VF4mnuUOzo6SHkmXUwaIQNkr1F17MILLyTljp+U0O1Pk+qTOIh9frSvGRRPE3dssYLsOg0QShIFa5wn8Xd1ddV9mcImdlVPjjdP0dN80iU0PFoiPkUoms7YtLkSWm9E9PT0+I0ib5R+VhNtVZnyPi7JTG4bkFy1I1rDH9ZojdZojdZoO7WdEXpYqVRqbW11wJsEMkICiNMtEhJMXGFXSL0Ly4kIIm4kOG1hYYFUWRfiTheP3mSh+QkxM2G+2yLKMWHuq70ycdfW//qT6/rD4tkpW6d7eN2xVJ4dADENwCTcNgnwXDcCJ16E7NnZ2YktKBHRh60sMWncTgVkjIdacYTrsbGxiDh8+DCenkT1Ye+SQDT838qpKEGa5NplbW1tRZT2GHfpYR7zVqki1SaIyn3DMjo5cJpsdLimCLz867/+64hIp9Ove93rIuKf/umfohq9mVgUOd6e85znRDUEUegVWGJRvFBhT5w4gUdTaSoRkc1m/bEJzAGuYd0VJ6zd7f+iABUKBbcGS2txE6hUNNQXD6dMp9NciZYj77jDvkijdX+hLkj4cRkU9oAEnhlvdKCNvr4+N/yoz24wpDU3N7sXQ9gffu7JmFSb/64shR3RzggeVqlUtCSOQtZUhbFx27qW34Ow5ZBIsCvMOFAtV66urnqZDK6/7rrrIOL7779ftzefWjQ5wW9Ol1LmVyZYWuLX2nufvdlQ8/Ysb1e3a78/3ZceHXM6F13tAE/n4ePs4/RJMCQ+KCfGxQhVKnD/OSfXwsKCn6QJXyC2xFtvvZUvIQNehNltYGCAyss8hENKZlhoQ8VEEgKHU6PG64eRTG1u1JWJjMe6j1bBR25WLZVKjr6IR1Cymkts4vFM3YEDByLiAx/4AFgecLKbbropInK5nJu5YOqb1dI2wEqRFiZbmbu+jhw58trXvtbHwu217ErSgPN7uQDk0nM/t/D1Pc2ACcnlcs6zZTx0Ek3wRSZEzioP+NLhILeceijzuBcjzOfzCXGZzyqYEuajTdCtD9ytjvl8nl9xYcrjBev1UgNKg6OJ3yfqe2zn1rAlNlqjNVqjNdpObWeEHobcUYueJyOAa/0Kn3U48FQq5RgQkqZdOUho9P5lOp1+/etfH1U56K677orTuKMld0v9j9ObIOqqcf5v3V8TCl+i1b2yLm5hInaxbihj3Stdl9VL697+LL/MZDKeEKqIeWYb4FqZ2twOg0FJg/JAj7vvvjuhI3IZK4Lad/z4cS7gRQTaAe978uRJgh08tqL5VDxo5dViiKOfiYWTo971lQS+gz9T4r/X+lJsvaeLhBFnWIidx7Ao6INbCBCgyGdbW9uHPvShiPjzP//ziHjnO98ZEX//93/veSwKWqGrz33ucyOCaE/NCYkQbK7jx4+7Zil93b8U8L9i0KO6TVZXV/3tKivhlcakdriS1NbWxtA8Qk/aiSOeKDfDVyefz7uOKBsdBOBxN6IfXsRPXV1dPNwDoRPQKgo99eBM4W02WaitesuvTvzpdJpXOHzJ+vq6mysFDrKDYjrOCB7W3t4u6nEcAdlY2EI0mQ29np7Occ+36Ojo8NLdcsN4TJcs3Xx5zTXXRJVQ7rzzTo9jVnNWkTCRb8GZ/OhJPOrnalv42BIuJVrivXXHsoVFsS7n9tGd7iH+3rGxMc4ImqQN4bTGqYdynHowNVULnPrp9sgjj9Ti+kTEyMiInslZoJhV4GupvfK1r32NwFQXkhKuC50ainmj244Z4bH46obgXD3VSQeizyFHsxwhXtg+EVULoW5sbDAoN0/JSYZtkIN7YGCAW973vvdFxMc+9rGIePvb3+6pYDr3GS9ONeSGmZkZxfFG1eSVy+XwmZFaJxM9q8ayKu+F6fJIS6VP8d5sNuuWN8HmciNNPkg5scLYv0sDTIjsty4JJTx2wgh2iyhLvLKywvQ6vTVVsfC5kmEWi8VE1mkYnHwiMcAtvXIBenwpu0MGxs1TIcdwRrJZmKXFxUUHQ9/mbVvwsLvvvvtTn/rUAw880NXV9fKXv/zd7363M5Wvfe1rN91004kTJ/bu3XvDDTe87GUv2/r72ra8vJzJZNzoLG+2e7kFj82VjlQWhmYdJu+7J4MtWi6X/RSgtbW1ufUfl8nY2Ng///M/R9Uf7rrgs2mVUwsi60v/kAglSFxD28LLVZcv1n1O3WdW6qFhne6BdUdR1wtY+9NLXvISr6SeKL3BXtVYEr7rMEWBJeM4m5iYqOtuRJmg2pY8JZzmFAlj9efn5z1iXkxL4Rhh5yC/cohUKhWCHThqPSU2Ti1KF6amh4UAOFUo2MHDlOjh+vq6B3ooPKQWmFFPc9abSqXgRtx+ww03RMTNN9983XXXRcS///u/a+x6CLMEf/qXf/kXP09RF9rb26kf/cpXvjLMV+rxBQy5o6PDD1mlfHn2lRLDpa9ADJ4jhRewpVogzYPj29ramDR3soYx5jBm4MeCwtk95VmClKtxicRwxfgwQGdsdCmbzUKNblhaXFx0RUqEDf14PEuc6hBVHzi4nDYEK7oj2rbo6Mc//vE3v/nNt99++xe/+MXZ2dkPfvCD+un+++9///vf/573vOf73//+e97znve9730cH6f7vtEardEardHOnLYt9LAvfOEL+nzjjTe6RnXrrbe+4x3vuOqqqyLiqquuuv7662+99dZLL730dN/Xff7AwEAChRq5SYZ+ZDSBDHketIBYdEsYtr3HDmH+bjo1MzehGLkl+qyzzgK8B5RYSjQJ5oNW13clQftnOrTqJiOfzu6X+NU/J2Iy/ZpEQrHb/SqnCY6vqz7WPrPuiBKNKw8cOOD43Iy6r68PcRX9BiUgm806/HkiEtXdJ5K7E1ojNEaBY4ntxNMTrHjffffxukT+RkS0trZ6akdier3zcarvqqmpSVbBsAC2RInhsAJpDi7c29vr3VBysWtgkuJbqlUQvQ8eSq4hYD9g7Lzone985yc+8YmIYAtTiiyVSnm9yosuuigibrvtNkxYNJSG4eFhcJZBzcfwPj8/7wQgLYq1wwiJ5rq+vu4beX193cNNNWphLGm23Q0WVbtIc7UCgHsKomrr81IV2WxWHjg6HGYEZurkD0Pzc197a2urBzYz8x0dHU4VrLv0Ud7Lk+fn590zIgAz+ukHVHO1PgNPxk4gOBKux1Te3NzsFtdt3rYFD/M2NzfnSez333//u971Lv17zTXXfPazn93i+9M1BbKznVg/7S7fHu3t7bV1B5qqZaCbDfm7paXFeRJNSfuJ/eMuU6XdcCPml+np6Yj4+te/DuCemxZPZ82r/V7c7mc6xhJmjS2MfolnJsB+6nKvBJvcwtqpR/nTEo43vzIRFkF2kQ5uD3ZYXl52Z4DQgDyJR5lA7glTuZxEYAtPJuHPvT6Dg4MvetGLorq4BHoMDg5yoydOyRuXmEk3DYmheniCXCYO1qeQbprO09pQ6fn5eWdXzM/S0pJw5cNsre7ileXK/Uzi9/40yl3m8/k/+qM/imrRliuuuCIivv3tb9dWu3/uc5/7rW99K2pyBrgSgC7K1rS0tNS6i8rlstIVwtgMT4MqyuWyF5AU1IXjg/C0hAscNpNKpbBw0mGssvLt0eTy8EwYTKya3gSDpKuOiK8gfieS5mr9AY89kXiN9Y+3p9PpBApXWJiJ+1YLhYJ715jzwcFBmK6zt83NTbc3bvO27Tr6iU98ggoatNnZWWLAaLt27SLz9HTf123FYlEisO/GhNKT8J/rXj74jfIkQ0xoYEoSchgbRXa4E1i5F+72J6rt4MGDP/3pT6Mq6Ts/S7S6etgWzCNOozMlvD51nUBbcMQtrqzLhPSKRNvCEVjXE8acnHfeeRGRSqV8r+oy11p0jLqTQ84GhwD+4Q9/GDWqIa2zs5MkJ4ehGhkZoaAzSc0wy4WFBe8nbW1tjXPHxfBKFdOWs+9Vr3oV16BbfOUrX/FuDw8PRw2P94NpY2PDhXpFRfoJBRkMDw87/5DW6Ci6Aouiq+4hlrvR1ZQLLriAD3/8x38cETfffHNEvPCFL8TL5brdlVdeiazgdWRyuRysgi+B6erv76cDflIrUwoZFJaQyWT4V4Km4iPC1Gu64d7xzs5Od6RJZ0LSTRTK8ezDBGKhxyWKvW0aHnTlVEhlwW6h9PAKNEuBWrkGnEql/GxJWIOcBSpS139KpVLOmXjU6uoq3aaHyol0Vr3N2/biYbfeeuvy8vL111//v/tYgN0i4g1veMP/7pMbrdEardH+f2rUuttBbRvxsFtuueX222//zGc+4/rQ4ODg9PQ0NveImJ6exvRxuu/rtte97nWtra0OtyqdzKUVpKFSqeTiJ/KIpCq33lQqFUEJhHkgahU+xYDREMPT6bQLeghQ6XQaCwy4Pp/85Ccj4ujRo7UZWhLwn43PqW6CV23oUV1TZEtLy+lsjH5jAkb5dC3Rw7qx+IlxJV7kPUQrmpubc8EWn4oCrnw5CoUCa+2xoE2nAsg+8cQTYdPrs7Rr1y6IxNNuLrzwQgpdfupTn4oqFc3NzXngO69ra2tzDQxDwubmJpSJAH7BBRfQYdQptLFKpeLWJ8UTKhQ2quTX3t5eqSYD6e2VSsVD16Rn8CI3rkon8Enr6OiA/t28mcvlPKxR5nGqVz/00ENRrdVy66238iuKl4IwufLBBx8MU4u9yAA66Jve9CbVHNAMSKVQqD1fYkVUgB9TwUZW7oF7mFBlNjY2BCmiUQvKy0la+p+7BpSX5p5IWXpdA1Z6out2lWppSkdIkcPPUVfkouPJ4+PjYSYopzcdd14ftVgsur8Q0kqlUu5LjohXv/rVqVSKOQRIbJu37cLDvvzlL99222233HKLF1aPiMsuu+yOO+4Qr7rjjjtwqp/u+7qNXGYHlxOVs1ex6SniWfWHoia4OVEaHBLB9CE681pNXJnNZiEU6AY6XllZ8XwU2cFhbBCrVPvaw11nayKNaYsIiARjq03GqsvzFMRRN8BEt2+BMb8Fo0qYxRKd2SK8hYXABb26uuopvfLJe7CywpdZLEcVUoKXH8d128UXX8zp7Czh6quvxrSIFAVnWllZ4Xx0SUh2Zv7l+lKp5L4Z+dL9QGyqFqXzHlYqFed2SkRz9sPRXCgUXPaSGCcXkX5SdQ8F5YcBLHlpm56eHvdBqvAQH/AaAlR/ww03fPSjH40qMT/88MN8JsPk0UcfDeO1bBBGhC1xaWkJVu024VwuRz9hWopcR7bQWjM0t44WCgU67E4gOdI8mU+/urVTIe/OG5Rj48VlBP3uqR1K1BP38tVxr3yxWHREKGFTJTJ2wgzFcoiGQWp5cqRiApza29vbHdJT6SKJc3g7t20RW3/XXXd97nOf+/SnP+3CJu1tb3vbzTfffMcdd6ysrNxxxx0333zz2972ti2+b7RGa7RGa7Qzp20LPewP/uAPcrkcKaK0H/zgB0iCl1122Y033viRj3zkxIkT+/bt+8AHPiA9rO73dVtXV1dHR4frK7IWeiSS5BrPR5Zg4tqVNDZu9AD9UqnkEEduJwzzIYcJ4H67PPx8mZDKE4hKrh5JTamLLlGr+kRNiId/TjykrjpV++Qwvep013uvantY1y7q/yrUgjJd4B5VTk33ltLsUqci+lgdr8NbKpXQ5370ox+FLVatrfXKK6/E9sX8oARcdtll//mf/xnVoAxIIpvNIh3zZFmuXAxHXyyXyxChQH4J3ED0RopXrGwirsHxGjQ/PJxuSI1z5V7hRbWptbJrKcI7IpaWlriSZzLqjY0NV4wgVKk+TCxlw44cOfKe97wnIv7yL/9So/7JT37CLmCby7TLqJlJxv7ggw++9KUvjVND7Lq6uvgX1Sehuyhb3OlBQZVMlINEa11QuZgQlb11i6KQmWjs2ba2NgcE4fldXV1en5M+LC4uJs6KMNXZwefy+bxr8HWNNEI0djwztx5pgExvOp12glHks+8dGRgaNTB/vkZKzenaK17xile84hXP/vvahm0aMmXPKOyKL73mspxkiWrunn7Blel02rNw0OLz+Txb3Q/l5mqZO3fbqOJD4niStyzsHJeNPqrH4okTJ7x0nl7kboPTuccSkxOn8a7Raj1DtXH/siUm4vW3QF2rG22YeKZfn2By559/fu2vDuSjVCdmSWcBc4iRDYNSa2srm/yb3/ymj9ffyKNe/OIX/9mf/ZmuIfh7bW2NvAg/jrUQGMeEy+f+G9mlnd9ElVUo1i4sQ4ufGMLKyopn/wiQiaOfV8Ba+vv73WEjNsMHTUJYDUw6o6Q6r1OsnCone+Xb1Q7wsssuY4MTdg/CfVNTEwmRL37xiyPiP/7jP+gtPTxw4EBUi7xUKhXqFvE0prqrq8txs2jCfBKH8LIyYvxeYJNpEVX4TCoEEVJRAKQPUGH6itH3qXBwelGduw+4q7u7G5pxI3A6nfaAT2UU+Ig0yW7elMPMzZW0crkMiSpPIAw1nwHCxcWAd0TbFjzs/3ajZK0w68KcVX4KSChL1HwKq8ALZUg9ctqViua+XIFasRN857S2tvKlu5cVHOEF5tXcFXzo0CGi8J2jJJSzRJptQknyI+nZOMm24Iib1QqzfkHUMB5vdeNEEoxtC+XshS98YVj6nZvvlc/AXiU/gabE3sRfT4Nlobu6ulDyCN8HYurAgQPezze96U0R8eCDD3oBMMnULkwoZoQP8AZWf2lpiaUXr/XEIDrf1dVF31BQFCjk+bMKLKIzsAEV88XtT9gIrVwuQ4fuUspmsx63LV3Qj3j+zszMeDAIE7Jr1y6HgNIzcX3deeedEUFa58c+9jHPC6YS28DAwGte85qIwC9w6NChiJiengYm2GGTyuUye1BVwVhcAUqxED5e+ERLS4uAE/VTOp12lzabempqygEP+bK3t9eTxnh7LpdzYCcdJrzIs+hEHj6W+fl5z0emJ21tbQ7hqDPBZTWdXZ7dCBl0d3fTJeejGxsb/IsyJ3uP805cjxsbG+4F3OZtW/jDGq3RGq3RGq3R/hvtjNDDyuXy4uKi5wAqFNA1FcVZ1bX7eb698isdLl1VBP3VUtH4lX8RD8vlMjId/0qd8hgwt1/rGro3NDSEUZEQ24Tu4n+lnCVwFvyZ0sm2cJIlin3U9ZltoZxt0eq66BI2PT3fRyFU39rql8VikTodNCkWbrlV0DYWqoQ/4+qrr46qwfAXf/EXIyKTyfzd3/1dVAuOENl/3333qYZLmJDLc/x1svBAVPhyFLOqjGNP2IA2ZDrzgPLu7m5PmJUFVai4YfZJYiDdAaYkVlfjZEHynJNKpYJK4bn8w8PDjifClwMDAw6RpeIjHqwIPMcHP/jBP/3TP41qtjhJ4jfddBNLgHnz7LPPZs5RIhmC0pYZL++Vg9njRRXMSXwjsccqt+Q4wuvr67UQ9X19fR5LLP2PG/3L1tZWd1QrylRKsGZpeXnZ/VUKhoRUOA1UppJ/PbpV5nGPnywWi2jSNIhKJXgc2GVgYMCD+GXk9OhWnYceyLrN2xnBw6iD4DxJlWGhHs/FUSq7H51YI/UvxL28vOyoYuITvhN0NHjtcx6SgBqSlYAbSXKq65tRzj/Of07qurUS6jI2NWcGNHm8vYdN9YqtJG6s6/dqbm6u9QwnupS4MWHJTHAvvudY5+0qsyILTJiA4iEzrHhnZyc2JYdUKBaLzB4zyXnR19fHwc2VCbsNCWFgqTQ1NTnOAtcn4F14aUdHR0dJLpEAACAASURBVK0ls1KtESNHlCMPqbIUMpAjD62vr0MAHpEUp7rx5bKihw66KI8d1+j5LjYJiM95g3/WeBUC7hWqdLJvWg0tAjTuuecezKTEViCNDQ4O8iVQKRgY77vvPqCnfuM3fiMiyCprb29nRPxVbAUnuGpYwwY2q/XkwiQG93an02lmxutXSJiA/JTl4p5Cnp/L5fyZCmpPVF0JA5RhZqC34eFhpQ9q5tPpNA9378Pg4KBPrLiUAn/CTja5YPV3bW2NL1kySepIDO6o0yG2I9qO6ej/pDU3Ny8sLHAkeayEZA1XLOQnYKW1jVlUNomIBoJD9IYFdnd3u32ZI6+/v78WD0YynTOMYrEIQVOyXRd4tpk8LjyT/f/kk0/+zHlIaFe1gZqKZ0l4vPztCeeWQjkSfrUw/S/BqNxRpOtrfWYJ95u+JH5Vvmu+Z7H8XNvY2GAd3WUip7qraD09PWgDDkD12GOP/c3f/E1U0zxB4+zv7wcOEb2NA/fAgQNwUAEe8tfT/qCQcrnMWeZwUKVSiduVqCvQsqiefSsrK15Zgya0TxpCz+7du7mSf1XIozbpbX19Hfndvbnr6+t+IDKW1dVVRgocmhwt+E6cqNbW1uRU1pNV9sh9Tueffz6FJlB2tY+A/ybOk8H29vbSN7688MILw3aEO4+V1aR6bJ6KLu3WQxgEzep7Vk/zYGCYVi6X89AJXtTT0wM9eIJ2JpPxuA8Jc46wDP9WaBhiLtu/Uql4/IWAH8We1WelrHkBsLW1NQeLUmosJ4YnNff19bGOHgS0vr7ursRt3hr+sEZrtEZrtEbbqe2M0MNUwDRMyApDJfD48tbW1toEJiW+CBI7Ijo7O10AT2jfbl5YXV3lFk/v7+np4UsUONU+QCACzkB98IA9qQtInUiIcgLVuqDqhgXGqQZGzY+bkmTy8hBNFSOmKb2/Vkura0vUu2pjf7futvS8Cy64IE6ND1YMlSPVStsTckFE9Pf3uwcUK0pHRweRbz4Vq6urSMQI7KSjveUtb/nxj38c1QIiH/jAByLi3nvvrU3GaDq1yLJqELvzCeUsn8+z4rxI6T5cgwKkqEs3EBUKBcT/hPLh4dfMUktLiyNIyWWFAO4+SAEs8aUgdD1n0d11+lIGyURJ+4gYHBx0G6bMYmiK2AmFluTbhJbL5egng2WlhoeH3bOICqLYesFto5Q4aJOQRFyF7ejowFvGoBS+j9FFZRDCwtMdyTeVSnm9U1lTMTifPHkyzBbNlXRGipfPOTNZKBTk5Iuq0bJYLEJI/KSFQGfy1VxaWnI/pUJA+dfNhh0dHW4iQu+XuWJHtDOCh5F37J4w2XkgOOgS+lhaWkpU4omI3t5et5jLS+Gpsto/7Af3rnV3d7sTWIB1HhGuTDU2pPuclc1KE2Ad+8Etik8//fTPnI1EbL03OQgTzNIB6wTa5jaopmoll0RESV3z4xZc9mcG6Dc3NzO9bsxpbm72EGSmt7u7GxblCHX6oMAEBjgxMVE7P/zLGjHVTz75JIyNM/eWW27hITxHId08nyu5kaMhnU6zuPzEQTY/P+9OC3WAvwQyKGHDuyQcS+fKEiaQ1Tj75O9xxp9Opz2jQFZrZoYbaeVy2XGweBFxUnEqANXg4KAvFo9aWFg4ceJEVE1nCujXaRvVbdLX10c8PRUAaKVSySNZcJK98Y1v9M7QnnnmGYf8j1MjyyWnEuLBv8RDqYIX1/OQbDbLcxgLdtTZ2VkXgsXIWaYE2hOrDL9hzufm5iAABW5ADNwCm0RKXltbqw21YPlETrSEH5Rxyc3hJnd5ScR6mTQPKVJmhZ8/27w1bImN1miN1miNtlPbGaGHTUxMKKrCQ48UAO1xDUIHlm82DPbU7SGK5fV4pLa2Ng9nwi6RTqc9PElRBp4FKY0e6QxJX91zO4MiGN3kgrjX0dHhRRcTLRFzWBuseLq7EiWGXQeSeLvFc36mdpUIza+b/kzbs2ePkIQ0Ibt27WJiPba+ubkZWw2zzZeZTMaRWPft2xcRKysrWI3qZgsggHP9ysoK11CnWIGIbteSCVHx9GEKgeKho7rEshCgbayvr3sUn/QhRoH9DVG9cmqtgEScETooqo+QIGjYRdfX11FlHB0qk8m425+meH0UBRkMfCuhSbS3tz/++ONRtWsxqwMDA/v3749TgypVfZjV5MnFYvGSSy6JKhCwIq0YCzOAiqYMa6Eo0QeupDNSuVCSmLSpqSnMs+wXQnt27drFB9ZFYLsMjS3MQrS3t7tpWtvfA2GEs0PfMJAwFf39/W51FEKK4pzDYD7cHsMQcrkcBMCTmeRUKoUVwQ+xgYEBd3OMjY3RMQwG2MNZ/d7eXjfVMi1ra2sNrKnt1TCUuzUG+tjY2PAYWcdCDDPxR0QqlVL5lTCwD49nVZE9buQnuFSpVNI2iyqhCHHOz6BSqQSBQoI6T93ZluBhGktEjI2Nsck9PrtUKtW16dXFv6htdQ19cWoZGuFm1UKh176XVrcczBaNKw8ePIiDwUv2NTU1uU2Y80gh75x9bNSlpSWew/TiXGlvbxdWYe17f+mXfikiqOK4vr7OCnIW8PZUKuUR3kpEYxL8rJybm3O0F35qampyf2o+n+dXjloZvTnvICosxqVSiXMZRqWD3ouDKKbOYRtxn1SqIDWso/w9zI+8R6xmrVQnhw0Tokg5xAJmBhmio6ODQ9aNusvLy14Ihs5vbGxwOwc9535UuQI2ZBj/fffdR0qZ97alpYUVZEc0Nzdr0aO6Zw8ePAg/8PpKo6OjmA35K8Od41+IM0F+buLLZrN0GDYg8x1zwsRKFlGWhW5XLQUHZmxqauJLd/Hm83nWETrnmbOzs9zifcjlcg4ygi23UChAnO7+n5iY8J2rzMUdhFt/RvCwXbt2KZTcXZ360jN7pqamfLNJc9KJE5Y56AoK7KpYLLqsLeeE+8PkgfAi67you7ubFwl6KsyxRJMKwjWKF6AnICRBtQle8uwZxhbX101nVt13t8LLc7ZFEljdbxIeOx/7Oeec46xC8cQewQw/6+np4bRy0aRQKDBdHIic5t/61rdqESD1dsq5fe1rX6Of3AjzkEuG213X6e3t5WnuUxkaGvLsCFVpcIdNd3e3u6kY5tlnn+34dUIv5Dme+a7QfKHEhglGAFBJ/BfYlb4UuLBHmZdKJUat7O+ImJubc4w0FkJVkr0ITj6fpzOwHxjb0NCQ0hzDasRwC3P+9a9/PYz4pQlFxH333Qf6F09T2jKzrVxJVyaYipmZGeaHfpKlfuLECbgdyyr9jwXyiPnp6Wm3xMgx6Y40qKJQKHhOntIT+dL1/kwmg64Pd2Tmi8WignT0ukKhwDIxIUpgEH5mWME5r5qmFAs/hVDp5ufnUZ09/zWqGZA7ojX8YY3WaI3WaI22U9sZoYchfbj5XvZ3dC8vYdfR0YH8hQSEWqOYWmRJ5LvOzs6mKiKwvlSdOp4mRFQkTa/ml8vlPJJYAPlujkMskh+FJuWG9zqC5/r6OpIpN+IPSDir6poN6ypMW1xfe0utd+10oPWu5sqi6MbPxJWe/5BKpWrr3ayurvIlU6Ggr1oTn8oXIM/y05EjR+p2lTcSyv9Xf/VX9AecCF6HJDs0NNRURbwNU86wHyqLIyLa2tp4r5vvWltbMRBBpSpN6TGHi4uLMqxFlSaXlpYIq8O9IYMSzidHJ1lYWGBm0HIUrs0zIRIRqgLbwly8Hl3JuFZXVz0LW2qc5yd4Skacmjfd1tbm/wrWndsBdL7rrrt4vqsdqCwzMzMUI8XTo1hHD6PfvXs348XwqwnkV/kI+JJXuMG2o6ODBXLs3aamJrawuwYzmYx7Z5UyzI1OhF1dXQ49RR/6+vqYLoYmc5GfLZDWxMSE1+DVPkJL5icG29PTAyC4e2rz+Ty5/DTpgtzCyaZaNhhydkQ7I3gYCeoODg0nGx0ddfBs1k+xvK7Lq+IzG15GJ67BBIEpYHBwEKOHIwjIK841XC+McGgR5X1qasrdYwn4DN8qlUqFa+ihMoEYMmwYY9r8/LxHf8hWVjfGfQsOl2hb1FWh1X2FRuFdqv3XX8oGBvt8dnYWRxFmQEXPczHHKwai2dlZWIXHdExMTLCNESn8rtrGHPJ24Dk2NzehIm7B9fj4449zDV9CEoODgzAY+A2fM5kMp6ry0iIinU4L4y4iWltboTFHApRxjFFIZuL0AVcQquvt7eUVwgyMiH379nGLg5G3trZCP1CjHDwcrMyhgtQdBVFGY7l1w/giXfJaAarSR9O+8FQExXfgD4O0wKj86le/yo3On1KpFPVcgO1QCgETC8edm5vzbE66lE6nvdaEgAQdH05IHKp4ElWi7ezsTHjCwvAvvMxKKpVisWBpQt+AUblk09LSwhx6Nk5vb6/nJyjixsPiJX8r1Uc/FQoFNojgRfjLlxw1qvgj/2hEIAC1trYyauZzm7czgodRSsADimQch4i9ToGKWOKIZj8os8fhZxJuW/EGP0QUawCdQdBskrm5OU9jFHSvvOthyo2Lq7TNzU2vU65gATYG+0fPdwy9LcI3fBSJb8I49887/3WDR7buRphHCi8OBRXL5bKDywlzz9OE5WipLaUm8NyK4V7K1eTOvKiWKJMQw5e8l4pW2uF0A4mB8/eJJ56AAIi/kOMEIoSoBB8FVXDj5OQkOVIJnVKBf2HZZo7NKBhJTiivqzk5OcmVbmCoVCqqZhnmd+EazjLlvXpdTZ7c19fHnMC/lRLrlca4YH5+Ho7IzDNLysLkdmS79vZ2znHGArLiN77xDQfK4vl79+5FURBIVURMT097SZRiscjTPM6zvb3dU8EUpNpkSKSqjwOlIZrQJUFHegTj+vr6Oeeco7XWhNQieqfTaV4EAz58+DBLzJUe7ru+vg6lsUbux4rqgcMEdnV1uehG9yYmJlQsNKqRqBMTEzyH10EShUKBp7EuqqTYqL3SaI3WaI3WaI32f72dEXpYKpVqa2tDyJINOiIee+wxT5gX2oKKTIaZFxCIHBegu7sbARB5XDVhEZk9Ib+lpcXBFBSUzC10RoHg7i5K6EDuI1HzDCThV3EjPVG8nAPjbmxs1GpUp7Ml6tct/t3iloRW9zOTwNQ8AJoLlpeXHccI2XNgYMCLESdix5kE1lRAErR77713i56g+d1zzz1hcWiOxConisOt0np6ehy9hSWemppyXCiaVGe0sbGxMU+cEjowAxQAOc/0QDgE5wRck6ypfEBFkz2cvcCXygFgYr10shIEpYFFxNLSktf8VPilo6gg2hcKBdaRcWHL7enpwQvITxQaFaF6heiLLrqIZaKxixcWFnjFl7/85Yj4zd/8zbBSSpVqMQoWHXrA31MqlRzaTZGraNJCGQ7DVPONn8/nfelZ8Y2NDVbHjZbLy8sO7MTYp6en3TqH/jQ5OSkPnJ68traGt5XOcM6sra152QQheHm9cvosuB9C7bm9u7vbaVKRz9zu9YNOhxK3PdsZwcOamppyuZwXoefzBRdc4Oep0mW8PgLE19XV5UEWMvSx69gV2BBk9mGzcTRUKhUPtdcRJoIL40yO4aTmXtyEOc5Pq1Kp5HA7DFb16QXCFBErKytensMf+Czbz/ScVerBWT37t8gLyMSKFTE0FVELM4sxkzo1eDvD5LwWOhxTcfTo0dr+SFC49NJLo8rDEsDhnESyDHMG0QcVhudXt1x1dnbi+rr88sv1OvntJANBXXx5//33R8Rzn/tc7D9MhZDxuIV/BTXkUfWMPZvNMnzM44q6dh+kTmGPcpJd2qU0pmJubu7gwYNR5Z3cdfLkSY5L+BPMUqnH8Fo4iozzXpNMtY+FzBQRL37xix988ME4NVlFrgEy/NgXe/fu9UiNjY0NyMMr8qgmtW+lwcFBOoB9UikHfk2lir7GzDhN5nI5ltUR8XO5HNe4j62jo8MFHfmzfbdCRaJe30F9fX0eSKU8NndJMqsDAwNuAlXmg1DutOJ6vqceKaFwR7SGLbHRGq3RGq3Rdmo7I/Sw2dnZgYEBtwIRQNHU1OQeaQSTwcFBr52IiLSwsEAUryNjZjIZwtUcrKWrq8sDn1wcjqqopexXDC+eENre3u6FfxRWrsD9qMlGdDmxVCohhNIHxMPe3l4v4MTt2WyWLwWJHTUGvWcT/fFsLnj2VsfENywWGgZL1tfXh9GDxcIw1dvbi6WI+WTUDz/8MKowoqhMQIwaLzfmmtPpYeAe/cM//ENUpVSBrvI6ROaLLrqIVyDgo3JNTk6y9KKKiFhdXSXujuJwCpTwQO1UKiVFJ6qa5ZNPPklXGZFCyT0aiADIfD7/nOc8Rx3m7QcPHmTUPETmPnpFZzCkHzt2jB3hQRwdHR2qPK639/T0KEU3Ih577LGIGB0d5WkYu1SnmMXiIfxVCrBDRnV0dDg+AJQ5OjrKpD3xxBNh+pBCEqKaDf3617+e21E7hFtP31SdWYHmojqhk7CgUpXY+yyBCg44/oXwfRyvh9uHhoa8ajYtm80qQ1xTNzIy4vAZKtUrS2zUlOdlDrkyn897VViGOTMzw8WeU6GsIWoNKkrILbcsiipT74i2Yzr6P2mXXnqpoAq86sHKygqHoFuuy+WyI0bLXICdARUbQpyennbVXjFjUKRXcygWi569wVESVdM55ykk++STT3oq/tYxgW4IlSnAy7vQeZk3uZ6NVygUPF5L5++zt4PXRbWovaBubxNf1n6vzsepaUnj4+NeW4RD55FHHvGIeYGwsFjscM5WhSw7oEMCB5LW0dGhaC5dc/DgQaiCsG9hvTN7ivcLw27wALZ0Os0oWAKVkPdow6NHj6rCvTpz8uRJr4UhZuDmcV6xb98++BbdgM4nJib44JBRgiyBGSj+FvLD0ahymvzKl4pOhFXAQUmbq1Qq9I04QyZwcXER/soByk+pVArODdyG8qXk6tOVmUzm5S9/eVRPXlE7ZMBaY2y84oor6KcqTjAV7CwF5TtiIQuxvLzsHilJnA60r+LXnmCgdAiW0jPtlNPpbirVOxWSJB3Tw9X50dFRr7YqqyP2bQWmRsTMzIyc7mFJI8w2+4KFm56ehuCRuSHF9vZ2r7lKHyYmJho8bHu1qakp4UI5FFsmk3Gnl+rTezkfWtOptUUE/eK8ATrLZDJe8kdRG5Aym02P8lB7HjI4OOiQweITteHpKvGeCPdwAFm6t7Ky4lWspJN52WKG3N7eTj/9cK/lLgnudbpprxuQsrVOxq+cAookdul4bGyMQ9YxlPv7+4V/GlUlYGRkxBdCMR0elF83qp5+jo6OevYuX+7du1cOzjBpwEu4ad05OGiCy3OHqBKlFRoQEWeffbZDHHFwd3Z2chDzF+otFouc9QI5Ywiceiwrn7PZbKJ4B+Ny8QW5qr29nYniFQpBghKIHZe64OlloiJHZuIUTqfTEhl1pbw4qn7CT467JvxZHG/8qzwWPiCmcBw//vjjJEeT/ry8vOz+MGEocwJothm7a0KIO83NzXQG0tIe98pq2nRe5YflWFxcVIa4FkKZ3cLWCjPneFW5yclJWKmHWuzZs4cuIVcpu1TlDMPqdzNpnjXU1dXlZyDPF7gdN0pb3UE8rOEPa7RGa7RGa7Sd2nYMs/2ftKWlpc7OTmQfZeZHxPz8PJKpKx8KNkViUok5rpEZMCI2NzcxMCL4KyzNr+Qhra2tHh2HvWVtbQ3jA02QnY5xIM3AoaalNLjekNDYXLvq6OhQ2cyoamN9fX2eIi2EWYGThhWD2CIO/r/n60r8pJh+pE4ldPMBaRq1rKuri1HQbaZ3165dieLCETE+Pu7pDVywuLjIcx544IHE9Yl26aWX4oBBmaMn+/fvRwD3WsAqUImkLx+b65SoNXv27FEEvDo2MjLCl9Dn8vKyo3gQ318sFl1phtLy+bxDXUA/lUqFa5C1MWgPDg6iQvEKJi2dThPU52F7gmtyYFxp/B6V3tra6qAB0tscLULIOG7uFtHSN6ZLHeNfXqRwO2x6v/IrvxIRX/jCF8IgYCAGOn/PPfeQDi8adrgmntzU1IR5DQWOwNTl5WWmyz1nwuvhRfykKi3ukVpcXMSUykMEPOZAP4r25OFuDFxeXvYinwIl94mV1dqL78iNWmuKnJ2dpcNeN3x6etrhrwTEjFaXKOvhZTa3eTsjeNju3buXl5fZXSw/K9RULdblPqSOjg63GrPSWn72v7DFFLQa1UNndXVVFu0w7ugVn8Vv2EgOLjA0NFSL7yIe4OXtE7lcAhCpRaVqaWlxx7ICeSF9d/vJVeCA6IVCwZllUz3c+sS/6tizBPVoa2tz96HcNm634bOSjZyHraysgCvBbtQA2cZIGEyL8oo4yOoOhOf/8i//Mre7Z35hYcHxq5jeRCRCgttxOmOz6unpwabEuqtOvKOgbWxs8ASuUW2gBNQhHUOEcsynXC7n/9Kx+fl5RsqBKBMW3lk4hEAlOCWxbiktyf2+SmNiE3GScrLPz8+7zZP205/+1CuqKFqEznO4CxCE45X4KYdXjwiKrXzpS18SSUT1OBamGksgP5Yi/qMqHuXzeYfbp/O9vb0quxW2v3iad2l9fZ1eAbHBai4sLDz88MN6BbO0e/du5oQXKW6CzjCHrPvs7Cw05kgZCwsLns2pjQD5Ye2EjwrxhCXmmUrmUYFs/jqIPhTV0tKiePqo7h1lhuyIdkbwsNnZ2aWlJXdC4G2en59nyaEGzqmWlhYozC3Ce/fudVBaUjIXFxd1yuv2Z555xl3B3LW6ugrRK9EyIjKZjCc+Q1Iq5+jHa2trq0NkqeqYa2BcUC6XPcBE3juvcyhbvJ/OSn50zxmn/+zsbOKs3yLrK+FYqssknHc6CLIaPwmEiaNWigibjVEwn9ls1ssA0vlMJsMqezHAYrHogId1eS2dec5znkP+LLMEE+rt7f3Rj34Up8JKra2twRv4kuUbHx/n8L3qqquiylxVyA0nEEn0a2trnqclEdhLabS1tcFjWCaFErjsLMQ/zj6d8syAV/JUYyZRN3lUNpv1+CZVmHSkbGUHI2zRT9b00KFDDM0V087OTjr8zW9+M6rxk62trUgtnNG8Lp1O86s76np6eryeCx6vb3zjGw4FIBfUbbfdFhG/+qu/GhHNzc3sF37lHH/mmWc8epBpiSp3Zy8oypQ3IsowzOnpafqGCksbGxuDM3nhwI2NDc4BpCWF9sDOGRo/ZTIZf68wKr2+Dy9V9ickypwfOHCAXcDTOLu6u7shfo8zUn1Uj1Lp7e11yViRwD9Xquj/29bwhzVaozVaozXaTm1nhB7W19c3ODiIFIn0gXtgaGgI+4bKJYTFJSJHq86kK/VS6fxfBQR6aUqBbniAELJSVBUOVaHlM//SpGnVhgjqs/+k/JWEYsGvbl5QYFhCw2MUaI1IoFKnEu3ZY3AI+yci9u7d674WlTB2CVGThpvB8YvX1taQOj0jp1wuMyj0Bl6Uy+Vc4ub6/fv3YyXbQszU/GAgYnrBR5+ZmcFELIDmMAOsx/6l02mUda6UD4nO8CV6TKFQgCp+4Rd+gVcI9VVd6u7u9hIH0I/Ij9uxLw0ODiKP+9ilsUHh6Ae5XA5TmOM7rK6uopEQ1SYTFloIt7ODVldXcdxKaeYnB55H01pZWWFOiAvny1wu5zgRzI88fB6PPjc3x1hI17vyyisj4rvf/a6D9jL2rq4u9F127ujoKP334plyGLMEfKniKQxNNkAMACw9gx0bG+NK36SC3YJEmclyuYy3m38FHcIp5Feur6/7jsD4cezYMSWMRpWw+/r6IDaWjFFH9ejgRtXc4e0MVsgjierwYTVi+MuTVbdoR7QzgofNzMzs27fP0zZVTNa/FAgNJOVgfTLCOIDQ8PCw5yqpkoLbyiCafD7PHuAnbRWRe1TNBU8//bRnaCX4RN2fEvBXDrSoKtWeGyuoN699TiuXy1gwgGwHXfu/3dRDpkJpW+6ckw3TMxmYyUKhgBOCU5Xb+/v7eaxKaYQFDbNXYTOTk5OcU7jKeEhLSwt1lTij+fuiF73ofe97X5xacrdYLGIgYg6JrRBeImTAiRnVI8Ydmfl8nlFzPW/XqQoXEQ/g3OfUmJiY4BZlO0TEyZMnXU7iRdls1lMesXYKbR3KlI3O3T/MVV9fnwMscfvg4CBz6HUS2traoAoHFWxublbidlgiBA/3OgmpVAq5wY1jqVSKX2FpxFaoWrrXOVLNB65ROhpGXZqs4m9/+9sj4s1vfnNEfP7zn0dUheyx0elwVw0/ZlKLHuZFhsfTJayO4+PjjIIRMUu9vb1eoEduUdaRASr3lA5z4PDlyMiIomy0RplMhlVmYnlIS0sLNOniuDwadEz1pukbV5L2rvA0IWzRE6/cjT38pz/9KabIHdEatsRGa7RGa7RG26ntjNDDent75SlFKkcvkZFN2c0R0d/fj/iG7IyEdejQIYRuj6l95plnEKAcJrWnp4eHe35lW1sbmr7HgHR3dyPOY0jB4jE2NgbILC1RAxM5TjUwucYDl9fX13maNLAwfQjzC89cXV31an48c3p6+lWvelUYwEftZKo+3rNpnlSroH8VNfYBehyd0F09WpJhnjx5EgnR0bMKhYKHhqrQMJImC8dUpNNpTJG8F5H885//vOPJPv/5z2cqoAeFNTMKr+CMwlcoFNDn+EledGYJZUUqL0SFmiLnP91QVW6owrN3Jycn0dW4XVE53IIArpRq5ufIkSOaEEVOegZCPp9neql9xQTOzc05yrsAa1gs+sDY5+bmoHD6idJQLBbpkmOqKUiKJcDU2dTUxO0oH1jt5ubmpLCGRVF68jhltXt7eylAysS+4Q1viIjf//3fR3GnD1dccQUVrhm1koUvuuiiqGqfKCgPP/wwi8VCKKjSQ95pzc3N9IoJ4a7Z2VkF2Ue13N3U1JTDaMlk6hG/XN/d3f34449HVeXyiDC9iHHdfffdDkeusEbGC7XrYpxAKQAAIABJREFUAqiCZWWSx8fHvfIAbd++faiPkA30try8zIrsiHZG8DDKRTobYFHX1tZYVAcLKJfLqmMSVWKCq0V1V0vfh1AgQeGFe+Qb5hdhRrBzxEV8b0NtAtqgKWLes5QSgNbu2tnc3EzAi/CXVyigMSIEXML2Y8jnn38+9M2/CSwl4chxmiSS1bwbCqfUKRYWP8lYOJjkzKMzquTJ2zl9HO9gc3OTKeVo4LgplUrciMUMxqkAP2xBrGaxWPS386KJiQkBLkTE1VdfHREPPfSQd0lQge6xU71gD27mpQMDA26Oo2MrKytyCmqSp6am6LASLTBbwXq5fXR0lC8dUiGXy3Hu0CVOoqGhIQgJWY3pVb6HVx/u6+vzZAD5IDGgebEVhfvjgJHJ3fPYRBu1ca2PPPIID8cOxsKNjIzQNyyEgsJiQzF2PlcqFdUqUm/7+vpgRW95y1vCqpXKx8OXRA8mkvkwJsMV2KQtLS0wcjygLOvm5qZwRsJc1yqw6aNmtt3829/f79iq8uY6jCrsqlwue50dZrWzs5NBQe2cP5ubm16fgVNodHSUreGhpAMDA9CG3svtvILYTs4i+e9hgcxYf3//DqqBeUbwsLW1tdbWVg/KgLCKxSIrx4LJOQ8dEPqs9A4likX1EOns7ORLJC92zsmTJ9mrjl4zNzfHMzmJEOVUiNkLmz311FO1fldlStHEDDy2XqHGtdEf4nY+9lKp5G482gUXXAARowqIg9Yi+EUNK+UD25grS6USD3dWLZXL/XaVSsWrQshe70HAzE82m2Vbovrw/O7ubtiqa2y5XM45MbP69NNP+yiYkJe85CXXXXddVM/TX//1X4+Iz3zmM3QYgVQSuoc1Qwbz8/MoKEwaDKm9vR1lghOTz0pVZgkUVkDneWalUkEHwkHFEbaxsfGDH/xAq8zT5A/jJFUulzCdo8obJicnPcaav1NTU01Wm4ZZnZ6e9jQjiXHMpBCzWCmHZFM/ve6wEh851r0mkdxFDp4Lz9MHtkm5XGaeoRBaqVRiIX74wx+KGFZXV3mOQvPZbnzJ30qlAufw0kiHDh3y9AahXzrMIxM4PDzMBxZLY2HL00+pUzBIJkRF7zzAhOefPHnSjQrsi6WlJdFDWNyNZ7Bx5eDgIC9iOZTtx0Z2oVAlZr7zne+IUOfn5znoeDszNjIyIsPV9m8Nf1ijNVqjNVqj7dR2RuhhYMJioEBOUdVKAUJHVY5eXFzEQiUkb77kSoRctLewsLGoClwLCwtco1p/EbGysoKMpgKJYVo8HgI6lk6n77zzzkT/pYR5jnMCQhsJXaVsXaEpl8tuSpI+VKmWsYiqUDY6OoqiiWKBUKZAW+HV1kX7RRtgFAiGwiiS/48rvYSHxyiq24rMdjgDluPo0aO8whPDFeRGt5WuK5Omvty3bx+KETI+a/Te977Xa4Xw0ve+973vfOc7I+Jf//VfI+K73/1uRIyMjPBeNCHpoPTQMcLT6bTjCQk81yHqVRCZmeHf5eVlTFgulbe2tnIN9iVE7Gw263VzZC6GOF0zWF5eplfYylRZ1F0vshA6VAqa4sbGBm/ndV55Oap2Ki6YmppSgU1RyPT0NLqslMIwwBQsqFLKPU5YwY2MyEMWOzs7sf7h8br22msjore3F+qVR4q+sTpS9PnX0QYmJyehBEyCQoDjS56pR2GEZIDSzrH10UNmdWBggHWkMfbFxUXFlIap+JCfGxiLxSL/Ml2cLd3d3WBAe16BitkyIbIb8UY6L/LzEhAixUcffVTvZR3n5uZ2UI7zGcHDpqamjhw54jlSqg0BHUAESpuHzrhGMASsMZuNK8NMKPrc0tLCLZwXCmdnc3q4to45WYHCMBG8Gm+hUHBTO1/KFEkTlpV8dWGuDq8UrM/uIGS7lkolji0aW3RmZkZRuXEajKvm5mb2jIyffOmhBAxWGDaO9afDy+2iQ0NDiBocxHw5OTnpk8brjh8/7syPLbpr1y6e5qbasbExJsHLej3zzDNauzAsDGf5km8cYEn+NtaFJysAXTVKNMx8Pu/B8bKRMvkICkJb90D29vZ2mK5ndIXZbMMig9ziJ0h7bG5MCJR28uRJ+IcKQDNMeuWYn8KodIyiJ598kiOYNeLJs7Oz/OrVIQQByiRwHG9sbMBFyL6SHd7lFXbc7OwsdOhpc6lUircjW3zjG9+IiCuvvBJewpMfeeSRu+66S2vH1hsaGoIjyhMWBk/Dl4JrYpk8iCOXy2FwdrD/hBtPaVtCi49qqmWpVJLrTrcvLS051gkTOD4+3mTVhThM+vv7mW2ISiKUKCeqYkepVAJ+hSvFjOk2U8HnQqHgdYhIp5H8tyNaw5bYaI3WaI3WaDu1nRF6WDqdPv/88xFJEI4wrWxubiLcEWJLeGF/fz/udE+bjaqkibCDdLxnzx5HuEeA6u/vx1Xrxe7a2tq4UoVfw3Q7XoeU2tfX5wCG/E2n0+gEbnkrFouuDwl60bukOHuvWqt0TnQCT/KvVCqEewkkNCJOnjzpqMRtbW0JCOCI6O7upgNYbBB1FxcXkRDdYb6xseHh5pJ5Naio2jDHxsa8FhrTWygUkGSZSaTyc889lyn1co5dXV2IluguWFqKxSLSLuItAVovetGLPPRR6ikjgiqUQUxqrQPcCcOFUTDVw8PDHibOuKamppgfOqOYCCaW3ra2tnoePYqFTFiOMdHd3e2qHm/v7+93XZ9x7dmzB5VL5kpWioer6jHTwtCYH8VEqNKCuq0sBY9kKZVK9FPWvLCaW/yrMHoeDjEofsExbljiw4cPo1J4pOXm5iZT8eIXvziqgTPFYpFXgJf4xje+ETpkRCQbRDWI3HNsOjs7Xdmitbe3Q7e+HE1NTcwhPRQoMIYZOswOUk0yGo/avXs3tzOfRAYODw8zpTRBcTpopMwq7FPey12ZTIagDE1XRCwuLmLY9LqjmUwGQn3JS14SZl2n80ydkMobOB3bri0uLnrlCLZxe3u7m6RUAZ3jkkNB0duqRhgGzOPcSwzGz1w4xJ49e9j/0K6CmthCbhJcXl4W4oa+VOi5EAQYiIcnaW97OHtdaPmEpbtSLfkYVqXF2Vuc6khLpVIqTqEJWV9f90AmVYZ1hCSlJXE754sGyyvYV5wv09PTzDNTJxANDyLldBseHvaENlUMYUW8nsvk5KSD52LIEjhsYn7oMM4n2MZZZ53FjZAKQ1hYWGCtmQFFVNPhn/zkJ1FlCT09Pe6co8+pVMoTiZqbm7lRNMbTYBh0XicaxytPUyVrVpA3ytjIpDE/slpLQgpjb3Aj/EyydtKZRCIRs8eN9957b0T09vYyM3BHLGBDQ0O8gr9wi1Kp5FF8gmuBnDAeypboG4o1ldPa8eOPHDniNUL7+vqQTSnXQj/L5TLXYNxTJLqnbUEGe/bsYUHZ+Mz8vn376CFTARvu6OhwjHnByvA0eIMOB9aRqVAQvyNdQWl79+71FD3E4tnZWfrG7Vw/Pz/PqeLlvJVDgnlTDn63CUNmQvRQGgaE7eHK27ydETxsdnZWdd8V+xARq6urkBEmbChSxWShBuWmsNm4EepZX1+HGjhzWf6+vj5OPYe2e+SRR5BM2beyyAuhJ6wGSiLvKmrSmRXe7WBRCrj39DK5yj3RSjzPX6Gjmf1APzlkw5xefEaod1eiwq/Z9sxqa2urczuxK4+jUdi9ELD0ZTqd9grFtLPOOoun8ZdjVDIvnVFdNAfFZ5gSUHgy/o/jx49zJUSi9HBhjUdVT+3p6ZEDL6ri7YEDBzjmnve854UhoNMNKT08n6OQ40kShgrXQQbuCQPjSsnRjs8pvxf/yqvK0/DlqAYeYIPkBStfinl2SPtMJqM4Ds18sVhEY4CpoJhms1k8QxCJ/EmwCobwghe8ICIefvhhukTEjeKYOFjvv//+qOptAuhCmUO7Wl5e9rwLiVPK7YuqYjE+Ps4cIjecOHGCo5+nEbnQ0tICe/bs+66uLrYkb6eHMzMzFJnjSwggk8kgK7hkc/jwYQ+jp2OdnZ0sAaNWVoPj9LPEfX19XnqbmUwUl6EpXYSFUM0/3uiJaE3Vct5eXmdlZYU5RERgchYXFz2VGwvBU0895arhNm8Nf1ijNVqjNVqj7dR2RuhhAMUirXhCqKBfEMOReXO5HPI1Ih6OENXAdMPU6OioIvf05ezsLLKSgo8jolKpIP7TEAyXlpYQsuSc40tHglBWsgNtyJ7gApT8H0KeDgPacN9VAqKev/S2XC57VBs6mZCu+NvS0uIKnPxhDmOq0EH0ANcC1bzElEp3YqdSSqbDfQlRl1uQtelhVEXLRE0yVAQ3SBYKBfkdoxrVVigUCM7GucKjjh075mhhPHNlZYW1Rmzn7blcTuWdoio4C5EL+sH09Nhjj3EL5MfzW1pa0NWYgVQqhdaCOY63Z7NZFFwmlglZXV11EyjjIo1EX3JlNptFUXDTbiqV8hrTKhTOpKFIqWAst6B2IKqryqKjhGxsbOANQllhXGNjY+ipaELMvBQ+x/y9+OKLXclmlsrlMpPGEGQOZVlRj9hco6OjPBMrwje/+c23vvWtUcXLZ0Rzc3MMCsrkIU8//bQjyfGKsbExUqeZH9ri4qKb0HnviRMneA7LgQL01FNPsa+9Tuk555zD/uJGZRkzk7wIMK3JyUkVdA6rUOoVNpjkQ4cOCU1bQ1AVZk4AVkfQcRCVyky7eRwKUcHxHdHOCB7W29ubwB/jQFxfX/ciEZwavb29HC7QpZCN+BIdn5ogxWIR6uE4hmTX19dR0nmRTPNcw5cPPfRQROzduxdS5hrsmTIGJqpHOgNQGDpnkFepl23d+ZMsdV4tc3Nzk195CAai1tZWuEgt3pU6o6d5OHtra6vvalX6cEeIuuQB9wpkkBMiqjt2YmKCG4GoUMVeXpTAmPfq1aolwRuZXtkSBT4SVc59wQUX4DhxSK2bbroJmeaKK66ICKK0BwYGeCM2TGSd8fFxP4OYwNbWVg4vzhSOnuPHjzsyk+J0+JWpm5iYcHYOR1lYWPATisNIAQgME/JraWnxp3HuLy0tMXxHuJ+cnIQf0yUMd/v27WMvuGB0wQUXMFIHWtuzZ49bbimy/MQTT8CN6CGE2t3dzY5QCAMLB6dxqJqf/OQnvAKOArMcHh7mRGYLc/vJkydhV/xl7Hv27OHt0MZ99933mte8JqoiIxxxeXnZATBFIVj83DW4trbGlUwXPZyZmWFFYD98+dRTT3kio6QHBujwV8ePH3fPGVtg165dXrpFyQlexlpOBBdDmVXV7vHSBIIVZXp5naCqoCIBsnCaffvb39bthw4dYul3RDsjeBgKAWvMQcNaChrR0YwWFxfZJ/wkacjh71jpqakpyB1CqetDknDtST8qssBxg+DG8d3X1/f1r389apBGXYLmp7a2Nle5pOuwWzgllWTmUWrS7dgP7AThM7n0p5hMdSMMPtEBZIX94yVC8vm80qI1Fcqidaa+vr7uGE6COMJNwrqwKE1NTaygA9wJQIh1YT6VAqxiHxExNzfnSccI2gLTorfyXbEiLBnyvsrbE53IVO/Zs0fJvLp+aGiIYXIwcShLC6RLgnz1BKaenh4ELKIVOPenpqaIPePtUrxcKMFJmajSQg/7+vo8FlQZhB7C+ou/+IsRcd999+Hc5S8TWCqVnD2zRjMzMw6DJAbj0bzKf3KNDQEurPZHVMWOQqEguCmRwfT0tOdyQUWqUAPzIEBDqJJ0+/jx40g/L33pS6Mqd05MTLBMrocNDQ25xAlr39zcVBJ6WCCiJ9Up3V7hfNoR7e3t3IL7jUfl83mHJ6VLTz75pAdAMiHNzc2+uNBGc3MzM8lCMGlPPfWUkhe14ir87SCTl156qde7ueOOOyAbJtYjUZuamnZQTEfDH9ZojdZojdZoO7WdEXrY8ePHu7q60ISQQRK2YFc4BgYGkHORtRG7oipFeinklpYWRDa3JS4sLHgJXQn4uAT4kueXy2XEKwQuXpRKpdzuJ22sNkh9c3NTzpKoivYyRXpwvGrXOgKQnow9DQGttbUV0RLJTqA7Hlsfp9o5peEllC3+OuZIAiLLy500NTV5GUA1YLd4mmqROJYHkuz4+DhTgdlHiMy4FphkrEyLi4v0k+sF8uRlAHEzdHZ2smTcTsde8IIX8C/WGK5U2DSkJYcHnWEmsdRdcskl6FiO4DAyMvLggw/q9vn5ebcwS96vrSOzurpKr1AsMDOk02l6xSTL3wNxonNjN+7o6EByVxRoRBQKBRYd9REr3MrKCrZTx45RUQIvjLJ3717GgmFcgLOeBCJq5xbUa/q8trbmpWKZq5aWFjpDY8nOPvtsZk9YXEwOe1xP+9a3vhVVPYy0wh/+8Ico9+h2mBDL5bKSNKKqXXV1ddFDTWxEHD58GMQTyknjfRDMB/2kDw8//DBbMqFAyxMfhrbMKQG9sWU2NzfpG+RKje/l5WXtwbAoXOgWeuMh2WwWXQ1PmFB96RLUqKIHDFCQQHReBWi2fzsjeFg6nd61a5dr+rT5+XmIg5+wsTz66KNsD8erTqfTEBM0BOuK6llWW9wrqtQj1GpF3ocl/fAiwVeHpZW4Q0IuKD8FmpubPf+Rv7lcjm54EpXYlWeGbWxsuNlHlaIYGptf8bUeO6CwZi/dsrGx4aZF7TQ3G4rxOydWJDEziSlDnI+TVxBwEZHNZnWC60XZbFbWy7CoCvYzxhylv/AiDm7O3127drmPBPPd5uYmsQBMCEfe3NycQ4wr+9XdPyrgxKTJYMi4OJiIzOaYYN01kwsLC7KvRvU0F2fikOX2vr4+YuW9PvLRo0f5l4czotXVVdX8FVXI2OVFcA4fPqzcvqiekr29vaqz7NProTosbkdHhwepc8HIyIhDeckRxV5wLM3+/n4vOM6jVGKGKB5Y++DgoIcUQb1DQ0M8nN7mcjn2IGwV2+w555wDf/VcrvX1dSZW2GMRcd555wl1LKomvqmpKfqGlZJud3d3qzR2VHMW0+k0vXruc5+rJVtbW2O5mQpIURmBjta/sbHhgGQJCEeYq5K16RvXKBPc6xAJg40Byt/B6yAtYZzSJSFmbf/WsCU2WqM1WqM12k5tZ4QeVigUcrmclztCDjp8+DDWAKR4GZEQVBFzEMNnZ2cVExxmQEOcdwCqQ4cOESvlJZeGh4dRtvgJEalSqfBMBH8k3/HxcdcUpQA5xrwwZjwPUUhaiIRIi4r687xpWRRRIxgC8t3CwgKivVCmoqaEppKjHdEjTrVzKjXbrY6KKPFwFQHbe1A+EuXS0hKB2gwNM1omk2FQdFgIF15kAFvQyMgIRjYApb7yla9wPYIt19BkqqKfxIkMDg56nBh3ZTIZFtQB0YWe7P72kydP0iVWXHTi2PYK8MFkh57R29vLex2TZXp6mqGhWUq95hpVdwyz0UFU3NXR0eHRcSrHilWNOaQza2tr0BivQKhXpA9zjnKza9cuN1ewZIIlc+z86elpVTGOqu36xz/+MbSBiQ+pf3p62uFFBADmdOv10KOqCgv4BlLB8nnkyBF0C5ae0NNLLrkEH4GSAXgmqptD6auQN9PL9uzt7fXSlKoboIpuItf+/n4oBzOAkNjYWWjJ/NTZ2em2Vq7s7u5mDqFhyPXuu++ujeKZm5vz97LEu3fvdp+CyhxChwkwewYoVJSIGB4eTlj1t3M7I3hYOp0ul8seE6gSvUQ0QVJQ8ODgINvYd87AwICYWVQ19EwmA8wzhxcbaXl52TEAZWXGRKCDJiIef/xx9pjHI5133nkOxqHN7Fh8CjyDIt0tofBLz1VSKfpEcCNHkhDr6QkHE5sTM3pTU5NzHTaJhqakBWe9CeArBTSG4cF7U1FgTwUbGxuDATCxiktkTpS0EBH79u3zSZDFFcskaUmSM5g9nsajenp6mG3WXYYvaEMRmxFx/PhxXsHpL+wlJopzXEcet/tBf+DAAU82Yur6+vqgNM7B3t5e3ojTgiEMDQ3RNwfoWltbIzhTcCFMCwsqVEPIgJnk/KWHfX19iAWOUXLo0CGsXqwyGQVnnXWWV1QhjF7FNj0qvampicVi1EyvUp3oEv6kyclJOux4IhdffDE7QtUeImJ2dpaHMz+seHt7O5sIxi9Rj36qLAsbE7GMxRoZGUFi8JQ+ESSTwDoqz4S/PGp+fp596nVV2tvbkRiYEGhYAfc0gZNBFRwjSEIbGxssK7sVBtPR0cGcQKjQ8L59+5guxq60VCjWc2w6OztdyGNHTExMeJEBSKK3t5fnILWwKM3Nzdrm27+dETxsfn6+VCoJjy6q/OmZZ57h9IFMOTVOnDjhjIo9MzMz45lk0Ec+n0eo9ITQtrY2trGLPB0dHbzXAeu6uro42ngmVK6QZddgstmsx0EIqNQhcVUxPVFbiCF4bL24Dieap7hms1mvbCJux68OkBPVcwf2XygUpFyGKWd1ERprFU0NCl8CrysUCoRs0EPOVomNXoYmm80iNwBcxMLNzMx41St2b1tbG9PFgaiKNrUFtZUw68lta2trCmiOashDc3OzFweBowh5ktMc/emiiy7iMHKqq1Qqgubi7a7WC7vZfYoyBjA0mBBEOD4+TrgBXVJaEn5fHqKpg3rhZPw0MTHhhPr85z+f+efQ5O2oLC0tLdzo/rBsNsvwWUee39nZCRl48euenh7mEPrhdU1NTfzKqQor6unpYSvRQwwno6OjzKSqHocVM+KZV155JTns0Mbtt98eEb/1W7+lJOIw/yJ7Ac4kkZRrEgBvXnRbQS5eiwdYr0wmw5w4Uy8Wi+KF6vbznvc8fmViBdCKlqZIHz5D0lzPjpDnFSpSIAzbxMHe9u/fz1aSQ5TJEcyYqG5xcdENFdu8NfxhjdZojdZojbZT2xmhh+3atau7u9uBmlDbUZyjKlbLAeZWC1Iy5STzEMR0Os2Vnlacy+UUMhtVnUAaG7KVkGOQfbB1YOivC7VZKpW8MqHiwbwCr2ot0hnEPSleSGoO9tHW1kYPuZ7J6enpwZiDaKaoS16B0KqHuPiWTqf9afSzXC47ynDdyrBSN31daCqewjWoudJaJLlHxPT0NE4OhFauTKVSaD88k7+dnZ2MlMX1OPuohmYxvUeOHMGF6cDKe/bsYe2Qu+lYX18fU8FfSGJ+fp5fPfMUw1dU1RR5WPmejmUyGRaLX1UX0Uv/KJyViDulWkfErl27vIqpCqkwfOF1RcTS0pIvqCIn0QOYBCT94eFhTx5XmLhb4NF1jh49msAliYhSqeTlGvCxlctlniP3IU92SDbUoNbWVnfZKt6dOMO77747LIne0W8VHc78oKO/9rWvJQadG9Hq8vm8o37LAu8aGHpJPp9ner32SjabRX1k1EzawMCAfEsay8DAgAe7suVnZmbQKR2fpbW1lRQIxWGGIUjJYBjm9+VF+AJVC9cLYy4vL3ONo/AsLCx46KwMkgqX3f7tjOBh3d3do6Ojbohjbz/11FNOYcJCxP3LUYKHXwBi7j9PpVIcCmxLlYT3ei5sv4mJCTaJG6kzmYywtKNqs3rwwQfdiijcKSVXRfXQyefzjMKzdtra2nijk6mCDngF5oLe3l6+xLRCO3HihJsU+NzV1cXYVTE9AUHCe7nFd0KxWHTbqcAahKYfZq7kXyQGQJ4effRRD7XnRXv37uXE91LrhULBba2KXHc8ERZuz549norAQ1paWpg9vBoKaucMcrIZGxtzUQZX0NTUFDcy8zDCqakpTGEYqFUUivMR7iggR16BgXF+fp455MCC25177rnMOZSGnXB8fJxrHFKLEemNyhVzd5pwCL2aHVeq5IcjFi4tLSncKapn5Y9+9CNEQA47mMGFF17oNmHmR6hmTAUMeGxsDNEN95jAMzlPeQU8T3g0cETI9bHHHhO6fBj2G6xFnAmKpYcM4fvf//6rXvWqqIIofulLX4K0nKjYVsViESJh1Cqsw/pisFVCIQH0LAcm1mw2yzFC55nqxx9/3GM6vve970VEf38/c8KNKn4NefAKxdkjIfkzNdvcyJaJKi/082pjY4NRsOL0YXFx0Q8cLNJhebHbv50RPKxQKDz44IOQOxTJemcyGegGalPsGbsFwuJAWVtbg4twruHWnpycdPcPO6e5uZnnOKyf8AA5wgTIy8OhMI7jusqK8kmdseXzeV7h+bnpdNojEhXH6CyQC84991yImKOE06qzsxNW4aFx6XSaUSciRLxgYHNzs+srnnytL8VLmHz3n0V1/8uxERGHDx/mvXoFHzy+VLixHs3B58nJSd7o+1agX8y5EnSYUiRfcRSviygIVNdIdB5BWux/5bTCSj0VVzl5dBse0NraysHEk/v6+vCLMBUwwnw+zzLh1kJZ6evr46CBnKBJwUN7mbSBgQE+MDPS+z3iBgLIZDKetwfLvPvuu3mmwmr4yeGaVMnM8/0VVMmNXi3zscceYxTwJCEb8UyObMXKoljjfWRyLrjgAsQdjmyur1Qqnusm0DgkDHbE9773vVe84hV6L9rY8ePHqZvjbk6FaEI/7Ijp6WneCBHy3pWVFb5UnUzID96JvghJZ7NZiIq1RqSYmZmBxmD5kNbIyAgdVpQjPYHl+0bYt28f1MuN9POhhx6CDvkXItSOYAnoQ1NTkwrCRVXdrFQqUO+OaA1/WKM1WqM1WqPt1HZG6GGEdSGuIjYinqhMIhIT8tfw8DASDdIf9uWnn34aWQbREuV9fX0dsRpBT+AUMohHVa5UJQ6kKuSg2dlZ5EREM4cYjqo0J/eYu5T4qxgnL6Qp15fHTaVSKQc1QBa75JJL0Kv4EilscHAQlUK+GWbMCw0rcUS1d8MQtR0Htq2tzTF/eUhfXx8zg21HsNzqalSNlqurq4jArAsy7/3334/Y6KipHR0drA5D46VCl/BqMs3NzeguDpQQVckdRYovZ2ZmvKi04NIRVL2mzO7duzF+etHUhx56iBFxo6y4COy8DrVv9+66PS0DAAAXR0lEQVTdQqtiPlFi0NJYo0KhgFiN2YDBzs/PuwouXRn1FzVXtgSmgnXBISRQDKYC1fCxxx4DB9lV5+7ubtcz1Ng1AiuJiMXFRYiZbnNXS0sLmh/PxIg3ODjopW2UusBa00+2YVTVI08k6OrqYplQJuQfYulRfeTH9Xol4+Pjbq9md6v2JnOuqrA81i2KXV1dPM3NsIcOHaIbfCknrsJWoxocf8kll0AkbooYGRlxYDBNBVqaSjJFxFlnneUgPoxudXXVIV1Yzb1793pBV7p07rnnipyiaptVlW22npthdkrbFjzs7rvv/tSnPvXAAw90dXW9/OUvf/e73y0zlFfhokENEfG1r33tpptuolrrDTfc8LKXvex0z//2t789MjLCjiI/Ay7S0tLiAeLs8127djneOa25uRmy4wSke8vLy+4O5bCbmJjwvBBoKJ/PQ2c0HtXZ2emmM5Uf45pEkWXHMVJMh/9Ll1pbWx0CKhHKocJRYQyGV0C1nZ2dclOHhU1zJeytv7/fkZloqn0uHhzG2Nxz1t7eLptt1FjhfY/t3r2bwwv+qrpf/Irti586Ozv5QJP1z6M5ZGyEQ7i9t6mpiZlk8nnUyMgI13DQcCIUi0Xm0AtbpFIpIkr8sLvqqqvuvfdevUimJ8e7o8xxLpfjGs6Us88+GxKFtOh8LpdjDqExrNbDw8NcCU+CZaoMDWOh8/v37+fIJq7BEy00k0zvnj17+J6xMBUyJrNNINpjx45BXTB1fF2Dg4PMDOyHU7itrQ3/H89E4Ovp6eHcd+jIyy+/3E3uMoeq3p4eos67B6u5uZmkeNpZZ51FN8gQILZ+cXHxq1/9alRNr1gUf/SjH9EBD5Iql8uITcLWYiEcF1SL6+5G9s65556L25KfEMuKxSKjgEiwn+fzeU4VJpkrl5aW4NmIy7xoamqKHABViWI+eS//skaaHweqL5fLPJwlQCqanZ2lM/wkH4S25PZv24KHffzjH/+d3/mdj370oxsbGx/+8Ic/+MEPfuQjH9GvYlre7r///ve///1/8Rd/cfnll//whz/8kz/5k6GhIUS8Rmu0Rmu0RjtD2rbgYV/4whf0+cYbb9xCo1K79dZb3/GOd1x11VURcdVVV11//fW33nrr6XjYyMjIoUOHkGUQHhGmlpaWkF7RMIS2gjD4/e9/P6oSojRuDBECxUDqRNRCqmppaUHuI4wenWxsbMxFYMHzIEUi0xFX8oMf/MANhkoH9lBAQe4iNnoqvoK4vMRzc3OzVxqjUpTSmbn96quvjoijR486LIXHSUdVEF5ZWeF7x7ZQrU4pW2E2TL+yUqmo9mOcGj6nDhMT/9BDD3kgnPz2jgGBelQqleikIrzDSu6iYcgUjEyaCIfxnFN+KhaL9BAxXMUDHYRF2daItLwOo9zq6io6k69jb28vmgG6ILFw5557Lm9nzp9++mlmA+2KL0dGRlDLeIWMscjOvFGx+Ko1FSaVEwLHJNClZ555RobusHQIVRdTt3O5nL/98ssvj4jBwUFPrqAE9qOPPspCMHbUuEKh4OUL6O3Ro0e5krdjwFxfX/cACsjspz/9Kf8yQLZhT08PM4Oyy849//zz0R6wuOZyOQYF/bBwhUIBmZi/GA8PHDiA/R9VmPm5/PLL+eDZJmtra1iAUQ05N1TFFEpjC4yPj0OHHjKTTqexBqGi8ZOyoVENhaJCZyA/6f0ehMznwcFB3yCMa/fu3UCsef3YVCrFeynrioF39+7djqLH2+fm5rDH3nPPPbHt27bgYd4Erqz2whe+kMyGiy666Pd+7/dQ/++///53vetduuaaa6757Gc/e7pnjo2Nzc3NieCieniJDTjeQXd3N8Txohe9KKpkvbi4yH72wsSlUgljFztQsXyE47M92LEqV+9BXF1dXV5Jj30+Ozvrxm6xK6ddWQgTLjSuV6KYf+kskANUiTg8mWGePHkSgoYB8+RsNuvoUJOTk15QUWg9DNBrr+RyOUe+EPy8J+qp0gQ9ZDfKKMe/jEUoIbyCv0LZwYjEl2zmzs5Oos54ryqMeMykQLAckoNzv6WlhQ+8FwpRjRgaTGhzc5Mp/c53vhPV6LK1tTUv3ckw9+/fzzD5SZVXsRdhkT558iR9Y5bEDHiv8xKJJh4geuLECXdm8KjFxUU4t4do5nI5zlM6AykqRNMhoJSQAN0yn+vr60yp5yMWCgXPZOD07+/v52k4n5jz4eFhxXbrryzSGNtZjmuuuQbGz0HMSk1PT8NF4OIw9fHxcSZfAaXMIQ/HCvfAAw/ApG+77baI+MM//MOIuPjiixExJbBGxF133aWMiKh6Xo8dO+YMg1mdnZ2lA9xOP0dHR1kIr0oRVYMhV6pyJj10D/H6+jpT6rA7/f397BpPLBkeHmbSuFERpBx38DDGfvLkSVaHn5TGCpEIuYpZ5QTYEW3b8bBPfOITv/Zrv6Z/r7nmmre//e0XXnjh2tra9773veuvv/7GG2+89tprZ2dnHQ1l165dqodS29Lp9Pr6OjtQyGwRceedd0INLCc01NbW5pEa/DQ0NMQRw7YXNJE84VGly4MHD0K1fvYtLy+zIT14t7W11Qtk0CqVinvCBNLjaE+0BBAwP0kfcgfv5uYmHX75y18e5r0DDY8zi34uLS0Js0ovSpQNW15e1tmkN0pf5ICQYuHlORKZzoksAp7DGcThOD8/z3R5qYjOzk7OC+ac5VN5ZQ/GWVxc9PosPHPPnj3CSPTOo/mxHBqXIywr/kVYR2Fx4fQTMwDSdzab5bBzsjxx4oQ7V6TyetZgW1sbHyA/oQlDM14Bbnx8HN2dVzAhe/bs4USDu8NL9u3b52n4nKd9fX3wYFQo1bXSLggTO7waNW1paYkp5dSTZ9fdeIzrmWee4XbkJKYllUpBRSwuXdq9e7f7CxlCa2srq0zHZEfxf7l9ZGSEf3GiJ1KsYAYPPfQQU4FBhS7t378f5xPaKlOxd+9eVhkeD2mtr697CTdl5jFSXiFkADLQ2Qsq4IJQwpIxLqE98ZeFnpycZGZ4BT05duyYG2nwQS4vLzsCNaeiAIuZCqalq6sLHCwYG2fg+Pg48+wYY4qm2RFte/GwW2+9dXl5+frrr9c3n/zkJ/nQ1dX12te+dnBw8MMf/vC11177cz325ptv5sN11133v9XVRmu0Rmu0///av/3bv/2/7sLP17YRD7vllltuv/32z3zmMy6LJdqll16K6DQ4OCh7QkRMT09vUXj0la98ZSaTQaJBXEXPEHgHKpqgg7xeJW15eRlxDOlGmOIIWQhuiK6bm5vIX9zO84UDjfFBEB6IY+h2WOSRxRItYQyUiuaBs6qD7NmsiWBoRHvM7jMzM147EYXyxIkTyNHIkoicCc1JFQBkzedXR8liPmXUYkEFpY9M6jX3hDXlYeVyaHk1kObmZgGQh8n7rA6jQCdIpVLyUKpjBw4c8DhMOat4pixv9IRoQwe6BbQsqtYq3o6VKaoCO4KwHEuMRSUQvfQPk1ypVND16czBgwdRj7w2aX9/v5x8UTUwZjIZZoYreVEul8OmxMTSmbW1NZfx+VsoFHAn8zqI8Mknn2S8XjtxcXHRvZ6k2e7evZsrHQtjaGjIdQLoZGNjw/FnFfHoaGFcMD09DRVxu/DAmARf8XK5zHZmKtiPk5OTEBWGkIGBAZaVsSiPmC9ZHXzeb3zjG9Gh2bm05ubmBx54QDRJ6+7uxqWE3ia0FwjPS1orCh8rN1MxPDzs9dypenHOOee41ZqflpeXuZKnCWocxxjkR162Ul/YIKin6+vrjiuGcWV2dvbrX/96VHU7HHtLS0voav/1X/8FwVxyySVyMGOq2eZtu/CwL3/5y7fddtstt9ySOHYT7eGHH2azXXbZZXfccYd42B133LFFUCJL7hlIkNTBgwdxbLoLWtWAsAhD+iqFxe7iAsUx4//EoHTs2DHPm2H/VCoVL9XK/hkZGRFgY9hB7/HoNJVsFpwVV3oyluIXHKpAYFT8Cw/D6zs3N+co5uJ5DI0ghVoeQOP04TylNTU1OXqTyqx4xWeFmbAQ7olUiTKVS2aqWRfEDtWuhf3QeUVhyLcUVbPhzMyMV5GnD/v37/ecIVXGca7MWbC8vMyI6Bj17J944gnGznRxVj7xxBNIAwow4ZnMJJ0XIgPsyoNHCoWCl5YeHx+H8XjRtZaWFiYNfgmBzc7OeqU6LlDSIX0TQ+IkdV/X/v37v/jFL4bBaEXEOeecw9LzTGSsjo4OD2Hn5M1kMgzNI4M2NzcdnJ4XHTx4kHOZSWYHnX/++UhIIgB6whIwFbgJi8Wiy0A8amBgwG9kxp5++mm3eTY3N9MNd6Befvnl7FO6feedd0bEK17xCngSxmHEhYGBAcfN4skXX3yxAP6jepjkcjkvdAeTm5+fh53gaZMIqwIIEfGSl7wkIh555BHGwk8YOS+88ELhb+n2fD7PQsCGNb1MAsTA6o+Pj7NYPI1ZvfbaazkJeR3zmcvlYFQQAFO9Z88eh8jZ5m1b8LC77rrrc5/73C233FKbW/e2t73td3/3dw8fPtzW1nbvvfd+6EMfesc73sH373jHO8bGxoitv/nmm2V1rG379u1bXV2FnUATeDV2794N++E45vPRo0fhMWxg2MzZZ58NGXGl6i94RQyFJ+ClQGLiUa2trewZyFr1Ko8cORJVPidEHA95kA/JY/l4kUog8le+n0T2T9RUS5H6xXEAS4P019fX2YHQOqFx7HNvHu8Hn9AhKw9cmHPOi9CrFhrXS6f0jBks9U8//TRhUS7PHjx4kG3J6qDfjI6OIolzxPO6J5980qMr+bK7u5vZ9tAMT4JWH7LZrHutdP6qmKEWV4UN8SLwU6VS4XZ4ia9UVN1+4vcumlQqFVVv8amGIzJLksN4An/pzL59+1hWCFXge/SKoTEtx44de/WrX60rUUEWFxc5vLge4j969Cg0g+SueA3kd/flKN+WdQEZq1KpIP4zBHkBmVJVgGPFOZcZAlM9OjrKxCK8wgIfeeQRJUTq7RdffDG7gBtzuZzn+fLTwYMHeQ7PRFq66667XvnKV0aVh0FgmUzG4YzhZKrSyR78P+3dQUhUXRQH8DcwmBZG6ZBOxOTGvkVTpCITbdJclLtCCIJoEYG0kGbZ0hbVLoJaSIIUtKhdSy0Dh4gmRG0gXKSQoJFMMzW+mciRgWnxx8NhZpw+4vsc733/30pnxvG9+8575757371XHlnEiYxTXvrYcFhxZuHFRCKBz+DbUObBYBBVW73kk2RuRC/eOn78OI4y8g3OoEwmg4yIs0YeDMEW4lokY+oRPwh+HIhAIKDHVssTSdgMI+yIHDY0NJTL5RDxMD09jUK8fv366OhoIpHw+/3t7e3Dw8NoAOno6BgeHr579+7y8nIoFLp16xYHhxERec2OyGFonajo5MmTmCK63Llz5zB95x9ls9nm5mZU1lCjQU2ttbVVr3qHqllXVxe2BzVoVEU/f/6MX/XiBfv27UNFDK0TaINaXV2V2VcdNTsU6l9o00fz96dPn1DfREsIKrCFQkGv0lkyPgxVM1TDf/36pRcmxyfz+by+gZMhBNJo5mxWHqWPDU8849bQdV00JWHDZM51vRCzNKvi22SSEdRz9ewbGxsb+mZLYPv10uk+nw9VYLS5ob4vyyvrJwnj8TgOFsYzoFK8vr6OCiYOLr7fdV29Rqjrum1tbTIGQM+M9eXLFz0rvKx1i+qtnoeioaEBN22oYKECm8/ncVOIw4qNl24/mXHccZxv377pKrC0WcmM/igfbAb+UNrKEJwoELx16NAhmb5LQlrmP9N9e+FwGFsVCoWePn164cIFR/Vd4Q9liBv+I+59cY8eCAT0ZNPSWI0qPz6JNoxCoYBbPdzBI4pmZ2exaziDsPGFQkEP/kOTfn19vX5IHcGwsrKCuzqciTgcEm849aTrWs8gIxPK4MtRx/3+/Xtzc/PMzIxufH716tWZM2eczaf4ZDoxPYgQI22Wlpb0ED1cPfbv36/XUMaZKMulYgQbPi9dmBhjJzOPIJD0naXP59OT9+PecWFhQa93ivvadDotDxVL0R07dgztOiAd8GhvxF3g4uJiJBLZs2cPvlxPz9/Y2Mi1V3aWXbt25fN5XLwQrLFYzHEcv9+PG3BcoWQlDpyBaG/EKSeT2uFqJePJ9Nqp0N3djRMDlwbEnEyJhiFE8og8zhl0kuurm6NGhjmqhbBk0ZPySRSLxaJuS5Rv091p6CtuamrCZ9ATgGtcMBjEpQHN/XIRqbgcM/5ceuZxvUBFQXoBccLrOa6kXRQkvenny+X0Q6JCecqMf3oHcYYvLS3hfMaVAn+ezWZxEZGexdbWVlnLSu/Rx48fcUBxsUMq+vnzJ05jhA0uDT6fDykfXUHY2mw2i4ssWjtxNX/58qUe5oh9b2lpQfHim6WzCqkXtZzdu3cjqNA8hUU9fvz4oTufsLW5XA7lo8cOHzx4sHyRIFk4CiUjkYwKBA4WEtLXr1+xF/hHKKtkMoky1/PHZzIZtLkh+eG6v7q6qkfx4/Ld1NQkjxE5m5lsZWVFrz+CymgqlULJ4MChdct1XeQPZHFEZn19PYoUD6Rga4PBoBx6lDbyFoJQZkrr7OycmZmRhW9QdOj8xudR5gsLC9gqfayPHj2KnUIzoIzhQ+Toyf7X19dRFJjxQJYMRDzoNYlQw3bUwDtHTW6HQywrXcj4LTmOLS0tsl62s3ktkrlG9TDtw4cPo2RQk15cXKyrq2tra0PA6KZd6Ws3AuetJyIiUzGHERGRqXwG3TP+nfKZ74mI6I8qzre+09ifw4iIyFZsSyQiIlMxhxERkamYw4iIyFTMYUREZCrmMCIiMhVzGBERmYo5jIiITMUcZo9/yuh3JyYm+vv7w+Fwf38/1ruzTDwev3jxYvmQ9io7bl+ZVCwETwVGPB6/evVqZ2fn6dOnb9++rReV9VokVCwHC4OhSLY4cuTIVm/Nzs5GIpGpqSnXdaempiKRyNzc3HZu2za4fPny+/fvSwqhyo5bWSYVC8FTgXHp0qXJycl0Op1MJqPR6M2bN/G61yJhq3KwLxiYw+xRJTpv3Ljx+PFj+XVsbCwajW7LRm23kkKosuMWl8m/z2EWF0KxWMxkMt3d3fjZm5EAuhzsCwa2JVrl1KlT4XC4r68vGo3Oz8/L63Nzc1g7FHp7ez98+FCLDdxuVXbcU2XizcBIp9OyAo6XI0GXg2NdMDCH2aO3t/fevXvv3r179uxZT0/P4ODg69ev8VYqlcIKT3DgwAEsp2S9KjvunTLxbGA8ePBgYGAAP3s5EnQ52BcMnlgD0yNGRkbwQ2Nj4/nz5wOBwJ07d/r6+mq7VVRz3gyMJ0+euK47ODhY6w2psZJysC8YeB9mrRMnTmCtdMdxAoEAlsGFZDKJVXStV2XHPVsmXgiMsbGxiYmJhw8fYqllx6uRUF4OJSwIBuYwa83PzweDQfzc0dERi8XkrVgshmXXrVdlxz1bJtYHxosXL8bHxx89etTQ0CAvejASKpZDCRuCodYPldB/5sqVK2/evEmlUq7rTk5O9vT0PH/+HG8Z+tTsX/D4s/VQUgieCoy3b98ODAysra2VvO61SNiqHOwLBq6BaY94PD46OppIJPx+f3t7+7Vr1/RTRuPj4/fv319eXg6FQtFo9OzZszXc1P9DyWhNWYK2yo7bVyYVC8FTgdHV1ZXL5fQr09PTe/fudTwWCVuVg33BwBxGRESmYn8YERGZijmMiIhMxRxGRESmYg4jIiJTMYcREZGpmMOIiMhUzGFERGQq5jAiIjIVcxgREZmKOYyIiEzFHEZERKZiDiMiIlMxhxERkamYw4iIyFTMYUREZCrmMCIiMhVzGBERmYo5jIiITMUcRkREpmIOIyIiUzGHERGRqZjDiIjIVMxhRERkKuYwIiIyFXMYERGZijmMiIhMxRxGRESmYg4jIiJT/QZNncjTGMVKuQAAAABJRU5ErkJggg==\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.5px 8px; transform-origin: 137.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe pointer layout of the image is [1 3; 2 4].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 256px 8px; transform-origin: 256px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn a four value vector that remaps the scrambled image into an original form.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 203px 8px; transform-origin: 203px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe displayed scramble is [2 4 1 3] making the solution [3 1 4 2].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 340px 8px; transform-origin: 340px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.5px 8px; transform-origin: 363.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis series will explore various puzzle pieces, orientations, sizes,double sided, and ultimately DARPA shredder data.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 326.5px 8px; transform-origin: 326.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMultiple methods are provided in the template to achieve re-mapping. Which will work and which will fail?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = jigsaw001(pc,nr,nc,pnr,pnc)\r\n% pc cell array of jigsaw pieces, all the same size and double\r\n% nr,nc  Jigsaw piece counts rows and columns\r\n% pnr,pnc Jigsaw piece size row pixels, col pixels\r\n\r\n %Four scoring methods created\r\n % 1 sum of abs delta       \r\n % 2 sum of (abs delta)^2   \r\n % 3 sum of (abs delta)^0.5 \r\n % 4 median of abs delta    \r\n %\r\n\r\n% Brute force\r\n %Try all piece permutations and score the piece edges\r\n  v=1:nr*nc; % Total pieces\r\n  vperms=perms(1:nr*nc);\r\n  [vnr,vnc]=size(vperms);\r\n  m=zeros(nr*pnr,nc*pnc); %matrix to hold created image\r\n  \r\n  best_score=inf;\r\n  for i=1:vnr % cycle thru all permutations\r\n   vidx=vperms(i,:);\r\n   m=[pc{vidx(1)} pc{vidx(3)};pc{vidx(2)} pc{vidx(4)}];\r\n   \r\n % Four scoring methods created\r\n   score=sum(abs(m(pnr,:)-m(pnr+1,:)))+sum(abs(m(:,pnc)-m(:,pnc+1))); % Method 1\r\n   %score=sum(abs(m(pnr,:)-m(pnr+1,:)).^2)+sum(abs(m(:,pnc)-m(:,pnc+1)).^2); % Method 2\r\n   %score=sum(abs(m(pnr,:)-m(pnr+1,:)).^.5)+sum(abs(m(:,pnc)-m(:,pnc+1)).^.5); % Method 3\r\n   \r\n   %sort_score=sort([abs(m(pnr,:)-m(pnr+1,:)) [abs(m(:,pnc)-m(:,pnc+1))]']); %Method 4\r\n   %score=sort_score(256);  %Median delta                                    %Method 4\r\n   \r\n   %fprintf('%i %i %i %i Score: %.2f\\n',vidx,score);\r\n   if score\u003cbest_score\r\n    v=vidx;\r\n    best_score=score;\r\n    %fprintf('New Best score\\n');\r\n   end\r\n   \r\n  end % i vperms vnr\r\n  \r\nend %jigsaw001","test_suite":"%%\r\n% all imdata 2019 are hosted for cody at https://drive.google.com/drive/folders/1TZkBMEEKHiFJExqVoJgj5VVeHbvOfTYB\r\n% a Text file of matlab urlwrite links will be added in the future\r\n%camerman.tif\r\n%https://drive.google.com/uc?export=download\u0026id=1WNWSIp29e_BM47RSiNwom9zMSUTUVKv7\r\n% future will show cody matlab imdata location and how to access\r\n\r\nurl='https://drive.google.com/uc?export=download\u0026id=1WNWSIp29e_BM47RSiNwom9zMSUTUVKv7'; \r\nfname='cameraman.tif';\r\n%tic\r\nurlwrite(url,fname);\r\n%toc\r\n%dir\r\n%figure;imshow('cameraman.tif') % valid\r\n\r\nm_cameraman=imread('cameraman.tif');\r\nm_cameraman=double(m_cameraman);\r\n\r\n%{\r\nd1=sum(abs(m_cameraman(:,128)-m_cameraman(:,129)));\r\nd2=sum(abs(m_cameraman(:,1)-m_cameraman(:,256)));\r\nfprintf('col delta scr: 128:129 %.2f  256:1 %.2f\\n',d1,d2);\r\n\r\nd1=sum((abs(m_cameraman(:,128)-m_cameraman(:,129))).^.5);\r\nd2=sum((abs(m_cameraman(:,1)-m_cameraman(:,256))).^.5);\r\nfprintf('col root scr: 128:129 %.2f  256:1 %.2f\\n',d1,d2);\r\n%}\r\n\r\n\r\nfprintf('Original image\\n');\r\nfigure;imagesc(m_cameraman);colormap gray %\r\n\r\n%{\r\nfigure;plot(1:256,abs(m_cameraman(:,128)-m_cameraman(:,129)));hold on\r\nplot(1:256,sort(abs(m_cameraman(:,128)-m_cameraman(:,129))));\r\n\r\nfigure;plot(1:256,abs(m_cameraman(:,1)-m_cameraman(:,256)));hold on\r\nplot(1:256,sort(abs(m_cameraman(:,1)-m_cameraman(:,256))));\r\n\r\nfigure;plot(1:256,(abs(m_cameraman(:,128)-m_cameraman(:,129))).^.5);hold on\r\nplot(1:256,sort((abs(m_cameraman(:,128)-m_cameraman(:,129))).^.5));\r\n\r\nfigure;plot(1:256,(abs(m_cameraman(:,1)-m_cameraman(:,256))).^.5);hold on\r\nplot(1:256,sort((abs(m_cameraman(:,1)-m_cameraman(:,256))).^.5));\r\n%}\r\n\r\n%size(m_cameraman) % 256 256\r\n\r\nnr=2;nc=2;\r\npnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=m_cameraman(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\n% scrambled image\r\njigsaws=[jpc{1} jpc{3};jpc{2} jpc{4}];\r\nfprintf('Scrambled image\\n')\r\nfigure;imagesc(jigsaws);colormap gray %\r\n\r\nv = jigsaw001(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=[jpc{v(1)} jpc{v(3)};jpc{v(2)} jpc{v(4)}];\r\n\r\nfprintf('Final image\\n');\r\nfigure;imagesc(jigsawf);colormap gray %\r\n\r\nassert(isequal(jigsawf,m_cameraman))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-06-14T22:50:25.000Z","deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-14T16:43:17.000Z","updated_at":"2025-03-02T14:00:42.000Z","published_at":"2024-06-14T22:50:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to re-assemble camerman.tif in grayscale from four 128x128 pieces into a 256x256 image.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe pointer layout of the image is [1 3; 2 4].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn a four value vector that remaps the scrambled image into an original form.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe displayed scramble is [2 4 1 3] making the solution [3 1 4 2].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis series will explore various puzzle pieces, orientations, sizes,double sided, and ultimately DARPA shredder data.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMultiple methods are provided in the template to achieve re-mapping. Which will work and which will fail?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image2.png\",\"relationshipId\":\"rId2\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkEAAAGxCAIAAADqHPV+AAAAB3RJTUUH6AYOFhcTwMqrVgAAIABJREFUeJzsfWm0pFV19q6qW1V37rkZhKa7aWWSUUQUkEFBMcZoErOyMmjiWmrMitEkKzERsxwSY9SlyyHOQ4IDTomzERRUCCBzyyg00EC3QEMPt/uONdf341nvs57a59Tb13xf4q3vnv2jVtVb73veM++9nz2cQrfbtUSJEiVKlGgAqfirrkCiRIkSJUr036TEwxIlSpQo0aBS4mGJEiVKlGhQKfGwRIkSJUo0qJR4WKJEiRIlGlRKPCxRokSJEg0qJR6WKFGiRIkGlRIPS5QoUaJEg0qJhyVKlChRokGlxMMSJUqUKNGgUuJhiRIlSpRoUCnxsESJEiVKNKiUeFiiRIkSJRpUSjwsUaJEiRINKiUelihRokSJBpUSD0uUKFGiRINKiYclSpQoUaJBpcTDEiVKlCjRoFLiYYkSJUqUaFAp8bBEiRIlSjSolHhYokSJEiUaVEo8LFGiRIkSDSolHpYoUaJEiQaVEg9LlChRokSDSomHJUqUKFGiQaXEwxIlSpQo0aBS4mGJEiVKlGhQKfGwRIkSJUo0qJR4WKJEiRIlGlRKPCxRokSJEg0qJR6WKFGiRIkGlYZ+1RX4H6djjjnmV12FRIkSJRpIuu+++37VVTgI/f/Pw8zskksuKRaLnU6HV0qlkpnpFTPrdrtmViwWC4UC/y0Wi/jebrfNrFwumxm+4y8+iKe63S6/9KsPbiiVSiiH79W3t1ot1rNQKKAy/Imn8ODQ0BArdt99911yySWuaVu2bHnggQd+qR7DK/DpfhYKBW1vlNj2sCvcU+gBElrBe0ZHR92dlUoF9zQaDTZzcnJy7969Zlar1SzrJcu6FG9/ylOesmfPnoWFBTyi1XAtwnd2b7Rd+MJOPuhYu58bNmwwszPPPPOiiy4ys3q9btk4sjJaeRIuNptN3ImW6v3tdnt4eJj9w4so3Mze+c53vvnNb8ZfOsf4Oh1rPNVsNvUVeKpYLOKn1qHT6bD3MFj9uoIvUhoaGkLhOtv503WLDhbriS+4x81eUKlU6nQ6f/M3f/Pe975XuxeTSnubM5Ndx5pbsIHojoH72RW6V3DH0Pvb7TZKw7Bi+DqdDm5GaayMlsk6uCEI6+m2JtDrX//6D33oQ6VSCX3O6aeve/3rX29LnhKWmChRokSJBpWWhR5WLBa73S41KsvEDVx3F7vdrmpgEEko87o7VeKDGEWFjzKXBZI+pTAVWkkQi1hD/cspNCoIV6tVM7vrrrtydILFk6uY01pyNDDtkKgqQ51SP1kmNQwzK5fL8/PzrAYkZTNbWFjgnevXrzez+fl5SJEQ/KGNdTodp03WajUOWbSXcrqOf23ZsoXvve666yxQJXMeZ02gh/385z+/4IILWG2K2BhQlbXZk04ddw3U7tWBoM6kEjclfRW9S6WS/uT9qvRQZQnXTlgZFOI0Ie1PbWa9Xle9wa0jXYBcuUrlctmpPu4RCxaUqpvWu5CHhoZQjgNdQFBzseg4AdwWoToiO9A1H0+hcGhgVDfD7i0UCloZwjkoR++kdgXi1qdTi5XUEVdVclBokOr6f0PAEKx3eczOzipaxZEmNGHZFGm1Wriou0CxWFQwkPNMkQHuF/xiAovhvXrR7QUoRMuxACDSdkXB618KSAxRNQu24BxSDDO6r7m9z13UTbnRaDiOaGa1Wg3bx4oVK8xsz549ZjY2NjY5OWlmK1euNDPgilNTU2Bm6Njt27ebWalUUkiK1dM9JQdLNLMLL7zQzM4991xeuf7663UaRMt0XXHWWWeZ2Wc+8xmMoGKJrKHb7FSmycESyaSJ+KGrOXMuueQS/uV2PX0dfkaxRMpqyhXcVNFPbrJRSUi3y2q1irq5B0G6fVNAUSKWSEYYjjW64j3veY/WttlsKpbo4DtXDYVJnfiCn7oq3UBwx9DKs5AolqiSNDvZbWWKBuOvVqul049grFb4Qx/6EGaLttq1fSAoYYmJEiVKlGhQaVnoYd1ut9FoqAYGsYsyoEIQlF9UArJePYl3KhJCSVYBH0phaj8HUc9Qtc8Zb3NMx/T+UAl6x44dIT65GP3J6Rw5UJsz+zvKgUBZ+VBrsV5ly8mqKhvW63WozrOzs5Z1GrHEAwcO8MHR0VHoajMzM5YBjNEGUuUKtTF3cWRk5OSTT+bFP//zPzez4eHhq6++2jJ1Kr8DUbcTTzzRzBYWFlTtIIyjTj1RhRjUbrfVz4jTFS3VQihr60BAnbVeNI/qEX7Ozc2ZgF1oIFQQjo461/Bfxaz4l05+ogiqprifbu1AQWEnq/5HFS0K9SuqRpBN8VvChg6w1Qcx0yqViiqjfHsUfFONjQqf/sV9AxUeGRkxUeYUzmX/qE+Zw+EdqKuaovPp0M9ut6vzB6OZby9YarQseJiJ9UinPuBsvcdkEuhipmqvC956+QeXgZbDyafvdeiKrp+hoaEQuKMHo2NaWiZmMPZr6937CKosHhJUCq1iBzW5sbbO/oe/FmmLYi/pLlkul3UnJeNX0wUfBLdbu3atZextaGgIXaTbU5QcEoW3b9myZfXq1dZrt3v1q18NI9kXvvAFy4ag2+sDxp5HtXfs2GEC+OjmTisptkUwpGq1qjsU2ZvaY4hdO45ogiWyaSb2RZXqms2mgtjcf9EJyp9c06KWIe6/zjvORAJTjsgX6U7NVmDcsdHXajVlk1ErF6Ulhdra7TbeqB3COalvJ0anlW+327rjs3pRCUxfhM9qtRq6IrM/nZSMVjuMXSVd58iqexGXnm4RHAjtpXa77dqibR8IWhY8rJ/e0Gw2dYI6fFl3DYpFTi4G6TZXLpdDyNsB2Q4xd8pWDodwDdE7d+/ebSLoKTlrU07JUW3MmXbCf/WnM6dFO0Hf6P7SatAXwI0OehteFejJnTt3wtFDLYvdbhd7KD6xyR5yyCGrVq0ys8cee8wyDhG1XZGXKL34xS9WEZiNfd7znmeZu8dHP/pRM3vooYfwiIvfOPzww83s7rvvNjHqoIY0yahyxp1X5xg3LxXS2XWOkVuwS4JarZZuhbRyRQ1aujuzTK0M+S7Kgdjh7FJKHH3Viiho6oucasj7Fe3gd8eu3IBa4Ifllp62xXq5F9es2rkd6wWxW7QPWbKOozPA61Rxsm/ORXqyRI15vMfMFhYWHNfEX6rxUzkeID0s2cMSJUqUKNGg0rLQwyDwquBGz8CoB05o+iL04QwnKixT1CIsYyIcqZjj3KX0p0O3CVyo+OZieKFh3HvvvRZI/aRFSlX9blvM49F7QgtTFFkisvfsZz/bzG655RYLjAdUejAi+/fv58WVK1cCYoIvPnQyFgtlCxhgrVabmpqyPtHi+joOhFo1TjjhhNBwUigUoHbAY/4f//Efzewzn/kMPO/VDlcsFp/5zGea2W233YYrilZxpqlznTOSKYbJWaG4X7PZDD3mCRuiZNS2XC7jHoUBW60WelIb2Gq1FEV0oLfeab1R6m5KREEI4L1OSXIQa4iZU+FTfci9jjXUYWWnKdQ2Nzc3MTHBwgFajo2NYf4oXMmBAHFRqyYNIsbjArQdUIFPbQtrGy55hw1QOdZRdluTKvp0hNbaEgxQexj9JweClgUPY8yN9RqfON7KtBz0QUd5DrkF4EMUwnJYWTiliCEoDwuNTyhZVynhLOw+qOHWrVvDhv8PAQKLL1ZbTRwjfJx9fsMNN1gv53PEpQUHeu4a6AQwqn379plZvV7XcnCRCz6KnWqd3c9zzjkHTVDRhK1TmAuD8oY3vOHhhx82swcffFBLQ2KOK6+80mRWKPrn8G00sF6vq+tzFPdjKIizpOIL/sVEBZfilMa2xXARbT6HTJmKM1NpCEqhUNCLbsjcTzyiqKM2Te905i7tJV1ro6OjKjGwH7RDiJLp4yMjIyocgGZmZtRW56RD/KTcELpauKZRGtD3EvnUESTTCqWlMMDLhAlp17nYAw6fS/BhYgF1BsIBwhKXBQ8rl8u0ozjvndDISVAe5EzQILKu0GDTzcIYnUSpTgSY8bwSjb7Ut7NwB2Sr29Xtt99uwf4bXcZR4m6Ywz/czTnGOXdFGxi9n+ZGdeLiolK5wb0F63b//v0a5weZulgsQqBWM3ixWASPUVbkOsexN/x8yUteYmLhU4XG+aRx+wbXVCqVSnRJMNEp1TrCYQVRxNYthqKP7qTOpcjFKqk4j26hg4AyjEJvBLqbmcpx6S6or+MXFRS4RztTkG7xLuZMiyoUCqywjovWEH/VajXnuuJcLk3sqQoAUOfmMOkrQKy8vtFNUa0h5WZ6+lkfB8hwrLXyqgUWegPYaTfVFtHjKaySu5P977xAtb0DQQNT0USJEiVKlMjRstDDID2FIBLxZQX6u90uJBQXYKHeYiCKRSCnsanE5HRzysgqKznkROvpBGEqQBDZvvOd75gZzDy808mJUT3MqY8mepjDWqMP5tzD14V+UP0KCc0M0QAGDpkOR7vdRrgY9BunGDmfsRBuckgvv6P31qxZY2aHHnooXhr6B3YzV0CFnnbt2oUqaf+MjIxAOWPAk5YD6vb6JdKtXGejA5ScNqlvpKwdPu7Gmo8rzOU813GngupusDjPdQ6zJ7W0Tm+mWo6OVoZAsfNyNDFBhXqblqmrgBVzQJyJmuugb32cNzhwz4Tcag1TKrfbbVWI+ToHJuNxRY9di5xZ1GFL2pPqpkjvVjdGCgawJsketrQIU0f3U85I5R8cWkW0HDlAUucN566W5tAGnZc0cuhKoMuye1GI7RBj+fa3v62F6BqL2rrdHh2NrdG3h17mbq8Pe4mLOeRehd5gNRYS9rYTERwwpWubNVTYsFKpaPOjQ8aGhOYxvvriiy82QepCzu1qjordf//9ihqh/KOOOuquu+5i4evWrcMXsFWGE6k7D7c8GlS0e6NZ3nXrZG0VNmSFtW6Uq/Qegt4aoUXfATXtsNN0K+QYhVIdJUXNNtBsNvVBvk7RTscsFakbHh5WOxxr6AZCw7ZYsej0Cy0F1jtvOVKO+eFO9ZJwicSUOp2OjjUFGuf3b8KutAnkyi5mQIFfckcVtpxnvwvps8GhQaprokSJEiVKpLQs9DDKGhYklFKhnhKiipYEG1WLd8Kg/kUAxFmwVbx19vOoS4gKwk4kxA0jIyNf/vKXLct76wAf/e6wMn6GkGBUs3T+bw7wcXqYq2eo37h7omgn2+KUibCGLN/psv3K7MaOrYqiMd1uF+LqGWecYTJwObqdFnLHHXeESuRpp51255138uLGjRs12wileJWLQUSBFK4kmAyiFK/KByd26A5nvZI+ozj0Iv9ScE+dYkzcRkzgce1zKgp4kGWqLzgrpg109XQHXIUwNb3+nEKjk9BBZJwVqnnw/tDZissT9WS11TmejjP4qdnoiQaptSI621lV5/HogBzUNlSIo/A4NVrnHhX6UbMnB4KWBQ9TFuUg/qi3W+jyREOImi6ijshcXY5Z6oNEhBR84E4dtdupMQD3z87OAkV0kzXc4q0XkXBXcgAT/tR/HUvT+8NXhAwjyvNc9fJxv/BF1ouy5pvo+pUQtuVpT3uamR1yyCHWy2ZcIaynwpvbtm3T9+Lz+OOPv+aaa3jPiSeeiG1IB9dk0LVKIcLc6U1Yzs097DRmSHJZaUIzjLMMORlI2UA3FsvFEXf5nELTl/Xyzijw7up5UEyYDJiMShFU56/vHtSed7YrbWalUgH70VabhF6RotIA+0fNVIXYuU6FzDva2c80ZM3xRZ1+nDAqDUQZW7s31xQbm+xhS46oE+isdY4eTj3SqU+DBMiBzjon+LhOU8qnKlG6XDhunUex9aIENX/+85+fnp5mlZxm6QoJ5a8ovm+9y5jtiipertXhPcXeiE4+lWN7cBfDO/uVGWpCrhNyyuQ9TodATJhavN3o8CCe0GWZjvW6Zx122GFwvcHFTZs2ufy5FkTmuhB7nSTOaEF+phs3JSe1o3AH1NIYaxiK/xabFTQ+qcDOBeXU4lDc4Q1q0uM4ut0/tNu5ISYbdqefa924qN3K0n52/hdsFOsZTcLUznIb6ixi/gS96GYva6KCtZNlnf0+2iIE9UPhc1EKOieZrRHEOoQiqfUO6xKnZA9LlChRokSDSstCD4OsobKhCw90OpAGzLuMLKp8MHLZuReqkE4hLhR2nHsxRaQQwXCIOWT8yy+/PJTponBBpzcwM+yc8GJU7QBFjVvOK5KFhIVH9cWobsS26D2uQPZqDiLarwLW20vur5GRkWc84xnhi/RBHleo70U2YZclBCHYzWZTwcO1a9ficBN9nNNAXRAJVTnPwNCznE5urkXhTCuXy+6YVi1HDTasm0KRPBhTZzsVKedJGCY3YapZVX14KIGzgzpDmlZJPVHHx8fRFiYbC2OcidGpOlgqlZz10WT66UAUeg9PB7m0Wywk9PRj/+hYd7L8+qolO2MHi9IJwELwIFrttjI3Rrp8uHcxv5ROm9D6sGRpWfAwWLN08mGi0NkU5HzNdYUXe/MlgtrZkQ0KefNxvZ8YgiIDLh0AF7/OM8KSXORmdtlll5lZrVbTCermXNT24DiZs3noX25CR8uMMr/onWGHuGq77Tj/vdGLIZPuxrLmO8sHCwlX7PHHHz8+Pm69lrBut6tWBwLFuu/ccccdFthdEF5233334UU42KxSqeBEGBC32kJgUqLXiUuwFA5rp9MJj5WJMn4yIeePHu7RDj12W7x7PJTAmC5dlwCTRekKcmPKOYnYA1eI9jB9HJAwk8h5CD86LuImgG4OlUrFnYVtYi90oKXWjekrFSZlDlVNpupkkag1AUR+oyKC6zS1yTls3xXoLJH6oEMyB4KWBQ+bmZnpdDpI8wPCUNXrdZXjaLqAfO2y4GiB3MKiUyQ0V1Aoc0pAOFE6sfxVTLqKJIH/9V//ZX20nCh1YwE6bmNyCo02JDQvRQH0kAlFfQ776YJOhdIK5Dzo9qDo42HFwkJCGwmPWdEIpFLvQW7cztROeeutt4Zvx8mZ9957Lx7ZtGmTiQCuI9JoNEJFqt+4hPFPPBIv2iGOwYQvchM1x02JQ+YSyDqHPe2f8IwP653nlm3E0DbcWVaausm9iEU57xjXXRYsuuisc9xOH3fZLMnp3QZi4tqnjohMVez8y/DTRejr3CDTCrvX6UxkxqFRn20JPVdtEYt0yVKyhyVKlChRokGlZaGH7dmzhyKMioTNZjP0NhweHlZ5E8fyrl69Wk8KZ3IBld/5PTyZkPJaKJBaL25jvZACXaKhGn7xi1+07IT4bsyNvtCb24IUwoaU/hyFuloIjkfRGIfYWJ+sBK5K7rtzL47eE5bpxEyHiOrFbiwlh2s7unrLli1OxsenHiqNO4tZYheItI888kjYS3Bx/MAHPoC/cMQMa+gQQm0LLToqerOXFEWABkbxX5UkSuVuHLUtlM11hlNpCH0du72Oc8604zQY7W1qJKrqOYXGeeg5738t04UcOIdyB+9bkD2ZSlLoHM93OcO2jk4UCafXsXas2rp4D3spTOlSylLxKhxKe6FqV8S39X4iN25FOLOF9pKOo7NoLHFaFjwMdi8mDTMZy3DHbzQasI4qlNRoNMDDMK3HxsbMrFKpaLp0LhhnnzAxmaj+HoUNnScxH4fh5KqrrrIAJoqaqaLGJyWuHActRg1LUW6n73Xu++yQEAFzhbOebtsK73Sr0bG3kF25nswxfTk677zz8EWN+eAQYFosBNtEtVrFvzDGMB+jDsTGjRtNHO6PPfZY62NPLZfLCpqRYWD66X5KNqChtTRT4b30hgjHmkOmnKxareoQcB/UeGQOtBN0TFiRboudLDJX5Tm23eUD1Ffg06VgJwqnZfJZlVMd7scxDc9JYauV6AHh4DiQi5zRVnMqagA7u0KlEBAZBnFg7R8lxvnp4+TxdKBHk0PWS3alHdJoNPQAORV9BoUSlpgoUaJEiQaVloUe1mg0irHjjggGKnzXarXUFc1JsipbMdenSp3d3jzCoKjbfTd2VKtDCHFxeHj4G9/4hpmpG5sFWosF+lYUyXT/5qg+/YrNeaPK5oXeE6qcCuUKCS/2wyGdid4E7Yxqn66ZUSxRxX8cFcYhgw8hFO5KpRLiNsyQhNOZ3fkGqgrU63U8iFz4PHPZebeHYJfFYnsdRscecNqSiSakYIAL23cCvjvfUjVFh2FEHWec+K++iw4r0/5nKgDnD6XNZBPC82NZJaogURjAJdCxwOncge3OB0RbHQW92b2qSUerRAw57EPit+5MA72T2pJOOYCN3Ft00+MmprBhuVxWp3yHOg4ELQse1m63MbSWDRUAn5GRkdAxiSCJevQWsgNqo/tLdAFHeQOxQZOdV1cj84m4oJbvfe971mffd5tyyAYcaMlKhlhQP9LKR3HFKCdr9571ACI05Pa1KNoZLTx6T4glOpbZr4b4Cz83b95sZqtWrTKzhYUFrSfWeb1e11gl7ubgcEB6+XZ8mZyctMxZsdPpgHu5maNwE6EhvIh7Vmhu5I7meiAK8SmPJz8LHejpEa4Xu71+m3REVJDN2QujfR4dDm1RvgOklsbwMr2/1Wppp7Eyyp6LWZSCLk8mmdQyu1lmfcd1Qpyt25thVaeiiYXJhAVqTxazwBsHRarQTONoFN4Mp0Gz2VTmR+DU1c3Ea9QZMpM9bGkR7KuYMWp+5zbq5D5MJpc1Bw/CxQOGjcnJSWxJq1evNhF5nNHLZB1G0f/QB4QPYk3+4Ac/gAbm1n/ITroxJ+zoTxoInUqh+4VjWlFmGVV93E/VNg477LAnn3zSeuOuojzMkVYpbFrY6n7rMMqtUewFF1zAG2ZnZzHKTm7QXQAtKpfLK1eutCwyzEXtIFAaqX7b7TYMYy7KWKlUKqlnuWPSTt3UfYqMUGvoDDaur0Le0Gg08NMpAaGfNzmoqilOLGPrtBqhGqR1yFHEVW6ggdk5AWm4LgEVdqwF4AdLw4OOh+mDLibPJSdUfkP1K1xKnOeKT5CLaH92s8huDfomD3P7QBhiwSAQpyu7yakt0mnA8+oGgpI9LFGiRIkSDSotCz3MArGIEqVq8RB52u22psZBsgYHZENIGRkZUesIhVaVqqinqxZIUkUK5ZfLZbi3QbYCSPWVr3wl9L51P6OSbI4i4rAOfo8ime6RHNuVkz1VbET37tq1i5C9iUqhZeaoes7bkC8NBcx+NQQ5zy7ghzjuEs6HK1as2LBhg/UqQA7pxV88RmT//v1hb7/85S+3zKu+2+0+//nPN7MjjjgC1X788cfZFue6pqaLZrOpTmucwy6VjAmupSNYq9XUi48qhSJgVIvDrFTUPlXLccAGa6uT37VI7+/2upISn1TV371dx5HTwDnTuyHWbP08aFStR/hkbqqDxgU3m03CnvpG7TSizdpqBmPoT77IlQYKDX5RCx91u7DnrXc9uu3O4bf64OjoaMISlxa1223mbNbZxmXstHjdtrDhDg8Pa44ybHOjo6PqKetQeEzldevWmVkxO4gBoV34XLt2LRPumYCcgLDAybZu3WpmTzzxRLRdIRZkvTt+dCI6LMh9hrOclXerWj8LMeNcFNicm5tTdsIUDCG/ib7O4WBRYNAhJzmtZiEvetGLLAuZAFHoCWvrqFQqPfTQQ5YNmUP/zjrrLDP727/9W1zEeS6YdfV6XTlNzuknTvThX6HbN/1E3IG8DnPTcrRpHAhtaaf3YHsuGU2fETUCMVpAoTMOiosTMHFS0NeZ7LYmfFE7mU3Wahd7j0aKWgFdQIKyDddLjmGogz4Zm95P3qCTsNVqhYPLPlfjKJ9yGblCG2S3Ny8U+0fH2qG+Tsh2va19PhC0LHiYiUuYS2IGdgJlizMYrMV5gulswDyr1+vgcLpER0ZGSnLACrzaJiYm8PgvfvELM4NBaHJyUvkiqFQqwboGX4B//ud/NtlEcihqYMjnZCEK715Em4GaQ5i8MccVkH+FiqYz5lGTiCboC+sfVdHC5lsg6TvQX8usVqsve9nL+JNeCVol6jGhyF8oFO69917rnTDdzFiFn0zV+pSnPMXkoBOtBidD6JRBaUBVGe4+qk5xdJyRVXcolqwKn+sZdfCrVqshCEF7qsMbQsWIbNUF70MxUhZYyPX603ZVKhUnd6LMKJ9T07Kzp3JuqK1OU3DxIoOoQiuym2kcCO00NxDqZuI8NVwMIrMv6p05VjFnL3T11J7kgzoB+PgA+SUme1iiRIkSJRpUWhZ6GNRtVZmJ3kAYhCRCCUjtYZQoQ5/avXv3Qp5yXvgwrsCUBT/G+fl5SPdABQE6XXnllUi+wNgjM1u9evVTn/pUy0LB7rnnHospGdaLIFmu7tKPQpG52HtGswMYHdbqdKwQeSvGzrmO2q7Yt2HyBddAp/BFLXb5pkGVeUGrV6+GekSjhVaYMr4F0QL4XqlU4Hboart27Vozu/vuuy2bUYcccgj0cgq5mAAKH7V7s+6ygTpFqWOF+g01NkwqWHoqlUqocnV6zxRmu7Qn8TpqbCqqE5x3DvehekSNxHlaMqMEO42oWtQhlkqhSWZkNwk1O1Sh9yhk+m2Gq8nFiTofQr5RK+MqVpRoHCDSXDVsGm4Ip1+n02E/m7g1qgMkAUw0EGo966w+liSdvdFNjCbeEJ8cICXMlgkPGxoa2r17N8YPGB3PGWIqKcvGb926ddh9kCQe02VsbEwzstC4hSmC/YJ8ESwKd+7cudPMpqengR/iL+yYjFrTGOcDBw4A2PzWt75lkhpRm8N1Hi6nfpBjjrXJUcg/eIVLJXTGLRaLGkvAGur2ytKU/fBQCcX0mTxJ+yenpd1YqirnScztzBkYzOxZz3pWmBnP4Tbcr523Cz63bdvm3l4oFJBQCoF9eNHmzZv1Rd3M/8IFbym/AdGkpFuSwxLdRcW36f2hwB2NK6Hju/XmS6Sjdgg+Wy+/IRatjN8NXAi7WbB16lwiX9T3MspQuZQbskKvjdZ55Sg7p2iiRDQaW0ysAAAgAElEQVRYN4dub+wwiOHMGp1GWFXZmwVrwfrECTiDFmcmytElQCugjlHUKMAhU5MnX+TiAgeIjSUsMVGiRIkSDSotCz3s0EMP7Xa7cH2G2g5dx6n2APGe+9znTkxMWOYT+Oijj5rZmjVr+IhluYJmZmZ27dplZkceeaRl0s2KFSvwovvvv98y8PDRRx+FRgXCxeHh4VC3q9frUN0uv/xy66OCOOcIUFQDo7wWalfd3qxLUTXOPe7gJn1vp9MJ00oV+mQSCV9h2bi4pF+qiwBrdaAlNa0wnsHpBM6Yry160Yte5Bpo4h+oKBMzxuoQ1Ot1nN3sFNPnPve5ZoYkYaDjjjsO0wAl06/V9ZJWG+ROjHQnVKlfKx21QU670q5z2pWD79yxeWg1tI2ocybLV1ABRLDLuc+p1usSHelo1ut1VaScxqav6/aewFksFqHHa1iC9WZmopuSqmXOIVaHtVwu60+2yDn1mSiFuoQ5qXQ0+bqohqrNLPSG2DuAxOXE0i+8XwvX+x0VshCLgaBlwcNqtdrZZ5+NMQPsMzU1ZaJHYxIAZiwWi+AiQP/gDL1lyxasB1wED6tUKvi5e/duE7iA+BjLLJVKiASC3/yePXtwUac+Fx62udnZWeuDIkYtQ44crtUPY4w+5V5BHhbdYkBRYDOnYmGLNKUTu0Lb65KquE4IrZXR3Ns0mShjO+6448I4PxeI5hhbMYsMM7Nt27ZFId+nP/3pZvbxj3+cf5100knYGqanpy0wKRG5Ci1S9MlWomnHJVjiv5bNSW7iLmIp7Ela4xTdIhTpcio6//uwB8jjQxSx0WigaerZW61WlcEQ+XRJ/0zCvBwirbs5UVmtW6PRUC7Cx7VnGGDnrGtmtrCw4OpmIg2AyOlDpsI5HO0ukLMaqmXROfE747FOVPJv56yoljM+qHEC7OQcDrfUaFnwsIcffvioo47CmsE0glLV6XSw+2iSmB07duzYscN6B3X79u1gQrhn+/btKAqbERQv3D81NQVzGsz1KP/QQw896qijzOzBBx+0TLebmpoKYyqLxSIK/+/h0YWYd0O+kSznnigLDH+aRF86Fc3xwrBwEA3mIHaL7hQ0CIUiM4sK7R/Wy0qjlvaFhQXOBwtyjLnSVEDBtLnpppuUX9IUhGkAWQQ0Pj4OIQam1lqtpsmQXPgOiDuRyuysodu2TBLIqkeAy2bEdkWDxqJppdSBgv2vGyI918Nh5RkxLnZb4QeWrMgE6+PMYxaYdlhPt5Scv4mJkZV+/yY+He70E3zRUJlCFv2p1aaEQR8fCwyTVIidWq9v57joe1V6oBYYSlfuZyc7tj7q6KFUyNyF1HjZarVCzXLJUrKHJUqUKFGiQaWBYbb/l7R161bIIPAMhE42NzcHsQViOJSqlStXQj6CW/zPfvYzk0NPVDiikxuBJjOr1Wp4EC62KOqoo46CUzUEcIhy9XodUI8z2ADndCpFiM5F9SH3k1JqqAlFEXPrr7SRqMo4/SZ8+0HLMdFacFExFkqCKms7f31WPszrY70KCosKQ7O/9a1vaX5VEKNZ23IUKjVLvA7T5qqrrgq7d/Xq1bB6qtXn8ssvVzf6kZGRM844w3rho+iwFrLYWKe76Hud0cv5ZIZ2lEJv7DBVJTWPRSX98BhGpULma2d9tGSqXKq7cMTDmeMiwaOv4+i7BRKaOXkP3ugSoGAC4KIzuTnToDM6qqJJ0BKd4Px1QztTVJFyeANfh7o5cNVlWDYZXH0d44uc4VbnOXtskdaHpUDLgofNzMwwQguEGbmwsIAx03O5tm3bpudHMCWHwhREUXS/cPC9zp7t27dDtYflzG06usnu2rVLEfBwzvUj3hne3O3j04EvbuVEfe6jFji3EqJPHRSujNqZ9C/r3fuirNoCPmeBic7Z9nSFb9iwQZmfw6lA3J4g/WArge/P1772tbBKmzZtuuuuu1gIcMWLL74Y/wJgPPTQQ5G3RR8kl1XojFmpNCcTd3znJhBypm5vOiLH2pUN0MtcE4J0ek8f5kDQiGXCXHVuO4gv3OhJ7jQinTZ0CcEK4os06stFp/Ez9L8vFosaNsA7FQZ0jF+9TlhtzbPFixrAED2b0M1Jl2FVZyatlQ5rdWk78LhGRzifGmf6ioYJuvhC7bqBoIQlJkqUKFGiQaVloYdNT087TxtnwQbAGKoC1kcToqwaClB04lJpd35+PorbKIoIeRbO+haoR/qTlYlaaPVLPiAQKmdRdcrd3w82DO3MVArDtodl5uCiB4U13Iuihbh64vMP//APzezCCy9U7YGFqPU+mswQ6lQ0Xf1pp52GY50xuKeccoqZHXvssZgb8OxYtWoVnPKd60qocpXLZUXnWIdQbW21WurCQARSneOZiETlccUATSIZTNC8KEKo6TPa7Tbi/TUXIjUSdZbp9uaTdPqQyyOsjzvEVbMgFnpdz+ljqWU2Gg2XDRmkagdL0yXMksMNgZVRTYgQn1beZTF2b3T6osKG1GtdsIQFGTLdiOvbnebt9sBo2uWBoGXBwzAdnYXJ+owTfYdAbuAVbnLO3yDnspxD7d4U2vBg5DEQoHw7U5S/aj3dxp1TVJQW8zr3osVQaD2KvsJx7n7Mz4IxcpinG2st8/DDDzezm2++GZs7hpXe3srDWJRuNBA4XBYifJ500kk//vGP+V7wuU9/+tOwueIVxWLxoosuMtlzTTw8Qdw6dYdiByo0xK7Q4AF1VWe1uS1qhZ2Nze2qygbYn1pP7qqYwPp49EWMD1O+WK1W1WlQ62y9SB1NOyovkpjnBT/VY554o1qPyBtcomRdyIQuXeoplMkBNWH/7pRXEw7h8Ftlls4P3gG/ChW6A1RdzJmCpbxBk1QRrHbCgdZzIGhZ8LBGo+G2BpJDga3Pdlzo9UB1mQMPyjacScDJQZj08N13Bhu+Pad1rtpRs21oZrA+XCR8o9v9+/Geg1abnRz2ttPD2JPhnc4GyZJDTw0LhsBk38fPT3ziExZkzeeWpwzAsVVVlZxDOWbIli1bEAWIv2D32rVrFxx2kFFzfn7+ggsusGD3US7Ci9pq7mi6dTpVxoneaotyEhgIF+ny4OT3MM9eIQuDdS4zbnfG9TAZUrE3OZkbLzeauO58SXQ3576vds1uFhKgzaRlEcT1q/u4MybpGueU1tEplUrqG0IxNzxl2zmsc2qpIkVOpiqX0xrVGme9zI9lhmdSFzLPICf6hAJKylufKFGiRIkS/W/QstDDQh2IkoiKwPwe9Y5z9gkTJy7nuaSer6Com1CxWISrFd4OG0mUompcvzudwGi9cqhSeM9/W+GL2gmir+5XGadM6J39NGO9M6oj5mCtEJyB3xYKBQRCYIhhy2k2myHgTJEZMqyGRpDgrNjpdGBkBSGxS6PRYIIYCwaLTVCMiMBUCPE5/0lObMVXCQ1p/7hMEIpkMorWpWlwCoqJT7beTxuSdrUzRRPKC/UwB85rky1A//BFx8hFQzv4AeS6gt+1Gi7DvZuNihDS5qRqHPXFMJFYMXaSMieVon/sagdCoHD1n1xYWMCDMHNS03JR2HhcwwbwnfZXHbJSqeSAyqVMy4KHTU5ONhoNMAwMJ+PDdPZgI5udncU2hA2OJykoYyMmgNJQMvasiYkJmATUuF2tVmH/19M1h4eH8SKgiJqROqQc7qKIeRSN6VdICGxGYcMoY7CAQ4Q1dNYj3h9lY6FDTfS2Yiw+LPr2bu953O4R96l5NzDuw8PDK1eutAyvg0GLr8ayZw4OLe3QQw81s+3bt+vcOP/883H/FVdcYRLfozsFTUEKVDJiSV3tCUy5fBYmXEShJEKRsOIw9YOyNG5k4ZnLrkwaCBXRYkqO8E7CYnqxlOV6d8FYusXzTp2HnCFqpyRT1yoxBA3kkug7zq3jyEJ0NmIgRkZG2tnZzVoHBS3bWWZ9PIhC3CvUOZ48Xt1wut0u832w6x577DF0Go5kuuyyy8zswIEDOo6Yb8ceeyzciI444ggzw7G6Rx55JDYlFZ2dZxl7PsfQsNRoWfCwo48+utvtrl+/3rLIZRgk9u7di6GCpZ034AtPWzazhYUF2HI1N129Xsd+hymCILPDDjsMqaQQEoSSJyYmsAliW4TKNT4+jimF5MJOeFRy4m1UQXFMy4nDOWYqvdhPoYlSKHGHhYdtidrD+OX/CQrPkkNGXiwWjzvuOAuSzGI+YG0/8MADZjY5OXn00UdbxtKQQpMxQ3jwuuuuC5t82mmnmdldd92FjQmJpHHajpndeuutlk2AQu/xyuQiaq7gIVu60WB3q9Vqeu6UMyK6ECJUEvONvCSUG+gO5zbiUNwhCKG7Oc2NqgS4KCiSBpa5Wef4E400JszSAmIHkk+oDws1J3zRdMBRRcpNVGaqUz4HooOJ04CVM0UhAcqdepQaNxO8ApbUH/7wh2Z244034icq7xx2lLnecMMNN9xwg3tjqVTCpoQcsM9+9rPN7Oyzz8ampKW5OLYlTskelihRokSJBpWWhR7WbreZmlqxjmazqc5OdChSeATAYK1WU6AAtH//fj3BGQJ7o9GAqhc9SBd3smTciVNawvJNJN+DKkaLl5tyTF/5hfSzhB20BKcFhkJ9tOT8auTYutxPhcVGRkb+9E//1DIYkEVBysY9mzdvNrP9+/cDRkZCKWAyPC7n9ttvtz4K8fOe9zwz+9znPod/kb0ewzo6OgqkkRqJTj/nRKd2Jt6vyBKTm4DopK4RRXycbodaW1X4iPupIsWKqbJF7U01Eioi7hxRvCh0Ey/0xlSxDoo6Uv3SruAN4ZmrbBdHPFw19Ol3IRNqI6TTf5hwh8qrPu7QTg6fDpaztbu1ozF22BOeeOKJT3/602Z20003mbjy456osSq6gWjPNJtNmC3wefXVV5vZxMTEO97xDjN75jOfyaeYOXogaFnwsMcff7xUKsH4BKsVRmhubg5zQo87OXDgAPBDTCbuCBr1CSpmBxyoak8jEC5ycWJDxCewymq1CoRKjf9RduU8pB3+Fp1tjjeEj3cXEX2VTzn4ZA4wGH0RcZvondFW5FQpahTEJnLiiSeq+wZHB4OLIQPut2LFCrXeA2+ZmJiAaPKf//mf+iIFkbAXvOUtb8HF008/3QTnUcM7WYvu5tZrGqS7kF6kPOTOpDeRwHRwnQcEPzV8m5ussgoXMOvc2bW3yVGU6ZIxKJ/jnq4c0U0DhRmJa+nrRkZGXCSlSWwcccIoBKrMjxMAj6hpkEhvNL7QJa7UnzRk6hyjgBKOY7v3vBtItO94xzuQb1ORYct2KpeuIZQpo1tE1JY8Ozv713/912b2m7/5m2b2u7/7u+g6Zz5cypSwxESJEiVKNKi0LPSwjRs3lstlnOAFpQcA0ZYtW3ADEi7AmtpoNPAF9k9QOzvZCJ+QsObn5yEMojTo+5OTk7hH1b7Vq1cDdYQ4Dz1vcnIyelhz6PJQ7D2cN3pnlKKugE57iP7lKEdXY+WjXic5P53AqBddtXPUzWgDc+iss87C8Wzqs0DYxyVhwTBhbsBPBw4alh0g52Rzze4DJd6yaQCZulgsbty40YKIV4eVhXATUzloM10+IdfVOUHf1DPUycIhhOonQvVIwTGnHLucvwoJFgoFTH5FMog6qppChNCFVIdx0E4541RUxZSKmqZ1Hxoa0nNWqTOF3h8TExMAZlx2ElVGCdhqz7Dy6iXBijmlEBXDv/DC+OxnP2tyuKDaNVyrWYeoN2/O6nZ6Ler51a9+1cy+/e1vm9lb3vIWeiEtfVoWPGzPnj3lcjmEkicnJ6GbY4vBRGm1WprUDjQ/Pw+nNexrWHL79+/HPMMUxIyfmZl56KGHeCecFVesWIH5Cm6HO0dHR2FWAeVAgtF52c8V0KGd/Up2FHVr7Ocl6AAKC8DAXxZajDLCfha7KOoY3tzt9bHE8B1zzDHgRvped/KLcyhHA/F4pVLBDsjAMq3nunXrzAyj32q1ABsiWwfm0tDQEA7fgWTDM4Xdvqaj7Cxhyt4sm3jqpMeBcEhdiBt3Yz6ofJ3LNhQOrsO33Y6pKaO6WWpER7rzcrsP99xSdlqmdkhUvqEjIgU+7TRKA4rjOX7sBkJnLHl8GDxKxq/z3JnTaFDUiUqpBbsEfA4h/bjYU4pcmqCLURCKN2ITK/Qe1hMdcbdy8RekjXe9612///u/Hw7Z0qRlwcPm5+crlYoe04BJ02w2XZpXE2EQk09nhsmyNLNC5r2tZvByuYz9LnQhsYyHYfbs2rVLD6wCcUpxyZlMPic4hzNS6+a+6yvCLnLcLgdVj1J0F3A6lnuFbiXRqkYZYVQXdI9Hmd9znvMcM9u8eTPUcadtwMqlgzU6OqqhSxip8fHxW265xeQ4MZPRgTcHHO5brRbOBsPGhNy+3W4XPzEV5+bmwtPuy+UytyGTDdEFbJlwJhCNN6HEQPd0x9iUZ4Pj0vXJRZ5pV9ClXktzOoFT40KDlls7rkUuJlpbzaWkKXGpBmlAAqPUNdcwd3xl0sVY4tO5uTldg87nRR93x55xrwiFg26vEZq6IOAZ+A05aVKVuVqtFpXV8K87DN35kpiYMFUS4gLUPp+bm/viF79oWbDjEqdkD0uUKFGiRINKy0IPGx4eHhsbg7ADLAiSGkPxIQ7j+8qVKwEDavRls9nERRBkwPHxccg+eBwObyMjI3A7hDZGDzTcg4uQeX/0ox+psEwJK5piw4nVFgTYs7ZR511QjkmJdYiKzPrefGTSaYEHNWW57yrpLybQ0tXQlalvf81rXmNmp512WmgnIBakoiiBKafWfPOb3+TjTrh+wQteYGaf/OQncfFVr3qVZRMAZoadO3eqT//c3BzTuJhYR1QTYsSx07YtcBfEVKxWqzpVqKboOSDUJFS7YpJ+qoMmWUL0It01u+JD6F7kkqarezqo0BuBzoqpOQ3EDEk6xA6XZtok7SW6PqoCR/d9TZfMntSeZ95bVxmUpkk0Cr3JqbHGrRcGZP+H40h8EvsGu8WFMFuAtfKiLhPnNer07+iC0ipxCQAtHwhaFjysWq1OTEw4k7LJ7EE6O56rhJ0FwDQ4U6PRwKTXhVer1XBxxYoVluGEls1FBanpvY2dAiXDL4DkNmKn9YeshctYWUs3dmJvPiToUMcoCOkgvhC468byNFqMUXEdLp4jhnVw1Y5CoLwInPDEE080SbDkZIKw8m5roHXh+uuvt175hvSMZzzDJNoPHkNArZngTk30a9as0TlJBNvlL7dg7+P3KJqnkYhkgVqak4FodjJhLSDKdoo68i9nazGxXTmcSokWYm0LIS9lG4RYwzwdlUpFnfiJ7Ck2y2q4A9zVp0NvM/G80ArrpGJXuOmnrWCZChGzTD1rhr2EbD7ve9/7rDewz1EhZgzmmtVxd5F50ZA+Z/J0yzl8+5Klpc7DjjnmGHflvvvu4/crrrjiAx/4wM6dO4888si//Mu/vPDCC6OFdDqd/fv3Q7LQaM1TTz0VbmOAfTG9pqen8Qo4ekCqmpub2717t/VmP6P3B7gXvBlpTgNxa1BhENyL2fZAbjvOUUFcuM9BFal+ZerPxZvT/hvkeIN+iRqWo4/zhhytzvFafNmwYQN/7t27l/IH72QeWBC9fsLdfHp6Gm44rp70+LBMQKlUKpgG8Ongzov2/vznPzezV77yleHuw2xG6jjX6c08SQtWmHipXq/r46oEuDJDrcX6jE4xdvJLIUsSCHJpD13/qG2GeIYOGWeyMjaXmxh3Qu3jI2oVK2SODFEhjz2p3eXYuZq+rNchwi1kdacsZnHlzsIXDlkhS0Cl41IsFi+55BLLPGBzXJ8ctMDvoeGNSqSTaFUO4P36RoraA8TGljoPs16mpbR169a3vvWt7373u3Fm7pve9KZ169Yh02WiRIkSJVoONAA8rB9deumlr3vd684991wzO/fcc1/72tdeeumlUR42OTk5NjYGyQLq1CGHHGJm69atAwyojj0UoCAw4i+avpzHFMR5TfPRzc7O0Fz41WoV0hmAJlhHXD5WlnnQhlMKUzyEf4XJKUgqV/YDTEJVptt7rKIDGKN4YxTcc38tBhsMkQ0H8bnuivYeACIk1KEnKi7ShoRoGHQdBu6xxx4DwgwoEk/t27cPzvFO9z3ssMPM7OGHHzbJG4Tk4nBE5GkJSCQN0+wZZ5yhk4pKQJgGl45z6m9NLEjVo9HRUVXOqOWEnUblw2Vdwj2aosL5s7HtoUGrm7nRO01IDVrEAPWNUGhqtZrTq0ymH9qlGKn1AmKFQsHdo9WmkhRedMAd3Yy1Gi5RMo1eqJga0tg/zuhlok+rAf6GG25Ammm3yqKwofsSXmQdVEtmk7VFrAmDz0zmRr+4miVIA8DDnvOc50xPTx9yyCEnnnjia17zmuOPPx7Xt27d+sY3vpG3nX/++Z/73OeiJczPzzcaDWxGaoLevn07fiqMPjs7CydXzEtgQeVymSzKMrxoamoKw4+c9GBXk5OTQPwREoTNcc+ePVh18LHWLDLWuyHSeg9ym3h0N9edxRnVHNeJxkI6tCHki1FOye/RwDKH++VAH2GB+T+ja9tdcaYLONMT7lNkqZjFfh1++OHWC+OsX78eEDGspCjzxhtvDM2NZnbBBRdYloAON6xevRq8EIzwWc96lpk1m82PfvSjZvZbv/VbZnbMMcfcdttt1mufYG/r2Dlxh2ieyih8UOMguXUqVEiWoIeSExxTrsD3hhzU2dhYeTVBsUp0GGHJzWZTbUL0jdIH3bRXvliv15WRO+ca3ENPdCcthZIc2ZXKsk6YADGRIIxqtJJGVwTkJPzF8y5CgPqmm27SO7XJi6RQQOl2u2rbY7doi1CThYUFNy7akwNBS923/vzzz3//+9//05/+9Mtf/vJ555332te+9qqrrsJfe/bsgfcEaP369dCxEiVKlCjRMqGlrod9/OMfx5eJiYmXvvSla9eu/ad/+icEky6eICCb2Ute8hJI5RC1du/eDbEF7hjMGw1hRP3gR0ZGAC1CeGGosmbyRZkjIyP0SDbxdVTUIgdFdFkJeFsUAAwt7d1YNLT1AgUONnS++CrtUuJWuMl6tYEo5uCAqShOGH1Eu6LQxwFS73EuIfoXuwJZd+m4rAHsjDFXURQ3HHrooQqLQYnfuXOntpd1QDjzl770JbYa04yEyXPDDTfgRVDLpqenw/dGddD8nlQMyo07a6gjQud4TWpMa3/oiWC9Xid8nU5UqnTqLEOPCQeWmuRG0QBkN70J0OG6HmxG5wjFzOkOw7frTyKE2od8rzbNOdyD2BXaz+rEQeINGvkAoquFrqCbb745nFT5FL0nOitcY113aU/yQWbgGxRa6jzM0SmnnLJjxw58X7t27ZNPPrlx40b8fPLJJ5HsJ6SLL764WCweeeSRlvnBI+GQ9fqSwdTBQVVlfGZmxiUuM7NqtcqYHsvghYcffhizASwQuOLQ0BD2CHgkhv6EFizgg1KhN8ImZ1qTXTk+4TBMk8Uc9VJzGRyiKKJ+53tDdDSsTE5Lo6zXlRy1nEGAgLFKuTLvjHrTtbNjShSnwl/btm3TinEngvfsgw8+yJI3btyoti5c/PGPf3z22Web2QknnGBm1113HYSh6A6uaLAj5wevfMuZvlw2+hxW4eQGJ5qEL+pmcQKu5zXWzR3u7AxvOo5kMMpWCYeGqUO6vR6Mzv2SIQcKlrqOVX99mvHcwTH6YFTCALE/wxMDHLWzrPl4BbYdStKLp+hU0b/6Yfu4qNF7IWwI4xz+ZYLQpUwDxsPuuecebElmduqpp1599dXkYVdffXU/p8RVq1aVSiX1v8AoVqtVHT9OU11XNA+4gcdFd5SGmRUKBXAvlekqlQou3n333RZMqeik18cduY07qpw5NSWq5ehPLvuoQwHuXLyZN2epOzkx/K53Rpt/0FewaehzDByTYYYaydjYmHKaqEs35Btm8tX+HB4ehpYGeQW0YsUK3mxmd955p5nNz8/j8FzQI488cuyxx7L5nJMhpykWi6HW64aDobu6ubteYpgafqpKwQmgThZ6m/UqH6ySE+11ztBzQTvW6XZOngMLVPsZg81V3SyXy2HCQx6hQl4SmvEKvSeqOFcLZefsAafmho/zRbp22GoGA4TdC68fpz5GQYh8JqdD4AQyN5eiQkxOmQNBS90e9spXvvLaa6/du3fvzMzMVVdd9aY3venVr341//rYxz529dVXz8zMXH311R/72Mde+cpX/mprmyhRokSJ/jdpqethr3vd6z71qU/dfvvtQ0NDT33qU9/2trfBmd7MTj311Le97W3vete7du7cuWHDhre//e399LCJiQlKQBCjXHCiymsO5aBByyFaJtqDSu60HqkJYXR09NZbbzU5D6IfRX2xHEWRhHwM4aBoHqjYe0oey1cBk2hetOZRq1X43QIzXrSoaJlREDKqfCCAHZq3S/GuSkyz2aSTPS8yowcuqou8a+yhhx6K6FQtZOPGjTorfvrTn5rZSSeddOqpp1oW8rhu3Tqnz2mro+qyM8OoJgQaGhpSeZxTVJUJdkJo2iFCqL1Ey5DWgVCbgyLxYPTwYvVgjI54o9GApqhdV6vVwmZapra6nL9OFwlnF9E893aFDZ0dzmGYWppTSVWZo2VRO42jE6IyYR/+sppQjodw1HgRpQFSv0hLnYedeeaZZ555Zr9/X/jCF77whS88aCHtdpsohy65ZrMZnsBUrVax37mTTAFF6gp3ATou4Y0W0m63v/Wtb1kwTUE57Mfds3gT2kHXQ3T3dxStWP49juuEcDxZoEvME70zZL39eiDasYiRQDAWhy+07VWrVc1cgD1ueHgYPQOHHXdws4KcT3/60yGgoDQ4009MTOAenCWP6Itzzz0XlbnyyivN7JBDDnG5kUx2TMeKNH0iWYJur/QgCI355CJ6OjM3WeVk3KO1n8kslWkVe3O9g6O4w0EcCOmCk5RDsMzQxMu2u9dJywkAACAASURBVHahn9GxNGFq1/VL16QmNzdRtZ6uHAfHqXW8Wq2GrXaWV3ySy2JzwNTSuoUVdksguk7DIYsWxSGLLiL95DkJA0FLnYf9PyHN74nv2NG63S5cPNRRp16vw0cfUjwGlfnZdILWajXMQvXsKJfLat5gbOzU1FRYsdCg5bScKLdzP52us0jG5nb/xVBUq3PvdYskFFq5S0b1sBy9zV2MrlhtYKfTQSghdhbqWFB3VCnvZGevaAbeoaEhlaCjIjPo9NNPh2EDZcJ1iM54kF2Q7+rEE09EaCAdH1yIrgX2DG4l+Kn6DTMW6ubu0kq5OC0GV1ngtsftWEfQJZDVPd3xMGc8VsHfNcH1obpd9NO81ZUUr2g2m7BQKhWygDmXCVAZDEPlosZddR7u9sZy0aSnLI1MK+Sars+dkQw/wYDD9rpGuTrk3+O4sj4YRTLY5xgILISjjz4aXxDPusRpYJhtokSJEiVK5GhZ6GHVapV+8E5/VywRYnin00EKYJUiKeKpVMUzKehAZQKRQdbGxWuvvVadm0FOe+Bnji6yeCzRUVimE8p4MUfuy0c7wzujbenGjgFcTJbuKDoabQWoWq0yVz0vUj1SzdvhbyDaZqC1IF2L0yFQyLnnnvud73yHF+GO3G63gSLiwec///lmtnnzZqCIDHVS9ElPGLfeYeKJIQpkMb2v6pS0XWlua1em86ZT1MiZlJiqWKc9u84FYFhwBiYHRX16neO7BqIVi0VkwwEEQvOSZtRlIdpdTgflWGNFqzGvXq/jpw6uIzYw9Id0XYo31mo17QQCks7QaKLmvuIVr7DMyOqWiVvUOTYFIArdbleR8Cha6AiF8MQoJN5DCCPg95NPPhlD8JGPfCSnnCVCy4KH4eQUjDQGHpoyV4LamQu9x3gTt3EuyGY2MjLSzWzRFrh4qPF2+/btObHAOu0KWYRWlHtFGYz7K0RjnBWH+5Hjna5YCxhG9BUOActZcu6vcLOzYAHnMMt87m5mL33pSzEuGk3BO+nIbrKJaLBgoVCAuAM4xTEYEO5ftWoVNyPLopunp6cvv/xyM1u7dq2ZPfOZzzSzRqMBBIzuG4CyQY4lKMOo1+t6Sgv9sxUwZK+G+ymLdQYwvZOTVkfEmYSjAYJabZeN0FXJAYyOJ+EGdIiif07sIM9TzykQK8awPyxzrW21WnU++hZM/k6WEV9/umRRaszjQDgLn0ppdPf/7ne/a2Y7d+60Pusin/2A4Lx20kknmdns7OyPfvQj6w3tsNiOwZ/YlCCpX3jhhRD1uCXiu8NjlzIlLDFRokSJEg0qLQs9DKCfppomqRRJuU9DLJksIETA+EVjnIu95/HAbPvEE0+EMarupzN9g6JKknPby/f7sEDlykEg88HJqE04WvmogMlqO5cwCzQ898ao+hitG8qBq+rpp5+uBwi40FrNj16pVEIPNKZW0ewbru1wjZubm6PqZmZIFnP33XfDMwhBzUgudfvtt2sWfOp/eJDavLbX+SWqfsOu0BT1BO5c94buCd0sOYV6PNLNRJ3FSS70WKvkvDm0q9lM7WSXdYlKZ+iX2Gg0VL+hjoVmKsTazQID8GCr1XIwsgX5edkh2l6HtSrmSQQ1dNB3VIh51VYqFRwd5xw1c1AWRzgiEZ0AP6Pt27dD9UccPYvSMtmrSEGOlNM4snXNmjW6IjCT5+bmFm+n+JXTsuBh8DHThUETggZscdjUV56QupoZ1D2MD9JxEV+AF+GwFZ4pF9243dtz0DxXyEHd4vk9xP26vaYvBzrllxa9M3yQCyk0JYb3h4zNNTAKbDpmiRchQSICKvhejCO9DTGazBzmUCATMwx2irAfzAzBXnfffTfuBPaIOl911VUwMCC5FOSnRx99VDuB8R5apahPNkdQ/2q32wC4XPSPSlQg8hVlWkxRzx0fT6nfJl+nkB0HRTd38hJFq0CtVivMrGh9tuzQpbvdbqNK6uhbLBYJ7lkgU2popvXx6dc6FHozbPG7g1JRefVdBNHWzntMTIPKVtvtNsDSnCRYrFi4oMbGxl7ykpdYlq8VUYb/8i//gk2mX1Fs5ubNm9/61rda70Hz7ChYIuE3e+edd/bL27cEaVnwMAQ4q4AJoYOnSelKmJ+fx1NONHOBLCzZJM2PCW/ARRj23WR1+7hbtzr/omYqJ2o5ChkkdxanBUZfpLwhp2KOOr1p+sJ/tZDwzmgzrXcfJ+UYyaDlRPuHse06rAy3CgV23uNO19XKI/f01q1b8SI40EPQ3r1799Oe9jQzO+eccyw7WszVmQfBaC9RF3G6i9NXwrZziNU2A8PJ9u3bsUNpIdRIVAxvNpt6aIvLoqkiBXm8dm8hc31Sj5JOp6NJFEE0LDklO/Rc59JzR5qFHiWchDRehq460fg5igj6om4W+KwLnwOh5ddqtTCbolvjbFGoT7tHctCOY445Bv8iRznYzHHHHYc7YbjlueFaGRwt9Hd/93dADiC4Y/j27t0LXggT3fT0tJlt374dsUCrVq0K67nUKNnDEiVKlCjRoNKy0MMgUlGd108VBhk+qcqW82cDUV5TaJEQOc9wsSxRvdbE+rjPRnUOUqh8OHSUt4Wmr34aUghvRm9wJTgIImqmcmUqUHPQd4WvcPXMQTsB9APEo+e6nmtsvYnko7AqWwSRVs8+5W2o2EUXXWRmf/zHf4wyTzvtNDP7wQ9+YGaVSgX5ZeCs/LWvfQ110GT2DotmfK4iftR1QgR1eHiYJ49r16kad/PNN5vY2DQEuJilr8VP9pJ2r0MdVDvnunAmKK0t79cjIDS5u/VmrKA/ui5S1ofughYklcc9PNyZlXE5ji1ARPg6Z4GzAGthu5zmhy+qvFL71Jbi++OPP47gilDZdeSWHu6cmZlB3aAkIaJj/fr18HrdvHmzmX3hC18wM3gq8nF4HiL1OYfp8ccfN7PbbrsNX4Aiwog7NTWFGg6EHrYseJgi6ZjW3Ep4crwF4DhWF7cbNWi35WgJ3slC8C90cx6nFN2jD/pXP+vRQR/Ui1GWEIXv3B6dbxvTHa3be4Su60lXpZAzRbls9HGXL4eFYDsG0A//dZchidIGLsJgg9rOzc0BnNFhHR4eBiqoLhIkvA4r/JFHHkE5gGiwFxxzzDFAGiHK4JOxiQwlDF0YLIj0MAkCUVeC+fl53aPxOTQ05FK5a6fhLya4UuiMk1k5qJMboh4lWnK321VnGQoK2kD+pfit62QXOqbQosvooXWm9wf3fQVLo7FuXN0um4YFNkgu/BAG7GdvVvkYf33/+9+HSOS4XSi/RlfE1NQUthp4isEqtmfPnrPOOssy33p4alx//fW4E3XAxHPdC4Bxbm4OlQHcjUCU1atXQwL72c9+FlZjqdGy4GHdbpcHHEQzeDLgxmTvU9Q4CnnzOx50B5PznEwT+VQXFRfnYsQxpRzNyT3o/CDcUolqV1GFz/0VtSiE23E3ZvpyVcppZo5hICwtPGaFVhwN12PUDu6B7bNQKKhjIXf2Rx55xNWN2yK4F7aS+fl5PHjddddZJsqcfPLJRx99tGXnr3IaqCkr2kDnieDscGp/7WbBrS7pYtixnL1Os1FVj22PhjOrCwweZ6YrLapYLCo3cmquKgHORktbl/I5MoaQ6/BMMvXetEyahA9IsVhUPZVNCP1T2EAtjZYzZVqO21HTcoq1SVibWl7vuOMO3XByziRzRGulStKYfjt27Ljrrrt4ESoaA7QxG7E6Go0GmomLYGzPeMYzEMKI7Q6Rjn/2Z38GD8Y/+qM/OmjdfuWU7GGJEiVKlGhQaVnoYXCojR7VihtU+iNEA3K+Ver4RKFM3W1rtRqEHUg3CA+69tprnbRrItS7xNhaJVAUUYw6PkWp3+MHfdA9Th1LQRJSGACXD4RG3+KgyGhyk2hpOEBd3cp5G3P4mlmr1dLsEtCVCdDp4FarVdgJonAf3PcRkUMFCCIw0h+cccYZeBCCLV4xNzdHFcGC6cfvkIh1EvIGldwLvcc5su1qD6PeHyrWhcyHEOTc/HRi00jm8nSovYfAoOa/4Ot06bkoTHdwhGbfoBVNW010VFtNPEPfywa65awoAgEYNVARoXV2bhNNWldNo9FQ739mVMEXVOl73/uemd17773avTlLz4049UXMMVhqX//615vZG9/4RswxZIcCJGiZMoo8UoQNUCWsCHYO1g7+Apa4YcOGaDTt0qRlwcOazSbtBJrD3uE2oE6nowdBkYfR48NkbStY4QJH8CBg5ampKYQZuaw5/exAJntu+Bf3oCgTUtOFs2fone6p/J9actQeZotjVP0Kj7alu7jkjaATTjjBMm8Owlk0EVmfTsO6HR8fB9vQ88O63S7s267T8PgLXvACy8BDjjg2kY0bN5rZcccdB3MaynRhsy6VPoh4I0AeFRS6mZ+3C4oPj/yITqpS74kzvEHBLhWnTFiFDgQ+adAKLUNRsYboupvSmueJgxI2s1wuq5MFAUmtEue5s2CFTJoIoYoy3W6X2b9MEFdlkCAmPHTmRh1QNhZbzb/+67+a2Ze+9CWTiEAQhzgqqznjHC5+85vfNLP3vve9liGEf/Inf6JnYsAkfNpppwEeh9ELZynMzs5CaHNmUZ1phx12mA1ajHPCEhMlSpQo0aDSstDD1AlKk70WCgWF+AgzKtRDsSt0PWIGAQhEuHNubg6Fa8kvfvGLIfJv3brVRP4KsUHqTP3Coi2QOnPcN9yzi4cN2W+LfJzVDq/3u6jAVD83k7CB/bxUIH5CYHdKFb4AJKFCg7fzpAKUhp8IlKYPmL6dygewxEsvvdREiseLgB6vWbPmiiuusEzvh/xLGBZzY25uDlCP5jFxncZZpMiBk9+jyKSWyTBhEIEsl+DDJHmHFsXcFur61I2dGOecMlAxen9ozxdiua2dEulu0EJ42CYIi3pkZMQ5D6s7D9U41W/ovMfMBryfyca0z5miXtW4drsNcA8Osbfffjta/ZOf/MTMHnjgAeszz11UuxJxUUUyC4UCvF6/8pWvWBZJcvTRR1977bWWAYw4ZPXiiy9W1JpFKW5EByigF5o6ZO/evdGM/kuTlgUPw94UZh5qt9tMv2bZDOMBvnSLN7Nyuaw5ILgThYAGt069ODw8/Nu//duW+QJdf/31FuCEjmFoNEyr1XKQiz6ir+vnyxcyg35cJ3pn6A3v3u5KcyW7v3QB86XRxxd5cWRkxCVxMLO5uTn0NnyOu5k3ne4+DH9RaAj85oYbbnD7jomRAyxzx44duAEvAg6D1Ii7du1CygP1MneoUa1WU07DHVMROZdY3TGtULIpl8taYe6Auo/TvdDl3bA+J4wgzY2rJ0t2xlEdiByonOKjAox8qXow1ut1rQy3Wl0R9K1XcxplCwzEbbfdZpJUBU59SIZZq9WUx1NcADgMCRV7+r59+7Qr2IE6ym7yu1Sc4ZA5WyDm0oYNG1BDzF62Gu/9/ve/b9nmcPjhh2NA8Uk3ejVJ0K1R+TfuX7FihebwBCy5Y8cOGMkGgpYFD6tWq9x9FAtmTIye0cDhx9zlNNXQTsyMsbEx9TamGQaPqwDO2XP++edbNqWuueYaPVeJpKzCHfmRw5miu8bibVTRt7uLzqQEcu+NtiVHG4tybm1dv0L0vRs3bsRuBaK0gfWs3t7OGEOdQLd4SCE///nPnYMJHoSBAWVC4aO/z8knn2zZ2StXXHEFnHpUSKLQg/vL5bLuKa4DnelLtRBWSfd6ZyhSa6VzZGef689onB8ro4Y03hBmctKWWmB+U2sTjVXayc1mM/QMsl6lx7nma2Op0DCv2B133GFmH/rQhyyL13StiGrA7Aqq6VqZKMIRijsk7QTqqSAyS+wSkIHg1F4qlfATuRDBhovFonJEHBG+fv16TDzEONKwpw2k+sWJx3Y1Gg0GUFvmdTI9PQ0pbSAo2cMSJUqUKNGg0rLQw6anp0dGRpxHqZk1Gg2FMiBWN5tN3KleRvxX/aacKAokutVqhb7RlUoFahneDpPJxo0bv/rVr1qWzSg0TuST8xbjRf3i3OHcPaAcK1e+B/BBy+zGsmH1KzDaiqgVMPzrnHPO0WzrNIpgcGHqYFtUbcWdzC6GIYP+/dhjj0XNjciDgPwF9OuDHI2UPxj9ffv26fmWTvFSwM16Fc1i7yk/NI6qxka9RNUdF0Yd5t61XhWEQn1UIQaxgSFmbr0mW6ecaQO5ykB8aZjel1iZKq/UFxW+c2ml3Nvxivvvv/9tb3ubZUqMmznReGQ3xxTtj07pfnbcsA+jhDtPPvnkJ5980jIYkEl/gGQiTRRyhsG/ml3BTMpwoAWhZyqViu5smNKVSgXzQU+ybrfb+/btsywIBOtoaGjogx/8YL9qLzVaFjxszZo17igETCwemoCRY5IhtUjTwMtHTHLba7IfLJVC7KwmfsEGh5KPOOKIV73qVZZl2Nu2bZsF5xtFbVfcSg5q0IoGcvXD/dy/+t2BSHqPM7zr2u72cY6Pst6wzGiLHOHOTZs2YQRVwli1ahWwIGwNYDOjo6PYIzQJC01QeIXeYMG2jkzwMNczFmrjxo2WOXrceuuteF2Y5YH+1pQtdHfmbo6fGrEUDWAgbKj3s0ztCnI7ZyrTSUKnJ50zrGeYaM25kLj4MDea+iI+pVyEpeWkrsix23EV6yp75zvfqelX2IehOZaOHg5L1FnheLyzcoUIIWFDN9vDpo2Pj0P6+elPf6qNxVYDyRg33HLLLZjS+Avg4RNPPKE8CUA6E6Zg2uOv2dlZvB3SFW6Ym5uDDQzVhpPR0NDQ7/zO71jmtbTEKWGJiRIlSpRoUGlZ6GEmwiBUKJ5Eh5/q3VStVhUGBBWyY6AVyiiVSqpXgTqdDgP1LXNuZKYy/IWLhcy9+MUvfrGZAU/4wQ9+APdZZ36PNiq8To3toM4dfLsW1Q8S1DKdPBvVwJzUmYN2sqgQoin0ZpUFOdc+nIdElUK9t6enpwuZU6hlsnkhc4djBjmTRMAQb3nkm1aeSgCC1mFOx8W1a9ci6SoGF86Ka9euxYN6cJdLaUiMzjkBah5qF4ahGaup+jgoUrUHN1jqP80+d/hkmOuEd+rrhoeH9SIHJRxHphcgeGWi+jiFxoR4KGgIbFBbVY+JdrsNteMTn/iEZQvKkZvMnDbaCW7WuUQtuK5QTXRKWzBvLXBFRpk7dux4z3veY5k/BQZ3ZGREvWRPOukkM9u/fz9yeCJmGSfbNZtN7BgaYsFEDc6RFVXCFMUeeODAAXWjxyFk1WpV8cklTsuCh9XrdcI4buXoPNOtRJ/FF32Q7sXQ9IEiMkiIO6OJd6IuOeYvV9c1uCFt3rwZUxlolfIzR1EsMYd5WJ8l56w+USNQDkfMuTPKhNyGGC0z2igHRaJPjjnmGDMrl8sQC1T4KGSeqGrrYtZU/SyXy5rGHk7Y3VjC4vHxcbiNaRqqww8/HAc6IzEHmOXU1FSIWdVqNUA9DNvABHDmMU0PQTajCKHL8+K2+FCYcIyNeJ3Oyai9kJxbOSL5sZrcaAZTDz3nZOjmm464Y4E64nQeBkU9CemICC8+JHZyaGcU03OGNCWislGvUcfjw3ne7XOgT7jh7N27FxapN73pTWb24Q9/2MwqlYqmDsGdp59+OnYJoH8McsUQYK9gTfSwZlpJyCBNMGFsNXDih4vjkUceqY6+S5yWBQ9rNBqdTkcVKZeKTXOF0cEXnAYThc73uqK6WeJwdRBot9shs2w0GmraxRY2PDwMUUjThw8PD59xxhmWHUb18Y9/3MwefPDBMELLLXtWSS/yr2iAV7huo2qcy7ZnAVMxMR7kWLCjNcwRXaMmBFdDcJS9e/dq7nYEe5ZKJUgYOhwLCwu0DZjsLBgRPI6gVHav9tL69esxSTTm7IQTTsAhYZ/85Cctm0V79+7VLR6vq1QqGnS4fv36TZs28V2Oh6n4z151bDX0/ncigmMYauXiPNeimE7Qkfq1kwV2xRLmBCPndq9qKFunCySq8NFRPnRd4XxTF5JGo/G+973PMjGCIqmy1W6vJxQvhgyVQIXTw8KBYLWdrBmd4UrMaQeNCkv+wgsvNLNbbrlFOxaGvYWFBTyiU5ryDS5CuqrX6/jJHLBmtmbNGuyBKgfcc889sIHh5w9/+EMzq9Vqxx9/fL9qLzVK9rBEiRIlSjSotCz0MOTjgFyjiYgajQakFQfp4Is60xNRVIf7brcLKRtYIuF7yD4q/Y2OjkI3R+w9RJ6ZmRkU6xLqQzmDkMV0R6GCQnnZCeM50p9TzsKA4qjeFsaTRjHMKOAZYiwW6FUWYCx8XXiRb8dA8Bw/9KEqPcVikaPM7q1Wqxgs6EMM9tTc9qoqOTrppJNwVpP6KJ933nkQftetW2dZCo+ZmRkIv6rNE2fGz3Xr1qkWwj53rndou5qyQDSnReFc9SR0Loh8nV6k+7tedHNJtSv6OqrfPNeOKnP0n9THqeU4S1hoBmY0tJZJHUu1sa997WtwEHe5PPRFbHUUWsxxsnUU4vDuZ78pHRZSr9dhfMLEu+CCC8zs0UcfxfzBJ0azVqvpCXnRdFAYlL179+oBpzAbdzNfRxQC3e6www572tOeZmbPfe5zzQz2trvuuus1r3mNmb3hDW8IX7HUaFnwsImJibGxMV2WRAuhXGM46SivsVyc+sqZyO3woDroN5tNTXGkOCHLZPIOtXLj8W6WLRsXAUxZL+5P6MmBSCZGjugSdd/ditXvrpCDLmb3Mx9FCcEuCzbifiUTC8IRJzBuO4CIa1u5HfPrYHSwC5DzgRcirQMHK0TVzjzzTGTDQ/9gazj11FOR+weZqzAlRkdH9bRcDPT8/LwGbMzOzoZnLnezxEU6UTn9lPqdKazuBhyI0EzlfCU0f40FtmHtXlqJnGlKy3QRXVol1yLlzcPDw062CEeBTVDL9DXXXGNm//Ef/+ES3+gU0mUVUhgK2e096zXaFTmSonsw54TSVquFRIsg7Ei//uu/jhT16gwyOzuLXUUT+TsAtpt5qcBmpkkX3SyCa/7atWthSINnP3jYs571rBwIdKnRsuBhpVKJKVWQ4JKTABf1zGUayTS/Ig+AUJGQNlIsDNjM5ufnsYvpplwsFnVxEt9XdwN88k6yNDQB5aBi2BZ37tyJO3WlcW2D+pnHXOeY7EThPaFlKBSWowbwfsqZvqifDBvWxDG5Y489NvxXDSGdTkcT+zIRM/oQAaSwpQ8NDUF1+9GPfqTtddqAmZ199tlvfvObec8555yD8uECh6lFLbAkh+3yZCn0D8EAZWlOtnBDEGZmckFjrKfOB87eaPfq2St8Kty4o1ZSXlQ5zHrZJF+kPMkdaOISOeoBYJzJ6ntCQRM/kXr0Ix/5iAUKdKHX89bFcjklKarRhkCFjog2MCoIgjArwFGiYhm9Ki666CLL3AWPOOIIRDffd999FhzTQ8jHzHbt2hUyVEpCsLRBmduwYQNMXzDfQuSqVqtwU4QrEw4VuvjiiweIhyV7WKJEiRIlGlRaFnoYTmroSDZSQiuaaANK1fz8vDu30OQEXkh/hPigexXEeYwHHGhAWKPRgOCvGfSHhoZwkUCTCYKBO51oqa7VRx99NLzwVStyAKOK2NYrZnZ7s7suxkiWo9V1epOZ5iCEJCfoRSGaHIDxOc95jkn4nR47y3gGiMCASkDMweM+WY5lGvDExASASrjvI8XUpk2btJ6/93u/Z2a33367HoFIfzBViOn3iC/Q+zH6fITamCakpq007IShoSFcVONcodc5vpA5BDrXR+ujsXVjh3RTedVJFdVWTeahjqa+18WHaeVd0l6ai/QIdabLQXYbOO4y+C+qD0WnvZuuqpy5Fqm2GlXjqJI6tU+xE74oRCa63e4tt9ximc7Ek1DgXrtx40bLAIOxsTHMGdyJ748//jiUPG0Xq4fKA4Hctm2bIkaMkX36059uWWwPvCJd0NESp2XBw1qt1v79+7meTbYG3eWp2kdxP7WgYruZn5/XKApAiFH/2na7rWlvgES3Wi3MLfzkJoJVjc+oxRvVW7duHUDFRx991IIVq5/Oj9mZwXS7YaBMdC9w2E4UTslhbDkUNdG5XZLlayuYETE8Oaxer6uZASJCrVZT5JapBQEGomPxoomJifPOO88ywBAW75GRkU996lNm9s53vtMyz/5bb72VZ7hYtjXQjUJfx8NBMKmmpqawm2iLilmKejWEEPR23YuLiiy5fInOE10jgnmWggMYc5ilmrXovuHMb+Ec40UthG/R0ySc+wYeHx4eVqwV9//kJz/56Ec/6oYsFJWiXhUhE+r0pk9zpUVBbydyOUuhyWIBKwrro4TZC04G+fj73//+2WefbWYbNmywzO770EMPaQQkYOrVq1drQsgcXluv1zVuhNI5ytSMo/lWgKVGCUtMlChRokSDSstCDxsbGyuVSqpXMX4QcgqQKMg1zsmY0p9mTeXZegQVTYzqGjdNABOiunNuxoMqSzIIF+JbFLGBttHtdmGhhbbBHLVKUeWMFLos0/M4KtOBosG/Uamt2Ht8cLRK7kGHZEaxHaiteDuPCoMoiq6jfqDuBhjx8fFx+FwAsYGyW6/X0XvoSTgurlq1ClgN7iTmjPfCKg5TfKFQwDRAf+J+l94FLx0bG3MYpnpqUA8Ls/12e71VqR7puDifBefao/60hONcAmITlUJ1wW5vQDGIypnqTAQ2nB6vijvXRZjMvt1uhx5JBFfxFyDEj33sYxrnG9VvCrmJbFxsNS4qTNqNJWpxj7jyo+Hh2p/5dNlll1mWKWPDhg1ADvT0Oxaim8lRRx318MMPW++C6qdFYauBnwjwmzVr1mA3e9nLXmbZvI2u3CVLy4KHFYvFqakpbEnq78ekGzrwrVYL80aP6Ob5EYCeMb1wtKZl8BFY4OTkpIuiN7PVq1drEkWww1arpaH4xSyBCPa7hx56yITX6uZFiwsPcjWz7du3H7QfojuL1rOQtLNhGQAAIABJREFU+WQ6i5e+PeqgRadqB3YpxkLSfcohNtHaOpwHObxxkSOIwQIvwWe73eYR9Zbx+ImJCfSewowrVqxAgJcmoNq2bRvQKpz7/rnPfc7MVq9eDScuYI+wtG3atAkc1PEehdowQ1qtFiBlVKnb7SKTiO77dKB1PaZ56NktYZ9HXexofAIx5FF3PVY7hJGjA0HPfp3SlUpFx4XcVO24rJieC+OMZNoDLPPrX/+6mX3+8583kdicZ38Ui9Y788FtF5MX+t86e7Mjh8pqNfLfS1jPzHDg+z333IOJh79gQH3wwQcZwGPZ5oCZb72W/qhFQ1thZi960YvMbMWKFTi1FVYxrA53RPgSp2XBw+bn5zmo6o9er9fVUER7stsUzKxarWpIEKbL+Pi4bl4qV5qEi5nZ3NwcHtF1u2LFClwE84NKcfjhh2N93nPPPVoH3Zi41WIrxPymEShH9nTkbGa4GJ7qNDQ05HYrLZABpCGH6yfNqat0ztqOMstCoYAsOFi66ECMgvXGzZBT6lHrq1evVpWCpnKExWhXzM3NQZiAvIJwtD/4gz+48847LTvV6e1vf7uZ3XTTTWEwBo1AuMgTMdTqsGLFCrcFh63m/NS0uVH12pnTdMLw3CmXbtFZrSwwnbrh02aWy2U9ZJw2No1xZg+EAhz7B8QqaaZjqtH/9m//Zmbf/va3+bh7L79rV7Bvoy7yUYtvqOz2GxHHrqI3RAW48HHrXQtQjy666CLo+vgJ7619+/ZpFlY08xe/+AX2tJyzpMOWWta9L3vZy1TgYA9EdcqlSQNT0USJEiVKlMjRstDDkDtDLWHEeaD0QKiHsHzgwAENZ4Z4snLlSsjjmn6UKTgVf2u1Wshcpda1yclJ/ITmxIzU6hHOaGvYYzTYk+nAQfg+OzsLhEoRRSDj+eTkRCUaCJ3C51zdVPyk1K9CPT+j8GOOphg6klmv0FosFtG9+EmXPJVM0b2Tk5NQsyAIswM1KwoMio1GA0dOuP7BT4wRunr79u1QzpAc6LOf/SwKQTmYIajJ8PAw7sSDgBCHh4cxuPhrbm4OOrTTHnTicSBUq6M6FeJU1EV0whDNc8pH1AEyqjqH0y/MQIZ/1U3RmegcaKnlEwLRewDbfuQjH7n77rutFyGITpioV60tYvqRwmQoIYWxB5zn+jpnmHRPuXwiGgP+pS99ycxGRkbUCsjIHJyNgDmGqI+JiQkdnWjrohevuOIKM3vFK17RkZTlIOr9A0HLgoc99thjtEhhbMC6aBVXFJ6ZFTF7mO2eqRYsm4LNZjP0g69UKqqbw3AyPDyMMtUcsrCwgKmpkWSFLIc6dklWTzcFen8omIPdcGxsTA+scuSgp9DRo99TDmRX/sElmlPOQTlTodc1P2cPOuyww8AGdKdev349E3ubJJeC6Ru9jYsjIyNoBTMXmNnMzAzMnNFoAcTN4P6ZmRncgzAaOnGox7wD0JjpzoTrYA/avXs3mJ+6nrNpuusRTHb9E4K67kHcWS6XlVG5CRAi5ySX8UTjUnIQNuvlNDQsqSTU6XQY0Kb1RHd95zvfMbNvfOMbllmgTcBhpRzIq9DneGWtmwP6Fj9RHU/KMZK5NCihx00nO1IDk2Tnzp1mduONN+L8CvQSfOKnpqYwDzH9IBY/8MADKt/kkKsegsxofXDgs8rQS5yWBQ8bHR0tZOepg7AP8tA8zW1IsxZ+YnMsl8s8fsXEfIqdEePNM+zp92WZANVsNiH4oxC8lF5YuJ9uSGBCsPFwbaixzfEwtsXMNm7cCEnNRQJF9SHnK9Gv96IyrwWCm3o59jMpK4XGmHzCnZs3b8baA7ui+4zq0/DXKGTugtgg4D1x4MABlIPuhRmsWq26oCWlCy64wMxuvvlmNBkjCN6Dt5fLZbwIQ8xANHQCxhpV2rt3r0bKj4+Pq92InYax1nhE5zKjgcCu2tyjnVocXnT5q+jQpDPH7bzO30fVRzIhnkrF+6NshuKjpj2cmZn5wAc+YGZbt241ESmiccFhmYVYml3XP1HO1K9A0C+r27mSo6tMlwnzSHUz304z27VrF77A2wjW8dnZ2V/7tV8zM+TnxF/tdltFkxyKQi+33Xbb6aefbkE66QHSw5I9LFGiRIkSDSotCz1s/fr13cyVHLoLPewV5WBqIrWgEP2DQK3yKU+2xP1Quer1uuJFEI5qtZraw5iMCvfgJ140OTmJFzH1lAWAO2E03EOfN9QEIf1AJKJQyeK9Zvtpb+H1VqsVmitoOcsJAotecRY7bftTn/pUVXegg05MTGjyZehkK1aswGEoqhMsLCygu2BUQ6jZj3/849CLmm8HpAPjQafTwYNwT4X6RSUJCjRo5cqVKA3jjqLWrVvXleiI8fHxUEUYGhpS1Nrl4MdEJc6jPYO/6vW6BmywfO1hFw6hEB/J4czO/GMCb2rlea6mLhMK+Aoe0g8ezYQ98l3vehdCSlSxiGoPYQNNZmYU0uQQH3QVOEXTvTRs9UEVIAt6MjxGwP31+OOPY44hFS+mxJo1a/793//des1p0f7p12lhMy+77DIEq4CcsXMgaFnwMOwjagkDlFSv18G3MHuAvI+NjYGLYIcCS2i1WrgTZiqMNPcgBiSZWalUwrJEaWBahUIBHA6Iljt6wyXId4GWJnYUENck3svkRmbWaDRgBMKDOE7JGav64TAW4y797g8fCa1r/QItVUQgUONsQnpnQby9y+VyeN7N3NwcLqIrwNhqtVoI8TH6Cu4e+OuWW26JVhVvhCv/+9//ftQHhy3hdYhDX7duHWqIk8PI2IAfMorDzCqVCt4Ljjs9PQ2XfX070xhyPphEBKugQDagNnm389JEF2KJZEJaSKE3mT3IWeOIPWqVXGBJQfx9rNdBiUVhmQAl+/CHP2xmjzzySOilEh0aVinEM6137VgvQ3XiIElnr+NMbu2E2GB0uy/0HgPtmqOvq9VqoZHswIEDkJb+4i/+wjLb1S233BI6kfFFMKdxHHPqhsfxukceeQTyLqaiBjUOCiUsMVGiRIkSDSotCz0MCZwAIkH6gzb2lKc8BaIitCVoTozThPAIsZonPkOzoYyDe6D1Q1Zau3YtPESccIT34h7cPzo6CnkHohNCGp944gl18XDpM9QZr9vt4h7UEGoiRTmokgDT9u3bF4ajRgXSKH5ifaS5fmoWKV/mdW5UoVcVXwr95tnPfraZ7dmzB9kxAAPSex43Q/VBKt49e/ZAaFW/xMceewyWcKjF+lRI6EO8HX7enU4HswiPwH3m/vvvxz24iCmxdu1aRKdCj8f3kZER4I2MrT7llFOs1wGHEJ/qQ6VSScVqamPRPB0q1GvCMwtUCnxRjJ3ql3rfcRydsqV+mNFXsGKqq1GHuPLKK83s05/+tGVOVaSov08OMB76u4d3Oq/axc9z1y4HAIZ/oYGrVq3auHGjZYomdg9irW4gNPUBnWswjXE0GhFFVaFAVHYxpekadlCwFKuj2Wz+1V/9lZm9+93vtixEh27YA0HLgoe12+3p6WnN14CZtHv3bgwk2AAPRgEYBYwe8BQje3SaMgGV+hB2u13sU9jL6C+HaQcsEfNj7969PNDZJP08SnMmgaKkaQd1Oh20Rc/MZH59bNksH81ExfLh+3CF93PlWjxFXbMW70a1adMmMzvppJPMrNVqoRXYncHP1q9fj37GAmY+yTD53sTEhEoD6DR3xi5ri2M2KcTgIt6LiCWwIlYDEgP2oAceeAATABF74KOlUskdexEaV+gu6DrtoJCdS/PhwGe9hzu+/mRQms5wOhmGNjbOSReI5pwk8SJtES5+/etf//KXv2xBpFTUQOhgZ+2BnAiETqejnRDyHn1Rzmx00z5EHV2tGIGgpndQu90OwVIOrgvCA3wNh1jKJU5iMMkrpBJbqVTSLKzanyZ4I37C1PLBD37QzP7hH/7BzH7xi19873vf69chS42WBQ8rl8uVSgVbGywZMBpt27aNthPLHKbn5+d5QJdJQBiUJ10Dk5OT2Iywl2HKtlot7FMoGZ80cjCQ2cxGR0fxCCpDR3AVw6Mrx61DjUBi/io8iJrQ10CTCkZTouUv5tC5I6fP9R63kKKCcLQ0tBR9iBump6cxImDSkFXXrFnDE5ktU9G62eFwer7J2rVr9b033XRTTk3ANW+88UbLBndkZAR9iE88uG/fPrqWsJAVK1ao0QJD/MQTT2CLiTplcGNSRyFOG2VXeIqnHuvcoGSDv1yUseOLmk/Wpf7SXZWe1lEOqtqYy7NMbxGNIbn00kvN7Lvf/W4Ymcvq6Qzpxlx7HM/rZl7pUUORsg3GQSv1AwzcPVoZp/npwqRTBjR+DATkm9nZ2dBszJ/aIQsLC5jS0MPe+MY3mtkjjzyi2wjjUDWGARfHx8e5HbHMQqGAnQc7Gz6PPPJIXKRPkJk98cQTEKkHgpI9LFGiRIkSDSotCz2sUCjMzs5CWoGwjO/HH3+8ynFMrACdRq1iExMTKs0R6IOKA2UcRpFarQbBH2ofkwmpqz3FcLwd97iYf5fyx9kn9CIELqLbeDuqhMaOjo4CZGMSJjObmZlB5Q+aqKYfHdSi0M31ij4o0QqIjqU6habxIFAUCC0ZPcnQY7wdzYT+PTY2pif+Pfjgg2F9KFPDWAU9DL00MTGBsYOMTGQYSiHqQMsH/tUUDOPj47C5nnbaaXiX02xM8kLpzCz0prpgRph+/dbvokrlfJG6ohV6M8JoDiQLNKFo2g6FARj2jtKgUlxzzTUWJIl2ljP1JKSB0PlPqkLDIVP9r19X5Ohz0TvdXzn2MIX4CBuiQ2CYIMCoL+XjqtGOjo7+/d//vZndfvvtlhkaqI6DqGqH88HB41jyv/Ebv/Hyl7/cMigbauL09LSeFQVi0q+BoGXBw/bs2bNmzRpFgTAnCoUC9kfsO9iY1q5dq3gIdsypqSlYaHlsipmNjIzA1I/dCmmlJiYmsNvqOiSaB6aCWbt//364z2Ja455qtcq4fZO9QBenC8vXZcwkMagDdsyVK1eiTLW0jY6O4iLM6XpeDGkxlrPF3LB41NFdwWCBPWPIVq1aBVwOg4V1uHLlSsQ8oD/R6nvuuQdiBKBFon9oNTyJ4Rzfj4fhJCfkr8OS5jkXeB2Y1oknnohXYC8Au3r88ccx9JwVZjY3N4fsVoiCKpVKwHMUx+OxALpLug2FliHHACwAJHmDpvagJBRNXRh2Bct3WRBdwiQT2BCE183NzcF1Hsit861wr4ua0/TtvFNZGlvkohScAS/aqJyLvyzYrhY7YvUqfVKuisLpyh3HxsawWt/73vfqX1F7oXYFswXhJ2y6sHI1m81vfvObZnbHHXdYdloTz+7QswWOOOIIvF33oiVLy4KHnXLKKczhBGsQ9sGZmRlsgpDKeS4XvkCCpkkJURTw28GW9OSTTyo4jvGemZmB0oP9lBML/8LkxuPJ4QuA/RQqxfbt27E7R43G0TWmi4QpHCnNmaiGuB9Mi7Mcbef+u/iz79wGEd2S+tXWXQyvs/KWqVDw+nv00UcZCGX/h70vjZK0LM++q7q6uqq36e7pnumefWEbBJkBFQFHREzEmMUkRs058aDZCPziEBU9JkfwKGrMUaJBlMQE8CQxJkJMjDliQIcIjLIMo8PMAMKszcz0Nj29VVcvVd+P67zXuep+ni7Il+/77P76vX/0qa563+d9tvder/t+Eily4MABRRsygR2LBQEDsdHR0YEboZpAyEW5aktLC9YFGjSu2bJlC3bFG9/4RpPT3cisORAG1bGXiLvBKLAEK1eu1LNUeIKPbioiLMIj3FygiBURFY7BcSmSxckGTalmZMgJDO0S1X9VnmiNae0oaEuf//znn3jiCVuAVKw6g88J11C0uy85ZBe7itpMerv73oFH3Jzrr64Wl7ZJCeoeFx1vKJlmZ2ehPCnI2fWWqg8GriDnarUK7Ry2LyCg9913H5iY7jdienfs2MEHXX/99TjpBmrHIqc0HpZSSimllNJSpWVhh506dYp1oeALZhENDXrxfHqe6MEWMrVni0CFYeEiKDIwv4rFIn6FAkXkITRxeLfZlELt0Uh3d7eWvacO6OJMJtBhFxLQKAW6Nz4+ricx0q+oxxZjyE1NTeinGiihOhl10YRUH45c5xbYOjyYBsYWbKxNmzbBTtVzALq6ujCT+BKZdmvWrNGFIC5RQflRVD36uXbtWjSuZjFBXPiXFq0eQ8p1B2YSxPwnDYiyCr6DgIceQpp6dRDzHItGpPg3ir/Xaxhn0taIiVdji6erhP2knYH9AyNgz549dUJ0OiJnY0WDVfVTCBQ1HnVlR6GP9gpsNbd76zgD6zjk+c7Wv8bMenp6YDxFSaObLucMP61aterOO+80swceeMDMvvrVr5rZ+Ph4GAusVqvYjahHg6ZefPFFOCGWBC0LGXbmzJnW1lYwQbBssMWRkRHILWXc1SSvCCyJoU5cQzegmVUqFdjmYJoM6euVaCSXyymyAN6t6elpZEyD8NPo6CjYK4iviuaF8BXVF8lJO5VMLS0tPHLMEknW2dmp6WW4fmpqCreAKcMnXiqVFvLAWPA2RqnONRwLHgEfL5Ph8AFeVoi0trY2jALdxvSuWrUK/VeO1t/fr+kNuGB0dBTtPP300+56R9u3b8eBF5r8u3HjRmgq4NT8jMbhw2SMTeUxdKa+vj5MKVazpaVFtQTydDxLfcLz8/MEqpg4A534Qc91P4DIOjUq5uoJOdeZyyTTB6lbkh/wUz6fx6b60pe+ZGaPPfaYBaXO3ONCQWgLCIM6icxOFLm+Ra+s8yB+Gc7kQrdEQ5JKztPrsuKUMIGHDh0KK0/mcjkihkxyMBRbDy50xx13YId/7Wtfs8QPz+w9xzfQ5le+8hX+dMsttywhGZb6ElNKKaWUUlqqtCzssN7e3rGxMZhZ0FNg9GSSAydV521pacGVcE9BXR0YGNCSRYClnT59Gjo+rsfnyclJtANXEi08LdZJVQ6gA3yJ9nt6elj9gUQtTw+7c5FkenvCqlQNDQ2KLyLsHiamQlfgaLUEiQCNslQqOXfKy6qiUS9QHcrn8wqB4emaUC217FapVNIiXsTRbNy40RL8BQf43HPPWWIlY1pOnz6NLgEDEh0I2n/rW9+K27EB8KDTp09rNQRML80pPUuaFhus/7POOgvTC5gi1r1QKLhDKdEZdQbSytFl5eKyEpVJPnLUetDWHK7VwQtDxxQrpeFf4hK1S1iymZmZL3/5y5ZYYBzIyxrinPkooiQcQhRDRBQ+xxI+wjXrXBp1ehjd7c6LUMdD6PAXamMRR+M6psVNcNfk5KQ7jdbEg4qd9oUvfMHMDh48CP8hvAJsSifWJWgrdXR0nH322QtNyGKjZSHDhoaGzpw5oxEUWMojIyPg3XDugU81NDRAGmnuzvr163VL4RTw0dFRcnnefvz4cSCCVC5OTk5u27bNEuHHog8KboaLjEcS61vhyu2QZzmOZmZzc3MKkmT0Tr1A2OulUkm5M3FlGjkD9x8aGooGAxw5pmDycrrrVXZqIX8SfqpUKvgVYE5G7+DnxCgwn83NzZBeeBA6XywWscpw32Fc5XJZCx5GZS06c8EFFwCIjFmCEOro6AAuWctKTU9Pw1WIL7F8/f39YB9XXnmlJcKVh5EiT2Dr1q0KMCO4zkG0TU6M1N5Gw1rO1Ub/W9QNqNdoT0y4noloUaclz1KABxWrc8cdd+zevdtEoGoPwx0SdliJT9ceOs7rNli0HpX+FM11cz10aMPolVGR5sqC6HmnmBBXhDAq8/B2cNTqNnTSjoT35fOf/7wlO+2uu+5CSMKlT4TvY7TB06dPLwlUPWixyLDdu3d/7nOf27t377PPPut++u53v3v77bcfO3Zs/fr1N910E87hrvN9SJ2dnd3d3eBl2CKoYtfT0wPLA7wSooiYDvAgntHFMzVMYvL6L8EUeqwX1SgtyQgZacnmw0+UdvgXxLcoWmYNH1wpnbDaHmG+MBTAyNrb2109IVyJUUDiwhBZ6M2pI8miX2Kw69evx7uNSXOAcoW3zM/P46ATrf04PT0NuaUnaDPMAD0AD5qYmGB+Oq/fuHEjUvqi/QRxflCtFdP7qle9yswGBwdhXrO4pYnxijlEhLVQKEDRwZXYDLlcDp3BlyMjI7iXqgZGpOnMZIuaQ+a4eZgTbbUyibe7xAldQYq3kMExoKVtZjIZ1ZOAINi9e7euoFMRHEhKe+JsJhdSCuO+3JPRYDDbDMfiNjMfF55+V5/cleGLOT8/r+fJgbLZbJjVHn3u3NycHsnEyzRDFNTW1obKiiCEtY4dO6bhQwKCVMY7i1aH8JWvfOUNb3jDy07CIqHFIsPuuOOOD3zgA+9973vd93v27PnYxz72mc985uKLL37qqaduvvnmnp6e7du3L/T9z6XzKaWUUkop/Vxoscgw4GdCuueee66//no4ZK688srrrrvunnvu2b59+0LfRxsZHBzcsGEDVBit+dvT06NfUluBoaAFZ2FaWWKxQdNfvXq15tvCyuEZmLgGxt/U1BRMe/yEv8PDw2gcWhUeevjwYZedqgOJ/uSCHKpRUqOnicnrWTZJ25ybm0O8EOn9Tz75ZHQ+XyGxh5gKph6rm4I+TFXSWWtg06ZNloD6cHtXVxeaxXziy7a2NtaCsiQSeeLECZg7CJWhkYaGhoMHD5rZli1b+PeKK65AXR+sMqalXC7joBzMIer/suYvtgH8NpbYfxrInJqawqhxPZ6+du1aDBPe5lOnTqFOx8tatLQwHFwwrBPm1GrnDNQH5fP5MItjfn4+BPHzsx78wTZxeIoGwLTb+KDhH6f1R0GVLhao3XYhKG2fs1FnPtmac1S49AYLjMg6jnR+4zwi4YgqlYpyDNem2sqEkrKHJgcI4Hqwkdtuuw1W/p/92Z+Z2fPPP2/BNuCGCbdBdFwDAwNLqObvYpFhC9GePXtQsxl01VVX3XvvvXW+j1JHRwfi/JZwNPB0OtmYGWZmXV1d8DSC74DdbN26FQwLkTPspOPHj4Md62HQK1asQOOIjlBoIbak8bP29nawQmxBpBBt2rQJBfpAfPkVD60lG6x2m87MzKA19Udxd8KTiTYnJyfBZPX9HxgY+KVf+iWTAh/hZOZyuVd+zCuLM7EbPImNfn/8pMFq1pZUpAmGefLkSUS5tHpWqVRSWA1iXZOTk8CJYOEwFYVCAa5IPBe5DX/3d3+n1SwvvfRSTAWdezrbeoIzhGWpVIIsxE+s1oFZgliluoBNBUbW1NSkyXyE1LvooAnLVr42MzOjVzpvoYoN56nDXxa10r8NDQ2aS8AvQ3hCoVC47777LKkB4XAiTlyFAa25ubnQnRuN8FmtOK/jrHYRrDoIC0fR+Ym+X1QmoldGZWcdzyT7qe8gsyBUa6FigdXBpvrIRz5iZqtXr7799tvNbN++fWFnogVBdCGimQ+VSuWb3/zmQt1ebLTYsfVDQ0OQIqBVq1aBBy30fUoppZRSSsuHFrsd9n+E/umf/gkf3vnOd6rjbnp6GrYIDGfaOjyLyxLjDJaZJSYC1P9isQjPG+x31i9Q5BtcXqwZgZxoWkI8XcwS44yFNkBQkVjuOvTJWK2CWak9HolX8iQnS9Q9Fi6BiYAhn3feefCy4l9XD5DnY8FkdAnXYaicaDrFI8zPz2MsMKSooaMzCuVqbGyEvaJluSuVCqYU7jsYu7Ozs7gRzkAYfy0tLTCMAAbBapbLZX06HvTSSy+pZvqmN73JzPbt26ddYk0ExSygzeHhYXXK4aErV65UDRodGx8fJ7AFk8wTDEzANSESIVNbaMNhCEHEwapS71RsdUUSAKkKe7W2li6IubFqLz755JN4rXS7uuIdbNO5K02MM6Uo2K+OE88N0F1A11md1qIml2vc3ajdrgPQcMar8/S667kiJkaSziHbxBPf9773mRng71/84he1qiHfWQVJsRHdOQqqUgIEF4CmJUGLXYZ1d3cPDAwgLmJmAwMDYFsLfR+lt7/97SimMDk5qaV0yuUy3E2QSQSYgT8iVgHuMz8/z0QxS1xJra2t+HLz5s2WCKGTJ08ijoIbsV2Gh4fRZl9fnyVeSqKh9HDOQ4cO6anQ3LthxozD1vM1CBGMLixBdqxhPND5559/5MgRS+I9lKDgvNEjEB1TwIvBYgRoXEU12bFD00FUKE/p7u7WcrSYn+bmZsgkuO/Qfnt7O8Sqeh0nJiZUEmNWDx8+rKPAhOzcufOXf/mXLTmK5V3vepeZffWrX0WHgeyH15EBVHQJ22BkZATV6DFpkKBNTU3wNCL8hs/lchnSDkvQ39+P7Yd/0SazI9SVRNGiX5L36fw7iB0ztML94PDWUfg1vuRhm4TOmtnf/u3fsq6NthzuyWhkiKvgwjZhSInuu6iHUBsJf1J5HFW2nOctKvLdl/osd1CtdsBJUBdjc+2Hoa9s7YkzLLsD1BtKTgOOSAHmdJowskg/s65Lpja5rVqtojwNWgNnW+S02GXYjh07du3aRVm1a9cuADcW+j5KqKeHuAg8kDzxC5IGryV40OjoKMQhGCJ46+joKK4EUwDrsUSdh9CCTXb69Glcg8gZbh8fHwe/01IxROEj3xYdKxQKOGBJifxF88MY0lc3+tzcnEufxJcK1HZF8PB6gPOuXbsWQhpMGRJ3enpaK7Lz/Aj3QsJKwyhgg5IlMf6HKylELTjcQXXP7u5uCD+Nir3wwgt4hCbVNTY24gO6jRE1Nzcru8SXGzZsgFCBGoE1+uhHP4poJTqGh370ox+94YYbzOz+++83sx/+8IdmtmbNGjwXMok8Cz3EOuKnQqEAoav4oNbWViZlm5T7coXEQt6XrT0Zi0ljDstgwvF1Pt2ZZOSYejuFVijDiAjH37//+783MV6VqrUV7h1wQ0dEqaxfuuc6yeTSe+vEmaI5ZNFrovLJUdQ6dAK1jsEXFb36rwt9uQYZj9N2AAAgAElEQVT1cZdeeumv/uqvmtknPvEJM9uzZ49J4gSI9roDyyg5gzg6uld+fsXPnRZ7POzaa6+98847d+3aNT4+vmvXrjvvvPPaa6+t831KKaWUUkrLhxaLHYaEUH5gpvOOHTtuueWWT33qU8eOHduwYcOtt95KOyz6fZROnTr1xBNPQOOAJQTLqbe3F9oKtHjo0cViESBpXAOVOZ/PQ/2HeoIrLQl34Ut8bmhowC1wKBPODl1J4dpU1WFh8IgpWC2MrplZqVRiXXkTD4nqSqxlxVidiT7LU55NTnxWrCMsktnZWcXUwoE2ODioZ7wuZCggkEbF30QZxBMxWBi7lvhaefamxswqSelumMswYfHliRMndNLwuKNHj6r6Dytw1apVaE1dtZs2bcIk6NGUx48f59pZYm24Sk600dEOJpnxNqwLWkZTlUoFv2IhMMypqSnMNjZYPp8HStbFPHRiOecai6L9pOE0VuJw1SIssK5o9uFLBYjSjtfbCZyDn3nXrl0mhpQLx2rnQ7e2bhvniEMfdAlca9GTzd03DowXBrRcRCoa5XK2S9TkCmOWrkvRcKOj+nhIJaCFf/u3f/vjH/+4JRWrnRNYm3Itc0uHb25mATBnHTN3sdFikWFheQ7SNddcc80117zy70MqFArnnXeengoPh0+lUgEXuOCCCyyBZnR1dSGqqSlHlqwxmBe2SF9fn1a4hyDs6upCxB5XwmeVz+dxJcI2BClALuJxYHOdnZ1a/Al/C4UCdqp63srlsu5IcjftEnH2EBgasiqXy3q+Bpxp1Wr1wgsvtCR/Dn9PnjypFR3z+XzoTmlvb0cH4DqDr3V0dBROSI0bz8/PK9wcPeRAMAnwYW7atEmh55jeUqmE3DXMJKTjOeecgymFuxLira2tDdIL4goB8HK5DJYNx+/ll19uZldccYXCRijaMSLsCqxRa2srak1heiH4WcMFo8BUr169mjlhHNepU6cwP+hMY2OjO+LEgoAWAfcqflxox0XOFHVCyaRLRg8zixyaoP8VisL28SvSVwhgCR1o1VoYvXPlhZgLq2XBvECxPE4yOYpyXvfEOvCNheTryz4oeg2IGkkowrPZbNT9G04XR416GX/0R39kZp/+9Kf1vIWoy9R1TP9lVkw0Iviy41q0tFhk2P9tGh0dhVIPpsmTtlWdB6M5c+YM2CWWk8g3HN0NQAHL+6r0ooBRngum09fXB9kJrgdeyawm3dBjY2PRfaahL57R7I5gMNE9w4pTtoCih3/BanlKi/JWqw2kNTY2ov94OiZkZmaGGXiWSDLmP2kMeWpqCrcrnIFKPZgXrJOBgQHMM6aOCcgKwEH8efXq1ZrQxgNKsCJ6nsuJEycg/LBGqCBFzuLmBx2G8QHzdN26dbgRWwVDOH36NNYaM4ARzc3NocPPPPOMJSJzxYoVGpxrbm7Ws1Q4505QmYRMlHnxtHul+fl5hwLVFVQxwBibPmghoB0mDYqm45V1cBD8SadXm40+K/yr0o5YlVCkObEa7YzzIui94ZdhP/+7Ms/1IVMLw6nTTiaTQQGH2267zcw++clPmtmPf/xjJ730xmi2OKh+ucWX7cwip8UeD0sppZRSSimlhWhZ2GFDQ0NQhK1W552cnIQ6f+jQIUtcWDMzM1CTodTDzmhuboYnTWHQxBzDboBDqbOzE5o7HgRL4sCBA0CpIrQD/X14eJhHFZucgeKgwBbovIR3a7Eo6t2ar0NFW50ztNtCBbxcLsNSRD9h3LBxapQ4OkRDibOzs5gu2CKY1VwupxYbTS7FghJ2j0fAzYgvC4UC/lX0/7p169Aa/sKQGhoaQpvoDM/21KL4GCaNbLSMyNnRo0dxJTYJS5zguRgXfK0rVqxgAM+S6ObmzZtho7/mNa+xxHvc1NSEbsBqhGG6cuVKGDTIx89kMlr/nnDt0HpgMSRVwGkzuXBaaF3xLueUU5uA74XGPrnxkB+pp3s7Yj9DcJ273jlCXSM6lmhUDMTKyM7cdNE198Sww65XUQxh2LJrpL6D0fl7Q7hg1Ne6ffv2z372s5ZUkHrwwQdN9gbIxTXdELQ1Bb7+/0fLQoahyB4WEswLTLa5uRksCSwMNfEmJibAm4DFRyCE54epY2rt2rVEPfDLoaEhcD2iVMysWq1qEWukBJw5cwZ8nME5fBmNZ2iSMn4qFAoa52d+GG53Scph4IQl6vEXvZ2bmwOT5byZAApYLi90+LS3twPCgPkk7ALePJWgJD37mMeeoY4Uhtnd3a3lvliNELfAR4ceWgI/cee5QAarQ7JUKjHuaAlivlQqXX311ZbEHtDUkSNHtFoY2hwfH8daQwvB0ycmJtAlzDzWlLEH7B8Uy3/uuedwC7ZfT0+PsmD1AfJLqinq5eP1uvTkUyG3pVpTxy3GTDJ1TdNpqbAad7uyzujT64srbW0h/6R+yVnSAGFUWFbrwvfdlS8rVOp0LBr6it5OVEUdgYoN85d/+Zd33XWXmX3729+24AgenXMnv6PQFTpjdXHr9HYJORIt9SWmlFJKKaW0dGlZ2GEdHR2ExkGthlI/MzPDdFpLHCYdHR1QwKG/s7IRvoTPCudalctlPQAQ5tTMzAxwiXgQLInOzk5cgy9RnXP9+vUwznAN/Jl0Bjp9TY0Y6p4wO3g4pIljQW0seupUcatUKvgVjQB9l8vlYAmF9a7YGbamjqlcLgenK0aEzpRKJcyPlmtiNJ7XmJwfBncubMGXXnoJNz7++OOW2Dc9PT14kKsxrzg6jKhYLOKJmF76EnENrof1ef755wOYqiW1br/9dtjlr3vd68zs0UcfNbOVK1fiifBhwl7v7++HbYcHYQJzuRzqisHwghl39OhRbCo8gpB3Fu83MVCcJ00NI16p0Bs6qdSAc240XTJ+GRpzVquVM3FCexJuDxM7Iwo6cDCll0XHOUxHHYpCSFyzzq1aB4/n0B91XIssjaYosOgowr5ZYA9h8//FX/yFmd13333/+I//aIn/QHeI1c1qp7UazrkbS3Rc/EnTNhY5LQsZBqcWuB4YDSIfLI2o1YxGR0dRXgg/QfgR3Y7NhCDHqVOn8G5jS0VjSLi+sbERIlBPDBkbGwNrA/gbO7izs/OBBx6w2r2eTYooKiPL5/PqNqS/DlIBXJJJZlqQzeGtIXHR24mJCY7XJK+A3cCVCq5Dy6y2rseBTk1NoVktQMXiHSrUWYId/7JSJYKIWBcsSiaTwQoCno4kvGq1Cn8j1gXz2dzcDFkI4Yc+DA8Pa+EMFKxiMS30lrErrAiWDPGw2dlZwPeBsMdU9/X14UY8F9f39PRgmJC48EvTk4kujY6Onn/++VbLTeg3Bjk+riypktTGVPFDryOjayZeNRUw9PvV8W7xL7zfWpQyGlyhV839GgLos7GzoS3GZFk5sA47psdVBVXUcReNrrlR12HuJOgi2MOTk5Nh9LoaOwTHYv7YarUKNvI3f/M3lmhsd999t8pFjSi7Np0M4wXhABlPcWsU7Sd2pp7Hu2hpWciwo0ePtrW1sdCiJZt+cnLSVbM1s5UrV2IhwaeAXzA5zN4kMoToiNphp0+f1uODyRwR9seXxAdDkcfuwYMaGxuVffDlD0HqlUoFHB9SE7uNZpxLPFIF3LUMWwQvTC6XgzCAcEVkyCSEhn9DO4z5tmo9VCoVzdd2JbL0uJNMcigwuCQJZbfQGuZneHhYeR8M4v7+fkwFYgmsZon3EJOMWoijo6PoJ66HQOrs7MS/GBES2FtbW7FkuB0du+yyy/AvrCtcyZwKbC0IzomJCXQGM4kCVxdddBEcAFA72trawrVuamoKl4lCSPEarACrWnnUZHGRM9WurFazmZ2dVSA7/2KiAFoJa6GRFoqHabf1EDKrVcui8TBnL7rvVQRS2rHbYeCnPq7hlSA70AK0pWhQkAsXSpEovKW9vR2nYGO/felLXzKz4eFhtbnr1IWy2HQ5Ue2AHlGQi/tXucoipzQellJKKaWU0lKlZWGHFQqFVatWQflVVWhkZOS1r32tJXoxdKtnn30W1hK8QNBECoWClu7mWWXQx8MDKi2BosFpmc/nibxnm3Nzc3gQvHnU91UzpZtIQ1lU9LR8Bv5OTEygG5oIHI0BzM/PayESWEXFYhFD+/73v2+JtcoWqP5r5Vm6cdS16LRs1sEygYnTCWlmU1NTmEm472i9wbTl0QFm1tzcrNXTme5N76UJMhCK7U9/+lM+bnJyEg+CLwgm1KpVq/SgS8SxKpUKqpdhQoDCHx4e1rM3eag39g86wwAGJg3JBrBjSqUSqhIj+6Knpyc8NNkB9jjzanBQrdaZpE2moUG2H/oSXZtcOLW5eT0mH0XTcbq34mzrk7Me+IJoHWqQMwWccab/Mu4bhUrSstTx8iyF6C1KUQvMmTLaCK8PzeLwQWopYi/deuut2L0A08MjHS0PRg9hNMzphhC9MurpjRLy2Yn7Xcy0LGRYqVSamJgAn9JXdNu2bcBxYA/RiQTWj+A8j9yE+IGooAMNrFALUG3duhU1HcDL4FlavXo1BBV+AqulHxxMExu6v78/ysKwI7UsULFY1LgrK2nhFYJsIGIi5FbVahUsGEPAZj19+vSBAwdMqkyZuNEdelsreljt60FWqy8SOUsY1magCKEv+DDPnDmDwlcYGkJKxWIRg0KH8fSJiQk9ZADu0DVr1oAdoKAUMMrMf9IDVKGssJ+IsXV3dytKBXcVi0UsKBYX/hZWnsQQsG1OnjyJLmHFuU+0tv2pU6fgb9SnW22dJ06g4u95vUJvnPtIpV2lUkHfVBfJ1JY4cp46Qu31X4zoPe95j5ndfffdIUOMBtUsJioc+iPKlB0qwZ0eHn2uG3voReT2U6XwlSdO1XEeWqBM6C1UN6FgoQ8333yzmZ111lkf/ehHLeED0XAamwqdgdHQlwVTGg7TjcV91qTMRU7LQoYVCoW5uTnFU/B4w+eee86SglJYv+7ubogK5fgrV66kMLNErS4Wi9BJsd5gc2NjY1oDMJsU7QUSAVsZoZ3nn38e2j1zj8zs3HPPJbLI5H3AlwrGm5ubA2fETxCBhK5orlJLS4tmdvNlhhhgtV/0BGYZODVOUHPAMIgQDo2AzzBhlsqy4xphzKZSqSBQpKlgmzZtggDAxBLTgTkh4NPMNmzYoJNAaxVWHTRK6hmYPbSGplasWIHZxrqz/i/2BtEuZnb06FE8AmATmNq9vb2YKIhezH9TUxNuV5t+8+bN6DZodnZWRYVjSW6WXMkiC4QWL1A7nkenhpEeVqVSq9pBLVgKC9egtTe/+c1mdvjw4YceesheATkO66wlVXqiMszdGBVX7noHdogGftQeoooQlnlzd0W/rNZWiQQVCgW0ibO+wHb+67/+i79aEs395Cc/iXBpnbQtDvZlIZoclxN+4fxENQxK3CWUEJ3Gw1JKKaWUUlqqtCzssJGRkdnZWajS6rs4fvw4NGj4f6ANHTt2TI0taPqDg4OaSQbbZWpqCqA+aO4MfcFugJEEd1ZLSwueCx8UYWlQz9EmjLnBwUHnRDKz5uZmxfLBEuIp0qxtbxKbQW951qJi62k5oQA8LAMGlvRkE2r0+BVPp4IGiwQmbKlUooPUgsIQTrUMnaUclJ4hWyqVoKiih4ghtba20tDh7c3NzbB9cSogFm5wcBAfYFMiLpXP5zFd8ObxRJvwQO21a9fCCtTktunpaTg2kcwHxHk2m4UFhtbggGX1ZNhhiMxdeOGFMAq567R0GQ+t1nwPGuV6Fo+z2DQcSzCeK94RGsRWawPhSmZcOByja83M3v/+98OLoOeALGTBhFvaGQF1rJzojdH8M6u1rmg+aq+ytecj0zcQ+m+jnjcXZ+L8hC67FStWvPvd77Zko37hC18ws+npaYXFvu997zPJ1HSWZYg2dFlfzuvoImfR+YnOZzj50S8XLS0LGbZq1ar29nYt1AQn2+bkpG2wJAbAwOAgvZCPzCCZ8otCoYArNSVrYmICwhLyCWyU0g74AnDVrq4uMESEoJCBFE0tnJ2dxaZ3qWA8D9rknCp0BvKGQkuR2aB8Po8e4npMzooVK8CYICeIWMEj4H9jI7gGzy0UCtoa+jk3N6cVGqMMzoXKFcvLw1NwDVSEarWqx63hroGBASRCQBvAlY2NjZAcaBN/W1tbMVIsrjIUS4AbmN4nnngCIUwtStnX14e1U2h1Z2cnpgJ/sSVGRkbwK2QnBoL6WJaI6s7OTsywotsrSbl9d8RzWI2eieE6vdEgR2Njo7aGzjAHXNUOAnb0lG2e/OIiWDfddJOZ3X777Wb21FNPhctKClWZTOwUOtdtlzoW5bz6JQUbRYtKYpAeA6SdCc+7ycQqSEXB8VYrU1E9defOnT/60Y8skfGMTOMasAiqehrmdBPCBFMTQIqLYEVdpvrKRzWGOtpDe3u71oBd5JT6ElNKKaWUUlqqtCzssPb29rVr16ojDgrmoUOHYFsALcZ6vqg8BEUGKLW+vj6o3tC1qRwBfQCdBe03NDTomWTwKL700ksw9RRNVywW4VmCFgaf1d69e1U/oqasQGToVlNTUxiFepny+TyeqCAuAufwCKhyHR0d+BKJwKBjx44p8BKf29raMHZYiq5CEk0Z3AK7E6ZMuVxW3ynmZ3Z2VnVeOnbwL6xeFHl69tlnFWqPB61fvx7WDBYLrtpSqaS+VodcR2ewcH19fZqKgEYIKAcwjAVE4FLWbbNp0yY1xwGUP3XqFG7EzMOYO3XqFCqJwEHN+vRQbGHhEWujdpgr5eX8fupLdJgOegjUcHTOQxpwFpgUbEqNdTbieogr8aAbb7zRElz4T3/609DajjqyFvJWRY318ILMAseAgehVCy3UqIPR2WF8hBadqZNlTEsIDgBslW984xv6YrrEEp3kpqamEKlRrS3hTzd+6Ep1SEv+rTM/9T0iZjY+Pg7WhFd+kdOykGGlUmnv3r0AFsLlhRBLsVgEYhtCi/hp4M3gGwRTnp6extYHX9uxY4eZnThxQv0wgOlns1m0o2X96PYBC2NReTSO/Q12HN1erl41c6HwCK0xUSgUFFVPLL6KQFxwzjnngLHCkwnW3NraivcWLRO9iVE7lKMe0pHNZpV1agERfklZgsnX+JklUSh46iBrt23bhufyEfigORIIWXV3d6v/DZ9PnDiBJ8Kbx2w2jBpzDpWCpbxQNBL8YuvWrTyD2xLnodWWs4II7OnpwdZCWBSxrvb2dohS7CL6VPEB3d68ebOKHxBzuVzJKPWEk5GpaOHkqHDihIRBMh5UrZKJ1SxdkpmLw5mwY6wyJNmnPvUpuElfOUWB3a/8RudBjeaQqSiKPjGaP2e1ygHfHb0GPzU3N+N1w+51eaJ65gNVBO321NRU6AxcKPCmHePyhUGy6DDrCza9fn5+fgnhEpeFDGtubp6ZmYHkgLIMHs0jphCHR4h+9erV4FDQqlDd9fDhw2BGeNXxos7MzGBHQg2nUxvMC7eDn/IkDqj/EF1DQ0OwAsErtTyjJVuK4bFw65fLZSYmc6QMfYHz4qGNjY36koCzX3TRRZBJ+BLWQ3d3N9gxxoKWW1pamIZlUg5KmSwPZEHfCE8ImUhnZydmBrYvpFQ1KV+LK6H9TU5OQvxgXWDy7tmzB2FLTBdAHy0tLVgdDA0P7ejoQH0pPU0mm80isQEDZAon3lgIIXw5ODioh0pjpU6ePAn0v54p09vbC8NRD5zbt28fRoQbaQHDssTjnn/+eYzUFYtSk8tB3kFkYbofXBqsnhDtOC+3lkbXXEhJeXoU6FGpVLABNFXu5ptvxonD0OfqRFzqfL8QRYWWmxa1XfL5PIaGLEMopkNDQ6FscDLMxWgVK0HJjU2IbVCtVrXuNpoiCsyBR/QRnG2dCiePo2lwUVH9suHG+jJs6VIaD0sppZRSSmmp0rKww37wgx+sWbMGKjAyT2EJNTQ0KEAcavuqVau03jkom83CWoIWD61zbGxMKwlBYX/ppZdg6oGgRk1NTUFHA6Gp1tZWdZ1Bt6L3THXAXC6ndYyIS9R/0aVcLqcloBwcURNXaSThEbAeWltbYduhNXfyHky0rq4urcwEKpfLerwyiMaZRs6ampros+XTYQBxzuGZ6e3tRRARNiLPrsSvcH7ip9bWVnwA0funiEQ6G4EbVH9vJpPBTGLy0dSaNWtwDXyesB3L5bKq4ZxqoCIxIrR55ZVX/vjHP+aDMPaRkRFFoF122WVaQhrE4utOAVebiX4/9dzyenzQksqu9Bf1d91jrg6IJl9XYxWHq9WqrjUuaG9v/9CHPmRJeAy40Kh3yxaw0uoYZ86GqFMyyoH6nnjiCW3ZWWA6avf0aN2NbHLsg4k7Xf0WGgYjsbxAOGqaR9H5cdsgNEZdaauFIJrhldEhL0VaFjJszZo1W7duxYbD6oKnnDlzBnsL3BluoqGhIazrY489Zkk8o6enB5IGoAMWxQAvgxRBRKehoeHSSy+1BEYPebZp0yakLmFLgc0dP34cARW8JIjJPf744y4EjSGoa56FFfREBrIzBdzTY66ntKBwAFPBcPub3vQmM3vhhRfwILgN9ewrS5xy4+Pj+F5rW/BNoKAy8WHqlWSIqj3Ar8gOg/ft27cPEwV3HHyJ1WoVK4IBQrTMzs6ikxC9LFGvRw3QFQz8vQslonEMED+Vy2X0ECIQD+rq6tIiLMxUQwwMj0Osa3JyEjqNrmNHRwfc1JCjzzzzDDzVyoIZondSJBoywZVajpIBLVceDLe4o8VC1kkJ6m4MUeYuGseiG5jeD3/4w2aGcuxPP/10tAJFtOSEdmkhyRT+FCU3FW6wTrqDoiD1aPk0PSed7l9sJyzExo0bsdbYVNFgVXSArod1ZonkIB4ve71euZAEXUKybVnIsE2bNg0PD0NQgTFht1EMYJ+BD7a3t4N9XHHFFZa40UdHR7FN9VDH2dlZGAo4hoo4CEAZkXwGjsbj6iHS8KC2tjZwH8hOmBdDQ0MOyGRyoooG4WmHOdOHiWL6pYpAMFDWbEXLGObJkychViGA0XJzc7O+GCdOnIA8YM4KuoQBKuZtYmJCs4ZZulfhCbiA+inEFQ0a/IuxEJqlQXIYcDMzM4h54EsIpNbW1t27d/O5mPlCoaB4E77Gms4MsdrQ0IAPeC52SCY5IwYEhGSlUsGUopIQwIrT09N67BmGuXHjRgwTP+XzebVoqUFr2haDIk7S4HqVNNhLPJAligVQHu2YV5RnaRK91bJCd2IcpYVWVvzjP/5jM/vrv/5rzEy04GGUdUaZu/symuTkAlo6fK3Z7RpZ6BGhJUQNTFVh2jeqPjY2NiKOi9tZKFUFKv6WSqVQPL8SEaLRTXdLtbZy9EIY1IX+pcG3JCiNh6WUUkoppbRUaVnYYYVCYWZmBtYP9GgAyR5++GGYHTBK4NEinAloQ/zU09ODoBeCaixNBPVKq7tu2bIF2T+qv4+NjUHH1xpFuVwOmr7TwlQvJsJKqz2BMrXF7PETtSdYG9TT0eFf/MVfNInePfroo5Zox+jnmTNnWLOKD3JHX46NjSFiBI+iC94Aeg5LaGJigqE7q4VW2gJqNYwY2EwjIyOYLmL0zay1tRVuPcw5lo/HKyugdHR0VM9nQZt9fX2s86udh/cSy8Fx6SkBxHACcQfDC6l15XIZ/cRZLfBkNjc3wwLjMT1mduzYMZ4LY6IsO5gZLlZ9n3aYlonJ5/N6uCgTj9RodpEwd/5kiJGjWwzkjiTVuwi4J3YxXFa8azfccANW5Dvf+Y4lZrFrrQ5VazPJokYkf6qDXaSdquaLqy6vj2BrOkx+iV3hDifSeKHVhoG3bdtmZiMjI3oqLKLpTU1NcKhERx01j+qYzuFw3Ljqk+v8kqAl09H/CR0+fJiVdcD7wKOZ+AzxhoXv7OzUs75AY2Nj8P/AXYDE1b6+PjBrMESG1vfu3cvbeeYF9i6iBdgoLS0tYHCQi0Dkuzcc5JyB3MGV5OASS/hFoVDANfhXRZElUhnJbYODg+AsGCyE8bFjx/BegY8jec7xAp4AgPeWj9AqWZhPnmmi5YsmJyfxqkMQ0qGEQUE2wA3LgBa4D89Cg9sT4Bp0vqurC6uDUUAeNzY2MkLJjm3evFkxLBQbaBPCEjOwdu1aIDUwn8T7QFgClYOnA99viYcQnuG5uTmIc4wFizs9Pa3JWF1dXS4zzAJUtKt7pGyIKCFcT/e4i+qbuPhAzhWpWQ106uqVFogKC6Sd6zAx/Wj5d37ndyypz3n33XebHBGuFAU1OKIsqf+rLZA4xcynEEzvhsDOuGGqF5HdxmvOMDkuwDUqn2ZnZ3ElgxFh+24qnByNSi/nRdQR1UG+8N6obHslEcdFQqkvMaWUUkoppaVKy8IOg9as+iYsiS1btqBKBSBkxHDDGgCugeXDoZhAVccF5XIZejQcBXAoHTlyBBnTRIjgdhhYyGmFhr5mzZrNSdFhE2NFvUagbHJkM8tZmSSuKqidaDroXyxGhX9hh6GKzPDwMBR5VR6LxSKGBqBdaMeAoFrCXgFlMhmt3kSdV8+mIlQSC6FoGtoEPC4ZU411gemMK3t7e2FCofNEEmKZ0GG4DQcHBzHnemLcxo0b0Q2sTjY53U0tSxh8Y2NjGBE69pa3vMXMfvazn2HsmC44jX/2s5/BoiVIEm1iJtF52N+Dg4NwTbPb0TMY1ZShA1bh0QRQhP40XqkeQm4VXUp6HV01YXWLRY8PdrXww5L2biBMkb7sssu4Qz784Q+H5XcX0v3D7xfySCu55AHnhFSguXMbagvu9kwmo5kwfB9d9reZlUolrZuMtI1SqYTdBQcMLWnXQwssrWg/oyjH/20voruSTpTFT8tChm3YsGFychLiBFsQUY3e3l6IH7BjfH7hhRcgY7DPIGbOOussuOxwJXhQPp9XWDMhdkDJA3aPpnK5HNgrQm48cxmZK+gYBEypVFLYHlmY4tHxIIKb8dbn9SgAACAASURBVJexn7AgGzkLbqcLEQE8iDQEb2ZmZuDkgdTHKcMPP/ywm0/FrENO0FulvIzvg+OSGkHh24t3BrOEaOXhw4dR+RvyBsjDLVu2IL0BqwMf3dq1a+HdhVsPj3vxxRc1QwBftre3Y7YVXqiFPNiH5uZmrbcEIUeIJnYRFrdQKGjxRvxUrVZxO+ScrpQlYb/Ozk7FoJIHaRof41g6XQR/qhShgHE5TxYUCeTf0NXGL1WScSyudEjIOh1XBXFEeFmwtbil6/DcKHAu+pNeoFdGbwn9ohaTHwtJR8X9sgCNHpNNVy1awAvCQvV4v/SAJGqr0edqb513lKTwSzd2F2sPW3bhRt4OrWtJVJxaFjJsfHx85cqV2ExQiLDbent7EQjBFgQPuuSSS3A6M7gPZNihQ4fwL3g9GEpHRwcsBlhg0LBOnjyJgJMipKvVKkQaojjADjz33HOQIoig4A1nFTuHlVA9Gm8OIbmqm5fLZRV+zH+qJiheS6wcavFIUcKbOTY2BgsMHWO9Whc80NaolkKIaubyzMyMO9IehP7jSjSSyWSAj8Cbg7qFPF5ZURi7d+/GYiGfAfJ4enoaIhCLi/bHxsYUGs7SiOiw8ov+/n58gNkHNaVarYLRYHUwFcViEQIPghAGX7lchkDFsqLzDPshYoqFGBwcZHUiM3vhhRcw+U65ViVGS1zyAzuvIjCKquCVYcmiTG2hWy6Nilua0Wrxk1PrFkUfWOpMuTmLWmESkHniAm/O+KhjUvxvcNUo737Z2JvrDP/FJOj7ODU1pWoEcz+0SCnfCK2cQOkVWrHVWkyHk/dRyaQKjdWdw+iNjnBWFNxUi5zSeFhKKaWUUkpLlZaFHdbU1FQul6GAv/nNbzazXbt2mVkul4PbClo2T+KADw3+RujRvb290KChcTMnGsYBrgG99rWvhTMKqhP8YGvWrIFOimRPQuSh7OD8QNXQTVRgEwdRVdCJVJmdM8fFUXC91h8CjryrqwvX4IgQWA99fX3QE4FIhFV0/PjxqJcDt/MsSlg/MHYZBVTkFfN5NepA9VZPpYFb1RJjC/MJZCCPAVRD8/Dhwwg+wSzD7ePj4zD1MF2wg2dnZzUlACPat28fFhRaJ8ypyclJxDWxbWCYZjIZWE6IcqG34+PjcGnC2wmP9AMPPIBbQBj76tWrMb1omYeLunTUaHREIy7ORgfxgvC0TIfJprMxiuxXY4J3heEiGlK6UZ37Dn/n5uY00wPvGq15Jbf5nSVRJ1AUJecn5OdweqNzzufq/PBVwpU8HUI9zPQ9qIMEXpZisahxBz4iaiVHO69T4WamjslV3zsaTmk2m8VmXhJ22LKQYSmltDhJURL0GilLorwJzwNjQFTLSpFPqXQkk1LnIZ2Brj+hEHJV/vgIjdg5mIkyZUIewNwhv+swXFsAcOGYe/0WLGDNdUJf0ThT1JdYrT1ggaelQwFV3Dyv1OPOc7kc/M9QXqFNTkxMuNhw2H/nPdaOLTQPr1DkR2cpm80iprAkKPUlppRSSimltFSp3nGo/3/Queeee/z48bm5OSiqr3/96y1xOt14441QiABzuO2228xs//79SFIGyO2DH/ygyRlaWg/+85//PEpdqMqzc+fOp59+2pJiHB//+MfNrFwuq3OPjkF10VBHVsUW5PQvR1EPD904JumoDuOkqj3hD3qjK/MBUOU555wDV6EL6dcJ0TP0bWajo6OqZeNBTU1NCiVwHjZFqdCZw/HqTGqb1eSgYR1mJpNxtXRNrBzXpkL7eFdokdBQcFBsd2iALVCTftu2bbq1CM1w1TdCZxfPKFBMR7UW7xd1/zI3Qx2G0edyb4RIjUpy5pZulUwmozUDQbRIlLiOmmftWuPC6e1uzh3pViFW1l2pQ+OD1FXLXaFr7fAveiVHrYnPMzMzYamLam1lFm2KjRO/o1e6EyR0jbjE2s/5+XkmBZm83doZthmyJg7z9ttvB255MdOy8CWCx4UIH9bL0Ro8pVJp586dZvb+97/fJEylpQdw5W/8xm88/vjjVnv04sGDBxWfBukIpm+1TCSTVDPSV8WFGaLBAB2IjtFEMmlUw+S1tKCakTJHOlVcTpjWOyfzinp4HB/Xf92JLcoWmULkIHkONW6CfFMezdgDiGPHr64wq3J8BwyrcxYlh6xMwRV00BnIZrNY3DrlVok5BLsh6k+XiaFEN14TKevcjG6xTHaaA9yH+oqTZJxPLXXG1XG3mGQEOg1MW3N5kHrqAiWTjsgC5muitbgH6cQyNOhu1Jeds6pqEyWfU3RMdogqHPPz85p7oCdhuultbGzkISy2wLujiRZ8kHudFZ5qtcJMT+ewYFlB7v0N49NkI0uClosMa2lpUVSry9PUUuhr1qz53d/9Xatdfqf3YdNs2LAB6A9EPvHlyMgIsn9gtXz96183sxtuuAFRXBBf1LC0XfTlJLmyQHqykVOgtBFeqZyXzE55NL/UN5asgf+GipurPKTWquthtbbyN4/10ol15IZWJ2CjIpD9VHLBFUqRsIYeamzqAHUqnJ3hYlcWWGwcSMgvGhsbVYufm5tTrscevnI7zK0dnuUgQib6it7uImduV7Cc1UIr1dDQ8LJ2mLOnnXz6f2CHuTxxE+vcJVrVscMUYZ9NzjZSSUOghw6QiKQwrGWy1vhXlWZX17GOMsrrdbEcKSqKC6F7eEmkhZHSeFhKKaWUUkpLlZaLHVYoFBCPUccUVSTnUVRHP4jOLlXfstns2972NkuQ1vQyAQsO2D0qTVx77bXqAaMjS8MVVJz1EVSOtNIo7ZLQgcYrnTqsejQr3GiX3O0gWqLq0GhsbFTHCysdh7obXTSqybrxOpy3aruZWkA5+xwq4Pwyav8B/d+QHIStOG93LKfaJQwXoRGasKGVw/445ToMdrrYAyj1JZqse+pLTH2J/11aLjJsZGRE33+4sDK11a/5pb4eLsDLBvH3kksusaR4B4qmV6tVQGbxE45vfvDBByHtdEOT3TgeFG4px7X5iobcnC+tK7foah3pxcorHbtxPiu+xiHOe2ZmRqfXyU4XxnOMRgfomI4OkO3rQpBQ+98l6KBxgLlZE8uJSVyvbVLqhMlY7Lz+RGmn/azGDiHkiivHZNUragPKNMnRwu0Xhfk47sM1QuO6/fL5vL4L1EjCpDHOuesSHoTbtYQHx6IVmNiak09uep0/Tceuc+78b6ByuQwIOx/h9DOTql3aJVedhBTWMeEt2kO3K9gl9ZYzS0zd3XyKxjK4e0NMkNuo1MNU9+Kc63TRe6/6HNUF3U7qkFwqtCxkGFZUt7Lmn5Igio4cORJuFAvgbWY2NzeHDYqCsPfeey+uZOqrJXkh3/ve93B2l2pztO0c6ECZuzNuVHV1rNyJQI0E0EzRdywaXqrGihKxn0xY1rfLae76JXmf6pIuLYl8TXuoB3fxGuf9x78QWg3J4cV6Vi95HwA1FNi4Bo/QV5rEhY5CyDRM5WJjtJm0t/olAX4upBeNLKrewLV2ohdfOhinzo8zZHWkZNzasnsiNRL9l9E7FzA2iV2pTTA3N6cBP3ZJd5p7y1yadjQWqDya0tQtlt7oQsUqyaJxSqcjciqiEFbVd9lPfMBOi75unLoQIuTseHID9R+4wKSrQ+bEpAVvbjZBZqKWIzrGSmyhCbtoKY2HpZRSSimltFRpWdhhlUrF+ayp7qm6AR2ERZhA9KKoQkTrG1fCDvvGN75hZtPT0/gVtZFwtu/evXuBXURlW9WwTPQ+C5QyknPHmdgZrtiPhtOcSRGG30w0RAtCdM6ccl1SG8sVnHWOKT1spaGhAe4ODZW5ZJqoA40rhTZh4CKNgecNYlCoMd/U1IRaWSguxROfWTHZEuyoO0yyvu9Lf3LRSrUJ6BLURmgl6xqRwgCPvQIbwmIOW6tV6l2KBbutt3DsmA06NsOF4JKF4Lr52qNb+JPaVbTmNTJdJ/pCk8s53sN8zUql4oCIOplcF+e+1ulyM+leN+2ng/hGXzpnppv44Z1TJ+QtfJF1IOySLlw+n9fgN80+/Kq4XzaoITciGNWad1jQRU7LQoYpaVB9ZmZGV1dfFat1a5w4cQKsEMSdpEcEXXHFFWb24IMP4hrl1I2Njf/yL/9iSZF4tDwxMaG7B0w5W1v2BjuJhzvoPqO80feW21R5X7lc1uzLaOiLM6BvDtmEwnzJKdRpSXixvsylUglqAboNxx1dQ7iRmZjaWjTH2XEWXIPbOTo8Apl5vb29yGTXZE/6EpWR5XI5hTxwfvRV57utCYLkcSGehSgV5dSOgTpfpcN9KJN1DkaQQzoQKxFmdjsmS8XCaT+43k2XCZNVbu7g/m4UTkg7/mgSTnMKnP7L68OUXnbJzSTxOBa40KlhKLKJHdMNQIGtbyK/URnPF9AdHaBToZTL5RSUj8/FYlEDh/jMaKXOElU9jRqWy2VF3PB4Jj1F2u00HTszEEJFYalQ6ktMKaWUUkppqdKysMPWrVt3+eWX33fffRYAoEOIndXqX7hg5cqVqtNRwVQXxHve8x4z++EPfwjdB7c/88wzZrZ582YYB6iPzlMZeXoy22S1J9XC5ufn0SY8YFC4eDgvyLmS1A3SUHtApXMlgRwiQP2EvJJ+GzVQeA3PjDZR8bRXRHboI6gjqymjDhM3FWwTE4Ij36rVqgLSYH41NDTgvEGnTSuajt+EiHA3M7w9rDRPfT/0R7l1oXqraIioG9Zq7TD6oJzprLcQ+B4+kY50RdzQvNaWS6WS4gy5cHpWHH8Kb89kMqgkQIivdl63gXPYckQO7GBiEKtFazEnZDU5J4GuuRD7WqlUeGoEb6/WHtYVhTNwYyvOkBsVtyv4M2rmTkxM4KA7NYxoxuFLHlPu8ClmViwWdcWJTgqdlgTsaHEcLhkegXlwG5sjWkKm2LKQYTfeeOOaNWvgzVP/m9V6Vxw2z4WgQtcHv8Q1iLhccMEFOJ0ZX0JodXd3Izx2//33m9mHPvQhC8SG43pE3JnZ/Pw8gJRwArDwIE+ONtl8+no4vu84r17J9CnF3fFKFasEpCkAl891jhd8cMDLMBrkUFi8TK/h/Ct3RkCrUqlAhmkUZ2ZmhofnspF8Po9Jw8mWoIaGBobEtA/02bKRam2RERdmUA+YS8ZwLbtylFH9w2HqFA8dDVNxklVmk+/r0juvrArUfD4fYuQqlQp2o0tLcCElEOqy60A4aSqfomKDDWpoJ5vNYslcKojuXmpFrs3Q6U0PvL65LvxDT52qbs7NqDqB1fIKdoxRYV5ZLBbD6WX4AMSCPmGORKUWaUn/py4E29RRk42g22AgbFND2pwQdHtJ0LKQYT09Pap82QIJTI4lgZxN4BR2lYjYUu94xztQMljxr4cPHwa+4OGHHzazG264wcza2tpcGrXJPoOdQQ1UFT1ogoVCATwaR5q50IVe71Qqjih09+fzeXW1k0HoJJRKJQhU9zaqbuheTmXuznoga3AuewsiQ4xA4BZwVSxrpVJRYDHzSTVayYAieqhRMYYZlK9VaxOfyeg1wcvlM6g6nMlkQouNpOHJalKKyTFiZ606OYduq03DeKSaO8xSCNENNBRC7YHj5TYIUU6Z2urJ5JJh8oDLL+RChxLUhf10L5nscFwZ2ot8TXgLWtAQJrsNpYcICF1Q7sxw7dht578J/Q2VpFK2JnE7Lwv/4leeuq4LrQ4VBjs1V4Hi3IlVkFpXfLqyCGZMujx0tx8WMy2ZjqaUUkoppZSSo2Vhh+EcZ/UMUFlWrYqKSegQp1/LKTIOamVma9aswZnCOAYadPTo0YsvvtiSmhH//M//bEldfKtV/51/xtlMzm+u2hxSqsfHx6uCqaXqGo6IQaCoyuyON3SxK17M1jK1+GBXot75OkK/ltX6G1XjdhQ1j+gPUW9VqVTSqj9UijVo4exp5+QMjUi6VZ3lpJq+c/Spws6qXaoss44Uh6AIT1rSOttzyYE16gWi80f9ja4ejV5Pd5PDT7q+WVD0gU650DijUw4t06MQBlCrsZLTLkjmzCB1es/Nzemr5xaOS+Dc7PhJN4Baq24q8vm8ephpmGoxJ667Wja07XQD6/sYDi0MxzoHIx+kI2VhDi3zAT+hy6bg4qoNygAqXBqKt2QAfknQspBhGjnQRSXOG3uC/hBlW5QTIUdjzAM7DGlJAwMD5513npkdPXqUV87OziIwhmjB9773PTN7z3veEzqdc7mcyhvHx12dNBAKM7LMEkJESJzCTw6FD4rm95BHIzIfxRfQ9aEFCPjG6ovn8NbOZ+UQECG4plpbE8+FqfRldqIIxNVx+T0YGtyhTlw5952KSRfrdplAdVAqLuAacl5iFjg65fgUCc7PqVOha5RJSiM6qaBPJM/SFaGUdR4/E2GgD5qP1VvJZDJhcIVpgtp56iIuzBwqLtxabj6da9FiiqYDMWlX1Yfpqn5QFGkolHFi3I5J5pHNGhum0zv01XPUWm6jUCi4Wifazyj6Q2MfFrxKOnuqjDLC5+peqqim5hT1gS9OWi4yzEHCuN4hH7da1uDEldpD1WoVEaljx45Zgi/I5XIXXnihme3evdvMRkdH0Q7MMog31Ah+6KGH3vjGN1rtZqUVqG/s/Py8iw1ol9Sk6OzsDJmsM6TImEK26MJpnBZlImQ0Li6ttzjWCeKVOhbeFUbaLWDE+pMzoJVLcq1V+WVvw6yvSu3BobQytU3HwlybzpY14adRo9OJTPd0/dXFaHX7Ee8HcuzYiVWVYS6n2xkr2mEo4/l8Phw1YzMawaLFFgotfYReqcMkaR+cRev0Kn2Qm9govIVmrusSPsCIgZlSSfIg3SNU2YIwoJXslJjQTnVciEa5jkXbd+vY2NiobxkGks/n63gvmG2mP6m6WS6XneJoskOWBKXxsJRSSimllJYqLQs7jDY4KQr+4WdVi6LH6EG7mZmZeeqpp8wMeUjQrbq7u6EfoazUY489hjZxS3NzsyV69Le//W1UolJFjwkozLe3AA0FIoZQh9DQ0AAvIhybL730UjhqXu/cFCbuF/g8XSpP2IiJgulMBAuKoIMqtUWYnOdE14VhBqeZagc4dVF3ioYNGKhjuRBe6R7EexWC6BDhSowsOg+P6sVuCO4nDa7wMmfAhR4wRkd0Ptma3k5nsu5zBuf4r4lxH4W6MQHDxODTv+65nB+dBCLrtDNsX/0HLnnLRe/UUcGX1NWjCd2587HDIelB1Z8ysRwpfum81qH16W7nVgz9DS7YyQfpkrGalMY+OL1qXtO20z2pL5cF3lHac/r0aDR6cdKykGHVpEKM1R7Z7IxriA1XpZvpurpNsVHK5bIyRDjHm5ubOzo6zOztb3+7mT355JMmRRT37dtnZsDZHz16FK5IHAbNsK2+hyCmgOi77fK0nCuSjgIzm5iY0BgJXz8F0FOGhXULXamq+fl5jYS5VFMNlTu3D/0t6oSMhr5cmErfWBd80jwEq5WLuVxO8xPoTnF5adq+E+o65y49SLlqtTah0G0qdttELjqhrtw56mQjm1PuQzGgUmd2dlZB58QsaICKnUfjysLcGTq8XcWA82RGfcLRIJD637iOIHrtsLXgzUPk0sUCOXZXVkqfzs/hWjP514VateSgY9868y4f2anCmVgSCL5kJFuni7dHs1TRpgvV66RR6mBECPFC62U2hZNMaBaaNPeSTgVf1VCsLlpKfYkppZRSSiktVVoywvZ/QozkW631QHwwdVi9K4x483YqZdB54XmDHtTe3g6DDAVdLrjgAjN76qmn0AJUSyA7BgYG/uEf/sHMbrzxRhOV0GHeTJRWF/vFNadPn7bEiGxvb8dIcWjWueeei4ciWK3FjeiCAPHpanxQyVUFvJKUldIkbud8iOrmoMbGRi1yQ61T/6VVp6Fy0HztaVK8zFl1uFJhnFFVHeTWnSuubfLL0ENIcgWLHXrIxOOqhlcuOZXRjctZY6qAO8wCnUI6FUpcHVdDXZ0KNFM0T4BJ4s5haIHfjzvTWag6IrcQ4TCztQcsuLwCnW0OU90qzj9Jk0tNGR4jqWMnFlTJuSu4WPoIcgNdO1dx2GGsnJllgaHpUuN11Jw68BaWoVFftFt959oN3b+V2toftAiX0DGYy0KGWQAFZqzLiSiTTaC7hw4fMO6DBw+aWX9/PxDzuAVW/KlTp/Al/CE8e0UxTjjoecWKFahKhVKKeGMbkuMclTUw9IV/ISNbWlrwBsJ1SZ+njhpOg+bmZnVCYutPTExoTWvnK9NiP5wH+idxo6vgruWIHERKgxYUVzrbmdr0MofoUyAyB+h8MjpAvuqaC+i4sIP7h065TCaDFVEW5gJaoGySFKVzyFwcXmMBfpJDVo5Pv5YLdoYy3jFZzpUqOtEvCYpTngtyKPMokD3aJd6uoybj1is5SyEcLnyEfoii4Z0nXFeQbvnosoZxOxNBZRK41RIemVh2hFOPFoKb6pVujcJRZ2IpoRSWUIUZr406IXUsbDM8QMBEJeJP0bjvoqXFIsN27979uc99bu/evc8++6x+D0tCiRd897vfvf32248dO7Z+/fqbbrrpF37hFxZqXO0w9+aojub4qQuKKtcbHh42s2PHjuFKvBWE5Opm6u3tNbO+vj7gPvAThNz27dtHRkbM7NFHHzWzd77znSY4Zg3RUS1yR4u56JFJnEBVM16PKyHYZmdngfiASGOfcTsUPTydG9qZXOpqd+57tqai15lcIJogIZNlFFOjKW56McD52qK9Lkylk5DL5TRHFe274D/3jJ6IAXIP4uNCcE00KlaNleJ0kp5mrgvKanzLMXftDDmaK56k5Xq5RlhQV7RJqyeTQsHm0svcMXL4Ei1PTk7qC+VQ+MpVOb1YF7epQC5ER/MxvN0NgQJGpSYn362yBYLfXa8T4kKYbqM6Q0rVLIaZlbfgQa5ksItk65dOrKphyi9dlqEa2bS89caogF+0tFhk2B133PGBD3zgve99b/iTk2qgPXv2fOxjH/vMZz5z8cUXP/XUUzfffHNPTw+MnpRSSimllJYJLRYZ9rWvfe2/df0999xz/fXXX3nllWZ25ZVXXnfddffcc89CMqySpCuSqKeoi88d56gmdrVaVWweTvc4fPgwlFYEn1AKnREp3A6D5i1vecu9995rtV6gsbEx/Lpr1y4z+/3f/30T1UyLe05MTCCgBcXWAefUlKzUVnmgnq4qHhpZuXIlOt/f328JFp9RHOdzUMuJmCUNDVaTwrVasSaXy4VRnExtDq+zKfV6l+7t9Gg1AqamphQVzQvUuoqe1clHh9As2ot6YKB2g7dzIZwaHsaEqrF6vi5q6HKrQZkFkvF1Yl2cyc1qRnCG9Uetp61i6srlso7ahdZoN0T/tSDKhaJohUJBy27RQFQLg/7h0GmZSUr3qn3JxXVeNef01pQJmtrhI6q1RQC4ZGrKOAe1Gj00Cp05qG8NDTUXAgCpe5yV+9Wtyudq512XHOAeX7p6PTpArVC8VGixyLA6dPnll4+Nja1evfrCCy/8wz/8Q+Rd7dmzB1AI0FVXXQUhsRBlaw8Q4UorrycWQG+Euy+bza5bt85qy5EVi0UIIZy6AkxHsVjU87+x217zmtf867/+qyVOSDzo+PHjONYZde6RavbGN75RXd44sWVsbIyQDUveW5cfBmIYxr3GOnY60NAO8snAWU6dOoUROc6rPqtqLWSZbFFz+4mwD0MCdBs6Bqq8zwFwQrHhforiU6h2RCPe0Zo9ID4uFBu52lL6zsOjLhrHjl3UB+QCJy6S4YKILhHKBOihHN+NlALVnVWGL9UbzNu1hKDjzs43GzrluLi6mm4TYoNZLbd1gpyuRRMPmIsT67KyD07OhZkMRIjoc104VrtntYkiUfSH67C7IBq8cNFK5TyqKPBGhjA1asUtoXFNPWDaLVwmVtSUIBfX+TDDZ9HSYpdhV1111fve975XvepV09PTjzzyyHXXXXfLLbdcffXVQ0NDegTUqlWrcARJlFTHVzvDanVY7hvsBrB1FItqbm52RcZMwFF6nAcEg9W+Xa2trVdccYWZffvb3+aX09PTGlgCRnHnzp3YTAcOHLBEsHV2duLYTPwELlAsFkPluqGhQZEa+j07z72rCvWmTZvwE8JjDs+ic+Vac0aDmoYOxOVQJ05MhmnCfGM1QYf2jeI18vm83s5OQnvV2LiLKBDOoJzXqTIad2FAS790pqQzU1yILswEIo92WcYujqKCCgKmqalJlXp2KTQKZ2dnFbuI9pubm51zAo2o5k5dRKGk0fE6UeS+VHHFGYhatNoZDlnlN38KbQU3dgcCpDBwChl6qDEhislqAmKyQKjoZzcheAG5ZBq4dZgXPj2UtW687pgVl/fGdFUdrL5KDrHl3srQtsvWFlFc5LTYZdiXv/xlfGhra3vHO97R3d192223XX311f+tRm699VYz6+npqSPnUkoppZRS+tznPvfz7sJ/jxa7DHO0fft21IPv7u4eGBiA9WBmAwMD8IlF6dOf/vT8/Pwf/MEfWK1WzoiLItAySeVvRW0xRcb5jkOMHFV7tfqnpqZQ3hcV62kqAVXf19dnSZ37Z599FoOCf/Lyyy83s9bWVj0+nFag2n/0m6sCRf1ObVAXJ1BQ5erVqzU0SBSyGjGzs7POiLHADuP8uJiiiXbsnCrqjXHwa/zEIzOcB8/EZNGnW63t1ZAUEyKU0UTVVffmfFJ1xaHjQCHSsmGB+kxhDxkZckNW041dcgacmq3Ro21ojWlokI/T4ArBrgrO5EPVhAKx9oezIdRWo3tKl4Alg8NDcNxU0LZWm5KRyNCPwuGrW8xZyZmkJotCOul2xu3wmkxPT2t5aJeX5vLDtIcgTotC+8gcQMyciaaCuX1rckiQmpvMFtBh0pDCK4ympqamMCL1T5APqDeVXaL1+cEPfpBffvazn7VFT0tMhu3fvx8cf8eOHbt27aIM27VrVx1Q3kk0egAAIABJREFUYl9fn2MNzrEAwqpbshvUkzA7O4sv4WqDk4pSRPkFD4zOCuw+m83C84myUs888wzaB8h+586dlmAr7r///ptvvtnM1q9fz8c1NDQgjRrROL5OeMlVsE1NTYXhd54+rK7ISqWCxjEu9hPZZngQZ8kdl4UB6jlMjhETZuJgDiaeJZcRrByN77m2Sd8sbtR6OVb7VpO76QkyrFTJl9wSJlssFkN0MuszaXjDiX/KSBd8wl7SOlguKUL5r5O4VqsnOViNLms1dgSXQ1qrumC1vlYX7+GjVZRSHivQgx1TseoOKlPRQqCQ9iGbnHavC8fMEMXRUJaoxCU7rgNkcAeI8MVX/aOaQPNDzySDiG5x9UbuZB2Le2t04Rg+cIWydBSUuGF5MB2jJSFzdp6nL5lZsVjUG7mmqinS86mKEWdSt8oipwiWaVHRtdde+8Mf/nB4eHh8fPzBBx+8+eabYU5de+21d955565du8bHx3ft2nXnnXdee+21P+/OppRSSiml9P+UFosdxlxmfGBO2PXXX/9Xf/VXe/fuzeVyZ5999i233AI8/Y4dO2655ZZPfepTx44d27Bhw6233lrHDmPJXUfztSV8oAER9et8DjBfxsbGLLHDZmZmoJ/CSEJT5XJZ8b5opLOzE1/+2q/9mpnt37/fxPWxZ88eMwNqY/fu3UBCQjOFqkUDUZ05uaTCPcwpqFpNTU0KtUc/6ddCbwnQV+OM8wM7DJ5MtDw9Pa03ZmqPmKJOpx4txelarbPLxYqphqv14Cw2zcKuVCqKNsas0iDWp2ezWQWI4nNjY2NOjrulrczDDNkxKtfquHPFyVxdf4UsZpK0ejVrsrXHQEexZw5pzbG4ZbIAI+dwAc50DpEIDqVGL64mlrixOACkdpseNjXW+ZPaBC5TXnvLb1wjCnJxgFu9kpngHJcOkBssNJq5/dzUqTHqUKPOQe1sRBPe4gw+nW1OS9gmb3cIFHW2O5eyQqaj8I2ZmRk6CcxqzHcLXKZLCF6/WGRYNJHZzF7/+te//vWvj/50zTXXXHPNNa+k8WKx6N4u8ix1TPMFwBYBkB2pYBQDYPFr1qwxs5mZGXiocCX+8mxAbAJEubC9zOycc86xpHgHD0bBhze96U1m9sgjjzz00ENm9pu/+ZvsGHe5cw0pJyWX0Z3tXBBA//N1UvnNV8sl05hZPp8HN8fYp6enIVmVB2VrDz+kg1E7g8etWLECj2DKi8kbq+VIKkl1EoZVTHKA1NNCxwsIU1EoFNRN6qIU+Ivla21t1fifQ4upoGI0zo1dWZKbeVyP/eYcRA5jRhYW8r5q7VnPjrSdhqRWmYOu6QdXtQHEUmGuOpGJ11p3Bdmcct5qbTmbKDjeeUedjA/rbFkgb0yOJNYZqNTm5FmtR5RfauYlZ8B5bk0Cfvolg8Fuet2NumQqtHK11Swpm0MZxrHgRpY9021AwamdYYAfD1J9mrkujHdoP3XF54LDqhYzLZmO/k8IsVBlXg5/oSeGZJJTiFTa5fN58jtLzJTt27eHPmueA4LGUZD3hRdewN6CoHrXu95lZl/4whd0f0PatbS03H///Wb2W7/1WxYAH/Qlma89A4VCThkTt6y+Dy4woDF5E4XaxHZxLFtPnGFZWJWvfI31GraMD5Bhzv+u7DubzZL161+15HB9U1OTKsLk/tUkh8GEkYXCcm5uDoBVnImDUY+NjSknJTtWMYAtwc7rpiI6WcWVWxf8fcMb3qAZXcR5qybk7FRnhTi2GGboZ2uz1MmvFUpOgRRG46xWoDokiK54Q22VSEdYCO5eNdbV0rJam5J6g05dpvZ4LXbS5UiFdlgmk9FucFbVbHWokzpz7gK32iWGeHXSGJFyAliFEAQMS0DhSp4jGL7dTpkgLEVHzbvcPgzbZMeWUM3fxR4PSymllFJKKaWFaFnYYVr0yGkioRfIRRSgtRUKBUWpQlVnaq1GenK159ueffbZZtbc3AzoPx536aWXmllLSwuiayB4FM877zyckwmP4qtf/WoTYKGqb3RvOviTGlvOx4J/mcSqeQVUxtV3wcRYfdDU1JSC+tBme3u7OtnpAtUCslQw0X9VrtkZlyCsJiZvVNAw19d5WbUR7Ty1Y8W/nTp1CvnsGBe9Vaoys321M7goDFia+K80yZ1mCq7EVBCH5mw1PCtqU+ojGCiKhr7c9Q5/Hz6OW0U7w0nTcGM0o4D9dMkV2pMwNd5qXSDOOOMOCYHstJzUBVJNUsjpuKMz3IJCLXpjpfagHF3ccM7D5zrEvHMG6iTP154i7bzWOnXZ2twDdk892zDOOJmKS+RMKmiZrlrdANyodZ6+yGlZyDAEpcMtZbUijV9qlItcNbydrzF+gtuwv79f31JIqampKXireO6JmW3ZsuUnP/mJJVuZIVns8nvuucfMbrrpJjMrlUqEzJr4B6JxV+xa3aZNTU14hAvX6+1of3JyErdrUatsNovn4qfh4eGBgQGOAkyZGTYanCsUChoIoas2RKvzEa6chPp2+I4pu+Htqlu4Ijr0rqARTQkAGKerq0txIkNDQ2a2YsUKDaRRNms+A09uwzXYNhyI9hAzXywW0SbEP5Ue50/TUjJOvGkc3okrXhmioumKdClHIaajklQg00hSJnYOiNWKSZeJqGoHR+Rcps4JaSIh0Ad15ZE4LWHKGt9Hdsx5EXVTaTkSq5VJbtJUPWK3nU6gfMA9XX3CdMs79UhVBKZkuKpRJtokw8AmL5ReScmk8tuSF8SVAFW9k85/N+2LmZaFDBseHl67dq3uG8a9deVAmUwGHFxX2imt5Kf6CmG7gC1a7dvV0tKC3YPWwP3PP/98ABQ1cPKzn/0MwTbICeykzZs3q/7FN1M3PdmTvrfUQ1Vx44saap3T09OaQ0b9V8Pg2WwWmA4wYox35cqVTuRbcA4IhYFyAbTMgJbySir1OhbKYy1OmsvlQlW9paVFv3SM24HisASK4pmamsIwIaio8+JKzfrq7u6GSDtx4oSZrV271sx6e3uBL8VfPK63txedx+KizvKGDRt0U7GrajNRikRRnVHlzAmYULRMT0/rG4HPxWJRKys6AEXoDOA1hD84W8QEkeTUf1U7KMWdXYW73L96pVNoHBzGvbzaGU1qNsEk80ryccccVPTycbp7CaZwh8Rqm9HKZ9QYdE0VhZHP57HxMNvc3hrRdDuBbiQT/cZl0ysPRJ8LhUKoCS1aWjLCNqWUUkoppZQcLQs77MUXX6Quqfqpww6BZmZmTp48abUZSMVikQBFk0wp1b9WrFhhggjKSD2IiYkJ3IJrULOjs7PzscceM7MjR46wkZGREQD34XsERvGDH/wgHgQDjkUigJXH41Dyg2Az/ARLYmBgADoXwPHoyenTp2FhoGVYD11dXRqiY/FyTAi6dPLkSTSL1lavXm1m7e3t9GyYOO5U66RqiSvVU0dYo1OZQzgcSQMnzBPQ2+nXUv8kV9wFCNXjh461tbWpHwYzPzQ05NJosLtwHIF2rL29/fDhw5bkAkKPvvjii1FlBjXGUJnlmmuuUWPCQRCdOuzCflGrJdzSzgelUSITN6AJUNtBcxVQTkOE8TztUkj0lWlxExdYcvEenV4XzXX5iG6WXGtq6zsT1kEf4U5QjC73D8iheXV6ORV6JS02/bJSW8qrISmppZaQi8zhQXBTT09Pa1GVaOqCeiksMIjVc0MnpxpwbJ+Oh8VPy0KGPfjgg62trfpuOyyvMrvZ2VkkHUMqwKuWy+VQwB5vPtx9q1evRmIyMNnYnQMDA/AR4Z2B0MrlcmBYujOam5vf9ra3mdlXvvIVdqlSqcAlhbDK448/jr+bN282s+985zuWoD9Wr16NdG+4C4ABGRwcBIoE9auQdffwww+DfQBLsmPHDjP78Y9//OSTT/JB7373u83svPPOC316k5OTCBFhCLOzs5s2beIkUFxp2X7GnBzXw8A1bct5ZTXsR86rYoNsRT2oZCIu8uHSBkxkmHOj6X5g+jOuwTDBRFatWgUZD/EPyXTy5EmsOLYBHxT6bY4fP45NpUfwZJL6nC5Vju1YACV3UUD1VkVD8Y7zEhYRRQ0o8IfKR5i6zjxIF7cLu+GA3bxSx+IyHxxUXbEkbuwq7VyiFTvDQvImcUodC8OcSvO1FS+dX9HJMJforZ3RXA5X1IqzpLvRbVoFHzkNDBQt0JqpzSh1QHn1S1NT1Jap1S0JSn2JKaWUUkopLVVaFnbYNddc09PT82//9m9W622wWg0INkRbWxuMGOCtcf3ExATMI1gkgL8/8cQTaAdHWb7uda8zs2w2i2vwd+PGjWa2efNmZ8qY2bp163ALTu9kmV2gAGB4HT9+3MweeOCB3/u937PkSEz8PXLkCI4DheqELk1MTKDbF1xwgSX6/tGjRzFMuLCI7Ec3FFvf1NQEY0Ldd62trfAlwiRtaWnBRCkUpaWlBZYKiOaXAsxArhqWUzDV8CJg3eFoQr8N+6/+TKtVLbnQamhS+9Zr4Cktl8swmrUqFav1OAsGXYX5iDaHhoZwoxpns7OzaNxVz1JYmtuZ9NRFkYFKNFN0S7t0XWc9qCuSf3Xp6b5TO4OgOAVess/q/KRRpYBYmM50WuqKz8/Pa8EU9lm7xDa12846caPmePFct51MbCaH8Aqde2zT1aEO3b+cFh01MR26OhYYeSaGFIhnmCmwMKwYYrL51YUOYra4g9iEs1StVvV81EVOy0KGrVq1qrW1Ndz0dCzoNuXrAZaETdPa2oowlXLA8fFxxZfDbUjULzzsgLoVi0VtDZy9ubkZHyAC4TZk38BAcdeBAwfQOP7SO4FuIyIFETI1NaWoNjydHBCvHETRzp07Ie0g/JRFcpbQh6NHj+IaSHHnJUPgbcWKFQzyWcK4V61apW8Li0WpG4dNaSCNQ3CBNL1dGyGb4zW6+q5yhLIYeJkmJibgG4TUgeAfHx9HO/iSyEN8idlGRLC5uRnFw7CadDDycAMTTLMr8GEiDMjXdKMqhtNqz2NkmMo50JQRc5aiUOnQl0gZrzNJGeZwniEenSlZugndKPjGaQ0Xckz1dlJa6L/UQRWMxxlTz+3MzIzqNNwhTpCbAGijKYzabcauVKy6k1RdxRP1hHPCVSdoampyiXThGjGt0J1brQ/iBjCpaqar6VLB2GcdSxR6ushpWciwu+66y+WcgphCBMKLdPr06X//93+3JB4G1tzW1oZ4GAQG/ra0tACHDSLvg7TDjXylEczHXyjjaNDMUK344MGDZjY2NoZbYLfBGjt06BCgAZAQ7LPKWp4eBE6q6bqFQkEVf9ze0dGBtGvwU/rN8QEwk127dpnZU089BTQHOD5DX9j0/Izngq2vW7fOzM4++2xMBf5FD125HUqpsBCcszbI3XTJmEqldeF4u76cNLnApxDBwpINDg7C9lX7kjV/ld2MjY3pI9BIZ2cndAW8/1jcubk5FoZmb9mm24Qqb6zWNIliJepA3qnLK5CBuraaccytVsCFxXhZpbZ2H2e1InnQ0X5SBoTGK7mk6iJWKxtoSYQnabEFVWgqST1onQESfwpTredqzzR3UVL3oFCwceGiDgNFVbgtTSi/tskr9XRmAmGqSYaJiRWoNxJ27wD0uEAzGRzKSW+PGvqLltJ4WEoppZRSSkuVloUdtmfPnmystCXVYdUls9kszI7nnnvOEvfd6tWr4U9DCApHWVYqFT1tgQ4iHrfIxw0ODmpNHXVPWaKOIQgH5KHVurzz+fzXvvY1S0JZ1BYVBk2TS40JdKylpUVjV+jJ6Ojoiy++aIk2B6NzZmYGiPBvfvObZoYyInNzc+r3o9apajjBdYDtATm5d+9emHqYNITo1q1bRw+nBZq+Urb28Av6jpjYwD5kaqv1UD9VFRiOU2II8Zdavy4WjZXQJmC2uI799OnTWFBYybSV9ZAX1abD7aeubFeDn1dqwI/bVU1hOp00+5txGudz4wzrbJt4llxYS61JF1jSL63WzeUad10K3b+0BTVURiMphORZrSuykpSMivrT3O7V6XUoPufx03CDg9Gz8yEI0J3ASZR/WJjfxXQZadPKAC55QIvuuyohbArNakCandfdnokdpUTbbknQspBh4HFRV7tyUkZf1ZuP7cI3FtfDqzY8PAzWj5a3bt2KK8EfcSXCRU1NTXBb0U9lZn19ffgXTifgOx566CH8izYBpuju7oZzDzLMAYLRW4TfOjo68IHReDMrFAr4UksasjgF3n8M5Cc/+cl//ud/mkgvTKB6irjp3ZsQOgCnp6ePHTtmiUh79NFHzezcc8+9+OKLLXGTguOzXqKuTj6f1xeYTFyDZOStKlT4DWYSU4dEglOnTsH9q37CaOfJGjRTimIDxBcemwRiEivOWpoapaCw1NUhTBxE9uEiYfzVAnmjDLRcLofuIFc2iUumwAR+1mA+eZ9LPtElU87LcJHKGwoh9fs57AmHHELPG2oPW+cMcG/r/Ljqi+pPc65FBbJb7WZmjcFQibHAX80Z5u1u1OqjK5fLYZmPbKxAPjMm9UtWUXH7x2Kk+5azGpZ54yy5aUmx9SmllFJKKaX0f52WhR0GNeSiiy6yBOxOqx8WFSDv+HJkZGTbtm1m9iu/8iuWHOvFk6A1DXZ4eBiuMy2TOjMzgy+hHwEs0NHRgQfBMsCVbW1tGvaHB/KSSy555JFH2HMABNatW4e+EbuI3vJoTfaBwHcFsDQ1NbGKoyUmF5VrXA8z8cCBA/CgsowhBqtKmUOuu7PyXNFedZoBw/LII4/s3bvXkor+sMkuvPBCmEeqn87NzalJ4aCkzmelRjampb+//8CBA5ZUysCX+XweD1KMshsRG9QVd4gJLcvL52I1sfr0ZKqxMjs7i244Z4CzSPRGauiqblOXVyQCQW6hzy2b1KhU/b1SW9uQRqda5yQF+nNxdfZo0KghpXg5XknbMfQQOvykS40PG7Fa5AsPqKRVFx6C5TKX2U/tBvewLjoHWAfh6Tqvs+3cfe6oAV0ddklXZ15qUrv5IZBVr3RJDrSVtWIq4f6hw4BdWhK0LGRYJpN51ate9eY3v9kSIDv8WkeOHMGa/eAHPzCz9evXm9nAwMAb3vAGM7v88svNDHf9x3/8B6B9kEl8N3g2ncnbBaC2ev9nZ2fV3QRauXKlvh4Qcm9961t3795ttUJoYGAAeD+4xQCOpxAC3o+ug5DdNDU16asOJlsulzXKBRjkSy+9FDrHTZiCBfBrEF945zVSPkXegZGiGApy3Q4ePHjFFVeY2aZNm0yyFMJzLuZri6AzXqhVdyG09u3bB9UE10OKNzQ0hG4fk9CCiYtGh0kQtj6XEYtQv2EanG6DUqmkbJ28g42bBHW0S9F4oYvNaMyJRA+tug1dkSFX3EiRkxQJUcatuwIvQmNjYzRqpXPI3oYwbqcTcK5UT4rKEkLGFUxP5q43Oscdg6yhA7ZSewyN0xtUOnJHuY2qa803XcUk3wt3QoqOTo+h0NkzkY7KRlzYz+kN6ullVFWzFDgPLiNlMdOykGFr167t7e2F/EDWMHbGgQMHvve971kirnBBNpvVYlEQHueff/7Xv/51q7UJyuWyVglC7KdcLjNbmT9NTEzgiZrfwxNVcCXu2rZtG5KrFHRw5syZSy65xMxgwYAYl4JIYCV1tUUourRKN2QY03VpfeInRX/wnYzGUUKhZcEbyFv4l/xIz7l+9NFHDx06ZGaQZAj79fT00LQ1iStoiSOiKmAuI+gFU3JyclLr9NC80BcYn1tbW3UOHWgF3BnLMTU1pWEqV60Rj2AAFXYn+sBqlsjKwO3MVVCW5KwH8iDlpC652FlCyrYYL8QHd/RGCLVw5/8yF0rBNc54VVFEyaRctampSSEh2JnFYlFNdo7IgTLwk0bsuO4hJITk9q3GjaLaAEetV7o4HH8KQRkuyhU1nekRCUOYLgZJJUnbpCAMD3Kj6azrnklKI7qoaph7wGRzF5i0pUNpPCyllFJKKaWlSsvCDiuXywcPHnznO99pSSox0HcTExNIv4UvEZjvlpYW6DJwTOGop61btyITGR4w4phhM7FIlZmVSiVo7tCytRiBSf0k/MS0aBPz6DWveY2ZIc+aYR7A/REzY3EQmoN8RKlUUmy9q4WDLjG3GuOFtggAZKFQgKMScTiozFTioLg1NjaqbujCG9DgkHvQ39+PdpSizi5LDJ1vfetbXKOrr74agUkXLcCg1Izbv3//M888YwkAEm7DFStWYLw8htQCdxP030KhoOqtU7Qx83BLMvOUKjDWXWsx4KeBgQH0DX5RJj7jXxAuoH+S+HLVgjm9oQJOJ5tz+6hHi2A8vZKbSks60eQKXW16C6+kK1LbdzE27MxCoaBPx66bqz1aGpTNZtV1RuNMS2O4ohjq2YseI+emhZ43/Mvs6TAlYK728PQotI+ubK1YzflRSCHN07BkCfGlII49dJYSmq8ujZmZGQVJ0nuvY2GbasvyMHH1M0dNw0VOy0KGmdn555+PGhl33323JR7Fjo4O+NDg6kEyU7FYhGDDcSSQJc8//zyOVEZtQzDZrq4uXEM8hZkNDAzArwVJA9bJAxVdUhR+1QSmEydOAOyAPYR9Vq1W0eaWLVssiYq1tbWp94DbVI9nxL7kyZbY6xhsS0sL42q8squrC0wW8tvFz/EIZiBplIvX4AOkuGMNbE3T2lysG51/+umnzay/vx+uRfyFipBJEtEg86BS7N+/H+3oScqUDS5Gogf4YlpOnjypc+h8idghDHxq6ILVEzRlAo1MTU1hLHpwc7Vaxa6gCxT7RL1G9AIpC8skJQcdqw3h6ZwfkAv46Wwzu1G9wfTRqZOcgl+9jgzHsocm4SL8ZYUk3Uhk3BqNA5XLZc1ncoWRnPNQW8ODGFnU1bRgi6oQokQM42F0RTp0TJilQP+kS0tQPYCbEN3gmZYmr4nDuOtiuYMg1GGbqT2Wk3fpQlAvwQceGa/zo404P+oip2Uhw86cOXPVVVdBVYf4Aac+55xzwKEgYGAVnX322RAYyPPFT9/61rfAGd/73vea2Ve/+lUzGx0dBbsBYwLfx2dLAmn4Ozk5SaXbkh08MzODLGOtqDs7O4sXEuIKvaXySMWWd1mtXCSGUM9A4alXuAY/dXZ2onGKSVzAircmJW0UusLsSydBNQSCRGm+hy69TC02h83ju2pmg4ODDzzwgJlpqGzDhg0YOB4BK7lYLCqngDKRyWSgFqBNTO/c3JzWxKPWqTlnjDyFx6wQbKaGBfm+JjUz2Elt1wIZRt0FGw8CmKfnoMMuI1hZEpVl/ZL5c47JhiBJJ6opOLU1Xq8p82SOGtoh96dc596o1GaLg6qxg7jy+byapBQhLtfNhO+7Bzkoo8obPl0Xi4NVhYwGorOWcLt227WpD3XmtVPyXNKhDpDD1OfyrnDOqQ2A+Lgw2MlXL6zrZrUr7kLgi5zSeFhKKaWUUkpLlZaFHXbWWWe9+tWv/uIXv2iJuoFatJdddhkwbED9wcE4PDz8xBNPWBJHgVLf2tr62c9+lv9ed911ZvaRj3wEcTUthtTR0bFhwwZLFFvo3evWrUMVD5yZCd18//79SGCCcw/I/qmpKSjyOKkSp6XQSQJTDz7PoaEh/ItsAdhMjY2NWnWXoTIYUvB8XnjhhWa2cuVK6FyIIcGgmZ6e1mgT0YyqpWazWTzinHPOsQSUf+jQITqjLFEzc7mcGmeqQpKoGOJ2+Nx4cCi6DZMLcamLLroIU4q/gPm1trbCnlbtOJvNavSRJosahSzTpQ5Y3F4ulzE0xiktSPpxCDQWNcZANAZJwsRqqOaBBx646qqrLLH49+/fj/H++q//Oq9hnzVYVSwWdV3cMZKgKELPGVIabaL6r24xJuqpDcHazWGAx2oNGmr9WorFWQ80ysMyFmxBDQtuLfUBNtSebzkfO8SyGkvGslpzh0/X8RJRrK4CN8+uTZcraVLHxD1a/aI8m0YtIaJq1WtCUzKKn9TN7JZMu+cMPvY2NA0XLS0LGXb11Vc3NDRogjD9+5AciLVAsI2PjwMlj00A5sjDQT7xiU+Y2S233GJmf/qnf/rnf/7nlvBTuLxGR0eVzbGSE/gyngsZcOrUKTwdWbcQMG1tbXgufoI4hMvRauEGDQ0NP/rRj0xyyNAynJDoA9rctGkT3gFIUHRpcnISXYKcgFwEv7akBBSDxpDcLH6PLC5IZUQZOzs78UQARohZV+QF5QSi+oov7+7uhusM//J0bPSHCW0YIMaC63E4QGtrK9y/mtUwNzenZf7pZoSk0eT0lStXQlzB9ziflJ/H0qNL9N7oq+4w7urvJY/T6oWMnClY/N5774UXEdn3K1as+P73v29J/UxsFbJsfZDVcknuOpUfzlMUhrWsVtI4hoh+8mQ47UOltqw7has+l9s1muermh9lgGpC5NTK4ulLdLWjLJDfLkfK+dMUdcJuq5R1Oc6cOk0+4cyr05KNhGEqd8wKnZa6gk6COrxPuPEob9S5yoxAh9fQdaGMnIud2RbN8FuctGQ6mlJKKaWUUkqOloUddvHFFz/55JPAAkDHhxafyWRggWnmKc/cAzH8jltwPayxP/mTP/nABz5gZoAewJzq7+9nPiyvZ54mvH9UNlHKnTUOzGxiYgLmDsyFt73tbWZ21113qfYHc6GjowP2IqqKAPpfLpehNbMOPR6EfzVlGMOxxLqCFtbX16dwA5gphUIBlhNsHXpjoMPCXuzr68OvrsoqDR1LvI6Tk5NojSALXIkOoPP0XGH2NMLf3d2tuEQmOaA1PRagWq0C4o+Owa/IhASdSRY4VxTG2NgY4TlKoX5KSPf/Yu/Nwyw7q/PeVafmqWvquRt1tSwZiQbNgCzLAiHZDLGDJ6bYDMaPDbZDAgbb2DIGOzEKOAPITgQOJgYz2IBkJSIWSoSgQUaKhCQkISShkZ7Uc3XNdWo4J3/87n6f96xC4FbdAAAgAElEQVR9pOfe+PreqqfO90c9dc7Z+9vftNf4rrXcqtbe3u7if0KLuempXq9/7GMfiyKvf3d3N+P/xje+oc1VNVHXcmTDTHjr8umVVO46ljQSR8pphG4TlvjvBoYwQV6dCDvuOpOEeldBotEuKgNjSiHhT6cJden6jZba7YRSNF37VBIAH/xKkeXdVZmEWff/o9H2qOcqeNwX34/BwsKCW2Wb4ksFlUwJrKMEVmyaRocmNc5BqsmKoCk0zbvWwiWurjY6OvrpT3/atXKIY09PD0kUMdlhjotS5BNfsru7d++O4q348Ic//L73vS8iXvGKV0ThTvvhH/5hgsYwLao0JYYpjh0WsFqt5h6X5FyBpnPl1q1bYVcMieu3bt1Kn9BoEg9OT0+79QDWJbCiO2PEVp1a9fX18aWn9VOtRRiSUhVwDUB/fenWrY6ODgZD55grR0ZG4DdOvJSZyYHvIl7wPPo/fvw4g6E32P+hQ4c8NEf0FG+iv7Gy2/gbfvz4cU+wpLQgPIKjIg9fQqKHhZc51E1oOjcwtjUmyGfKWA4j4oEHHgjjx7fffruejsNMnYssJmNmmF3LfUhdXV1+ALRKZWOgQNW+kslaJfeJh6yJrbofRf97ai5ZNZ28al5eS0UPcsxq08KP4j2+PvJ9Nr3GF01iWfKfNS0I6XuXWIuDXTs6OjwpT0pV5WNIIEAdLb9GC+IMVWubzLlhJ82JWHtjlZY0Tp+XqNCaaOuCh+3bt+/IkSNsDzoBvofnPe95cC/Fl4S5bWkSrvEJIf5Te37nzp3vf//7I+KDH/xgRLzpTW+KiGuvvRb5XQjviFhcXOSgALVAF9Qb6623t9eT9uJeesELXkCMrXsyjh8/DoMEgXLOOeeE1ad35jEyMsIscO1I4oY+QjFF8lyObiuifOgH71rT8riVSoXBoNG6MqfOWVW9G+XoomgU1ZNzRY8rv5xtRUyVYDVhYqzfnjzYEmu4BqYlwu2ESZTFX/XUv1PAnp4eFxFEj/waet67d69/qapySFT33HNPRFx66aVO1puGNot1efBQItzOmUR5U2SuD0aHoQy1aCsC0VK6xfLu6B8XIjXNFG7lokwaRsoE5tqnZpR2x7U6PcJfIqlozlq0sK6aiBU595K6WZZ6heVx1iJm2VTRdJFCB9UfpK1xuUEnzTupN6YHS25RGg9S9J4vdZRO12pua4bZtlqrtVqrtVqrpbYu9LCHH374oosuAsieDMpu+5I8QjgtioXMYkIDhpUmQYHDovihD30oIt7+9rd/8YtfjMZETZIQUXroWTIvapngTy4W0f+WLVtItI9axl2Tk5P4e4BiA5G/6KKLPLG6WkJ/8WXZOF6tVlM2LP4pK2fRKEHLHpLAZmVFShIiTbY1GaPCpGMfW7LRuY1F4m3aR18EKWpc6XpqWyPgPjls3AImo1yywjmALTmNPAy53hjYmzKr6rluqiX8+dFHH8VbxgQVcez7IuVM4Oww30zCBIb5e1wzkO8qyeYJdM5DeRDmXByZiihoitBLLig3wGrp3JKpjZbBOUw/cMOdBua1RfRPqqQq9SUMs16eoDQhH6Fs0cloWY4TqDeWL5E29iz2W28JeKnT66+J3kfXwKThpcQCPjY/rhpSCnxuYetXV5ubm8OnoqbD7WZDJXLG0ugHWlYg/7u0tIRtkP3+rd/6rYj40Ic+RGTPF77whbD3kEe4tzkdFJ0zcU192dXVdfHFF0cBHpGFBKMlwwZn/6M/+qOeXFz01M+uOJm/DxqDkw/dlaxqyfLOvW6nEiFLAUnem794yeWWXnh/G/W/O5baGmvJpzwUzq4SzhuymFY+8XjfuHq9nrwyUcoVlJ6eEkn4gxJjS34aeBh+0BtvvBGjN8dYPN5tShpDmfW2NSZ2cjOaWiLcTpTrzQqVpX0XuU9ohSjl/hB788Ov16rsGaoVCf0Str58tJaXl5NTwPcxUfymSTR8Rqnpdu9cvKScjjIaN11HupxTQ5zbmwQjP2lNM3okxp/AO35Cao05+5OBMTnVWjxsdbWBgYFnEUjV9IaXQ0/Sfut1guYiI/P/VVdddc0110TE61//+iiS2CYuooPFlxAmYeTKtvWlpSWihfD/iz+BtBwfH48CUfLYY4/hqyvziWh84YXC8MOqibugJ5lOnCxpYGGsJdE+JwfqJHnOfZ2dhImhJm+Bq3GaZplVCH9RHlgadiq9oZ0qU0mxK9eH+vr6fIRJvHVXUFtjmZWmjrT29nanO4TW3XXXXajgShoZBqf05U1u/8QXnRXVajWHeNCEYPROxBeTIu7eXDEGJ4WanQ9Gh8d3R84bv0bKmW+BT8Qf4WueZt30y8RQU35IH3ZiWj417abLfGLnzuzVVVMO6uujTSlvbhqMMDV+qFJMnvcphc/3XSvphzBhQVd5WzMDbbVWa7VWa7VWS21d6GEqNhiNgkkKfJF+4/o7TTpBWUnSP8997nN51u/8zu9ExH/4D/8hIl7+8pdHBJU2o1SSjobVSMCzsjmls7MTXY0EVJgNw2z06vnWW29917veFUXiIkmXPncJuW5JaysgiGWRWXqY1BR3/MhR5DgxB9FFSZVpanVkwAwJjVbOFddvpMa5gVFA5OTb069hkn5Tz5AD2KSXuIKiMZc9iwr6aZr/wsV/KR9NFQXtaVkE7u3tLefrkqzNcmlgngxJIMCmcMGyc07OJ59ROpNaedfDpDT4ZmmyflTUyup48r96lEiYshV2ipKC6OZxIQOTO7Z8AOqN5V20OGVMoG53TTrto0ZYduNJu9IJj1JcirpyTZHDLD3Vx9BeFE31PZLq7Pp0Mr2oqkOy6tNDslus5rYueNjQ0NDy8jJJEffu3RvFDs3NzeFmePzxxyPi5ptvjoje3l4c1L617e3tZSy4SKcHtZx//vmUDnnnO98ZER/96Ecj4vLLL/fnyh2tilxhNNdfD3lx+EjIM3hrUV6sTABDHn/8cc8cKHh32dTW09PjPjN370cj/cXIGSXjgy+IELr+oLZmmdySjU5kQqkL9VOlMdmPenbyqhn5CywYBfSCIAfBtX2+es/dZ7ZSShgYFhjgdhhhFtxqlLADTpgkM/m8ki+k6cfx8XFwQGAWmJHS7Sc3DJTR5yLa5xFL6UGJsTkyKNk8m/ogtZtl54o+OnhE++UWMwUGNHW4OuWt1+vJJRlG99NKPotrJ/3UdNguSCVzpSQ2D+1o6l5NHKKpRyMN3h+RBua3y37rX9ZqtXLEpFi1n2HxWp9yFFx2TbSWLbHVWq3VWq3V1mpbF3pYZ2dnX18fKhciCXjCc889lwy5NCx15EePRjOFrDHJzetCmTIVPe95z4tC8H/3u98dEX/2Z38GsFBJGbirnEdAxgoXyoRHAq/BmB955BG+JG0HAcjHjh278cYbI+INb3iDxrC4uOgSYqXI3cDtyOaSf5NBjNvL2UujUReRlUypPcIMGugESqnlkBkJmD42JXly/LQEfBdpJaqXrVUJgig8uu9gMtGkWbs5TuGuPk2NOZn4vCvP79VWxAUnm1UZlqZrWN5LL70Uk5TS74aJ/02NbG7oSzDxtJLJ7lc+Ksn0ra58pgm0kjAOCQseBj1vCqPwjUt4KG2uK3xK4NQU60HTkJK2FGZhTii+Mnwj3ag3wmetA+aHSiufFLKIDIPU8MpozDTB9O64OUcmZZ97pTHVWRp88pKklVzNbV3wMEoelA+9PD0cF1mu/Z1pGriezAVufunu7uYc/MiP/EhE3HHHHRHxrne9i8ovl112WUTceeed3N7Utu7gePiTBs9g6PnRRx91LksgUW9v79133x0Rr371q8NelXJen+npaTej0/Ps7GybQdcSq9bb6McdzqRbPLVByneg2z2LR3ql3XMp55PbtWSATT2XGZsKY6a58G57nJ9EhJRTvOwZisaMRzxdkVLOdeSloPF/T0+Pyw0SJnzRkiGLdJTVavW//bf/FoWbkxv7+voIAjnrrLOiSEATjUxUQkCSk3xbk8VMib7CmJBnXRKbSY60aOQZYQe7afiUr7mEubIAJ3bV1O6XHuQfBcNzftyUZC8uLropW9MsS6gaYTKSJzNyRHR3d/u2pjV3s/zCwoJ/1Gjdd6XwR+fuOnXpEb44vmjiTMmX7CbNBBBdE21d8LBarTY3N1dOTqPmb87S0pIX6dEZ8qOs19h7U7CnjmZEUJP329/+9u/93u9FxIc//OEoaN9dd92VCiuElX/1E7mwsOBvKQrl6OioEjxGoT6edtppZKUiYyxJFNUYkpwo7k1JaeiQ91mHhYUFP9C9vb0+YJXnUH7hKJGG5Igue3GWlpY8F6rYldNHXe8sUCwhvZbad82C/wVa8SslIiSmxZX+uCTQSBurF1EHmqwiiD3GuVqtiu3pp7GxMRKYOWHSs8io+dhjj73sZS+LwhUK4H7btm0kegbvc9ttt0XERRddRA8elrC4uKhScGGKVNlRpGRanmErVX5JsQf+Za3IfpsQAa7/0XSi/EFLS0suPiaoQopcdC4rui/+Ecau0g66TKMd9COaQtDSCMvcTsyyaV4xFxHEmVwSUoT1stVA0WBcG4tGFsXrLDhV0tv86ZqXv1/aOF/thP5YE63lD2u1Vmu1Vmu1tdrWhR42MzNTqVQE/IuS88kFqIWFhbJWnjJ40hKsSJjmBNuNiD179gBWBHZPSeilpaWvf/3r0WjXWlhY8Nsl9LkFg9Gec845eNccYV+tVgEUkQiYrPxLS0suVXF9KhQrnL0rCtxVrVa9QrFEdb9GPjOaJFkXpaXluP/GnVVhSkxY2q32IjVqlJRCmsy/jnzTg5IW6DfK/OtaoPx2KeNRWB1e34goGSF9eTl1krvdasQ0X/3qV3/mM5+JogTrjh07QMlSpgcP6He+8537779f/bDFx44dQ9vmy5/6qZ+KiLvuuouIe3Cq0u1ca9FO+cLS5C902TxMGYpG+0T6WG9MsCSrpq+Mmp+ihAJ1BUjoX7dkasdTh75ZCWQrt5PXRkjmcW+VZoVyErrdjYdpgrI3JBRugtqHGT98EWSESIn8y7qsXPXlUi+pJTuqpllOHbKGDImxTngYfKUcdqO9fJYQEE7P9PR0MotHRHt7u5fe0bvhdgb+zs3NAfTAFnTVVVdFxAc/+EFqo3zzm98MI4iep7Fpdjh+OvvsszEfOXs7fvw4cBUoIMVHduzYUfb3yMnh85KTQ7EHUcrntrS05EkNeA+np6edcKhPJ9lUfD58+LCDzkUFytEwiQqoPrK/YMlL4T2rJrViCcJcX161VtzOD4PqvtOSiaypfUmWN253852YtMsr9PbiF7/4vvvuiwhK9hw/fpzUl4wQW2J7e7ubsLhR7JxFuO666yJi586dyFsUHiOvR7IlKpVi2aEVjedW21c2qCZbmfiEG73l6/Jx6qHpAEQJ3qL/mzIM58q60geT3D9JfHGnwHJjoRM91M+kzobTCok+irUKO/zOdMU8koMqngE8onfH3ZNdXV1sri+IjnTizc6NEmNzrizp0y384t9roq0LHoYXxA+o5DW+hMDpPfQjlbJt+rHGzRaNwLDkrJZThCPy4he/OAqmdfXVV+Mko5Pvf//7YUeq6eGTa5dJ4Wwjl7Hwb/5cSlKde+65HH1HdgiJwJUoH0tLS2VvihBoYtWutSQ3g7vK2gqUGrMgjq1SJGZ1wNXy8rJinsKQGo59EOOsG6Qwxds6YKejKE3pAddSFJwvrqysQOsTBMD3WgpQpYgaDisOB9TCUT89PT1OBVSIxx31AtfAb8jdfPfddzsv1ALyjx9U6SI8F0zH008/zV7jK/3FX/xFOvGgMWEBHE+h0Sau4GfSd1PImpQ2yZlBU6E+Afz8ccnBnHiY4y3F1BNKxXmncnE582tvLJCmjSirXBENaKAU3UiTOuUiEa1arTpLk7GkzKSbJm+TepTiIH2ECVzTFDySIhfLgYwijAmCVFZzV21r+cNardVardVaba22daGHRURHRwd1T1ysloERS4ssQq6/I7kgcUdJFEVaQSqXFp+qrvCTi2/Eit1+++1kB/7N3/zNiLjhhhsi4tFHH5UxKgr1KJk1JNiC1H/kkUfUc71eB6A4NDQURS3p/fv3l5P3pBACKTc8sWLZeiQ4y1hBP8xa17OGcrZFyTQE1lFFpd3oJKHPDVn1xoKBNInq/CQ3nicsl2jvqh5NPjbHgCkcwoXcJJuzp3ois2YFVHdUMM6ImJ2ddX1RUrxPkKffcMMNL33pSyPi3nvvjYgzzjiDpGWu7iiYz0X7aFR30lpRE5W7+vv7m0YLOTqOCUon8D1SkhoHi8uElbD15VT6lUqlXExyubGIiR6XtiAsCXJSPnQeNK+En5TVIZ0xvzGh/32EOj8p4M8daUm7cidCV1eXG1H0f1k3rdVqvhH6yYck44F7E/gy+WiTD9KDz9qapZxuiiBNyTtWeVsXPMwNOL61Mhv6m1Or1eRK8R6SHY/v/dzoZDi90Lvt/IPbL730UvIoci7f8Y53RMT111+PTQlSKENN2QkkXkL1YRxgsr/BMJjInXfeedFFF2mcOp382tQk6IOXKUnmyvRiRERPT48wKWFsj+YcUX4m+kSwCLOBeM9tBvSXrQN7WjI3lbmdolkVwRZGrZyp14rIbp+Rsmf5kJaWlsp5CxcWFiRqRMGZRDFdJlBuOrdnPv7446A5+OmXf/mXgW/QORbCK6+8EvuzdjnMruXEXXQKDBF/Ozs7qW3NXzqRNxeA/ubNm8P8mr4pKRQsWTJTbFa51EC9iDnzLdPu+AkRX0wQkrLDRsvr3qZoPOFyczroSaCMhG5w1uLrGY2Ot8QGdEKassDygyQiOF/UO+sgDskNvmjLy8u+kjQJRjRth9+YXnyfV1ptiSBl9MCqbWtmoK3Waq3Waq3WaqmtCz2sq6tLQZEuAQmP5PJsykqQQLEp8bNbeIT+KEMeOjs7PcmQTJcYka6//vooIlXf+ta3fuITn4hGibveWKdYEiX9YJmkUJmsjiAeUVkeeughHuSCmGRzV0yXl5fRlngct6f4Zelqbk/r6upy/UYSpSslyh3sYjVgCqkyPFGWK4c1MlmpgL47mrVbeBT47GqxFs1vVzxDstS5ORdjsqIUHCcixHyChymq2qcso7RW/ld+5Vd4IgVOZ2dnyT3NryjZP/jBD0DEPPzww2E2vbL2Kfut67W1Wu3KK6+M4vT+9V//dRigiRF+/vOfDzOguUohSd9VENkw/IVKxq6kVCV7uKtQ0iTKoINoVOv1fxmiJcVd2+FPTIC9ZzHx6bmu82k9XelRn+WIgvbGepXquVyerVarqbC7HqT00D7aSmNWM1rKQERLqUNossr6K6DD44bQtdXWBQ/jNDuj0s6V31jFW7hXbGFhAeK+bKUTBDNzEHatKN3iDpvkBFJSDPr5yZ/8ySgoy+jo6Bvf+MaI+PSnPx1F9o3l5WVsbgK7h6FvSZ+IA2xiYoIHwcOAs8/OzgKyx9EiwwXz9XQbXV1dTi9kNfIUUAJq+406+lypdzJZVyKio6PDRYRk0HD629vb65ZJlZkvJ1EUipr11Mb5+6/UiJ74irt6e3vdr5lImP9VjvCUBZEFcTzhwsKCsxadEE+VwmF41atexY0IMXfddRcMkr1j1rfeeitL6qYhNTft1hqTIXHXyMgIfXKcEkqNR5CjUnY/b21tbb4yusC3TEtdJoXJfisTmV8peu0HJrkznWmpz8TekqXRD0lTG6Zm5Kku0lwSNL9sa5XlzQF++tIPTOLceiMSyJbm66MHSYAIs/v5NHX4y7lRolEITrKFL4hoy5poa2ag/5A2MzMDiU9NgajJ/u5kq1ZEF3lEiBSvstG5o6PDQRkSfKAmHFaBPhw7ThbEj3/843QOJ4OxTU1NuR9Foj1fgtvGh3/bbbdJHYyCyHZ1dVFybM+ePVG4WBYXFz3WRPQlZXKLkhldojqNeS0uLvp7KG+ze/hHRkaiVLBGRIRZA1IX8XLIslQfd7xpQSR/aEhoeHq62GEKCY9S+lotCF9ycqSCOJCBlZ+bm8Njl3QIZ9WCkJRjB8fGxpjab/zGb0TERz7ykX/6T/9pRPzwD/+wepNoog2NiPHxcRI9Oz+uVCqILzg7SaW4bds2nvulL30pInbv3s00f/CDH0QE0Wnf/e53I+LRRx99xSteEUWNOtbnXe96FxGN9KbA+TKUoL0xuXACKTjIRfgL13IUtuFUtd5Ys03srYxZUEiWWtkjpd6cxEsPSy66lFEszCNVsRiSBOKXPFQ256RFEP92PlcrIvNSmlCfkS9Iej2VoyAlvopnyC6mlXTeXO52NbeWP6zVWq3VWq3V1mpbF3pYb2+vHCFJeHEpUv+7VV11Q8p62MrKigPSJN+5CIPILIXP83pUishTfsI7dcEFF1AAGrPYz/7sz0bE3/zN32DqcX1Rw+DpZBi68847XZrDiTI2NkaW2P3790eBQKtWq9yIDqGePRhW/WPmknDt9hBZTrxIIz2PjIy41iITqHvXeLqgWcqmERH9/f1uxpFe6xqtNCfXeiVX+nJhjE2ZTOlEiTbQFCWbu7fJty/M9hUm/nvMeKWxOK8OWPKZheXLINL50UcfJeNGeemisBuzg3v27GFSvuOjo6PKlaXtuPnmmzke/+W//JcoLIr1ev2f/bN/pi0444wz6Jkw/C1btui5IyMjlBBinGhv7Y3lvGkrzcqsCEbvZsN6Y70SrWoZrJi2TOqCv3RNsz3J4eenKAELk0PRLZOyx7jhVyqXa1fJzKiN8x3UKdK7rwfppJXtqGrJ/KvVDiM4ftKiUW1NS5eqyJYV4qSTrfK2LngYLi5Xq/V2lXGltVqNcwY114sn+1gUJi+h2z3/RXd3txtSZK8D0eCWpeXlZa6BEil7BaaeP/zDP4yIq6++OiJ+9md/9iMf+UgUTgsdVsxl9Akl2rFjBzFhbiuT64skij/90z/Nl+4ZSuzK10qvjcARnllftzPTFHYD0WfYsuk5gxR9KfPF+fl52JWvvEjDimUoT041Qd65EcutDLBOWZSxwu1L/LSwsODkVelLGCF9im24QxQDo0oNJLcN/zAvrjx+/DhrDv++5JJLgOckSDcmvsOHD0eRt/70008ntwsrz99du3bJuhtFiYMdO3YAzSdzlebFc3kQ7rcdO3YwU3iYDirc3eMEhLgpu7XSRiQzmvix8zkxNmcbTQ2D+jLhm6LkC6w35q2n6ag4a5G448ygVqs5rU876C1ZmNOwEw4+MdQooT/0WpUD0VYaS6kliTaZGf0VLg/Yf3IJXryzlaej1Vqt1Vqt1VrtH72tCz1sZWVlZmYGX3cSXtweIhmnnN9BgpKL//K7Ora+Wq2WgU89PT0OsZN86lDXlSLNLmUPEYf/9b/+1xHxx3/8x7/+678eEX/5l38ZVhPSUY6M9sILL8TUQ0O2OnHiBFravn37wkRml4v1JbU00QmEwWUwaJ9LS0s815Fy1WrV9bBKEQnuSHR0F6ksLs8q44ljQHxN9KANGzb4fEEu1Go1TyTI46rVKpNCW+X6+fl511Al6roqI5OgCleq51QskQsmJycZG1eirMzPz0tfCXP+KzOIHwPXFBWE62L48PAwKhS5PMgfvWHDhg996ENRlD/FFPlv/s2/AQ3EXr/mNa+JiNe+9rV33XWXVlvahiuj9PzRj370Yx/7WETQM3O59tprf+3Xfi2Kgpw///M/H6bcJ0N6GWgg5TUZn11FECzQlS1BQlgZV3Yrjdks1ZWrg0n/k4PAtZCkFKrzsLQGTdEfCdrnhjj972+WvnSNX8THVSiZyhNRCkv2SNOBcYOKL3U0apZSzspKZ1gmaD4mOrma27rgYUNDQ52dnZBgDoGoVVllbmtrUy7XMGrlBFEsDerjjhC9hwmBhsHQM0coEbA7yQYHB7kG9CB3ve997/t3/+7fRcRb3vKWsFAwp/t0Mjw8jNkHn8dKkeCcShzY9KgifcUVVzgTknfKgeysmJCWArXzXPiiLG+eNpcbNSrHeVer1XLKEmVNdfut/ATJY8dQMcTJzMjYMNgyeAELHbK4uLjo5T35K1ij245SYIAsvR5lKHwp3MsNjLXGlGNY//r7+1kl/JT0eeGFF7qzrb+/n4ViDelty5YtMCpWibkfOnTITw6RZN/73vde9apXRcRHP/rRKOLJPvnJT46Pj0cjkZW1ikZ02qlTp6g8TmNg9913n9u+EijXbdEi8akeoxvbRV6dOvvjwvgNY3YMqlhIStrLjc74E2NrSseTT85lC/Hj5MZLVtMoZZLjJ4Un+ppr2M6KlJxaNvAwA2MCvidwZpTs8BozffqCiNY1DcJzhl2tVlM1nNXcVgUPu+OOO/78z//8O9/5zuDg4E/8xE+8613vgtDQbr755o985CP79+9/znOe85u/+Zs//uM//uzflxuFMHjz2WOIyOzsrOJhdXF7e7sHogosIP9/2PY72IEXtbu7G9rkhbgUFOX+9kqRmJEmQubuIhhSR0cHORWvvfbaKOLJKE6vK2kDAwNXXHFFRHzxi18Mo6fooLyiiOSvfOUrXSBVJ04llaXJYzAVTOMq7NLSktNxxUjBVPx1EjheaBf+ekYovfB0Tp96HB85IRIbfV8SlXTBWRXimaAYG0OiT0k59MlRkSLloAzYjPAstBSLzRgA7OhsOD77He94B1PDd7Vx48Y034g4fPgwmAv8YQ8++GBEfPjDH3bx5ZJLLmGn/u7v/k7PpdbP1NQUt6Toe3doodFu3LgRkD3CE3t0++23wyDB1ksPLlPztsasS1r5pKCEaTnlxGzRSF4lM9FEjt1YIkLvz20anpiepRfEOWJCQySzSnnW6ekp2xNNSk95fdRn0xpv6REeGSbltRwtECVGHlZb3Bck+RcVCFh2+63atioGes011/zCL/zCLbfc8qUvfen48eP/6l/9K/107733vv/97/6xFdsAACAASURBVH/ve997++23v/e9733f+95HCrhn+r7VWq3VWq3V1k9bFXrY5z73Of3/gQ98wDWqT33qU7/2a7/2kpe8JCJe8pKXvO1tb/vUpz513nnnPdP3z/SItsYEqTT5rhBMZBh04S6VGHZTwOLiIrcjZUty8UBUWeHKSn1nZ6cnNZdxzAOfsf6Nj49zDV6xP/3TP42IH//xHwdsRs9K4fGCF7wgIr7yla9EYcKKQmMAbIZn5fbbb7/88ssj4uTJk2GimVSoKMS90dFRdBHm1dPT4zU/FQ3ts2aa3d3dNcuRoeYwRT3XIdTSsTx/sbwFDNgF1f7+fvdaofyp9DYzkuGU5VJBr7BSaq7RVqtVruFx/KT0xx6W0NXV5REasrgSHO0idl9fn6f3ZVO2bt3Klt14441+0tCElAufbGEMhm2tVCrM1MOZ29vbQd4/9thjGufP/MzPvOxlL4uI//7f/3tE/Nmf/VlEvOY1r+FXpsmwt23bRnSz27Ve+9rXEteMIVQmLNeuBLdLlrewXEoe+SD1y79MuPCkciXTZRlmLFsLJ1wdJtR42Y0nQKOvv2x0SZlL+Hv/smmYgS+IbIlOi+TbK+tYeq4sE66rceY1eLeOyLzZ1F/o746so+otSubfVd5WBQ/zduLECWVYiIh77733ne98pz5efvnlJGF6pu+fqck0lNzLvtNiHl6nWHvp9DQBbaEsYlfciPlOqeXdcK/cie6c02H1918IAspdwqje8573RMQ111zzohe9KCK+/vWvR6na/YUXXhgRX/va18KMcs5m9u7d+2M/9mOaZnIX8SJBRjVOFW3BV+cJQQYHB/2N1YPclcXf3t5eKC9T0yphlXXjmFabJpMO9lVf3sQjGa0y4jsKQzgaoU7CsCe8xlwwPDzM0/nSXZ7RCDNZWFjwzIoqB8NSUJ1ZjNAxCwz+8ssvf+UrX6l9HBsbI1WmH9d3vOMd5DP81V/91ShyaXZ0dFDjlD4/+9nPRsR55533rW99KxqNXfv37yfA6+Uvf7lW8jnPeY4LZKz/29/+dmIzGDBv4s6dO/2QiGFQYMHtk4pgkZc0LICpjHUKYwNh2HGPwUrtWSxm8/PzSkgWBqBIsVzugUvBADTZ/bxz8dEy+KitMUclTZKNo5wkodYtyYh4mE9Qa+VeMY02QVd8YSUr+4w8bV40SgNtRUKZlI6kvPKrtq06Hvanf/qnP/dzP6ePx48fR66kbd68GbTCM33ftM3MzHR0dED78HhDnk6dOvXlL385GssHb9myxYU7/pfbP0HyXIqUAM7Z8ggb+br5y9NlnkZ4pKmWlbtYpaYASOP/3/7t38Y9hiMEL5d0OxIBA99QKRkUIKjV1NTUgQMHIgK4B2NQvkT3lMzNzcEPmPXc3BzXQLgFkfByYixFd3c3PXC7XiTXmVBWRLwc89Ld3V3OWChcorO3Wq3msdVKqeykE6Wnvb2d+XpSKwVTS9nicaoZpsmKBbIUSirmYAd5wtxnxprPz887CWOcp59+urOrnp4eT6l8/vnnR8TWrVt54h/90R9FwRfr9TqPIPCZWDGJUN6+8IUv/M7v/I4G80M/9EMRMTY25u5GZILp6en//J//s84YPG9gYABFnOcykkceeYTecNTpZfF4JsfIRCM1b0ol2xpjeEWFnWQnEIfLBIKSyttUBmWk0Gy9X07xE9gkKUlNQ9e50nlYvbFMtr70j1qQMigjGumPEE+eW0vRn/7uiBU5vdKi+Vz0OM8SJz2saWDZ6myri4d96lOfmpqaetvb3vb/breyVZKYoNVardVardWato9//OP/fw/h/1lbRTzsk5/85C233PIXf/EXLqps3Ljx6NGjIIMj4ujRo0Cknun7pu2KK67o6elBAEeVUXJYBMxkfXKcD74uZUhysHhPT49nrBB0zfUMHqQMSa4TqMoiOpAqPXINOpNG4vZGssF+97vffetb3xoRn/rUp6Kw29x5552CaOvK++67zz1Y0rHQQeHrCQ3lKkWlUnHtamxszCHd8ql47IGyQznETmEJ7llMorrrf4qmUlxamPLhll7Z6FJqA3eECF+KoOoOv9nZWQcKyt578ODBKBI7oeW3FxV8GCcbLYidp8taXFzkaNEzX2qabt7s6+tLyGzXA9CuvvzlL5MnjAbOXnoGH9H7b7vttgTq4y/uMVden3zySTLCOA729NNPZ6YAIEFXSTV0z+vQ0BDKJR+lXSVLQ5SEeon/7gOTwuT6UPrSrSPJgCZgcHJ9udKW0IO0pHY0vdK1HO2Om/iSj00z8hs1d1ePdNLcE6ZH+5BUHSIF3pXXR3cljG55XpqRF4Joa2v79V//ddmN/tN/+k+x6ttq4WE33HDDV77ylU9+8pMOU46I888/f+/eveJVe/fuBbjxTN83bdu2bZNvRqFLYQTRN1UMo15E70ZEb2+voxXkGIAuO5g+GlGqydnrHoUNGza4Uq9SJvSDkU0mdbeS8//FF1982223RQSw+3/7b/8t/X/ve9/Tg8iF/8gjjzivhbb29vZiS8SIBKtuL1LFe3rGsbExN9FMTk6616pWZLpi2IqjioiTJ0+6IV652936zwWydjrkXbzBbUHK4O5EZG5ujn/cayXC5Lsj0IqwEozWI5AEN1AmKo2zWq06wEQx5k4vZCf04D8VZ3F7Gke9t7c3hW35yjDsvXv3OmPT3JkaXAQsRm9vr18j3xX8Bis9URnXXXfd7/3e7/m2RsRf/uVf4nLjPCjLIuvz6le/OiK+8Y1vRMT09DSWaoBFavDFP/7jP/YZOTPQbrooIxOrh6noghSoy19/H2XSd1tZeqJepbKnoNZYMibxBpr4U9mwWSky3DsrEhdxDpGEGFlcyyh8WcI9EagwR25OlyicTKBlQIqOVgoeoPmBkVV2TbRVga3/1re+9ZnPfOYTn/iENCG1N7/5zddee+3evXunp6f37t177bXXvvnNb36W71ut1Vqt1Vpt/bRVoYe94x3vmJmZeeELX6hv7rrrLiTH888//wMf+MDVV1+9f//+00477Q//8A+lhzX9vmlra2vr7e114A2Gl4WFBU/zg8iTcA3S+l1VR+SZnp52gRHNSY7TlETDLW9CcqN2IAJz5fT0NKg/D+Hs6upCfUx5BKgZRibf9773vRHxJ3/yJ6hQxLQiE2zYsIEvHTA9MjLC3CkfRZ1fQexUXjkiJiYmku7i0eIymZa93Js3b2bWbm6amppiFl4NUlBgz8wUjZhAQd3onBHKMMjYUDHpSpZeV51limFITETgEUaY9tFRW/V63RVN2uLiIsvrBkPdnixgnpwsBcPqvCUwd5QAAlIC+AcjOU3GUpS8n/iJn4iIc845h5miM7HyW7ZsYcA89/Wvf31EPP/5zwe4gR7GXa973es8XxcZp1ZWVi644IK0j1u3bt21a1cUKoJnKlFLEES3qslC6GWyBXnw1t5Yll02cF862ejceiEc/LOY+LQdbn7QI1wDk9boG9o07Fo/OeZFBpgECgtTNN2CUmmsJuo/pXXW//6g8hr67cnyKWzw6m+rgofdfffdz/LrK17xCpJ2/9/8vtzq9br4DQ1498DAgLupeGOnp6c5mlwjg5LXCuHlnJ+fh7VARHSkkkUiIk6ePOlsgNsXFxfhMfQpWLDTXAbW19fHr05q5+fnOWdA11jD97znPWS4d2/KpZdeSuyRZ9vavXs3RV74CM/r7u4+evRoFBzC62GG5XySHyjM7OOMX4n/3YooIuJwQeY1ODjImrAgmqAHA+jpvhSyAdI5w1aVRW5BGILNyPDCFOTI9IgCzcix8jJvij5GiQoIOclfPrqQNDc3h9XUGXBbkYfCfXLRaCWLaMCSyZIJv/Goj+XlZXq76KKLohBQnnjiCbCIcB3MfVdccQVPBFjICLds2QKq3tmqyvQ4mZMBltdEjkyH2qcamN6acp1KYzVIcbuyKTI5gdSnM6Fkrkx2vxTm5cKlAH4eeyAZwpeCljhECglI1VISrj2MSbtAvLS05ItAE1d28UjeOO+z3lg4VD85QxV7cxNoU4zlKm+rgof9Y7eOjo65uTleOfdPdHd3HzlyJAqawhkaGRlxvACMraenxyOf2PXdu3erkm8Ubn8JxR6rKJeS00oNyfMz6XWiN8nUHD7wBUrL5H3i+vrGN75B2Bz5FRUJ8Pd///dR5GDEq/HmN78ZugbTIlOw0iYJ7MDtSvDIUnj2Xs3X68J47sQoGAbku7OzkwGz8lpqvkT8V+wXg6FnJXaqFwHmelAClMsTye0QZcUjMwzmohSOEBH3g87Pz7s/TPFkHAMYFUNSkZeVIjtlRAwNDbkSID8oE+RLRiKNREgZp+M6Sw7HF93HnMByydHbVkSdRxFJNjQ0dP/99+vkMJjPfvaz6O4sL/3/xm/8BkvKBCnf/MEPfpA+qdqDtHTixAkwL8Sopb0mHVrCuKdWxp7o8PsFChZ2ULsWzZFW4hC63U0vSTFyVrRcVGdO43TTi0ArZSdZNPJpHUVnZuJzrv+leORk+CkjNZomoBILTHP3YetL13rlUPTzVk7nuPrbqvCHtVqrtVqrtVqr/R+0daGH9fT09PT0ePC/qhWgRrjpTKUiEEUVMOumDCXGdfsk4P7l5WVX9TSAZBoKS9buDp6pqSkkWURsSaB8iSIlM6Mqj2jwF198Mbk5QEX/7u/+bkQ8/fTT2BsxMyKhnzx5EiMSughq36ZNmzytu1LaeBFkVT12u8SBAwewa7k/QzAqF5Y1a0LOmdepU6dc/+NGfal9YbJuWpQezNhYELmsHNnFIk9OTqpGZRQazNzcHF+6I1P4SZaClY8izxPTFNqQCXKNgGQepS50q0ezuu1II5Q71u1gKUxYNs8EVwsT1Um3QRarp556ipASCqygP1UqFRJtfOlLX4oi5uTw4cOsIcorCMannnqKsf3N3/yNflpeXmb1aE1BbskF5YqULHXJ3uhzVyujzLXmbtqVGqe/Hk8tl1XZ9RWNlrSkhbiSlG5MbqqEnyzj2jWvlPHO9zpZ8/xGaVe+5srv5X+lxjVFJPp6JqgkTel+1kRbMwP9h7SOjo7+/n55ocJwqPwD1cOyr8xDStvBT55gm59mZmbkr4rCmDM3N5cQ3hGxsrKizsPMBY6flrff3f60DRs2uJ1KFarcOK6kZ6AzQFpjr9u8eTNp+mAzfHnPPfdgYMQ0tHfv3oh4wxveQEgZ42RGs7Ozjq2YmJjw6DEIWa1W42LPz6SCNZ7Sfnl5mfG7MFEvkgW4nXBgYIDOHTGh+Dnfo87OTs9fJZSBB07RiXJq0Bv9y2/H+jC8jo4Osagwt1bZ39PV1eWFY/hyw4YNfOR66L7qzjh7q9VqDteWiODUSjKQU59arYYd2Nkk89LUqCK2bdu2Rx55JIqoL6ES4MeIcdx48cUXcw1jQB7avXs31JnH6WTyD4sm0klvbt1aWVlxAUXcwrmXmIHPWgf7WcqsJAtYUzqe3LoJZeNXppROKaFUWNUFz38hdpWCHcsQ9noRNJbYm8s08ivrbPvSuQtWG+GWRgk9aS4+EoeEaM1d4FN9hjXR1gUPm5iYmJqa8kBd1XiE9jkFTAE6SjhEItGtW7fqp+PHj7vPTElmaS6byxLtOL3u7u6K5X6FtWzbtk1elij8Q3qx+VKdcCghEBDcqakpxkYGWHL5v/SlL3XkJFlLbrrpJmbqHpqbbrppz549YdQnIiqVihfw7O3t5RGME1pZLyLtakW2X0boqS/lE/LehB1A1YPTiLrxIIYtes0byKwV+1VOGdfZ2Yn7UEHHETEyMuLuH+EvPApbHJoBe8iaCoC5b1W3JK8hfaJussgCATEYyL0oL7Oenp526Ufav9MUUV6GIQRvRDznOc/ZuXOnjisP+upXv+qHX1WBQC0yI0quvPOd7/S6ZSmkj8cJfumaUKLmNHFlDkn6qRyArE1MZ6M8d70RrrvUi4TOcoO5KpO0K/nq/LmuGiaGKrmzrIfVG+PuaYIg+oza2tqcUSW1zAcvh58PvqOoTZjUKWeWGrNTM/3koEqJ1x7kLmNVUyTO6mwtf1irtVqrtVqrrdW2LvQwkHVoP0ncQPvx5CBLS0uehhzxPykfkkARgZFolEHKYfEqy+JgZaGoPXBKRgBXaGgrKyv05voihT2jMOaocIybxRDG9+7dq1RJ0QiYjEL1YZxHjx4FoIj9Tdd7NizBvZimYOKeRZe5SIXldhmdHL6POau9vR03jMPTlYfCtbEwU2pEoHMcPnzYnV5cOTs7iw7EBLXmjumXnw9FwUHqAwMDvhHoTMPDw2wW1yvbvfCfUagpMhsqmTJLxw56DIBMSTxo3759FMPkZNIkgLtPaNOmTVyJfwuN/7rrriPD/R/8wR9oOy644AJsxX78arXaDTfcEIU/jMEQJRaNKoWOomYR5jZOyPJkZKMr91rpBUxaC/Mq+5AEkaclQLkD7Wq1mutDeumSI80tb9JIXO/UwFzJS5pTQv2ltB0+lwS85J310C65D/yhYdqkP8jjvWSV9Wukh/mV8lL73JU1zb2VaUfWRFsXPOzEiRNyWtB0sABieFRTFMTITc8CsLolYWFhAVrm/h4FBvGlrH/u5HBnTBRnUU41P6AqIszYlLA8LE4LNkA+vc2bNwvNH8UJnpycZILQdCD1Dz30EE/3BICVSgUn2ete97owODtXkpuqr6+PUdEUoeVEgSvb29shr17Ba2FhgZmqdABz2bZtWxRMV8ZGZ+TsVMr2BH8aGBhguXCAwTb6+/vph2u4a2pqipXhpwS1gHfSYDbRWGhYGfkSxl2p7rWe8/Pz7DhLoaouHjQmtsqGYqZ+6KGHqInz6KOPaq8F6XZSe+aZZ7JBLBoDu+SSS2B+4+PjGiHYjWikvFpebpfZLcVIxTOkE9TtbpiKRiHGHxqNbECMLYEj3IKqd62Mhq/Vaily2cegY5OItf/6LIFTutI9dmIJyXYalqqqHC0QjbxBqJPEmWg+zZXG8mMpW2N6hMd7ScJwYUL+yDJcSCGPvuZriIFFy5bYaq3Waq3Wamu3rQs9jLShrmwJL+f4YKSV48ePI6q70LqysuIJF5BcTp48CWrAM0FEIVB7+Shhq3iE3P5c73j9qakpnoiZC0F4fn4eNYKnI6EPDw+jgTFacvwIVOnVh/v7+7HUoXZQk/eRRx5xOZrre3t70c88wnpmZoahMoaTJ0/yj6tcmzZtYhgoAfwdGBhAMWKV+L9arSrTbpjBlo1ggtK0hOYPA8I4soA13759u0PsZHX02qSKs/Y0HyphjJJHb+glMzMz2FdZw0OHDoWlI2GyrOrS0hJKj3I4RcQDDzzA1tOJTLVsq6+AGnXgvvKVr2BrRWlWJe6ywtFelEul/d3f/V1EDA4OYkVkvkx2ZmbGZXzh9Fhzt9TNzs6qUEMY0M7FcykKbprWl66mJFyA3y5VyZNESOFrmnDW9Yx6Y1Z4KWdNY5wTQs9tbsk26CmqU2InaTDluOmU+0Nakd8olc4XwRMmRKPxsL293dPZPNMihNmiebulRrvGpvUsFz/Tg5oaJNdEWxc8rFKpKOIBUsguHjlyxF8hSJ4K1LpRbvPmzaLyur2zs5PbIUzg17u6uoQ3i+JILS4uur+Hn2ZnZ13fVz0OR9wpGZXjp/GCVKtVyByEGCIrG4uSYkRET08Pg+cnbh8aGhJ9jIKqbty4Ee4F4B6oW71eVwHJiGhra+OJHHdl1mBq1GOEJVQqFa7csWNHFGbDU6dOueFO+S+cMwHvlokPliZ0ojtXWJapqSmXRWBF/f396twXxKkAX6oYKWeDfdywYQN9wtK4a2hoCAbjSUkWFxcx5HoWeblY2BdRDVaJlWejleyHCK3R0dH/+T//Z0T81m/9VkRQhWC5qMTtpsjZ2VmOx9lnnx1mmXRuJxRccgNHxODgIBWfOQ8c1G9+85v/5J/8E62kyKgzS1n/UuKMMM7k1iqxloSDTza6sEQbzqqTKVKE27NLyLbmxueEDk+ONO9Nj0jwyJQpP8wcl1xfvuaaER95ZxVS6TzJp5CGvdKs3rQm6GdYuEQ3eqfB6Er3U2gM5c0tj2o1t3XBwyjUy4mEdMpNBTkg2lfb5hXN5Xj3cB+5N1zIgq7t3r3bxX+895OTk2KQYb5xjyiSr4tb0Fq4UkG4jnFYWlriiYq7iohjx47B2OAiqm7sNWK460UvetH/+B//Ixpf0cnJSUYID7vwwgsjYmRkxGOWT506pWRXUcJDwwJZn87OTrgXJB5up/LB/IUKCzEPh1C2U5cYWKu+vj4YKmNQTB5joze+nJiYcGcA/rbDhw/7tgqnIydWFKJMvV73lLVKoekF5xQZ7SEWKm0D86M3VGeljKIBSHnxi1/sQsyb3vSma665JiL+5E/+JAoVVrQvAUzIh+nsPGUX9Fg67RGdaH0wG/D/rbfeyhpeeumlWqWmMAFl2EoIcieacjAnvSGMXbnrJTmBpCqV4QlJUUjZb6VUlUHnimRIGpuzFr3UPraE108hAWWGIc7tmlBnZ6dTDI3BR6jby6FdWmpnRR0dHS4i6K4UHB0W9c87LnLnNybdbk20NTPQVmu1Vmu1Vmu11NaFHnb06NHt27cjIHtpSgX98ZNsa1SXwJSkxKayJkXhfFJ4IDfy08zMDEIWt/PTgQMH0AZcRu7u7vbMTNw+PT2NBubFVqanp12NS7KS2xwUN02fyjThSiG3X3LJJZiSAODx5dzcHPoK6hRxr+Pj425WFYwe+R2tbsuWLW6Z5ILp6WnPNsIjTp486ZWmseb19vZ6dVA0NrkGWTqJuooX1k/KJMIE0Z+WlpaYGoopYum2bds8oJjHzc7Oou5g99Miu90GLbm7uxt3F5v18MMPh3kUeC5pdrX1dMJdx44d40u2A/U0xb2+5CUvoebyTTfdpE4EgPYk0QMDAwxY6ULCVJ+XvOQlEf9XivqDBw/eeuutUTIQMX2Phu7o6Piv//W/RuGckzCebG4+pKTrlC11soCV4YVqelBKjR+WnMxddAJqukaSFK+VxvIuGlJCQvow/Keurq6yF1AWQv9JmHWftYiDaznyi5eT/oRp0mG2aH/HU9i1v1xhltsoRUNLifTCoVocB5FqzdeQS2xd8LADBw5MTEw4cFaIZywnbhzr7u72UCeozxlnnOH1jhVyxEHBo8BdKkns7rexsTE5P6JgGzt27HCQuqLK3CLB8IRuYAwMSfFDMAOFuJWJXbVaFbhDo63VapdddlkUWACa+BPXY1Hcs2ePhxCcPHkSCg6vFWNjPI7+EHXmGr29zj+UyJ93hvlqrbwqjaqIeZAT/GZ4eNizb8CGK5WKZ31kVU+dOsXt7GNbkXfKETcy2LrHTjnJPCUHNr2BgQGezl++3LlzJ7ucUnIg/bgXViKIQjLe9ra3RWFpJK6rra3NU1Zyxubm5ugNBx7Wv5tuugm2R1kiNuXmm29OiTrDXKeeX0a+PYQYNk5ZQpLZsOy7klyVYAJNLYT04wbGlNtC4ShuPxT6w3172uLkrGoa2lV+rux+qaKKo8BkyfRoTg2mbIqUn8lZoPiiQ1eE7E8uujTgMGnAuV2lMeNJ08QuiVWnXFO+aCl0YU20dcHDRkZGhoeH2STEZGKkhoaGPE2UsH9QH8gxhKxarXoiOOiL6mrSJ1cODAx4mS4VtORLzg30aNeuXRw7Htde1NWEkqI98P5s3rwZckyTu45fUdHwPHV0dOAJg+vodlASUHwY5+joKJkVv/rVr4YRMjpHE5WikLI9qWSJeqvVakrfFRYKxpq7Y2l+fp5neXKpEydOsLyeOlIIRr4888wzI+Kpp55CZ+LpTHN0dFRAUz3o7rvvBuzghUbbGnNUwhfHxsbkxNIiT01NeXkXGPaOHTsYGwqQ6oWikvIlXU1OTnIeWE+4zqZNm7xAGl9u3rz5uc99bkT87d/+LR8ZMDVdmUKtVhPkJ0xU5x9mzfpcccUVEDiezmi///3ve7pO1ufEiRMuqqtxjYfKRSMbEFF2KinOVI5/Ev4iRTU520ihYAnO0DTmzAuq0dqaFRwpTzCBM8PwKWnwrglpDAmRGIY2dEFBkWTlMC89QhjCcqSdfO0+eKEx/ctnys9b9rGJBXpqRCUg1bDD+PeaaC1/WKu1Wqu1Wqut1bZmmO0/pOFuwXxEKI9sa+5rQaHp6upC/EcTUvoMpOaUrglRnU74f+fOnRgq6RP5d+fOnQjgiPOIug8++KD7t5SviOd6vFS1WlU+iyh8M21FkhEk/UqROVtlD6OwMlUqFSR9RbDxOKZGeY4777yT7+kNexSPu+GGG970pjeFWfYZsIMGR0dHsT4hzzLavr4+RsjUsGvVajWPQOIRY2Njjm5HCRCckttV8oN+2A4ldmJD0RS5UqvNgsgJVy9yrEThChoeHma5PBnxzp07lb4rDBvpNl4UpqNHj3I7w5aAz+2ubbCz0SjaC/aJUffw4cP0du2112oHoxDSmTtqnw7ALbfcEhFYIDs7O9H48YQpP8vLXvayKBJ2rBQJjsvRQvv37//ABz6g5dJRkXgejSZB9SZoZTkDhSyETZ1VtKbgeEHPE7AwTLdLSpXj9WVvTPVrfD3V0oD9AvqUYbPsfUx2v+Su9iwhKszEl1LmfHmThdlVrvb2ds/XlVLgJ7df2VspzKoPLBqVbO1UC1u/utrmzZsXFxfZMy8Asbi4SEUlQOSwjc2bNyvJYRQ7PTw87KnYRM09jxHs6tChQ16MSgdFJDgsNAqDGMeaC6TaQ38Jt5qamuIWTILiT0r+pNtnZ2e9lIZwE567XRkOeQR+FIrWq+o8HIKef/CDHzAXDIxCajBOyGhXV5f7/1JgEEyXMezfv1/xBmHvTL0xnomNYAAsMmy1u7ubR/AldkX52JivkuUrjioKtjo4OIh110ugiV7AYxK4mZUXSJ1H0AkLODo66pEPsmeyI07darWaG5PFnzgG1ptkVAAAIABJREFUXH/BBRcwtt///d/XFCYmJjzcWwSRZ3FIWLqFhQWVntGC7Nq1i6SIymAZpXBdFu3ss89GyHMSJo9UQrd7FK1yMJbDp5r6kJIXR6tUBtA39ZxFo60sRRAnK9yzZAJMxr3kQ/KPcn15bEaK0EpMyMUXrRK744MXND+xaveLV4ooZvH18tMTwt4FFF3gltskTHjoajRabld5a9kSW63VWq3VWm2ttjXDbP8h7ciRIwJquw5x4sQJJOInnngiCsvS/Py8Q8n5/+DBg2ghSLLPf/7zw1KwIwijTh0/fly2vijE2yeffBIwIVnh0R7a29tR3RCW0Z+Wl5d5IsqWonSJZgW4QYnenTt3um8WKX50dJR+PAR4eXlZqSvUZ7VaRROiT8CKjz32mOtDQiUQDf3zP//z3O4ltZjg97//fa/OLKy5i40CuXhRMdSjgYEBVAp3ay8uLjLCFSua3NnZye2qmh1m+EJvEwoDayfqjtJnOOJZxliG4bLn6OioF5AToodpYjjl+qGhIV9enShPD7ZS1EplR7wy9YEDByhQCSTkAx/4QBnr/MIXvvDd7353WBmEMAE8+eRdpUBTPOeccz70oQ/pGikrNSv2xuC3bNniNbQksJdtZcoLldQjV3o0TseXy/blZ0NXlk2RKbBXY0hgB253yKvUMl/JhCHUja4OSrdrGjxQxjqq1mvTTMdu3kyAezeERqP2mZAsUqrKgQFNLZmVZsn+oxRbHSWso7avZUtcXa1erwtt+Pjjj0fhy9HpwUYn7B8Ei7+KrMIIAK2HddXrdUg21If/Ozs7hcOOgqpu2LABniR4ekTs2LFDtTSjYFo7duxgnIwQynLw4EHKK3P4GNjhw4fxi0B/YbEDAwNehxeT4IUXXqj6ilF4fUZGRpTSKSIoh/jEE084SYKT9fT0YGl80YtexDjdQMFSbNq0yUH5ylgIE2IYopXuloOdq96Nm5IUZsAWIFIo0s6dFnJTOfR8YmKCWxiY+udGODdljo8ePepWRwCB09PTbBbsio1ob2+HQbpX7Omnn6Y3loJ9Hx4edt+nQJtMkAPDvI4cOfJHf/RHuv3gwYM+a0jJ1NQU+/sLv/ALem5iLTRRNPbuP/7H/xgRF198sVPShCNnO8gksnXr1uSPCeMN+hgl9HYi2Y7ejpJxz29PQDvvUxbpNJgw147/TQDIFEMmft/UOVcGNDZ1zjWNmmor4q5Sno7y7iRsPU3jpGlfUuibz9p9uiJibrTUyPlSls+mu+Mrn8SjNdHWBQ978sknBwcHOT2klVKKIzQwPiKVb9q0CdcLGw9nGhwcdAs45GZxcZF/YFTuPomCUUG8Ojo6YFRQNM7QxMRExdL7coaeeuopJWMMCy9zX67q2UOCeQTjnJ6edhWKKR88eNAjXfg7PT3t+WdRJQcGBmAATJP/t2/fDjmGzF1yySXEPgsvHhFdXV1eUAYetrKywmozd2lC6JHMncEMDw/zRC+oNjc355SF9azVanAROLeCxD1JFYs8ODjocdCsVa1WY2wAPSDfgloQWMaQDh065BFI/D8wMOARwSza7Owsa+gIFClnirhgHVxbZVM2btzIgC+44AJ65onwRfAaL33pS3/pl34pGrU64cs9uDWFWL3nPe9hJUHee6JL0UqAHmzx61//ehdipDml+shhzpUEjmhKjv1jciw54EIz8io2whA9i2aQOCgtIeyThufeuK6uLgUa+9ybBpYlRhUWa+zcsbOz09MpPEufStfpTsd4Bq7p+6KaeUlD9U68T3GmpPuW2WpSc1d5a/nDWq3VWq3VWm2ttnWhh51zzjlTU1OIyWgPCPX9/f3nnHNOFCqX1BRsUOgESnXhOZm4cmhoCNcXf5FnBwcH3XyEA0ywPWW14AKhufT31KlTaHVcryokqIaITkokwa9ewjhhjlXNmbnLY8eDPCEsNr2f+qmf+tznPqfb+TszM8Ng/tf/+l8RsXv3bk+wy+NmZ2fplo9oOdu3b8dyy5qjAMlz5hC7er2Ow8+rtMgjhcqFNXVoaIhHcKWS9npODfqXMZCnKyRWxZejkFInJia8QikTGRsb40bvU6ZIrmHMPT097J1KfjMkZWGOwngoYR99iBN1//33U/Tyla98ZZSKQ95zzz0R8cY3vtETC+E/27Vrl+dillGO2//Fv/gXEfGGN7yB84+m6IrC1q1bmRqHBK/h2NhYQiSGeYbcKCeHjesZsr8lj4tbMtVJOeF9pbHyclJE/KOuTFqgv0ptRTy7O8CElU1+KdfS0lxS+gzXYsvpS8LeRN+ypH75+qiWQlO9lpb8Yd5nssoms23Z+ReNGmpbW5vr8U29j6u8rQseNjw83NfX5zZrdmj79u0QYixgGH9UmZ7zTa6KgYGBlcbCwWG2dWU6j4iJiQk3YQHl2Lx5szJKREEB9+3b52FJsk5A4OoWXjY1NQVZ50GyeTI7svapooryfWjwGzZsaLOEIHh9xsfHVS9NC3X++edff/310QjCnp2d5Vk8vVqtyokVha3s8OHD2Mc8pmpubg424EiEsbExd7wjPczOzgLp9ni7s88+G64A/6YKycjISM2yrbMsc3NzAk1E4d3s7+/nIxMUiIO9o08Gr9rZDJ4xPPnkk2yWZzyp1+v0ye4w+I6ODubuK9DZ2en1WQSpV2FuLcvzn/98knGkqB06f+973xsRe/bscZpCre16vQ5f98IflaK8y9e+9rUosiaOj4+7rRX+rQK+jJPzk2K5aAq0cskmeaQET/BrxJCSJyyMRPq8Ko1p75siSmjiOsmp5gXAZCVLBkaPrkn4cncXpdvlRCwHyS0vL6vwTZpm+nKlsaJK0+CB9GVTe2wSEXxIgma4kba8gL50stn6lU1vWZ1tXfCwhYUFkQYXgVVKg3cbaqUy3u5D0keuhPaJ0EOp8aY49Y+C2z322GP0AwVUZC7kw8sVTk1NcY2zjenpaZ5LiizSEC8uLnKjGGpE3HrrrTj/5WuJiNnZWQgi13OBkgTCR/Xyk+yVBFRKkOMuqK985Ss/8zM/E40ws8HBQSg4IWv0Njg4yJrQFODlYJmUDAlS6ypsFGHpcKaVlRX3CcFNn376aSbouJuOjg7UQTgid42OjnqOSoE+RNa1R+3t7aw5wcKoU08//TTnhwMAH52dnYWmeCYnoVRgVOJk3CiOGAafExMSFDYi3vrWt4bBzLjyda97HTd6Ek71xkJxnPbs2RMRt99+u4NWaKJ9rqyk6jBSKZiLV1lUaRJ3a6XmUqOulO8necJ4kC+F6wfxDBzCk+fWi8hc0X3XbPQIp/ii+y7aCkyhEkv+XNf/xNiS/hemxvnfpo43hYKlxzmz15dl1tI0YK5er3s8a1OnGq1arTr3SgVc1kRbS2NttVZrtVZrtVbzti70sFOnTi0sLLzwhS+MQqD2xK9RiNXCobmuhkiysLAAmg7NBgFfsUqusc3Pz6McoGOhWCi/g8ozRsTpp5/uJiweNz8/j2jJleQW6u3tlRwXlniewXAjUzjvvPP4kqcrVQS6hXv4hEfHHMeCnHvuuRdffHFE3HbbbVGoNbVajbkz2UOHDmGdAzjHFFQ80+1LBw8e9BTACINdXV0yAEZha92zZw8zReXiSikNnnZrYGCAlWSzUPv6+/tdmUAt1pWKEGCa5GtGyWOVuru7HaCIYrq0tMSsacizO3fu5BE8jqe3tbV55hEBO1kENyz39vaiY7GwmqBrD7VajW5xZam56Uyxg2U/0+Li4tVXXx0F/pba0FdffXU5DblG6GEJbY3VIGVlcvE8YbLTRx+tsq6ksL8wL07SnFwta6o9NFUNtXSuumnYfnvTzjXlZAhNqSv8/xSrUEYGNsX1LS8vO1ZeVybEZphC3HSV/EHPBJssx8Z1dHSUwyE08rRWa0gVWxc8bGlp6bTTToNk4F2H4nd2dkIZPaZqeXkZ0u+JjmZnZx2Uz5eLi4tuo4MoT01NKQ9eWIIlbofBQPIqRXJoZU+PiCNHjrgNgStnZ2c9pTr9Ly4uqgRMFNjxsbExj9Pigo0bN3pUHFOoVqvQSpaCE//4449jwsLRcv/99/NEDyV+y1veQpTSZz/72SiQLEtLS9jonLi3t7d7Li5+qlar7sGGxw8MDGB15Oksxb59+1gEzHFIBvv27XPIMoPfuHEjX/p7OD09zZegFQS0UcWyKHh8b28v+8KQOAxTU1PsixLzMzAfdqVIP0/Me7JruYShyt2YiHFhYoqUQ0JpIaFKnBy8gL29veD43aPZ09PD8XBq9ZnPfAY78F/91V9FgTq54447nsUjAlOhVks0Uka3Fup6saKUmYl/kkUrSlFNz+LvEd33KxMaPllcm46QJjbg9jRxbh+GGCqHRCbBpn44f5DsoinaLIyDen79FDSW5puavyaOf9HT5Qf1RUimSG9NDZKae/LbtbD1rdZqrdZqrdZq/+htXehhKysrBw4cQHrFDoNor+hdbIPY6CYmJhBvPV3T2NgYpjMaEvS3v/1trFWew0nyKWoZKsiZZ55Jb0KNR8SxY8dQFNAJZCfEUseViO3bt28XJlBDGhwcdDFTiSSYC6IWt/f09GCyo08smTt27OAfBoYEOjk5ifWJ9XnooYciorOz8zWveU1E/PN//s8jYnx8nAGQtuOBBx5g1ooUjiIX/v79+1FQUCZk/ROiMhqlVP3DwLq6uriFBRHOHpAFtkcumJmZYb6sNqtUq9VQodDVCNDu7e11UCWtq6sLG+Ydd9wRlv7AYY1MeePGjZ4cAX1xfn4enQkl0su/6VChJW/dupUtQBdkU8LMR8yLrVyxApWf+cxnfuRHfiSKJGc8YmlpyRW4z3/+8xHx/ve//2Mf+1gU5cQwSDqUI0xVYrWvuOKKiMDSrmVJ2ehd6RHSJ+k3TMFBkqkl+IYrCikwIMHZkwbmg3eNJGkeCihOSqHfogvKw64XKeodrpJSgUhjcxSYlLMU3UwPfo2GneBjYSqXa2Nh+pxGm7CgTZVIjdlzams3nYxIdywbn1dtWxc8rLe3d8OGDdiUOC5Yrg4dOsRH+BNfKhcDdjDY1dLSEmTIy53s2rWLEwONUHwGtA8qJsOX2xmwRK2srECM3J/x5JNP+gupODbPMSj4HK4dOA39Hz58mGEzWTp5/PHHlQcrCsp76NAh/uEEY2Ds6+tjGFwPK/rFX/zFs846S9Nsa2tj+nyJY6mjo8Mz1mMrGx8fh2SzMlDV733ve54KUqlGFFYVBY2uVqs+a2oWy/yr9JL89ZwjooOwK65RtWuWlOuZwpEjR7CIymAYlrKEsyHvC8vLNZyNgYEBj75i8JdccgmBDZ7Of+PGjSwXg1f8ma/5z/3cz5Gb4+abb46Il7/85Tzo05/+dERcfvnlYazFEWUM6eMf/zjI0j/4gz+IgiuLNLMRAnYiDfzKr/xKGNbcjVeKhiwbA4UypynoyrF5NNF9Z4Gp2n2CgCd8/7METiXgu/vhZNp1vpiQ6P5TmmClMfNk09vVeJbnL41GPtc0jk3N0e3iImU5QIkZaQJ2+vqIPym2zxeQsaWnp8C7MIDommjrgoe1t7dPTk56LDDIjjPOOANxnlforrvuioharQbZcn/Y4OCgx3Jxzvr6+qA7qD4q38wj6FkhOBBNJVrkdsEH1DO4AN2ouBMfJ9Tq1KlTHGLHjvf19Xk5MSG56RaeR9u5cyfqjus66BPRePTvueceJVVihEqgFQXjn5yc5BFMDeYxPz/PrAkJkIzPjfSmNI98RDVRhkOfC26q48ePM1+Phh4bG/MsU8KA0LnXsJa3kmt0JLx0i3I9cyVuLSFfeC63w5B6enpYKC8x881vfpPFh2WyAlNTUywXEgZq4r59+0DAc5bm5+dRib7whS9E4Vl80YtehJLH8krndtfgj/7ojzLZX/7lX46Ir3/96/qpq6urrJFs2LDhqquuClNQ2J2k7kRJ+UiYBWcGTYPGxJmcUifAfQpLcpiJ+KI/SG9ZAsr7WxONxDpR58TJkhLj9yYcRErUFOb68mEnNpAgEonTl8O92xorqmiy5cAADckrU6ccjFpbR6mkBZF6HY1OytXfWv6wVmu1Vmu1VlurbV3oYcSWIudiwMGEJakcEQbB+ayzzkK+Rg9D4q5Wq25jQeI+fPgw6osbnb73ve/RGwK7vC/lhC6dnZ0I14yBp09OTiIfUXmZtrCwwK9oY3K/IU+h26HBjIyMoBR6Iebt27e7rUaJPDC5CLsYEQMDA4j26E9Y/x544AFcJjjJjh496h4p1Me+vj7VftTSHTt2DJmUIWFA27VrF7oFjS+PHj2qfqJA2Atljv0Wc9/AwABLKuwi/bC5aDaClbImdIIOKgOjazBPP/00vbHIpIFeWFhgm4ACskpnnnkmVkGHnh49erRiuZu5/sSJE2h+fCQ71ObNm3kEWh3bd/3117MRYAiV9AGR+aabboqIG2+8kQF88IMfjMJO2Nvby3P5iWG/+93vph/OG0s3NTXF8rp56i1veUtyH0Yp4FoeF65ELRbO28V5HbCyKpPyUCSMe1PfjCyTUdICpSCm3mgpxa0b8NWJ650yx/nKCL9Xxkw+kzpVLhXd1lhi9FniBDo7O90TpsBwv0bVn8tORH3j+HvFMyRPYbkGZltjen75BdcQLnEt5dj/P2vPfe5zzz333NHRUcgH/Eb2IsfUQvflwaYpnYSzNCHdcdhANCGg9XodesFBwWq0uLjI6YENyHqOy4ROdHY93zlU+NSpU26fhFsorRTP5TWoVqtMTWkXImLPnj0wSwbPRIaGhrgSwAVNrxPkA2Z53XXXXXbZZRFB6NjS0hKWMfxw3/rWt5gXJjuIJhzx2LFjjvpl0U6cOOHpmlj/iYkJps/cMbEq2b+78WZnZz0NnRLGQ17ZOHnvGAzjlFWN21kQeZIca8N69vb28iuLwJenn346N/IgRtvb2+vOOQ7Mvn374JoMTOn5mRErQP9f/epX4dkygfpBZdgveMEL6AdGBe+87LLLWCjGRpkVQYo8zED11Rg2Caje/va3MyrnN7JBuU1JUAsHZSTflVAJTZMhJUca37tdK0Hk/RHJfCeWWXZWCUYhTuYkW4ytzHpFsp0riAU2dWilEZaZZb3IvuFTq1QqnNgEoHBPWNNYAj092Xj96d6JArx8sml3NIayu1Ff/vmf/zni12puLVtiq7Vaq7Vaq63Vti5siRdeeGFvby+SLAIj+lBvby8WKjdhtbW1IWsjDqsiJcKL6k9GRK1WQ9RyR/3IyAgSNNoS/ff392OxQZlALh4bGxMgQj0vLCygKHgBxuHhYQaDHM04d+/ejVrmNbeOHDniMHr6HxwcdDyLQP88l4Szjz32WBjS0q1Al156KThDpnDkyBHyJZK1D5Wru7v729/+thYcwN6uXbtYc0+zK9SWzCNMEC2NJ6Jn9PX1eSwBBsnJyUnQkl6TTP3wXG4fHR1FzWVBpL2hnDFBgVM88SD6U7Va5RZ+QvWcnJxkuVR+OqzuqEMrf+zHfgwtB2w9gz9y5AgGZ8/Tcfz4cTctqoGjIdicYgKp7d27tym+nIX1nBoqqA14hByMy8vLroclyxKDKdfDVEvpa1Vdr4zzrhQ55n2nklFOVjivH0ZLCp+0HFdl9NCmmpCDFduKZPZ8TJB6R1XUG+sj01LKkgRsSYP3ZBxS+DgkHqGRFC/d64vvuYzTylSKRMmOmxc43hVEvQJeGE+hC+VAgrXS1t6I/w9aT0/P8vIylh9P79Tf3w8d8TIZk5OTTtHgdouLi5Ahr3M4NjbmiWthXQsLCxxQB+/19PS4Nw6G9MADD6hKSBQ2q3379nlGdiHW7rzzTg0GevHUU09hBlQQGH9hFQwJQt/b2+uYJSjvgQMH8AxxDRfMzMxAAaHR9Kz88XAp1QhlRkQsfe5zn+Oj0sNHxPe//31HosvvwjDgrzzoiSeegKFC8Rnhvn37uMbT7M7OzvJ05svrd+TIEZnLtI89PT0O4q8Xma4QUIAscmW1WuWjl4/p6elhdzAUI2GonDdzYZEPHTrEfHmckqdwxjyjR61WU5xZWN1wb4p84qRRVPqWW25J6Lgw6pMMWW7SpCkCiWtERj1Blzop26Dkm2kKK0/xT25/S0aqBEEs5/wVt3MO13RG7e3tbiHUg/zp9cbyLuqtXI4kYfNS/eimAV5py8pOLy1aiqJLhS69+covLy/7R2VbdjamC3iEl58Ns50++9OVsNjZuZZ3TbR1wcOOHz8+Pz/P9vACo8HUajXqh6mOVERUiqq1grlHRGdnp2PrUS9OnjwJ6YTYnXvuuRHx0EMPeUgQ9Hfbtm185OmCcnA0+YvSsHnzZqJxUQ3JEdXX18fZhbHBLY4cOULcK5yMIZ122mk8CI4Lbb333ntBYfgYxsfH0a5ca1T4iwNYDh06RIErwbsffPBBDRjqPz4+jt2cWwh8VklryDGEu7e3l2HAP77zne/wJdoPY2PwP/jBD3giqo9g9DwCPgeDOe2001z8h09Uq1VeTiEvImJxcVGqiTZXIcCwH7Z48+bNCLYqPx0WiAZpEJoGaQBZhJ8OHDjAysCkJXEDkyETo+RipBYPTo+I++67T5v7TAE9yTX1TFe2tbUxa3ac7Xvuc5/ruoirIOXeytxLqIGkrPiQmuLmBT1wPaOpFtg0f5V4rfAmYUzdWVHic9IzUnRzmNbiOpPiw5yTdXV1Jb0qStB8xtDR0eEaG3vdVkQZp8IoDjBJWqAvskLI09LxEQG3aUvcLslD9ONDEgZtTbSWP6zVWq3VWq3V1mpbM8z2H9K2bt06NTWFZIFAhNby1FNPoUKh9CAyT09PI1YjnkslR1vCpoTI09bWxpXcjqKwvLws6HwUisXExIRLQFx/5MgRjGzI7/x08OBBRHW3IShLCFISDqGnn34aFQr3GIi1Rx55BLXDFT7l/EXpRCBdWlpCs0SIY14nTpxAC+EvSuTOnTtRUPDlHDx4EJvbrbfeGhFvfOMbI+K8885DtZW2FBHd3d30gFOQbMujo6N0zrDx+qABawtUMZLnovDRVaVSQftBlWFI+/fv92AAVTT1KGNWcmJigmRaKD2q6ewuSTZ3fn6ea/CEoafW63WuYctonZ2d7A7zogkAySni7FWrVVYbGynbNzo6CmKe2OQU/CtRPYHduYbe3Nuh4+d2rVqt5mDF6667LiKuuuqqslQu0KCHYaQjLb+Lq0fSdcro7XqzvFBtjYUxE8CvHHEcjXpYqmgsrYgtwDqi57pVTcEDygUTZjb0RU7aVdJEXXPSSBIW3+fbNJ28q3TR6KJLK6NOXP9LyexT5HJT1TlVSAjTfT2tQUdHR9NyBKuzrQse1t3dPTw8DEGEknImtm7dCjuBhgq1wZvgaciXl5chlw6Rn5mZgWRDATltynAPBcRi1t/f79WWsarJGgNVBRdQrVa9oiZjXllZOf/886NAXsCQFhYWGAyUGsvV/v37HcIAs1lZWYFnY0TivT106BAUn+shyktLS3xkMLDAgwcPei6uxx9/HPPa3XffHRGvfvWrI2J8fByOyODlzeadYXmZu1yDLDJXnnHGGYyQ5WJTNmzYwJewK3hYX18f6+zOvLYi/RW8RJ4ziAJXqnSLw/dVybqM8K5UKiwpPTNaVaZmfbABrqyswKhqlqpqdHS0XmRjiQKa8fWvf53b4Ytc/9u//dvYWh0hnT62NdYUFmqDuTi3kwnLOZlg4nzE0rt//362Nfl7HBEuA2OZCSVeqwUs++2UAsq/bOp4k1+qfH00Mpjyc8NYkcyGzom1PhjYWXOZ6JPAGlZttSlDdSB7U1uirvHqMEtLS2VPmGbkHFcxEsmeWY4BkIDSlMsmR13immHANH+QvHFroq0LHnbo0CEFMEG8kM3PPvtsZHx0CCjLhg0b2EgOn3Yd2GGq0gKBgyjzVnR2dnp1D2WoI8QKuk/qv127dsH8eJEg9Fu3boV3Qs3rRdIzroHBcOYGBweh4IrC5srvfve7mrjKfzAYRquMUywFao0SG0KhvEJNf38/YbPiZDwRhkGCriuvvBIyzY30KT0MqsG8JiYmmJo7qyYnJz3eVkF1sB9eJyH6PKSPux544AH6gbmyOwsLCwwJDVUASC8Lp81lW53obN26lUWDgyK1nDp1ijX0wt9KMgn+QjHje/fu1Vw4BkKi+gG76KKLeDpNugh/leDOXSCC2LkuInblLpOmFJ+l+Ku/+qvf/d3fDaOPYRSQaxLUomnQWCLciSOGSfpOlHVBiidzGi2/VNM+Hd+YFL6E1HAYTl9fH+8pB0bOKpUQipKbygdcayys1TQnk3itq1ASPujTV7JSqZSz/SodZXnuUeJM7khLG5GkgeRyCwOy+petXFOt1mqt1mqt1mr/X7R1oYdt3779vvvuQylBA0NYnpiYcHMTFxw6dAgxRJUGI+Lo0aPI+HzETriysuL+HhSF5zznOQALPb9Df38/NyLFk/1oYGAAnxCaATB6afFuau/u7kbkx7oFYu3JJ5+kT5kpwvBaiP/ctWXLFgaMQIrS2dXVxVI4zC8K3QtzqAIJXDnbtGmT9/a1r30tIq688kqyY9xzzz0RAQzyxIkT2A/RCTCECsXntY/n5uaYCxBEOrn44otZWDcQDQwMoNUB2GOPfuiHfohhcyX6ZV9fH7vM1FCqnnjiCbYAqZPhdXZ2kmYXBVpCqAy56vmMM85Aq0P/w5a4b98+pfTVkB5++GFVPAiL4ePpHhRRqVSuueaaMB3ILWAsy8LCgmsYfOlBVGEWMNcbUswZjQd95zvfUexj+eku+Ctrg4v/wvK5vJ9A6rTku2o6pBRolZCWZb+U8okkz5BfkwCT8laW3Xh9fX2eeF4r4BFXGqcve5psgjXypW/E4uKiZ5nR8gr0GCULoS9L00ekHZfDzKevKXufUhOTWua/rom2LniYGwA90nZxcZF/nDNt2bKFfIlYG8hUpPrI+KLVNPM5AAAgAElEQVTweWzatMnB3Cr7Cw11Y1d3dzeHAxrN47q7u6GS2KlUatlRALxF4iLwRYHU+RV2BWcSveBNUyp0SDx4BKVloh8vMfPggw+yPvwkoxZ2GGhuvV5nqNyIRfG+++5jao4dVywX48QQOjg4CAsnIaScK9BTZgHdv+uuuxg2f5WiEF7IGjKwCy+8kC2DTrHd7e3tzNcjApWtf6UomszGlVM4Tk5OKlI7CjnjwIEDfGQ36XPDhg18xB4Lb2bfdd44WidOnHAiyyo98MAD+Ep1ozvwlLfMzVzqWaVnohQD5AG2tWblg5eWlm688caIoDhcopLOaVLYlsZQ5jdyvLmtbGlpqYyY8M6jZP1L2IpniaZKMVj8w74sLS0hvvgBEPxHpSfYXF9eiY9lY2mCp6dFc+EjbZZeJUflaOOaIuabcpEyuxLc33vWwHycHR0dHhiuBfR9TIbiNdFatsRWa7VWa7VWW6ttXehh7e3t4+PjoPJQepDFVD4ODAKi/RNPPIEi5cLO3NwcAo7b6Pbv388tqEdILvPz88h0oEWwqi0vL3uWd/SSoaEhhHoUGu4aHR1Fs3GD28DAAIoCGgYj2bhxIx8ZPE/v6+tDvwF7hmh21llnIfJzJUqkbEGojxI5QXy4zWF4eJhrUOB6e3sxe5K2A+Xjy1/+Mgk7CPS+//77I2Lfvn08gj7R3tra2lhDBiNTG2oHu6PSlA5XUVE3bF9oSyzdAw88wGaxHWifmzZtYhHYawnaFaushrp59OhRVtsrRgrFwwQR2JWxDBVNahD/sNcchpTMHiVg8+bNwHBQSRntRz/6UQpdEjLxvOc9j6ECCQEE9M53vhPtk0XmEY8++ihW6y9+8Ys6G1Eyr0VJ1lajc3KGpea4ErUUUu0GRukQ5eQUAmUkk2Cy0YUpfE0hiE3zSHmrVCrYMHiDZmZmvGg7L93Bgwc5M+y4asOWFU0FDzQdYQJ6uMYmPdghQkod4IBGXqulpSV/usagtP36W6/XXYtNyIumaq5f2TSAPYUuaMHXkB62LngYOcghghxoNuzhhx8GJ4aVjFO+ceNGThj8BuPY4cOHoVCYhiCgnZ2dkCHeHJDoZ511FgSL1wMf0vDwMJwDxwmGr/n5eZK+Q+JVUQUYvRfbPO2007yyCUd/cHAQZuahTp2dnTiT+JIxK58I8WEQZb05Xv1yx44dDJvrIeIPPvigOxj6+/tVkyWKV/Shhx6CcHALfr6enp5ycryxsTH4scIb+Es/GPcw3KmuCoNBJlheXob6OOBew4AngaI8fvy4FxDgp9nZWXgnN9Lzli1bSDKCe4wxbN++HfxkpUhEyQnhkMCZlK6eR7DXKl/AeeC80dXQ0BA7Qp+ww2q1yslk5QcGBn7/938/IsCXsi+f//zn3af42te+lr12aycjrDfLoa5SDMll4iElsumVYY2VSsVvbwrUliXqWXIUpeKTyYoYz5D3qN6YAF4tYfNonjRuZmaGrcSLzBuh2BXPntrf3+8JRROwsCkD8FlHo2FTiUAdl/gsNVCUssR5WDT6O/W4MlvVSibLp7vJtWJlx2SSbxQImOa7mtu64GFtbW1DQ0McI+gj51i4UqRsxRi6lxtVYHh4GBoBYaIKyYEDBzwsCVzA4uKiJ11FKzp06BCdQ+b4aWFh4cUvfnEUfBHq39HRgb6iPE9h/jDGicT92GOP0blnP+rq6oInIYFC6Ddt2gQ15xouOHbsGKyIK6Hpi4uLjk+Bsq+srLAI0NPp6WlYPotw2223MbBbbrklIn7pl34pioi3p59+GoUDno12tW/fPiV/i4KyyM3gmPWuri5/t9my6elpVD32iGXp6upCYfXcXSdPnqSfiy66KAqN9vHHH2fwvNuw4Y0bN0Ls4HOoaIODgwzbUxMNDQ05EAZhRVns4CXs5tDQkB8tZfniUMHnYMZSDRV7m4rpRMSVV16J1wpbAvri6OioJ+1tqruwgJ2dnV6TTDOCZ7uEIZ3J/XaKakrKmdNoKQpl5Uwql2NAErcT33U1JYWspZCsFL3rt3Ok6/U6AgRyKgPjLY7ihMvi4lPTgzzcWOqXoz8SO3c2oEA9lwmSGpdWMilzfqV0srKeKhe4H4OVlRV/elOkRqq94sJEtVr1BKSrvLX8Ya3Waq3Waq22Vtu60MOq1eqDDz7oOWYQtUZGRrz0IpLI1NQUMrsD2efn55HmkJ3RxsKqqERhUUROD8O1R8TKygrSPXqbTJdI+lyDnnH06FGHiXNlW1ubpwBWSiT0PznSIqKrqwttACmMrrZu3YrqxkcmG4UO5IjH0dFRgd2jQFHecccdiIFcKdMHY6PNz8/fe++9EfHTP/3TUUDz77jjDrQ6ekMp3LZtGwvFEyVLsrBcyfKq9gpzR30cHh5m0VhzuZeYC18Sa3zs2DGMtFiW0Ck7OjqwvzEX/EkTExMKBteMFhcXUXDde7dlyxZ8gdzOlk1NTTFgPrKbMgShlqFabdiwAc2Jv8JPOoZwcXFRJUCjEJbR16MwO+NuvOyyy5KDyv/3nxSwwYMEKP/VX/1VDSapFK54dXR0lENf5UsuW6KipGcknGGUUk4ks1hKLe/BFbreNUXBd7HHYhGRhiE7OVcq3NgHk0IgfNZJwS0HKQvdnsybyVMYlosrFbUp674JGSjtquyN05dum60X4d68QVLjfLNkH3atV1pmy5a4utry8nK1WsUXBY9RbTAcDCR0YPt37dqFwwbCBF1bXl72giM0RUp5/Z6NGzd6HkJYwsrKCld6yQ9lLOQaLhgYGIAIQqdwnxw+fBhmAAGFET722GPYgmqWCu/hhx+GAeORIjDgnnvuYWy8zNDTLVu2cF7lWI6Iffv2YQbEpaQE86yPOIpzJqjq4cOHGf/tt98eEa961asi4rzzzrv++uujeIWwg42MjEA+6A2RYmBggFExCzx2F198MS8bPV944YVsB/ZVGBsYh6WlJdYQniRMx9///d+HlU2JiEOHDjlTERhHIPsoiJ36pHFOJiYmEGVosEPhfTgG9LyysgKPl3uMB8mWFQV3PH78+Cc+8Ykw2AiDYZVYun//7/89DI9tgqGOj48DjnfzZjQahfQNHzkGcPp/+S//JSvzLEmJ1Byt4N2m2xN8I8Vy0RJjc+xJW5GC3TuJRmunJwxMV87OzvLuqMx6+fZaY2pE9ea8QdN0L5eG7YZNGYHLhk09NBkYnXPLh+3ci0O4sLDgnMaTfaQhyUmWCpuVRYS2IodL4sqpTE8YY1sTbV3wsI6Ojh07diAmQ+BQaCYnJ/EJQY6h+C984QvRVLgSQrOwsADUwl+5yclJtl9Iv4jo7u6mH4i7XmZP0FcrkmyiM+HeoM8tW7ZwIvkJQtbf3+/YKv7u2LHDQZW8vaqWCRNiIs973vO8VBV9njx5EhIGTWcK4+PjrAycDFjBtm3boJL1IuDanTo8d//+/XBW2MYrXvEKOmFq6Iv4pZ588kn4q0M0T506hdLDXOATnZ2dDvHgS+A5UTA2KPvZZ58Ne/bsl1NTU2hXSAyQhtNOO41HsK1CWrIUzAhQTBT8w8O0jx49yjhZGaSigYEBGCRDYkYbNmzgvDFa1YRD3WQlUSgXFxedreoWJ3N33HEHWwlb5RTNzMwwNR6kk8awPVOaGuO86qqrOJ+ugYnMlZMbSaFJPMwpqRink+ymGLkEVkyJGbkmRYmVH1RprKvJ7lSr1RSd5v0kzpRU2MR0/SfnZLXG1JG6q+xOS749RZ75Gko9chmI90Lr4+60FMSt9fFKSWl9vJMoSQBRisnTArZqr7Raq7Vaq7Vaq/2jtzXDbP8hbWpqSjAbBA3sRbt378Y3gzyOWH3o0CHEVbcvtbW14XrheoHNuBLVBw1maWkJb5AnxWhvb8cyiXpEwZGVlRXJ7GF6ho8cPWN4eJjvEdUR22dnZx1whfK3srKCNwgVRMl2PacGatDS0hIaCQ1n1ZlnnskTmRdTmJqackB5vV7HT+ag4eHhYWbKNFFTzjnnHIyfXntz9+7djmej597eXjrnuSidk5OTiI2eyWlycpIHITYyl0qlgrsLyCKS7MTEBM9F5aKr/v5+XwpWqVarod94bFylUmGE7Djj7OrqYrWxCaO81mo1hoTFVSeKR3iVbeEDldmLL7GIYj2uNyYEQuJ2R10UOtajjz6KavvNb35T66msVMk2yADe9773afDC3SXcfBLSfUgu/idJv6nKpSgof0SKD3PdJakpuqBsT0tKkpCWDIOTJkeRX9m0uIzGzz5q6ZK6w0/uFNSMvCqNrnTNRth9d/gl22AC8ftMBS/00tu6zE210ll9ZYSwL0d9VRoTOvumrJW2KnjYHf+7vXONjbLoHvhQSqGlCxS5BelFQyFy0bZGahCFSrwQE0NCQmK8RtHaREIlRv1m8QIxMYaAUQKhsWoMXj4QE00RTECDQGtoSQCjEhEqYttt2W7vUOj/wy97cjrP0veveV/o7p7fh2a7z23mPLNz5pw5c+bw4e3btzc2NoZCofvvv//FF1+UVD2Ej2vorZxze/bs2bx5c1NTU25u7vr16++7776r3X/q1KkZGRn0KSgYdEkkEqHHoV/jfefn5zPfg5eMM2fPni3+CjkzHA7TS+L3Y/VVQ0NDcPJg4sSJXIjWuRzbEp6JH/oXdOTUqVPpT3E00aSmTZtGOiL6XBxTLS0t/MvPg44pMzOTdVR6J5S+vj50Q0lJiYs10O7ubh5EXVAGHR0d8vuXwssMFoVhKa6L9fgowtLSUmLr0bLffPONc664uBifJL2z7Hah3SMMFLq7u/mSFyEhJFRNdxBTpkzRu5kwc3nx4kUKg5CpV3Z2Nl2/1mThcJgzOQdP8qlTp3gQk1WsAfjjjz9QpcgTUU+aNAmdpOXT1dXFUWrE/ZuamlC9vFaU1qxZs+glqQuPKywsLC8vd86tX7/eqcT8WpNpV7A8vbGx8fXXX3dDs1t99dVXHMUFSktOT09/6aWXpL7S1Xp7cDjlrYq7zEsfysjI0I4pCVXQK3Olvw46pmQhmhfooV2Rnl/Lc3Z50RxyWxfQEHGXl3kRIpzj5Uv0/tVC0Ort8uXL+su4dQFvRYFXMC1PUZZ6+cSVeJvgeJEaUtrg0oW0eDsASECKRpIoJgQjQodt2bLlmWeeeeeddy5fvrxx48Y33nhj06ZNclSUlqahoeG11157++23S0pKjh49+sorr0ydOhUTxDAMw0gRRoQO+/TTT+VzVVXVMBaVUFNTU1FRsXTpUufc0qVLy8vLa2pqrqbDxo4d297ezhifkSnT77Nnz+ZLBumyiTA+Is5hWH3u3DlCPBiwy3ZTelROOEMoFGIkhYmABZOenq7zbsgGYwx2KIM8HWuJob144ZYsWeJi6hz7ID8/HzOOoT1/BwYG8Obh2OTM9PR0vJcSQOGcy87OxlWIhYehkJOTg1GIGUfB6urqiESQFFBEBnKUek2fPh07gGJQzl9++YW4Sv5iWZ49exb5UE0+t7W1UVPuRnhhQUEBMQs6DVV/fz8jfV1NCZmh8Bi7f/75J+NNvRq6uLhYZ1HBNO/u7pbdLJ2K7+IDLwKjvL29napx/uLFi51z9fX1WPMUjPe+aNEivLucKZtr83ReHC7ZrKwsHdcwOHQvYy9QW9sikUiEjB40JxADBcMaK3lgYIBXoC08cXbpaO/BwUFt9XoONz3SHxgYiBvZr20Lz80YN4JRGytiPXgB6MHQfHlQMNmHC1hCXlyiF7Gpn6sr6MlHyqDr61mrXnygfrq8Pr1mGVvQi9tE8rLTmPY9ipXsxU9qV6SURNclaNlrmQcXTsgagIRgROgwTVtbG74RYfHixdFodPr06QsXLnzuuedwTzU0NFRWVso5ZWVlH3300dXuSQw9006oHzrc0aNH4wakT6GvjEQi2n3PZ9nTGc+SzGdwH7QCnV0kEqFt0cXTn3Z2dup9HMSNxhP1TNikSZPo5jif4O/e3l68SdoT1dPTQ7FRRTjl5s+fzyPocyW8mxLKPpnOuQsXLtDjl5WVuVhn193djW6Q6G3n3KxZs1DqHMrLy9O5/2nrkUgEDdfY2OhivWptbe26deuc8rI650KhEKUizxafr1y5gm9QL5hrb29HpLjjKEMoFEJhMKPJi8jNzdXbSHJ+KBSSbTPlHZ0/f56jOguGuO+QJD/7nJwcHfCJ0MLhsISPuljEY319PXWnDNy5tbVV7/xCO2ltbeXtMDAiS8g999xz5MgRF0iCDnFnJrxVTdqzlJaWRqMiuhLF39LS8vHHHzvn1q5d61SfrjtZ6aN15K108dqrJrpWuw1FwQQzengzQ/K4uJFv3pRbsLTSm+uJIk9WIkmtvbyNQ70+OhisOCqWksObnvTq6wIKQ/S3pzud8iVyuUySaSetrAsMumpFtXixkTqsUXReMFRSKqKj8MVVq7Xj6KFbb49wRpwO27p166pVq+TfsrKyp556av78+X19fQcPHiwvL6+qqlq+fHk4HMY4gGnTptGhxKWrq2vy5Mk6zyafm5qaeH/0ZXTZsvqSLpv59tbWVibG0Df0Hc3Nzegb5h5QMKFQiL6MoTeLopYsWSKbWrmYhZeRkcHlND46O0wcF5uwQYelpaXpbE90x93d3fS5shuZc+7s2bN0r/Qvkq+WImETyK+X+RgkQGnPnz+vF8NSzrS0NC7HWk1LS2OS0ltihaogmgNRHD9+HEOHgQKTMQcPHkQUTOChZjo7O1EYOiOXLKbWOSr7+vr4QLFZgd7e3o4MmQDjca2trVRKZ3vq6enhLVM1Dg0MDFAMNKj05lioMqfo1MYoOmHx3LlzERpjL+JK6uvr0dy0IkSdlpbGfXQ0TX5+/ueffy7/Soeoc0z39/fTAHQ8i0yZ6PROY8aMYViA4cVV0Wi0rq5OaiG6R4/SRAV6Ye4ukDnQ6xB1T+2GJmGSgumhG0gnG1QJbqhd5S1j8ualdOR6XAUjeNakp7SCVos3USRWS3DvlStDt7aREuq9sD1jVMtQli7ocYOsE9A2lrfsWmw7L7mlFoVWhFJ4vQhP19epgYLZYf+SmpqaaDTKFDds27aND6FQaOXKlVOmTNm4cePy5cv/0W137drFhxUrVvy3imoYhpF8SJebKIwgHVZdXb1v376dO3cOMwQoKiqSqOiWlhbG1M65lpYWnfrIY82aNX///TcefGaGMI9CoRAWGEN7JlcikQgDeZx7WAahUIiBLQ4i/FFXrlxhVM7wlpF+QUEBpWLIzIzdiRMnMODYBZgRpeydqHO8jho1Ci+QTok0a9YsLCHqKDYBo3vZHcY5l5GRQbGxwCTVL0cZ/jMMz8rKko0nXMwSnTlzJp46vblifn4+Fols5Kgjy3kdEyZMkEXE8mU0GiVtB7t7MFuJ5eRiA0Z8j5hBLrbMAMuysLCQulBODBrZ0lpH4S9cuBBRYIMi5FAoxN4i2H+Irr+/nwuxrjizo6MDpyXNAMlfuHCByHUMTUQxceJEvZBZ8orpyNJvv/2W85nVo7RYrnv37qXBMEKn8IcOHULmsmcHN2eghsO8t7cXV/nXX3/tlJWDzGkkFGZgYADpYZLK3j2Ube/evc65hx56yCl/g2d8eMF1+kztUfTC4UBix735MG0ieAGQ4E0XaUtULtcuVomb14GIkkfKW0atg/E840N8ekjS89F53kunZrk08mXcaScQf512kHoTeBRGouGDzlLP2yz1ijuBqu1jLX831EoW8YpD8vnnnxfD9P333w/WZaQxUnTY7t27a2trq6urvQVSHidPnkQZFBcXHzhwQHTYgQMHhglKjEajkydPpjOiK6Qr8aag+YnOmTMHhcHPnvP7+vo4qvd/ys3NFW3kYkHnAwMD9CYowi+//NI519vbS1HpT4m67uvrw/9JwWTTE3pe5IADs7OzU5v//N7C4TAPQivTWzU3N+MfY4YGp1xHRwctErVKUx4/fjz5k9Be4nDnPugn+t9Ro0YhLsJhsrOzdfZ9BJKeno4oiNRgtVlnZ+f333/vYmk7qPu8efP0XtiSNwtfKP438SiiXxk3IN7MzEz6aOqORmloaOA+Osa9r6/v7rvvdrGVc9RlYGAAbycL1yQFF5d74R5avAjw3LlzvHpRqM6548ePIwq0Dp7Pjo4OFBWFZ/KvoKAAOdOGEV00GsVhCOIhZwcAtHt3dzcpExnfMCkry9o8v5/O10VhUJYiiocfftgFpmGkVw26+NxQz5vXxesTrgxNeChZ84Px6F4QvxepoT38Y8aM8aJOnOq4tRdO5u1kMV9wjVTcebj+/v5gWhPp8bXWkRP0XOCoUaNkywtdo6BquTJ0xxkJytBnevlEQB7kReo7NYbw0ot4ek4XXl/oxevLmeZL/Gf8+OOPn3zySXV1tV77CU8++eSzzz57yy23ZGRk1NXVvfnmmxUVFXxfUVFRUFBAbP0HH3wwjAk8bty4/v5+7W5GS40fP56WrXdZzM7OpoEytKdHy8nJYeSOnUFHH4lEdJYg2ZqEbovJFca8kUgE+4MzZcsubDvdp//22286dI3xe09PD1/q9bZnzpwhtyk1Yvh/44038i9lkCro1UJ0zaFQiPZKrSXJEyqQzo7P6enpPB0F093dzYU8Qtb5UjZ6fProlpYWakEPjiN33rx5aBGUND+krKwsFKrepbO3t5fLeTpbbs6YMUPPsfHipk+fzpcIGUPq1KlT/EthCKMIh8MMOLTmnjx5Mm+ECvKiCwoK6BR4ZVi0t956K1qT89F8CxYs4PeP4UUbHjt2LC8CtcowpaurixfB3SjJmTNnKCeimDFjhr5QAoVoqNr2jRtiJ7pB/x0/fjw1paXJrFhwZsgF1s+6oZ2pUzaWtpm8wDkvZkFrEc/k8oI49OX8DJ3qwfXl2tSTMstePHwOWnVeGkARGkdpabI4VZsvw8hHbCatAj3d4IW3eMpVT3pJcKO+UOSpyy/y9Aw4pyb/PAUcnPYTZNkfhyxf4j9j7dq1XV1d9MhQX1/PL7yiomLHjh3Hjh1LT08vLCysqqrCO1dcXFxVVbVp06ampqa8vLwNGzbY4jDDMIxUY0ToMLxncbnzzjvZazHIgw8+iJPqP5KWltbf36+3NGUU397ezpBEB5uNHj0aS4WRO+ORnJycYF6ArKwsBunELjIMz8vLw8Wk567S0tLwH+qh6KRJk1hsxFhbxl9cziiJUf+kSZNYS0RhmOqYPXs25iD+Je6cm5sr8yIuZhN88cUXuLmwFCVInefimsNqvOmmmxAFlcW0On36tM7WLxu3y2bWzrmff/6ZMQRP5xF//fUXRWUa5t5773XOzZ07l0dQTUqYkZFx1113uZilKDYHBgdPl3zHSAbBYszV1dXxRD2FWVJSojPk7t69+9FHH83KytIpi3AMZmRkYKvxBvG8NTc3Y6FiC3Krjo4OZI5jE5+e3E07q0+dOkWR+JL7S3p+boIz4PTp03pja8lUq/PWjxs3DqvX87TrgT9Sevzxx3fu3OliVjIFa25upsDHjx+fNWsW25aWlZUFfYmefRM3BFGcY8EYOW9DFhFOMI2Ft+5NewvdUNvFQ+6sZwFk6Yu3BiDohGQbmhdeeGHLli1ajNwWC0yeG9zSOm4tvDh4CcL0zDI+6M1IuYkk0daRlvKv3oJHdgzXbdgzc7W3yftX7Gl+INu2bVu7du2VoZvgeIvbEoIRocP+11y6dCkcDuNiopkS3v3TTz+xowcuQZl8osOiF8A5JmuHmbrA3SSbbuiNo5qamuiwmOvC3TRhwgS0EX/pUDo7O1ED6E6a7A033MDMGZ2d5Een36fX48zs7GwCprkJafVbW1sJuKBblDiXRYsWuZgHjJtkZmZSQQqPCmlpaWGuS/L7OedOnDihM8c3NTXxL/VFdV26dIn6UhhmxX7//Xc0DaJjOVRRUREyR2EwCvn111/58TBNRb2OHTvG74oBB+5NSfbPnRFFaWkpr4ynQ19fH+dIssFoNNrT06MX6vHeb775Zh10Lku40CJUU7bMluRYTu2rgp6jYDxu5syZgyqjI0Jubm5G0+io6/Pnz2s1MGbMGB1fwJehUEgnBvP6F5r0yy+/7Jy77bbbCFTR0R8XL16UvJTOuT179jjn7r33Xh0rL9v+6l7Sm7CRYZZTUfiex0zXRe4ZDHMYHLqU2/OAyTncP9g7e4EMcr4ujHjetK6VEZh+hFzi+Sf1OlHRZ14Mi/6ga+2U1nTKp6dfPQ8aM2ZMcL22lFOf6Yb6Br0gfr3MK21oXn95xTrixsWyZHm+Yi3JhCBhCmoYhmEYHilhhxnGiEW21Yb09HRtTcpYntN0CqjBwUHcnhs2bHAxK7mvr2/16tUu5r+VUT/RHAsWLGhra8PUPnPmDD4GwlvE7NP2jc475QKpzT1/mgtYV2Ln6Yg7uTyYIUk8mV4y4uBq38FYll7tS5RF32J2BCMDvVgSLxhE222jYjtGekk09JSE2GTaaPZSy+siXbp0Se/Zra91Qw0pL4+UZxV5+wDEDSzU9qJXeDmE49qTj1OO4oRgVAKV9d8RzHxvGIZh/Efi5lsfaSS/DjMMwzCSFZsPMwzDMBIV02GGYRhGomI6zDAMw0hUTIcZhmEYiYrpMMMwDCNRMR1mGIZhJCqmwwzDMIxExXRY8jA3gD66Z8+eFStWLFiwYMWKFSRxSDIOHz68evXq4JL2YSqefDKJK4SUahiHDx9++umnS0pKli5d+tZbb7GdCqRaS4grhyRsDINGsjBnzpyrHTp69Ghpaen+/fuj0ej+/ftLS0sbGhquZdmuAY899tiRI0c8IQxT8aSUSVwhpFTDeOSRR/bt29fW1tbS0lJZWfnqq6/yfaq1hKvJIfkag+mw5GGY1rlu3boPP/xQ/q2urq6srLwmhbrWeEIYpuJJLJP/vw5LYiEMDg5GIpE77riDz6nZEkDLIfkag/kSk4rFixcvWLBg+fLllZWVJ0+elO8bGhrYO53fBkoAAAKSSURBVBTKysoaGxuvRwGvNcNUPKVkkpoNo62tja3aXGq3BC0Hl3SNwXRY8lBWVvbuu+8eOnRo165dy5YtKy8v/+677zgUDofZgQymTZvGdl9JzzAVTx2ZpGzD2Lp166pVq/icyi1ByyH5GoPtvZI8bNu2jQ+hUGjlypVTpkzZuHEjOzgbqUxqNoyamppoNFpeXn69C3Kd8eSQfI3B7LCkpaio6OzZs3yeMmUKu0VDS0uLbPGc3AxT8ZSVSSo0jOrq6j179rz33nuyP1lqtoSgHDySoDGYDktaTp48yb6Izrni4uIDBw7IoQMHDhQVFV2ncl1Thql4ysok6RvG7t27a2trt2/fnpmZKV+mYEuIKwePZGgM1zuoxPiv8cQTT/zwww/hcDgaje7bt2/ZsmWfffYZhxI0avZfkOKx9eAJIaUaxsGDB1etWtXR0eF9n2ot4WpySL7GYHtgJg+HDx/esWPHsWPH0tPTCwsL16xZo6OMamtrN2/e3NTUlJeXV1lZ+cADD1zHov4v8FZryha0w1Q8+WQSVwgp1TBuv/32rq4u/U19ff2ECRNcirWEq8kh+RqD6TDDMAwjUbH5MMMwDCNRMR1mGIZhJCqmwwzDMIxExXSYYRiGkaiYDjMMwzASFdNhhmEYRqJiOswwDMNIVEyHGYZhGImK6TDDMAwjUTEdZhiGYSQqpsMMwzCMRMV0mGEYhpGomA4zDMMwEhXTYYZhGEaiYjrMMAzDSFRMhxmGYRiJiukwwzAMI1ExHWYYhmEkKqbDDMMwjETFdJhhGIaRqJgOMwzDMBIV02GGYRhGomI6zDAMw0hUTIcZhmEYiYrpMMMwDCNRMR1mGIZhJCqmwwzDMIxExXSYYRiGkaj8H286g2lY++lWAAAAAElFTkSuQmCC\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":3042,"title":"Fill-a-pix - Solution Checker","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/fill-a-pix/rules Fill-a-pix\u003e is a logic puzzle game similar to Pic-a-Pix (aka Logic art) and Minesweeper. An example starting board is shown below on the left with the completed board shown to its right. Each number in the board indicates how many surrounding cells, including itself, are to be filled in. There are (up to) nine total cells associated with each number: four immediately adjacent (up, down, left, and right), four diagonally adjacent (one touching each corner), and the central cell (where the number is located).\r\n\r\nBased on this logic, all 0's and 9's are determinant, as all nine cells are either empty or filled, respectively, for these numbers. Also, all 6's on the board edges and 4's in the corners should be completely filled in, as they only involve 6 or 4 cells, respectively.\r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/1418.gif\u003e\u003e \r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/1420.gif\u003e\u003e\r\n\r\nFor this problem, you will be provided with various boards and solutions to each board. Write a function to determine if the solution is correct for the given board. The board will be filled with NaN's where there are no number clues; these cells should not be checked. The solution board will be filled with 1's (filled) and 0's (empty).\r\n\r\nA related problem is \u003chttps://www.mathworks.com/matlabcentral/cody/problems/3043-fill-a-pix-solver-basic Fill-a-pix - Solver (basic)\u003e.","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/fill-a-pix/rules\"\u003eFill-a-pix\u003c/a\u003e is a logic puzzle game similar to Pic-a-Pix (aka Logic art) and Minesweeper. An example starting board is shown below on the left with the completed board shown to its right. Each number in the board indicates how many surrounding cells, including itself, are to be filled in. There are (up to) nine total cells associated with each number: four immediately adjacent (up, down, left, and right), four diagonally adjacent (one touching each corner), and the central cell (where the number is located).\u003c/p\u003e\u003cp\u003eBased on this logic, all 0's and 9's are determinant, as all nine cells are either empty or filled, respectively, for these numbers. Also, all 6's on the board edges and 4's in the corners should be completely filled in, as they only involve 6 or 4 cells, respectively.\u003c/p\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/1418.gif\"\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/1420.gif\"\u003e\u003cp\u003eFor this problem, you will be provided with various boards and solutions to each board. Write a function to determine if the solution is correct for the given board. The board will be filled with NaN's where there are no number clues; these cells should not be checked. The solution board will be filled with 1's (filled) and 0's (empty).\u003c/p\u003e\u003cp\u003eA related problem is \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/3043-fill-a-pix-solver-basic\"\u003eFill-a-pix - Solver (basic)\u003c/a\u003e.\u003c/p\u003e","function_template":"function [tf] = fill_a_pix_solution_check(board,solution)\r\n\r\ntf = 1;\r\n\r\nend\r\n","test_suite":"%%\r\nboard = [-1,-1,-1,-1,-1,-1,-1,-1,0,-1; -1,8,8,-1,2,-1,0,-1,-1,-1; 5,-1,8,-1,-1,-1,-1,-1,-1,-1; -1,-1,-1,-1,-1,2,-1,-1,-1,2; 1,-1,-1,-1,4,5,6,-1,-1,-1; -1,0,-1,-1,-1,7,9,-1,-1,6; -1,-1,-1,6,-1,-1,9,-1,-1,6; -1,-1,6,6,8,7,8,7,-1,5; -1,4,-1,6,6,6,-1,6,-1,4; -1,-1,-1,-1,-1,-1,3,-1,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 0,1,1,0,0,0,0,0,0,0; 0,0,0,0,0,1,1,1,1,1; 0,0,0,1,1,1,1,1,1,1; 0,0,0,1,0,1,1,1,1,1; 0,1,1,1,1,1,1,1,1,1; 0,1,0,1,1,1,0,1,0,1; 0,0,1,0,0,0,1,0,1,0];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,2,3,-1,-1,0,-1,-1,-1,-1; -1,-1,-1,-1,3,-1,2,-1,-1,6; -1,-1,5,-1,5,3,-1,5,7,4; -1,4,-1,5,-1,5,-1,6,-1,3; -1,-1,4,-1,5,-1,6,-1,-1,3; -1,-1,-1,2,-1,5,-1,-1,-1,-1; 4,-1,1,-1,-1,-1,1,1,-1,-1; 4,-1,1,-1,-1,-1,1,-1,4,-1; -1,-1,-1,-1,6,-1,-1,-1,-1,4; -1,4,4,-1,-1,-1,-1,4,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,1,1; 0,0,0,1,0,0,0,1,1,1; 0,0,1,1,1,0,0,1,1,1; 0,1,1,0,1,1,0,1,0,0; 0,1,0,0,0,1,1,1,1,0; 1,1,0,0,1,1,0,0,1,1; 1,0,0,0,1,0,0,0,0,1;  1,0,0,0,1,0,0,0,0,1; 1,1,0,0,1,1,0,0,1,1; 0,1,1,1,1,1,1,1,1,0];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [0,-1,-1,4,3,2,1,-1,-1,-1,-1,-1,3,-1,-1; -1,-1,5,-1,-1,4,-1,-1,4,4,-1,-1,-1,-1,3; -1,5,4,5,4,5,5,-1,5,3,-1,1,2,-1,3; 4,-1,-1,-1,4,-1,-1,4,2,-1,1,-1,-1,-1,-1; -1,-1,5,4,-1,2,2,-1,1,0,-1,-1,7,5,-1; -1,-1,-1,5,-1,-1,0,-1,-1,-1,-1,4,5,-1,2; 4,-1,-1,5,4,2,0,0,-1,-1,-1,5,6,-1,-1; 5,-1,-1,6,5,-1,-1,-1,-1,-1,3,3,3,-1,3; -1,-1,5,-1,5,3,-1,-1,-1,-1,-1,-1,3,-1,-1; 5,-1,-1,6,5,-1,3,5,-1,6,-1,-1,0,-1,0; -1,-1,5,-1,4,3,2,4,5,-1,4,-1,-1,1,-1; -1,7,-1,-1,5,-1,-1,1,-1,5,5,5,-1,-1,-1; -1,-1,6,4,4,4,3,1,2,4,-1,-1,6,4,-1; -1,5,-1,6,-1,-1,-1,-1,-1,4,6,-1,-1,-1,-1; -1,-1,-1,-1,-1,-1,3,2,0,-1,4,4,3,-1,2];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,0,0,0,0,0,1,1,1,1,0; 0,0,1,0,1,0,0,1,1,1,0,0,0,0,1; 1,1,1,1,0,1,1,0,1,0,0,0,0,0,1; 1,0,0,0,1,0,1,1,0,0,0,0,1,1,0; 0,1,1,1,0,0,0,0,0,0,0,1,1,0,0; 0,1,0,1,0,0,0,0,0,0,0,1,1,1,0; 1,1,1,0,1,0,0,0,0,0,0,0,0,0,1; 1,0,0,1,1,0,0,0,0,0,1,1,1,1,1; 1,1,1,1,0,1,0,0,1,1,0,0,0,0,0; 1,0,0,1,0,0,1,1,1,1,0,0,0,0,0; 1,1,1,1,1,0,0,0,1,0,1,0,0,0,0; 1,1,0,0,1,0,0,0,0,1,0,1,1,0,0; 0,1,1,1,0,1,0,0,0,1,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,0,1,1,0,1,0; 0,0,1,1,1,0,1,0,0,0,1,1,0,0,1];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,3,3,-1,-1,-1,-1,-1,-1; 3,-1,-1,-1,-1,-1,0,-1,0,-1; -1,-1,3,4,-1,3,-1,-1,-1,-1; 3,-1,4,-1,-1,-1,-1,3,-1,-1; 2,3,-1,5,-1,4,4,-1,-1,4; -1,-1,5,4,6,6,-1,4,-1,4; -1,-1,-1,-1,-1,3,3,-1,-1,4; -1,3,-1,-1,5,6,5,-1,-1,4; -1,-1,-1,7,-1,-1,-1,7,-1,5; -1,4,-1,-1,6,-1,6,-1,5,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,1,1,0,0,0,0,0,0; 0,1,0,0,1,0,0,0,0,0; 1,1,0,0,1,0,0,0,0,0; 0,0,1,0,1,0,0,1,0,1; 0,1,0,1,1,1,1,0,1,1; 0,1,0,1,0,1,0,1,0,1; 0,1,0,0,1,1,1,0,0,1; 0,0,1,0,0,0,0,0,1,1; 0,0,1,1,1,1,1,1,1,0; 1,1,1,1,1,1,1,1,1,1];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,-1,4,-1,-1,4,-1,6,-1,5,4,-1,-1,1; -1,4,-1,-1,-1,-1,-1,-1,-1,7,-1,-1,-1,-1,-1; -1,-1,4,-1,-1,0,1,-1,4,-1,5,-1,6,-1,-1; 4,-1,-1,0,-1,0,-1,3,-1,-1,4,-1,5,-1,4; -1,-1,1,-1,-1,2,-1,3,5,4,-1,4,5,-1,-1; -1,2,-1,-1,3,-1,5,-1,-1,5,5,5,-1,-1,-1; -1,-1,1,2,-1,5,-1,3,4,-1,-1,-1,-1,-1,5; -1,0,0,1,-1,-1,5,-1,6,-1,7,-1,6,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,5,5,-1,-1,6,-1,-1; -1,0,-1,-1,4,-1,6,-1,-1,-1,6,-1,7,-1,-1; -1,-1,-1,-1,-1,8,-1,8,7,-1,-1,-1,7,-1,3; -1,-1,5,-1,7,-1,8,-1,7,7,-1,-1,5,-1,-1; -1,2,-1,8,-1,8,-1,-1,-1,6,5,-1,-1,-1,5; -1,1,-1,5,-1,5,-1,3,-1,-1,5,-1,3,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,3,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,1,1,1,1,1,1,1,0,0,0; 0,1,1,1,0,0,0,1,1,1,1,0,1,1,0; 1,1,0,0,0,0,0,0,0,0,1,1,0,1,1; 1,0,0,0,0,0,0,0,1,0,0,1,1,0,1; 1,0,0,0,0,0,0,1,1,1,0,0,1,0,1; 0,1,0,1,1,1,0,0,0,1,0,0,1,1,1; 0,0,0,0,0,1,1,1,0,1,1,1,1,0,1; 0,0,0,0,1,0,0,1,0,0,1,1,0,1,1; 0,0,0,0,0,0,0,1,1,1,1,0,1,1,0; 0,0,0,0,1,1,1,0,1,0,0,1,1,0,0; 0,0,0,1,0,1,1,1,1,1,1,1,1,1,0; 0,0,1,1,1,1,1,1,1,1,1,0,1,1,1; 0,0,1,1,1,0,1,1,0,0,1,0,0,0,1; 0,0,0,1,1,1,1,0,0,1,1,0,0,1,1; 0,0,0,0,0,0,0,0,0,1,0,1,0,1,0];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [0,-1,-1,4,3,2,1,-1,-1,-1,-1,-1,3,-1,-1; -1,-1,5,-1,-1,4,-1,-1,4,4,-1,-1,-1,-1,3; -1,5,4,5,4,5,5,-1,5,3,-1,1,2,-1,3; 4,-1,-1,-1,4,-1,-1,4,2,-1,1,-1,-1,-1,-1; -1,-1,5,4,-1,2,2,-1,1,0,-1,-1,7,5,-1; -1,-1,-1,5,-1,-1,0,-1,-1,-1,-1,4,5,-1,2; 4,-1,-1,5,4,2,0,0,-1,-1,-1,5,6,-1,-1; 5,-1,-1,6,5,-1,-1,-1,-1,-1,3,3,3,-1,3; -1,-1,5,-1,5,3,-1,-1,-1,-1,-1,-1,3,-1,-1; 5,-1,-1,6,5,-1,3,5,-1,6,-1,-1,0,-1,0; -1,-1,5,-1,4,3,2,4,5,-1,4,-1,-1,1,-1; -1,7,-1,-1,5,-1,-1,1,-1,5,5,5,-1,-1,-1; -1,-1,6,4,4,4,3,1,2,4,-1,-1,6,4,-1; -1,5,-1,6,-1,-1,-1,-1,-1,4,6,-1,-1,-1,-1; -1,-1,-1,-1,-1,-1,3,2,0,-1,4,4,3,-1,2];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,0,0,0,0,0,1,1,1,1,0; 0,0,1,0,1,0,0,1,1,1,0,0,0,0,1; 1,1,1,1,0,1,1,0,1,0,0,0,0,0,1; 1,0,0,0,1,0,1,1,0,0,0,0,1,1,0; 0,1,1,1,0,0,0,0,0,0,0,1,1,0,0; 0,1,0,1,0,0,0,0,0,0,0,1,1,1,0; 1,1,1,0,1,0,0,0,0,0,0,0,0,0,1; 1,0,0,1,1,0,0,0,0,0,1,1,1,1,1; 1,1,1,0,0,1,0,0,1,1,0,0,0,0,0; 1,0,0,1,0,0,1,1,1,1,0,0,0,0,0; 1,1,1,1,1,0,0,0,1,0,1,0,0,0,0; 1,1,0,0,1,0,0,0,0,1,0,1,1,0,0; 0,1,1,1,0,1,0,0,0,1,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,0,1,1,0,1,0; 0,0,1,1,1,0,1,0,0,0,1,1,0,0,1];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,-1,-1,-1,-1,-1,-1,0,-1; -1,8,8,-1,2,-1,0,-1,-1,-1; 5,-1,8,-1,-1,-1,-1,-1,-1,-1; -1,-1,-1,-1,-1,2,-1,-1,-1,2; 1,-1,-1,-1,4,5,6,-1,-1,-1; -1,0,-1,-1,-1,7,9,-1,-1,6; -1,-1,-1,6,-1,-1,9,-1,-1,6; -1,-1,6,6,8,7,8,7,-1,5; -1,4,-1,6,6,6,-1,6,-1,4; -1,-1,-1,-1,-1,-1,3,-1,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 1,1,1,1,0,0,0,0,0,0; 0,1,1,0,0,0,0,0,0,0; 0,0,0,0,0,1,1,1,1,1; 0,0,0,1,1,1,1,1,1,1; 0,0,0,1,0,1,1,1,1,1; 0,1,1,1,1,1,1,1,1,1; 0,1,0,0,1,1,0,1,0,1; 0,0,1,0,0,0,1,0,1,0];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,2,3,-1,-1,0,-1,-1,-1,-1; -1,-1,-1,-1,3,-1,2,-1,-1,6; -1,-1,5,-1,5,3,-1,5,7,4; -1,4,-1,5,-1,5,-1,6,-1,3; -1,-1,4,-1,5,-1,6,-1,-1,3; -1,-1,-1,2,-1,5,-1,-1,-1,-1; 4,-1,1,-1,-1,-1,1,1,-1,-1; 4,-1,1,-1,-1,-1,1,-1,4,-1; -1,-1,-1,-1,6,-1,-1,-1,-1,4; -1,4,4,-1,-1,-1,-1,4,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,1,1,0,0,0,0,0,1,1; 0,0,0,1,0,0,0,1,1,1; 0,0,1,1,1,0,0,1,1,1; 0,1,1,0,1,1,0,1,0,0; 0,1,0,1,0,1,1,1,1,0; 1,1,0,0,1,1,0,0,1,1; 1,0,0,0,1,0,0,0,0,1;  1,0,0,0,1,0,0,0,0,1; 1,1,0,0,1,1,0,0,1,1; 0,1,1,1,1,1,1,1,1,0];\r\ntf_corr = 0;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,3,3,-1,-1,-1,-1,-1,-1; 3,-1,-1,-1,-1,-1,0,-1,0,-1; -1,-1,3,4,-1,3,-1,-1,-1,-1; 3,-1,4,-1,-1,-1,-1,3,-1,-1; 2,3,-1,5,-1,4,4,-1,-1,4; -1,-1,5,4,6,6,-1,4,-1,4; -1,-1,-1,-1,-1,3,3,-1,-1,4; -1,3,-1,-1,5,6,5,-1,-1,4; -1,-1,-1,7,-1,-1,-1,7,-1,5; -1,4,-1,-1,6,-1,6,-1,5,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,1,1,0,0,0,0,0,0; 0,1,0,0,1,0,0,0,0,0; 1,1,0,0,1,0,0,0,0,0; 0,0,1,0,1,0,0,1,0,1; 0,1,0,1,1,1,1,0,1,1; 0,1,0,1,0,0,0,1,0,1; 0,1,0,0,1,1,1,0,0,1; 0,0,1,0,0,0,0,0,1,1; 0,0,1,1,1,1,1,1,1,0; 1,1,1,1,1,1,1,1,1,1];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n\r\n%%\r\nboard = [-1,-1,-1,4,-1,-1,4,-1,6,-1,5,4,-1,-1,1; -1,4,-1,-1,-1,-1,-1,-1,-1,7,-1,-1,-1,-1,-1; -1,-1,4,-1,-1,0,1,-1,4,-1,5,-1,6,-1,-1; 4,-1,-1,0,-1,0,-1,3,-1,-1,4,-1,5,-1,4; -1,-1,1,-1,-1,2,-1,3,5,4,-1,4,5,-1,-1; -1,2,-1,-1,3,-1,5,-1,-1,5,5,5,-1,-1,-1; -1,-1,1,2,-1,5,-1,3,4,-1,-1,-1,-1,-1,5; -1,0,0,1,-1,-1,5,-1,6,-1,7,-1,6,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,5,5,-1,-1,6,-1,-1; -1,0,-1,-1,4,-1,6,-1,-1,-1,6,-1,7,-1,-1; -1,-1,-1,-1,-1,8,-1,8,7,-1,-1,-1,7,-1,3; -1,-1,5,-1,7,-1,8,-1,7,7,-1,-1,5,-1,-1; -1,2,-1,8,-1,8,-1,-1,-1,6,5,-1,-1,-1,5; -1,1,-1,5,-1,5,-1,3,-1,-1,5,-1,3,-1,4; -1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,2,3,-1,-1];\r\nboard(board==-1) = NaN;\r\nsolution = [0,0,0,1,1,1,1,1,1,1,1,1,0,0,0; 0,1,1,1,0,0,0,1,1,1,1,0,1,1,0; 1,1,0,0,0,0,0,0,0,0,1,1,0,1,1; 1,0,0,0,0,0,0,0,1,0,0,1,1,0,1; 1,0,0,0,0,0,0,1,1,1,0,0,1,0,1; 0,1,0,0,1,1,0,0,0,1,0,0,1,1,1; 0,0,0,0,0,1,1,1,0,1,1,1,1,0,1; 0,0,0,0,1,0,0,1,0,0,1,1,0,1,1; 0,0,0,0,0,0,0,1,1,1,1,0,1,1,0; 0,0,0,0,1,1,1,0,1,0,0,1,1,0,0; 0,0,0,1,0,1,1,1,1,1,1,1,1,1,0; 0,0,1,1,1,1,1,1,1,1,1,0,1,1,1; 0,0,1,1,1,0,1,1,0,0,1,0,0,0,1; 0,0,0,1,1,1,1,0,0,1,1,0,0,1,1; 0,0,0,0,0,0,0,0,0,1,0,1,0,1,0];\r\ntf_corr = 1;\r\nassert(isequal(fill_a_pix_solution_check(board,solution),tf_corr))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-02-25T02:55:09.000Z","updated_at":"2025-12-31T18:50:57.000Z","published_at":"2015-02-25T02:55:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/fill-a-pix/rules\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFill-a-pix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle game similar to Pic-a-Pix (aka Logic art) and Minesweeper. An example starting board is shown below on the left with the completed board shown to its right. Each number in the board indicates how many surrounding cells, including itself, are to be filled in. There are (up to) nine total cells associated with each number: four immediately adjacent (up, down, left, and right), four diagonally adjacent (one touching each corner), and the central cell (where the number is located).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBased on this logic, all 0's and 9's are determinant, as all nine cells are either empty or filled, respectively, for these numbers. Also, all 6's on the board edges and 4's in the corners should be completely filled in, as they only involve 6 or 4 cells, respectively.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem, you will be provided with various boards and solutions to each board. Write a function to determine if the solution is correct for the given board. The board will be filled with NaN's where there are no number clues; these cells should not be checked. The solution board will be filled with 1's (filled) and 0's (empty).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA related problem is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/3043-fill-a-pix-solver-basic\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFill-a-pix - Solver (basic)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"},{\"partUri\":\"/media/image2.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"}]}"},{"id":44757,"title":"Lights Out 6 - 5x5, 13 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require 13 moves to solve. For example, if\r\n\r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\n\r\nan answer is:\r\n\r\n moves = [1:5 8 13 18 21:25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44756 5x5, 10 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44759-lights-out-7-5x5-x-moves 5x5, x moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require 13 moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\u003c/pre\u003e\u003cp\u003ean answer is:\u003c/p\u003e\u003cpre\u003e moves = [1:5 8 13 18 21:25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44756\"\u003e5x5, 10 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44759-lights-out-7-5x5-x-moves\"\u003e5x5, x moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_6(board) % 5x5 board, 13 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_6(board); % [1:5 8 13 18 21:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          0 1 0 1 0  \r\n          0 1 1 1 0  \r\n          0 0 0 0 0  \r\n          1 1 1 0 0];\r\nmoves = lights_out_6(board); % [1:13]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_6(board); % [1:2:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 1 1 1 0  \r\n          1 1 0 0 0  \r\n          1 1 0 1 0  \r\n          0 1 0 0 1  \r\n          1 0 1 1 0];\r\nmoves = lights_out_6(board); % [1:3 6 8:11 16:19 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 0 1 1 1  \r\n          0 0 0 0 1  \r\n          0 0 1 0 1  \r\n          1 0 0 0 0];\r\nmoves = lights_out_6(board); % [1 4 6:9 12:16 19 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          0 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 1 0 0  \r\n          0 1 0 1 1];\r\nmoves = lights_out_6(board); % [1 3 9 11 14 16 19 20:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_6(board); % [1:2 4:6 10 13 16 20:22 24:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          1 1 1 0 1  \r\n          1 0 1 0 1  \r\n          1 0 1 1 1  \r\n          1 1 1 0 0];\r\nmoves = lights_out_6(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 1 0 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 1 1  \r\n          0 0 0 1 1];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          1 1 0 1 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 0 0 1];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          0 0 0 0 0  \r\n          0 1 1 1 0  \r\n          0 1 0 1 0  \r\n          0 1 1 0 1];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 0 0 1 0  \r\n          0 1 1 0 0  \r\n          0 1 0 1 1  \r\n          1 1 1 0 0];\r\nmoves = lights_out_6(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==13)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":"2018-11-15T13:42:34.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T19:15:14.000Z","updated_at":"2025-11-29T13:54:32.000Z","published_at":"2018-11-15T13:38:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require 13 moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 1 0  \\n          1 0 1 0 1  \\n          0 1 1 1 0  \\n          1 0 1 0 1  \\n          0 1 0 1 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ean answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1:5 8 13 18 21:25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44756\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 10 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44759-lights-out-7-5x5-x-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45329,"title":"Castling-01","description":"Given the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Castling\u003e","description_html":"\u003cp\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Castling\"\u003ehttps://en.wikipedia.org/wiki/Castling\u003c/a\u003e\u003c/p\u003e","function_template":"function y = castling_01(a)\r\n  y = x;\r\nend","test_suite":"%%\r\na={'Ra1','Ka7'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rh1'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rh1','Kh8'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rc1','Kh5'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra1','Rh1','Ke1'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Ra1','Rh1','Ke1'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rh1','Ke1'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rh5','Ke5'}\r\nassert(isequal(castling_01(a),0))\r\n%%\r\na={'Ra8','Ke8'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rd8','Rh8','Ke8'}\r\nassert(isequal(castling_01(a),1))\r\n%%\r\na={'Rh8','Kd8'}\r\nassert(isequal(castling_01(a),0))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":"2020-02-15T23:25:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-15T12:59:18.000Z","updated_at":"2026-01-23T13:34:49.000Z","published_at":"2020-02-15T23:08:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Castling\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Castling\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44709,"title":"Toads and Frogs Puzzle","description":"On a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\r\n\r\n      T T T T X F F F\r\n\r\nToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\r\n\r\nWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:  \r\n\r\n     F F F X T T T T\r\n\r\n*ALGORITHM:* To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\r\n\r\n*ILLUSTRATION:* Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\r\n\r\n T T X F\r\n T X T F   Slide\r\n T F T X   Jump\r\n T F X T   Slide\r\n X F T T   Jump\r\n F X T T   Slide\r\n\r\nHence, a total of five moves is required for n = 2 toads and m = 1 frog.","description_html":"\u003cp\u003eOn a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\u003c/p\u003e\u003cpre\u003e      T T T T X F F F\u003c/pre\u003e\u003cp\u003eToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\u003c/p\u003e\u003cp\u003eWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:\u003c/p\u003e\u003cpre\u003e     F F F X T T T T\u003c/pre\u003e\u003cp\u003e\u003cb\u003eALGORITHM:\u003c/b\u003e To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\u003c/p\u003e\u003cp\u003e\u003cb\u003eILLUSTRATION:\u003c/b\u003e Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\u003c/p\u003e\u003cpre\u003e T T X F\r\n T X T F   Slide\r\n T F T X   Jump\r\n T F X T   Slide\r\n X F T T   Jump\r\n F X T T   Slide\u003c/pre\u003e\u003cp\u003eHence, a total of five moves is required for n = 2 toads and m = 1 frog.\u003c/p\u003e","function_template":"function moves = ToadsFrogs(n,m)\r\n  \r\nend","test_suite":"%%\r\nassert(isequal(ToadsFrogs(0,0),0))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(1,1),3))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(3,4),19))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(2,7),23))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(4,6),34))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(8,3),35))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(4,8),44))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(7,6),55))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(5,9),59))\r\n\r\n%%\r\nassert(isequal(ToadsFrogs(9,7),79))","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":178544,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":"2018-09-07T17:35:43.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-08-03T12:58:09.000Z","updated_at":"2026-01-20T13:33:38.000Z","published_at":"2018-08-03T13:42:48.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOn a one-dimensional board with n + m + 1 cells, there are n counters in the first n cells representing Toads and m counters in the last m cells representing Frogs. The empty cell is represented by X. For illustration, if n = 4 and m = 3, then the problem is as depicted below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[      T T T T X F F F]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eToads and Frogs take turns moving. Moves consist of sliding a Toad or Frog into the empty cell or jumping over one opposing creature into the empty cell. (Toads cannot jump over themselves and neither can Frogs.) Toads can only move rightward and Frogs can only move leftward.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhat is the total number of moves (i.e. jumps and slides) required for the toads to switch their positions with the frogs as depicted below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     F F F X T T T T]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eALGORITHM:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e To solve the problem, whenever there is a choice between a slide and a jump, the jump must be made.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eILLUSTRATION:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Consider n = 2 toads and m = 1 frog, then the algorithm could proceed as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ T T X F\\n T X T F   Slide\\n T F T X   Jump\\n T F X T   Slide\\n X F T T   Jump\\n F X T T   Slide]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHence, a total of five moves is required for n = 2 toads and m = 1 frog.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60551,"title":"Jigsaw 002: Intro 2x2 square. Local Cody images","description":"This challenge is to re-assemble camerman.tif and other grayscale images from four 128x128 pieces into a 256x256 image. The proposed method is best fitting edges to line m=1 and b=0 for 100, 90, and 80 percentiles.\r\nJigsaw001 was to show how data fitting can achieve results but is not a good general method. Tim used an elegant anonymous function of f=@(u)norm(diff(u))/std(mean(u)); along with a couple other methods incorporated into Jigsaw002.\r\nJigsaw is similar to the real world task of In-Scene-Calibration of a scanning sensor which was best solved by smoothing and usage of RobustFit. The template tries to simulate robustfit with edge smoothing to account for gradients\r\n\r\nThe pointer layout of the image is [1 3; 2 4]. Return a four value vector that remaps the scrambled image into an original form. The displayed scramble is [2 4 1 3] making the solution [3 1 4 2].The four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\r\nAssertion is adjacent Chan and Row pairs will best match m=1 and b=0 for 100P,90P,80P of error.\r\nUsing 100/90/80p gives 88% of chans having \u003e95% valid adjacent channel determination. Using 90/70/50p gives 93% of chans having \u003e95% valid adjacent channel determination. Bordered images fail spectacularly.\r\nScoring function 10*abs(m-1)+abs(b/10) used at 100/90/80p with 4 out of 6 voting to determine best. Tie 3/3 leads to a best mean comparison.\r\nSeparation of Vertical and Horizontal performances is consequential. This was also gleaned from Tim.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 841px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 420.5px; transform-origin: 407px 420.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 366.5px 8px; transform-origin: 366.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to re-assemble camerman.tif and other grayscale images from four 128x128 pieces into a 256x256 image. The proposed method is best fitting edges to line m=1 and b=0 for 100, 90, and 80 percentiles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 361.5px 8px; transform-origin: 361.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eJigsaw001 was to show how data fitting can achieve results but is not a good general method. Tim used an elegant anonymous function of f=@(u)norm(diff(u))/std(mean(u)); along with a couple other methods incorporated into Jigsaw002.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377px 8px; transform-origin: 377px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eJigsaw is similar to the real world task of In-Scene-Calibration of a scanning sensor which was best solved by smoothing and usage of RobustFit. The template tries to simulate robustfit with edge smoothing to account for gradients\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 222.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 111.25px; text-align: left; transform-origin: 384px 111.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe pointer layout of the image is [1 3; 2 4]. Return a four value vector that remaps the scrambled image into an original form. The displayed scramble is [2 4 1 3] making the solution [3 1 4 2].The four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 305.5px 8px; transform-origin: 305.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAssertion is adjacent Chan and Row pairs will best match m=1 and b=0 for 100P,90P,80P of error.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eUsing 100/90/80p gives 88% of chans having \u0026gt;95% valid adjacent channel determination. Using 90/70/50p gives 93% of chans having \u0026gt;95% valid adjacent channel determination. Bordered images fail spectacularly.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.5px 8px; transform-origin: 374.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eScoring function 10*abs(m-1)+abs(b/10) used at 100/90/80p with 4 out of 6 voting to determine best. Tie 3/3 leads to a best mean comparison.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 318px 8px; transform-origin: 318px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSeparation of Vertical and Horizontal performances is consequential. This was also gleaned from Tim.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 222.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 111.25px; text-align: left; transform-origin: 384px 111.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 289px;height: 217px\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkEAAAGxCAIAAADqHPV+AAAAB3RJTUUH6AYSFxkeO/V4dQAAIABJREFUeJzt3Xt8FfWd//HPyE1cQ60mUaxEtD+IQtQc1KbSaogRMW1tWaFWfz9rLEWzdLvr0bUmu24XtWKh+1tgV9tY3SLYuuIW6rVrKLDmLCIBlROopLJqL+CiniQKAQOo7Xf/mGRycq5zbnPmO/N6Pny0OXPmzHzPMJl3vpf5jqGUEgAANHRMsQsAAECWyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK68n2GVlZXFLgIAaMYwjGIXwZaRxS4AAMBFzPRSShW7ILa4oh7W0dExb968adOm1dbWLlq0qL+/33qrMo711rp16xoaGqqqqhoaGtavX29rT0eOyPe+J1Onytixcuqp8sUvSnv70Lv/+Z9y0UVSuL8+Uuz9+efliivkE5+Qigq55Rb54IOhTxlG7H+WQ4fkb/9WPv1pGTtWLrhAnn66UCUH4A+GYSildAkwERHlAtdee+2GDRt6e3sjkUgwGGxpabHemjx5csKPbN++vaampr29va+vr729vaamJhwOJ1xzaAtHjqjp09U3vqF27VJHj6o9e9RTT6mLLx5atbZWPf+8KtAxSb33iy9WTz2lurvVvn3qa19T8+YNfTBZeQ4fVhdcoG66Sb35pjp6VL38smpoKEjJAfiDSxIhI64r8f79+y+88ELrZbIMu/nmm1euXGm9XLFiRTAYTLjm0BbuvVfNnp2+BMn+Fd99V510kurvH1rS369OPFFFIkop9frravZsVVamjj9eXXSRevbZ2I/b3LtSqrdXnXhi+vIsWqS+8hVbGwSAdHQMMKWUK9oSo/X29paUlEQvmT59elVVVX19fTAY7OrqMheGw+Ha2lprnbq6us7OzjSbfuwxuf327EtWXi4XXSRPPDG05Be/kM9/XsrKRES+9jW5+GLZvVv27ZMlS+S++7Lfe3e3nHDCsCWnnCJjxsiZZ8rXvibW11y9Wr797Sy/CwAMMgzDbEIsdkGy4boMu+++++bMmWO9rKurW7p06ZYtW1avXj1jxoympqaNGzeKSE9PT3l5ubVaeXl5d3d3mk2//rqcdVZOhWtslJUrh16uXCnXXz+08auvlk9+UkpK5OKLpa0t+70vXCjz5g29/PKX5bHHJBKRLVvkS1+SL35xoN/r9delu1umTJGxY2XSJLnnHvnoo6y/GQB/0q8DLEZRa4GxVq5cOW/evI8//jjZCps2bWpoaFBKTZky5YMPPrCWf/DBB1OnTk34kcmDjhiGeu+99IVIcUyOHFGlpWrvXqWU2rtXlZaqo0cH3mppUaWlqqlJrVql9u1L8Nljj7W192XL1OWXq48+SrpCW5uaMkUppUaNUpdfrl59VR0+rF59VV12mfqbv0m/fQAY5LYIyIKL6mErVqxYt27d/fffP2LEiGTrVFdX79mzR0RKS0sjkYi1PBKJlJlteons3r179+7dY6qq5LXXcirimDEyd6789KciIo88Il/9qowePfDW978vGzfKpz8tzzwjU6fKsmWxn500Kf3e/+mf5Be/kCeekJHJ73mYPl3eeENEZPx4WbFCpk6VY4+VqVNl5Up59NEsvxcAn9G6/TCaWzLsySefbGtre/DBB8eOHZtita6urvHjx4tIIBAIhULW8lAoVF1dnWYf11wjP/hBrgW9/npZtUpE5JFHpLFx2Fvnnivf+Y78/Ofyyity110Z7/2RR2TNGvnlL+W441Kt9sorUlEhIvK5z0n0+af/uQjAGdq3H0Yrai1wwObNm+fMmXPgwIH4t66//vpNmzb19PT09fVt2LBhxowZjz/+uMpubP3hw+qzn1Xf/KbatUt9+KHau1c99ZS65JLYD6Q9JpMnq6VLVcyAyRkz1C9/qbq71cGD6kc/UhdcEPup1Htfv1595jPq/fcT7O7SS1Vbm3r3XbV/v3rqKVVRoR56SCmltm1TDQ1q1y51+LDatUtddpm65ZY0JQfgey657OeLK77MtGnTJg9n5dmWLVvmzZt3/vnn19TUXHfdde3t7dannnvuuVmzZk2ZMuWKK65oa2tLtvFho/P7+9Wdd6qzzlJjxqhTTlFf+IJ6/vmhd0WG/ZfMPfeoUaPUPfcMW7hhg7rsMjVunCotVVddpX772wQfTLH3T3widu9Wnm3cqC6/XJ1wgiorU7W16pe/HNrgs8+qc89VY8aoiRPVHXeoI0eSlhkAPBdgSikvtIemVllZuXv37mKXAgCKSa8ZpOxjvkQA8DhvDN9IyC1jOgAAheDhABMyDAA8zNsBJn7LsCymrU8xoXx3tzQ1yYQJcuyxMmGC3HSTpJ0qJL9lTjbPfooyf/ih3HmnnHmmHHusnHdegulEAHiDZ+4AS81HGXb0qNTXy+9+Jz//uRw4IFu3SlOT/MM/DK1w993y/e/HfmrhQvnWt+TNN2XrVnn7bfnrvx5669prxTBk0ybp65NNmwaWOFnmhAVOXeaWFtm0SZ5+Wvbvl0cfleXLJRzOc5kBFJ2n7gBLyfspbY1L/P73Zdu2YXP2JmQYSW8Xfu89mTRJensHXh5/vLz11tD0vO+/LxUVcvDg0PqRiEyZInv3inXf9uHDctpp8tprUlYmb7wh3/mObN4shw/LOefIHXfIF78Yu0c7ZU5R4Pgyn3KKvPyynHbawMu33pKWFvnZz1JtH4Be/FD9svioHpbjtPUSN6H8V74id9whe/bIRx/JH/4gf/d38ud/Pmz9HGe6L0SZRYa1PRqGvPBCTtsH4Cq+CjDxVYblPm19zITyP/6xbNsmp58uo0fLxIny8svS2hr7kVxmui9Ema+5Rm64Qbq65MgR6eqSefPk7bdz2j4Al/BJB1gMH2VYjpYvl/ffl+bmoSW33SYnnii7dsnhw/Lqq3LCCXLbbbGfuvJKCYflrbdERN56Szo75corB976y7+UQED+4i/kkUcKFSTxZV6yRKZPly9/WU44Qa66Sr7+9VTTCwPQhX86wGL4KMPsTByfTMIJ5VevlhUrZMqUoYnjH3ss9oO5zHRfiDKPGSN33SVvvCFHjshrr8kpp8ikSVluH4BL+LD6ZfFRhmU9bX2yCeXHjBn2Uik59tgEH896pvtClDnGj34k11yTzfYBuIE/2w+j+SjDbr1V3nlH5s+Xri756CN56y15+mmprU3zqQ0b5Ic/lOeek5KS2Leuukq++U35zW8G+pa+8Q256qoEW7joIlFKli0TpaSmZmh5XZ38x39IT48cOiRtbYnrQ3kv85e+JFu3yuHD8rvfyYIFsm+f3HJLmq0BcCffth8O49z0wkUSPW99FtPWp5hQ/oMP1O23qzPOUGPGqDPOULffrqKeLD1M1jPdpy5zsnn2U5T5iSdUIDBQ4JYWdehQ+gMIwIX8cPW2w/uVUOatB+AxPm8/jMagNADQhlcfoZI1MgwA9ED1K56PxnQAgL7MAGtZG2hZGyh2WVzER/Uw6x9+xDGjPjH25KrTZl5WOX/0qKGx5292b2179f697726eE44auG29t2r9vTuGDOq5JxTL5019S+tjxw6+v66XffvfueFD46+/2djPll58udmVf3V8WM+6ViZExY4dZn/+KePnv/NT17Z80zfke7y4ydecW6w8uTpeSwwgEKgBpaMv+phi+eEF88J3/2VzY3Tl769f/czv/6n6Hc3/uahhqk3x3xkw64fX3TmV2+/4tlv1/2072jv0zv+0Xrrsa3NhnHMgtqH7/7K5gW1D5tLnCxzwgKnLvNzv/7n3/WEb/jc8ruufOGamkUvvP7ovv3Z3kQNoPC4AyyNIo6JdIY1tr55TXX08gOHI997+tL49WNWi3bo6P47n7rEevn3T3y2/2if9bL/6IHvPnFR9PoHj/Tc9XTt0Y+PWEuOfnzkzqcuOXjkPaVU98E9q14M3v103XefuOj+56//zb7/SluYhGVOUeD4Mn/v6Uvf73/Hevl+/zurt/1tio8DKCI/XKJz5K96WIwP/3g4o/X7j74/dvTQbcNTT637Vdf9+/vf+eOfPn6vf99zr/5L1afqotc/fsxJp594bte+jdaSXf+z8YyTAmZ7479tbT7jpGm3zXryji/9qmHqzZvfXO1AmWV41fsYkd/3dGa0QQDOoPplh4/6wyx//NNHPYd+/+zO5ZMy7Ar6VdcD558+23p51bS//3HoxsXPNZgvP3XClKZLHor5SOD0L2/77drqCV8wX77yh6c/e8Zc8+eeQ384Z8LlY0ePE5Ezy6adWTbNgTKfV3HFv7+88Mrzbj/puNN6+996dsc/9h0uwMOnAeSGALPJXxkWPZ7n+NEnBi//d/uf3fT6o0c+PFB31jesJc/+etlxo8fdMnPtSced1tu/55kd///ZXy+9atrfR3/q7FMvfrJz0f7D754w9uT9h9/dd2D32acOTBU1/dNX/8vGa6o+ddnpJ543qfyz48aWOlDmK8756/bXfrLqxZsPHH73k8edeunZ83/fSz0McBHuAMuIvzLMHL/38ccfvt3332tfubvjzV9cNuVGOx/c9PpPu/6nfd7FPzzGGGEt3Lmn7dbL14wbe7KInDzu/1x9wV1LfzU3JsNGHTOm6tT67XuevbTym9t//8y5n5o54phR5ltXVN187oQvvPHOi7/ZF3p25w/qz7rx85O+XugyjzpmzMwp35o55VvmyzciW0uPr7CzNQAOoPqVKT/2h40cOXrCiVXXT1/64puPHv34UNr1X/nDszvf+tUNn//nUSOGzUs/YsSo6Jd/Ehl5zGiJc8HpV27//TMisn3Ps+ef/qXot079xKRLKhuvu+gf/+rSf9vQ9aADZY6x5Y3HzzvtirRbA+AAAiwLfsww04l/dtqZpRd27nku9WpvRLZu+e3qeZ/70ZiRx8e8NfXUS3/+yt3v9v3u448/fLfvt2teXlh1Wn38FipOOk9EvfD6T0XUhBPPtZY/GJq/++1NHxzd/+HH/f/9zpaTbNSHci/zyhf+au97Oz/849H3PnjrF9vv6TvS/bnJ/zftfgEUFAPos+avtsQYnznjz9t23V9z5lfNl1bPk/mD2Yj3sy23Hfn40F3PXGJ9auGV/2WO9LvyvNvW73pg5eZv9x3pHnds2Tmfmjlz6l8k3FGg4sr/+PW/1J/dFL3w0rNubP/vh/e+9+qIEaPOOOn8//fZJZmWOWGBU5f5wjOuemL7ve8e/O24Y8vOm3D5jRf/eNQxY2L3AcBBpFcuvH/smLcegGsRYDnyb1siABQXAZY7X7clAkBRMIA+X8gwAHAU1a88oi0RAJxDgOUXGQYADiHA8o62RAAoODrACoQMA4DCovpVOLQlAkABEWAFRYYBQKEQYIVGWyIA5B8dYM4gwwAgz6h+OYa2RADIJwLMSdTDACA/aD90HhkGABmLeeCRUP0qEtoSASBXBFixkGEAkBMCrIi8f+h5BqbJMERElBr4wfwZQC7oACs6+sMAIBtUv9yAtkTfMX/p+NUDckGAuQT1ML+I/nXjVw/IBQHmHmQYANhFB5jbkGEAYAvVLxeiPwwA0iPA3Il6GACfsn+fCQHmWmQYACRlGIaIEiHAXIoMA+BTNqtfVnUNLkR/GACIYUhMVtF+qAXqYUCWmLXLq2IG0PPv62ZkGAAMBVV89cuaaxQuRFsikCVm7fIe2g+144oM6+jomDdv3rRp02praxctWtTf3x/97rp16xoaGqqqqhoaGtavX592OeAYLnceYPaEGQYBpiflAtdee+2GDRt6e3sjkUgwGGxpabHe2r59e01NTXt7e19fX3t7e01NTTgcTrE83uTJkx36GgA0NDh0vtjlQFZc93fHgQMHZs6cuW3bNvNlMBgMBAKNjY3my4cffnjnzp3Lli1Ltjx+gzw/DEAKVL+05oq2xGi9vb0lJSXWy3A4XFtba72sq6vr7OxMsRwA7CPAdOe6cYn33XffnDlzrJc9PT3l5eXWy/Ly8u7u7hTLASCZmNshCDAPcFeGrVq1qq+vr6mpKb+braysNH+gURGAMIOUh7gow1asWLFhw4af/OQnI0aMsBaWlpZGIpGJEyeaLyORSFlZWYrlCRFdAGR49YsZpLzBLf1hTz75ZFtb24MPPjh27Njo5YFAIBQKWS9DoVB1dXWK5QCQAu2HHuOKDHvxxRd/9rOf/eu//uvxxx8f81ZjY2Nra2soFDp48GAoFGptbTXHIiZbDo3Ez1AHFFR0gCnF7X1e4Io/Sc4///xDhw5FL3nppZfGjRtn/tzW1rZ8+fK9e/dWVFQEg8FZs2alXh6DsfWuxRQ+cEzMFIjwDFdkWEGRYa5FhsEZMe2HLWsDIrJ4Trh4JULeuGhMB/yG9IID6ADzNu//61IPA/yJ9kM/oB4GwIOofvmEK8YlAkCmUoxrJcD8gwxDBhgND/cjwHyFtkQAHkEHmA+RYcgAFwe4FtUvfyLDfI07tOCMfJ1pMRPPRy0nwHyKDAPgRsniKtGaeQ4w/rbTCBnma/yWwhn5OtPit1OgAIMuGJfoO+bYQn5R4XJmMMW1GcafvXl+igp/2OmFephb2G85AfzA/I0wjKHfCMOQ5jWBwZ87C/cES34HNUKGuV3em+b5/YReomPMkkX7Ib1cnuT9wTxaz5dI5Qy+FdNCqJS1JMurFhnmSdTDMubkb0LU762PkNzuV7h/o5jfr6jGQ+tRKYo0goUMczt+UeFDLWsDzWtiF5p5tmRuls/94lfJk8iwjPGbUGj6HmH/1A+c/45WhQyIxth6AO6VsNblh78SYBNjOoC88U89LI9iDlrCDuDoVsT4O8YsGR15/rG8gbZEIG+8d0FMeKEv6NXf3GyKJBPJsj8MnkSGAUgsYZA4MFDWMAzz/mVrXO6SuWGlpGVtgi6xrKPUe39w+BP9YQASS3iVL9Cl35pBynwEWMIVgHj0hwEoJrN2lXrsBn1XSIa2RADFZzUVms2G8e8CCVEPA1BkhmGISPOaavPl4jmM2oBd9IcByI9MH+vTsjbQsjZgPgDM+mOaAENGaEsE4DQz58zZpEgv5IK2RL3R140iMnuw7GePdbrGTxmc8AErQFq0JQJwniFxww4ZPY8s0JaIQsl6EiB4m9kBFvWyiGWB9sgwvZENRefn5tyYVsS0TYtKDQuwwXk3EsyCCNhEWyIKRamh/zyPyoQl4YxQppgamMknZwgKhHoYkAe6XIUzHYWRBWvjMWG2ZG6n50eQwXlkGJATLssWM72SxWR8gDGYHrkjw4AoXu/dcjI2zH1ld0QdqC/CG8gwAPlkBY8x0EmohNu/UDBkGDCMIUq8WBkrRA0zRW2pZW2geU314jnh6NEuhmE9x5I6FvKDeToKwustUl4WP4WENxQuw0wxcWVllQw+VyXmIZZkGPKCsfXAMF69HyCPX8qMopa1gcVzwslGIUZHVMyuoz8F5IgMA5ABK8AkLrei614yGGPEFQqKtkQA6aW4czkhq2mRDjAUFPUwAICuGJcIIL1k/V7xq1krDDbxUP1CAZFh8Bq3DSz0xiDVTNsSAWfQlgggJzEdXQw7hJOoh8Fr3FbjcVt5bLJ/LxfzQqGIqIcBAHRFPcwJ1p+0/K0KHTnTGea2jkxogXoYEMUweJxly9qAOQdH6tWsFegAQxFRD3MCv+HQUfTzwGT4uPlCoPqFLJBhABKIjytm7IULMddUKt64swfIQooqFwEG96A/DPCTDDv8uPcLLkdbYirUwOBbS+Z2xjTSkF5wIdoSgeJzoNV6YBcSVQkb3F/coywNEWleUx398ejxHdGDO/IYbIytRxZoS0yFgdbwGqVa1lSb/4kkOMVb1gaa11THhwjzJcKdaEsEii9FzSNfVbS0Wxg2dF6pxYM7TjgcsRDtilS/kAUyLBV+qVBchWgGGBY/5im+NiAihuH9ngV4j1syrKOjY+nSpTt27Ijpu6qsrIxZ01ph3bp1y5cv37t374QJE2699daZM2c6VFZARBzpv1EqbzEW35UVuy8RMYyhTrI11SKy2DDE6hiLehdwCbdk2A9/+MPbbrvt61//evxbCUdkhMPhhQsXLlmyZNq0adu3b29ubi4rK6uuro5fE9BPVAOi06lhGC0xoznmdjpbAiAD7mo9iB9DmGxUYTAYDAQCjY2N5suHH354586dy5Yts7NNwO0KOk4xqv6YYLCiVQObE2akINzPLfWwFKZPn97X13fyySefc845N91005QpU0QkHA4Hg0Frnbq6ukceeaR4ZQTcKKbZcKAnbLCB0jCMgQH0c5N83mrKtDLVSDA0Hygit2dYXV3dDTfcMHXq1CNHjmzevLmpqenOO++sr6/v6ekpLy+3VisvL+/u7i5iOYF8ykc8pJrwcE21iKi5nS3W7qLCaaDxUCV4C3Abt2fYAw88YP5QUlIye/bs0tLSe++9t76+PqONWANDituoyOyL2tPwn3DJ3HDzmiS3dim1OMUn478sYQb3cXuGxaiurt6zZ4+IlJaWRiKRiRMnmssjkUhZWVmyT9EfhnzSZHie2XK4WInIsEeoJGZ+I1IKutFsno6urq7x48eLSCAQCIVC1vJQKOTAoESm7YBIVNXEPCHcek6Yj7JMtYZV+BTfIsW3c/fXh0+4vR7W2Nh44403nn322aNHj962bds999yzYMECc/mCBQsmTpxojq1vbW21Wh1dS4e/3TFcTHua+/4J07ZumjG2ZG6n2cGlVPJYitkW4QQduGVsfcy9zFbrX0dHx0MPPbRjx46RI0dOmjRp/vz5tbW15lttbW3mPc4VFRXBYHDWrFnJtkxbIrKUYwdY4fvPEu4hvvplzc0xtH7MuPl0A+6TcscFBL7llgwrHDIM2fNEhi2ZG46pXA28jO7YI8OgJ7e3JQIeUMR7hZfMTT45b15Kw33QKCrqYUDBJbjO26ii5VKLsybwzWYj2fWEef1KAneiHgYUnMOX95gAKzjSC8Wj2dh6AKlFP0KFcIHnUQ8DisFGvCRbJaZ2FTUblCEiMb0DeY6x+KmnyEkUFRkGeIRSyrznOPtYsdP4GL91DafggmeQYYBmhkbGDy6IGZyVh8mw4m4ay21zQKGQYbY49rvMRcMOXf7ud6CchigxYtsP0xbJlPRDKbamy6GHb5BhQPHZj4ahNZUyrH6wwYX5qX458ykgH8gwWxz7JeVqYIcuRymP5YxvHowef2h/DH1mRYqexUOXg54b6pnaYWw94HbRz1IeXOLg7ATxuwdcg3oYvEXPP6QzLW9hB9Cn3LFTewJsIcMAEcmuSyrRsgyH5dgZZGENEsxXk17C75r0APgpt/z0XT2CDIO3cBFKJ2GjIC2F0BT9YUCU3B9MnMkw9xRPo4x5y0hXKvsPVU5YQKIfmqIeBohI3F29addMsSzDQBhqgTQSfNwwDBE19BTmfDBnjHL9Q6qB9Mgw+F70tdzBC3mC2BscARg1aN5Qgw+lTCH6qZV2RizScgjPoC0REBF3XNeHP1XZGkBv3rxsa7iJpH/kSvSgE+pe0B31MGBQ/u7kjR2cmOlgxcEZpFrWBswli+eEbU0TZUP81POAvsgw+E7sIHL7PWH2t5n1J6NmkIpfK/EilfHzwqh+wTMcvNu/SCorK3fv3l3sUsBFCnEbdL4yLOEEHIlrYMwPDdAflpHcx10DKRiGmOMPhy0yDCWJTjvHB6EALkRbInwn18t+ojpXwjkvbO0rqgYWG2Ay1HmVeDsEWBQ9ZxlDrsiwDPDrob1Mr3N5uS6mnptq8P7lpGPos53+Ktc1AR2QYfCfHMcf2vhsJilpZDwkA4lw9PyJDIP/uOZql/QRKoO1JcZtAKmRYfCTqCiw1aiW/O3oqTGSrTa4i6QbyduoYPvbIQzhLWQY4ITo9kv7T7AkcYDUyDD4VI7xYPVh2dmF9SRkpZx9BDPgdWQYkFcpu7B0DLDo+a6KWxIgHhkGFJxZ/ZLhtbbEYRe/lNHwQHL6/VWYKeaaQtENVL9ip5UaeDdVhjEwEUiJuaaAbFjPTbbxcK/EfykmnisqZmniBkmmPQMG0JYIJJdFO97wmtOwAIuuWg0+GCyB6KUkFZASGQZkw8azkgdmkEr2tq2tZLVrwD/IMCCv4qtfiVawW8Eir4CUyDAgH6LqVU4OoB/aLaM/4EtkGJBcyskME37CZoAZoiT1DdIAbCDDnMN9ProbnCMxEaUM83mVOfwDZ3GGDK3MiQVfIsOAzJgzRsUu1HACDsADyDAgMyquFTCLAMtD3tnpAKOTDF5HhjmHy4i+kmVBmgH0GUq6GaIISIIMA7JU5PZDO7sm8+B1ZBiQnjXZochArcjIovoVdU/YwLjE+A0krHKZu7ezu4QfpxoH7yLD4Hc2r/BmjgzOPp+uBpb32IibMphkAoQMA7KQZRNi1KeGfopJJmsWD5sVr+S7SLMQ8AQyDH6XyRXedgdYIWIjZpskE0CGATYlHcFBmx5QPGQYkMZga19hIipF61/CDjB9kpJwhwPIMCAVwzDSzGvIFRooHp7jDCTFDFK5SPygaiCvqIcBiZk1sPTDAwvXZKb/IA4NiwzNUA8DEshzDcwcK2/zuZdZbBnwK+phwDDRUyDaSjHqGkDxUA9LjL9uPSLDf8jBERxq4EP5Og+K1TVUuPof4A5kGDCgsCM4Cjc0n4ogfIy2xMS4LHiEvX/IpO2Hup8HupcfSIcMg98xgB7Ql1vaEjs6Oq6++urKysr4t9atW9fQ0FBVVdXQ0LB+/fq0ywH7CDBAb8odrrvuuq1bt06ePDlm+fbt22tqatrb2/v6+trb22tqasLhcIrl8eK3WTjmcADoIsvz31X/zK4qDOA4d/0RWllZuXv37uglwWAwEAg0NjYBUAGUAAAOT0lEQVSaLx9++OGdO3cuW7Ys2XI72ywQZofTSHQHWBYfFnHNP7OrCgM4zi1ticmEw+Ha2lrrZV1dXWdnZ4rlRcRlRBdm+6Gr/npLisHxQEpuH9PR09NTXl5uvSwvL+/u7k6xvLi0uCr6nGYdYNbjm1OsAPiY2zMsL6yhIs40KsKlzBuYC3rRL0TLHikFJOf2DCstLY1EIhMnTjRfRiKRsrKyFMsTIrow0AGWl20RKoBruL0/LBAIhEIh62UoFKqurk6xHIjWsjbQsjYw0AFW7MKkEtPpRR8YYI/bM6yxsbG1tTUUCh08eDAUCrW2tppjEZMth5YKPHJBWdMVFiLIogvPzE+As9zSvx1zd3N0619bW9vy5cv37t1bUVERDAZnzZqVenn8lmlLdLuC3ZfgxAgOx26qSNHZFvMWA+7hG27JsMIhw/wppzvA3IkMA+K4fUwHkAXNBtALqQNkiQyD1xQzwAoaRSk2G/MWWQjfIMPgHRq3H+pYZsAFyDB4RAGrXzT0AW5FhsEL0gZYAQcPRm+anAOcRYZBewXvACOZALciw6Ax+x1gBYwhEg4oHjLMU7R+hlmmvU5uHEBPzxngLLfPNQUklEuAeWoyQk99GSBjZJinmKMKNK0G2C95dIC1rA0UsEyZ0vfoA3oiw6ATwzDiA8xdMQbAQfSHQRv56gArck0pv31mVPvgb+7rFc835vz1BjeO4MgO4z6A/KEeBg14J8CE9ALyiQyDq2k8BSKAwiPD4F7FnEEKgA4YlwiX8lT7IYDCoB4G13HFDFIAdECGwV2ofgGwjwyDixQ6wOg/AzyG/rBhmHyuiKiBAcgU9bAhpFexODaAXikxDCphgHeQYUPMCxwc5nD1iwADvIQMG4YLnMNoPwSQC/rDUDQEGIAcUQ9zGjO+CjNIAcgTMswjHBo1niyBozsS05WA6heAfCHDnOaRq3e21cmCBxj1XMBPyDCPcOiinWw39nZPDQxAfpFhcAIdYAAKgQxDVjJJI6pfAAqEsfUoLKcDTKk0+cp97ICHUA9Dobix/dAMMOabAryCDENBeLP9kHnvAZchw5B/ZoC1rA2YLxfPCadeW8SpVCB7AG8hw5BnmdXA9OqdIgIBlyHDkIZZnUpTlxKRJB1gaT4Y/bAAbk8GkCEyDPkRX/2yE3sihBaA7Hmx4324ysrK3bt3F7sUHufNERwAXI/7w5ArAgxAsdCWmDH7/UOe58Y7wAD4CRmGLFH9AlB03r8M0R9WCAQYADegPwwZI8AAuARticiA7zrAin3LGpNbAamRYUgq5gJK9QuA25BhsMWnAVbsr1zs/QNuR38Y0vNpgBWIYWg2SyTgYmQYklJKRAw/DF4FoCnaEpEU1a+C4JAC+UM9DIkRYADcj3oYYhV3AH2xR7MD0AkZhmFSV7+YKxKAq5BhGOKG9sNi7x+ATsgwDMgiwJxv92PeCgDRiv93d6Ex529aWXeAkWEAiot6mN/l0n7ofJAQXXbQbQn/YGy9r7mhAwwAsub2elhlZWXMkuiGwXXr1i1fvnzv3r0TJky49dZbZ86c6Wzp9EaAAdCd2zNMhodWtHA4vHDhwiVLlkybNm379u3Nzc1lZWXV1dUOF09HvnuEis/Qigj/0LgtcdWqVQsWLKitrS0pKamtrW1qalq1alVetuztGVnN6hcBBsADNMiw6dOnV1VV1dfXB4PBrq4ua3k4HK6trbVe1tXVdXZ25r47M8C8GmO0HwLwErdnWF1d3dKlS7ds2bJ69eoZM2Y0NTVt3LjRfKunp6e8vNxas7y8vLu7u0jF1AMBBsBj3N4f9sADD5g/lJSUzJ49u7S09N57762vr89oI9bAEDs3ijWvCQz+6J1OBTrAAHiS2zMsRnV19Z49e8yfS0tLI5HIxIkTzZeRSKSsrCzhp3x+jzPVLwBe5fa2xBhdXV3jx483fw4EAqFQyHorFArlZVCiOabLMyO7CDAAHub2DGtsbHzhhRd6e3sPHjy4cePG5ubmG2+80XqrtbU1FAodPHgwFAq1trY2NjbmZafeCDDDMAgwAN7m9mtcR0fHQw89tGPHjpEjR06aNGn+/PnRYxHb2trMe5wrKiqCweCsWbPit+DP+RJJLwB+4P0rnQ8zjAAD4BNub0tEpggwAP6h2bhEpODNAfTOP98FgD7IMLsMw9UXUqpfAHyItkRbXD4BFQEGwJ+oh+VHEVu8PB5gHv5qAHJGhtnizgup2zrA6LoC4DAyLD+cv3B7vPoFADaQYbZYPWEuSY1cAsxt3wUAskaGuUXL2oH58tPOdOVYDcx+kUyEIgCHMS7RFqUG/isQmyMe8zIFYqG/CwA4xvt9KlrMNWVnNAQdYAAQg3qYixhG0goZAQYA8egPcwWlhjqfYp4fbRiGiBIhwAAgFvUwV4uufrl2lhAAKBYyLHtm01++omXJ3LD1v4PbH9Z+2LwmEFVXy488lh8AnEdbohNsDlJPFmDm/7esLVTx4Dxn5jThXkB4HhmWvfxeF6ytJZtByuZNWtntFAB05P3RblqMrbcw/hAA7KMelis7jUI22xIJMADICGM68qNlba4DLggwAMgU9bBc2Rlwkbb6JcM7wMw4LEQHGAB4CRmWH1nnDdUvAMgaGRbLgeHI0a2OzWuqC7UbAPA6MsyNaEXUCY+vBorH+w1ZLhxbn+wOMGiJG4mB4qEeNsCxP6Yz6gDL9CmUKBoCDCgGMiwbWUdLLiM4/D5Y0X51J3pNB/42Ib2A4iHDBhR+5rps2g/9m1gAYAP9YU5gAD0AFAL1sDRy75EiwFyK8YSA/siw7NmJt5a1geY11S1rA/HrMF4DWmDcJdyMDEsjl+qXcAuzm3FJBvTHnL/ZM+MtYciZ7YdKqRTrpHgLcA8z60l8uJP3u2qcH9NBBxgAOIN6WJ6ZAZb7o1gAAGnRH5Y3zCAFAA4jw9LgEcwA4FpkWPaseFsytzMmwBipAQAOIMPSsJNG8TWwhHMb+n3CQ38zDIb2AflHhmVvydxOoQMMNpi3CRNjQN6RYRmIGWpo3r+ccA4OvynuVA5MJAH4lvdHImR6f5h5QWxeM9Tul3aUvI4Zlt/JAskwAEVBPSyV+PRaMrfTrH4tmRuWnC+aRW9cylcBivstiC7At7jHOVbqC2J0tTX3AJOoOkRRcPUHoDXqYQkoJS1rE79lGKJUWEQWx1399WrR0qKQAJAaGVY0pAgA5IgMs2tgmvnkwUMmAYDD6A+LZRhF7qMCANhEPWyAYQyMp29eM7Qw3RyJAz8oxRwcAFAE1MMGmAEGANAI9bCk0laqojvAqIEBgPOohwEAdEU9bBiqUwCgEeZLHMBDLAFAO9TDxDAM4REqAKAhv2cY1S8A0Jevx3QQYACgNZ/Ww2g/BAAP0Lsetm7duoaGhqqqqoaGhvXr19v8lFn9IsAAQHcaZ1g4HF64cGFLS8uWLVtaWlq++93vdnZ2pv2UP9sPKysri12E4uMgCAdhEMdBvHIQNM6wVatWLViwoLa2tqSkpLa2tqmpadWqVak/4s8AAwCv0jjDwuFwbW2t9bKuri5FPcwwDAIMADxG4zEdPT095eXl1svy8vLu7u6Ea5JeAOBJGl/cp06d+tJLLx133HHmy/7+/s985jOvvvpqzGqGYUyePNnx0gGA3uzMcFR0GtfDSktLI5HIxIkTzZeRSKSsrCx+NX1DGgCQmsb9YYFAIBQKWS9DoVB1dXURywMAcJjGGdbY2Nja2hoKhQ4ePBgKhVpbWxsbG4tdKACAczTuDxORtra25cuX7927t6KiIhgMzpo1q9glAgA4R+8MAwD4mcZtiQAAnyPDAAC68nKGZTcjsNYq40S/6/kD0tHRcfXVV8fPApfii3vvmCQ8CL46MTo6OubNmzdt2rTa2tpFixb19/dbb/ntTEh4HLx2MiiP2r59e01NTXt7e19fX3t7e01NTTgcLnahCm7y5MnJ3vLDAbnuuuu2bt0acxBSfHFPHpOEB8FXJ8a11167YcOG3t7eSCQSDAZbWlrM5X47E5IdB4+dDJ7NsJtvvnnlypXWyxUrVgSDwSKWxxkpzk7/HJCYg5Dii3v4mNjPMA8fBKXU/v37L7zwQvNnf54Jpujj4LGTwbNtiRnNCOwl06dPr6qqqq+vDwaDXV1d1nLfHpAUX9xXx8SfJ0Zvb29JSYn5s5/PhOjjIN46GTybYfZnBPaSurq6pUuXbtmyZfXq1TNmzGhqatq4caP5lj8PiKT84v45Jr49Me677745c+aYP/v5TIg+Dh47GTSeLxHxHnjgAfOHkpKS2bNnl5aW3nvvvfX19cUtFYrOnyfGqlWr+vr6mpqail2QIos5Dh47GTxbDzNnBLZeJpsR2Nuqq6v37Nlj/uzbA5Lii/v2mPjhxFixYsW6devuv//+ESNGmEv8eSbEH4cYup8Mns0wZgQWka6urvHjx5s/+/aApPjivj0mnj8xnnzyyba2tgcffHDs2LHWQh+eCQmPQwztT4ZiDyopFB0Hiebu+uuv37RpU09PT19f34YNG2bMmPH444+bb/nngPh8bL0p5iD46sTYvHnznDlzDhw4ELPcb2dCsuPgsZPBy/Ml+nBG4I6OjoceemjHjh0jR46cNGnS/Pnzo0cZef6AxNytaT3BL8UX994xSXgQfHVinH/++YcOHYpe8tJLL40bN058diYkOw4eOxm8nGEAAG/zbH8YAMDzyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7IMACArsgwAICuyDAAgK7+Fz0flVOG75fQAAAAAElFTkSuQmCC\" alt=\"Adj Scatters\" data-image-state=\"image-loaded\" width=\"289\" height=\"217\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = jigsaw002(pc,nr,nc,pnr,pnc)\r\n %Brute force try all puzzle map permutations \r\n %Try all piece permutations and score the Horiz/Vertical piece edges separately\r\n %RobustFit like scoring with 100p 90p and 80p of edge vs interpolated deltas\r\n \r\n  %mpathc used to display progression of processing\r\n  mpathc = mat2cell(zeros(256*16, 256), ones(1,16)*256, 256); % 16 cells of 256x256 zeros\r\n  ptrc=0;\r\n  \r\n  best_score=inf(1,6);\r\n  for p=perms(1:4)'  %Tim\r\n   m=cell2mat(reshape(pc(p),2,2)); %Tim\r\n   mpv=[(m(:,pnc-1)+m(:,pnc+1))/2 m(:,pnc)];\r\n   mph=[(m(pnr-1,:)+m(pnr+1,:))/2; m(pnr,:)]';\r\n   mpves=create_sort(mpv);\r\n   mphes=create_sort(mph);\r\n   \r\n   score_setv=calc_score_set(mpves);\r\n   score_seth=calc_score_set(mphes);\r\n   scores=[score_setv score_seth]; % set of 6 scores\r\n    \r\n   if nnz(best_score-scores\u003e0)\u003e3  % 4/6 or 5/6 or 6/6 best_score\u003cscore  100p 90p 80p\r\n    v=p;\r\n    best_score=scores;\r\n    fprintf('New Best 4 score VH');fprintf(' %6.3f',scores);fprintf('\\n')\r\n    ptrc=ptrc+1;\r\n    mpathc{ptrc}=m;\r\n   elseif nnz(best_score-scores\u003e0)==3 % Tie  Choose lowest mean\r\n    if sum(scores)\u003csum(best_score) % could select 90s and 80s only\r\n     v=p;\r\n     best_score=scores;\r\n     fprintf('New Best 3 score VH');fprintf(' %6.3f',scores);fprintf('\\n')\r\n     ptrc=ptrc+1;\r\n     mpathc{ptrc}=m;\r\n    end\r\n   end\r\n   \r\n  end % p perms 1:nr*nc\r\n  \r\n  %Displaying images for various New Best configurations\r\n  if ptrc\u003c5\r\n   mpathf=cell2mat(reshape(mpathc(1:4),2,2));\r\n  elseif ptrc\u003c10\r\n   mpathf=cell2mat(reshape(mpathc(1:9),3,3));\r\n  else % hope for 16 or less\r\n   mpathf=cell2mat(reshape(mpathc(1:16),4,4));\r\n  end\r\n  figure;imagesc(mpathf);axis equal;colormap gray; %Display Path to solution\r\n  \r\nend %jigsaw002\r\n\r\nfunction mpes=create_sort(a)\r\n% a matrix [L,2]\r\n mpe=[a abs(diff(a,[],2))]; % Create error column\r\n mpes=sortrows(mpe,-3); % Descending sort of deltas\r\nend % create_sort\r\n\r\nfunction score_set=calc_score_set(mpes)\r\n% calc scores at 100p 90p 80p for a 256 sample matrix\r\n% score 10*abs(m-1)+abs(b)/10\r\n% score_set [1,3]\r\n warning('off','all'); % Some puzzles are poorly conditioned\r\n  mb100 = polyfit(mpes(:,1),mpes(:,2),1); % 256 samples\r\n  mb90 = polyfit(mpes(26:end,1),mpes(26:end,2),1);\r\n  mb80 = polyfit(mpes(52:end,1),mpes(52:end,2),1);\r\n  score_set=[(abs([mb100;mb90;mb80]-[1 0]))*[10;.1]]';\r\nend % calc_score_set","test_suite":"%%\r\n% all imdata 2019 are hosted for cody at https://drive.google.com/drive/folders/1TZkBMEEKHiFJExqVoJgj5VVeHbvOfTYB\r\n% a Text file of matlab urlwrite links will be added in the future\r\n\r\n%To access matlab cody local image folder may be accomplished using cd\r\n%jigsaw002_pwd=pwd;\r\n%dir\r\n\r\ncd (fullfile(matlabroot,'toolbox/images/imdata/'));\r\n\r\nmc=double(imread('cameraman.tif'));\r\n%fprintf('Cameraman size %i %i\\n',size(mc))\r\n%dir *.tif  %*.jpg  *.png\r\n%figure;imshow('cameraman.tif') % valid\r\n\r\n%dir *.mat\r\n%fnmat='imdemos.mat'; %trees cellsequence contours mristack\r\n%fnmat='trees.mat';\r\n%whos('-file',fnmat) %mat file contents name nrxnc size type\r\n%load(fnmat)\r\n\r\n%Possible files to use from imdemos.mat\r\n%  mc=double(circuit4); %\r\n% mc=double(coins2); %\r\n%  mc=double(rice3); %\r\n% mc=double(circles); %Bad image with top/bot black\r\n% mc=double(eight); %\r\n% mc=double(glass2); %\r\n%  mc=double(liftbody256); %\r\n% mc=double(saturn2); %image not great\r\n% mc=double(vertigo2); % Pass 100/80/60  Fails 100/90/80\r\n\r\nnr=2;nc=2;\r\npnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\n%jigsaws=[jpc{1} jpc{3};jpc{2} jpc{4}];\r\njigsaws=cell2mat(reshape(pc(vperm),2,2));  % scrambled image\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n%jigsawf=[jpc{v(1)} jpc{v(3)};jpc{v(2)} jpc{v(4)}];\r\n\r\nfigure;imagesc(jigsawf);colormap gray %Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(circuit4);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(liftbody256);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(coins2);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n%%\r\nfnmat='imdemos.mat';\r\nload(fnmat);\r\nmc=double(rice3);\r\n\r\nnr=2;nc=2;pnr=128;pnc=128;\r\nTotal_pieces=nr*nc;\r\npc{Total_pieces}=[];\r\n\r\nptr=0;\r\nfor c=1:nc\r\n for r=1:nr\r\n  p=mc(1+(r-1)*pnr:r*pnr,1+(c-1)*pnc:c*pnc);\r\n  ptr=ptr+1;\r\n  pc{ptr}=p;\r\n end\r\nend\r\n\r\nvperm=1:nr*nc;\r\nwhile nnz((vperm-[1:nr*nc])==0) % want each piece moved\r\n vperm=randperm(nr*nc);\r\n if isequal(vperm,[4 3 2 1]) %don't want first standard perm to solve\r\n  vperm(1)=1;\r\n end\r\nend\r\n\r\nfor i=1:Total_pieces % scramble puzzle pieces\r\n jpc{i}=pc{vperm(i)};\r\nend\r\n\r\nv = jigsaw002(jpc,nr,nc,pnr,pnc);\r\n\r\njigsawf=cell2mat(reshape(jpc(v),2,2)); \r\n\r\nfigure;imagesc(jigsawf);colormap gray % Final image\r\n\r\nassert(isequal(jigsawf,mc))\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2024-06-26T13:41:17.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-18T22:29:32.000Z","updated_at":"2025-04-23T19:19:12.000Z","published_at":"2024-06-26T13:41:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to re-assemble camerman.tif and other grayscale images from four 128x128 pieces into a 256x256 image. The proposed method is best fitting edges to line m=1 and b=0 for 100, 90, and 80 percentiles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJigsaw001 was to show how data fitting can achieve results but is not a good general method. Tim used an elegant anonymous function of f=@(u)norm(diff(u))/std(mean(u)); along with a couple other methods incorporated into Jigsaw002.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eJigsaw is similar to the real world task of In-Scene-Calibration of a scanning sensor which was best solved by smoothing and usage of RobustFit. The template tries to simulate robustfit with edge smoothing to account for gradients\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe pointer layout of the image is [1 3; 2 4]. Return a four value vector that remaps the scrambled image into an original form. The displayed scramble is [2 4 1 3] making the solution [3 1 4 2].The four pieces will be provided as matrices in a cell array, along with size of puzzle in pieces and piece size.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAssertion is adjacent Chan and Row pairs will best match m=1 and b=0 for 100P,90P,80P of error.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eUsing 100/90/80p gives 88% of chans having \u0026gt;95% valid adjacent channel determination. Using 90/70/50p gives 93% of chans having \u0026gt;95% valid adjacent channel determination. Bordered images fail spectacularly.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eScoring function 10*abs(m-1)+abs(b/10) used at 100/90/80p with 4 out of 6 voting to determine best. Tie 3/3 leads to a best mean comparison.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSeparation of Vertical and Horizontal performances is consequential. This was also gleaned from Tim.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"217\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"289\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Adj Scatters\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image2.png\",\"relationshipId\":\"rId2\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image3.png\",\"relationshipId\":\"rId3\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null},{\"partUri\":\"/media/image3.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45330,"title":"Castling-02","description":"This is a follow up of problem \r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\u003e\r\n\r\n\r\nGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Castling\u003e","description_html":"\u003cp\u003eThis is a follow up of problem\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\u003c/a\u003e\u003c/p\u003e\u003cp\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Castling\"\u003ehttps://en.wikipedia.org/wiki/Castling\u003c/a\u003e\u003c/p\u003e","function_template":"function tf=castling_02(a)\r\n  y = x;\r\nend","test_suite":"%%\r\na={'Rd1','Rh1','Ke1'}\r\nassert(isequal(castling_02(a),1))\r\n%%\r\na={'Rd1','Rd4','Ke1','Bb6'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'d1','Rh8','Kg1'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'Ra1','Rh1','Ke1','Qd1'}\r\nassert(isequal(castling_02(a),1))\r\n%%\r\na={'Ra1','Rh1','Ke1','Qd1','Bg1'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'Ba2','Ra1','Rb1','Ke1','Bg1'}\r\nassert(isequal(castling_02(a),0))\r\n%%\r\na={'Na1','Rh1','Ke1','Qd1'}\r\nassert(isequal(castling_02(a),1))\r\n%%\r\na={'Kd1','Qe1','Rh1','Rd8','a4','Nf2'}\r\nassert(isequal(castling_02(a),0))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-15T23:34:53.000Z","updated_at":"2026-01-23T13:53:26.000Z","published_at":"2020-02-15T23:38:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a follow up of problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45329-castling-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the position of only one player's chess pieces(some of them) on the chessboard, figure out whether castling is valid or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Castling\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Castling\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45247,"title":"Tell your secret","description":"A secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\r\n\r\n* z(1) -- each person can continue spreading the secret\r\n* z(2) -- each person can tell the secret to only two persons\r\n\r\nn.b. outputs can only be multiples of 5. ","description_html":"\u003cp\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\u003c/p\u003e\u003cul\u003e\u003cli\u003ez(1) -- each person can continue spreading the secret\u003c/li\u003e\u003cli\u003ez(2) -- each person can tell the secret to only two persons\u003c/li\u003e\u003c/ul\u003e\u003cp\u003en.b. outputs can only be multiples of 5.\u003c/p\u003e","function_template":"function t = puzzle_tell_ur_secret(n)","test_suite":"%%\r\nn = 1;\r\nz = [0,0];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 2;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 8;\r\nz = [10,15];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 20;\r\nz = [15,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 31;\r\nz = [20,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 64;\r\nz = [20,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100;\r\nz = [25,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 768;\r\nz = [35,45];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3000;\r\nz = [40,55];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100000;\r\nz = [55,80];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2019-12-27T00:41:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-12-27T00:10:25.000Z","updated_at":"2026-03-13T11:59:30.000Z","published_at":"2019-12-27T00:41:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons? Output will be for two scenarios -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(1) -- each person can continue spreading the secret\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(2) -- each person can tell the secret to only two persons\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en.b. outputs can only be multiples of 5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42769,"title":"GJam March 2016 IOW: Cody's Jams ","description":"This Challenge is derived from \u003chttp://code.google.com/codejam/contest/8274486/dashboard GJam March 2016 Annual I/O for Women Cody's Jam\u003e. This is a mix of the small and large data sets.\r\n\r\nThe GJam story goes that a store is having a 25% off sale and ordered original and sale price labels. Unfortunately the labels came commingled in increasing numeric value. Find the sale prices.\r\n\r\n*Input:* m , Vector length N\u003c=100 with values \u003c=10^9.\r\n\r\n*Output:* v , Vector containing the Sale price tags\r\n\r\n*Examples:* [m] [v]\r\n\r\n  [15 20 60 75 80 100] creates v=[15 60 75]\r\n  [9 9 12 12 12 15 16 20] creates v=[9 9 12 15]\r\n \r\n\r\n*Google Code Jam 2016 Open Qualifier: April 8, 2016*\r\n\r\nComplete Code Jam Input/Output included in Test Suite.\r\nThe women's competition had 500 entrants. The qualifier winner was USA Stacy992 using Java. The top 60 show strength in USA, Russia, China, and South Korea under \u003chttp://code.google.com/codejam/contest/8274486/scoreboard?c=8274486# Contest Dashboard\u003e. ","description_html":"\u003cp\u003eThis Challenge is derived from \u003ca href = \"http://code.google.com/codejam/contest/8274486/dashboard\"\u003eGJam March 2016 Annual I/O for Women Cody's Jam\u003c/a\u003e. This is a mix of the small and large data sets.\u003c/p\u003e\u003cp\u003eThe GJam story goes that a store is having a 25% off sale and ordered original and sale price labels. Unfortunately the labels came commingled in increasing numeric value. Find the sale prices.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m , Vector length N\u0026lt;=100 with values \u0026lt;=10^9.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e v , Vector containing the Sale price tags\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e [m] [v]\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e[15 20 60 75 80 100] creates v=[15 60 75]\r\n[9 9 12 12 12 15 16 20] creates v=[9 9 12 15]\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eGoogle Code Jam 2016 Open Qualifier: April 8, 2016\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComplete Code Jam Input/Output included in Test Suite.\r\nThe women's competition had 500 entrants. The qualifier winner was USA Stacy992 using Java. The top 60 show strength in USA, Russia, China, and South Korea under \u003ca href = \"http://code.google.com/codejam/contest/8274486/scoreboard?c=8274486#\"\u003eContest Dashboard\u003c/a\u003e.\u003c/p\u003e","function_template":"function v=CodyJams(m)\r\n% m is increasing value vector of length 2L\r\n% v is length L vector of Sale Price Tags\r\n v=[];\r\nend","test_suite":"%%\r\nm=[15 20 60 75 80 100 ];\r\nv=CodyJams(m);\r\nvexp=[15 60 75 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[9 9 12 12 12 15 16 20 ];\r\nv=CodyJams(m);\r\nvexp=[9 9 12 15 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[480 640 1047 1396 1638 2184 2481 3308 ];\r\nv=CodyJams(m);\r\nvexp=[480 1047 1638 2481 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[495 660 1953 2559 2604 2685 3412 3580 ];\r\nv=CodyJams(m);\r\nvexp=[495 1953 2559 2685 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[384 512 1005 1340 2037 2716 2973 3964 ];\r\nv=CodyJams(m);\r\nvexp=[384 1005 2037 2973 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[270624780 336144033 360833040 448192044 736130808 745857189 981507744 994476252 ];\r\nv=CodyJams(m);\r\nvexp=[270624780 336144033 736130808 745857189 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[147 196 267 330 356 440 810 1080 ];\r\nv=CodyJams(m);\r\nvexp=[147 267 330 810 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[222 296 1533 1767 2044 2356 2541 3388 ];\r\nv=CodyJams(m);\r\nvexp=[222 1533 1767 2541 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[450 600 804 1072 1137 1497 1516 1996 ];\r\nv=CodyJams(m);\r\nvexp=[450 804 1137 1497 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[1788 2384 2697 2967 2991 3596 3956 3988 ];\r\nv=CodyJams(m);\r\nvexp=[1788 2697 2967 2991 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[4131 5508 7344 7803 8397 8667 9792 10404 11196 11556 13872 14553 14742 14928 15408 17631 18496 19404 19656 19904 20544 20925 21816 23508 23598 23949 25872 26208 27900 29088 31344 31464 31932 34496 34944 37200 38784 41792 41952 42576 49600 51712 55936 56768 7440174 8148762 8601471 8837667 9290376 9664353 9920232 9920232 10865016 10865016 11468628 11468628 11783556 11783556 12387168 12387168 12885804 12885804 13187610 13226976 13226976 13443489 14309541 14486688 14486688 15291504 15291504 15711408 15711408 16516224 16516224 17181072 17181072 17583480 17583480 17635968 17635968 17924652 17924652 19079388 19079388 19315584 19315584 20388672 20388672 20948544 20948544 22021632 22021632 22908096 22908096 23444640 23444640 23514624 23514624 23899536 23899536 25439184 25439184 25754112 25754112 27184896 27184896 27931392 29362176 29362176 30544128 30544128 31259520 31259520 31352832 31352832 31866048 31866048 33918912 33918912 34338816 34338816 36246528 36246528 39149568 39149568 40725504 40725504 41679360 41679360 41803776 41803776 42488064 42488064 45225216 45225216 45785088 45785088 48328704 48328704 52199424 52199424 54300672 54300672 55572480 55572480 55738368 55738368 56650752 56650752 60300288 60300288 61046784 61046784 64438272 64438272 69599232 69599232 72400896 72400896 74096640 74096640 74317824 74317824 75534336 75534336 80400384 80400384 81395712 81395712 85917696 85917696 92798976 92798976 96534528 96534528 98795520 98795520 99090432 100712448 100712448 107200512 107200512 108527616 108527616 114556928 123731968 128712704 131727360 131727360 134283264 134283264 142934016 142934016 144703488 144703488 175636480 179044352 190578688 192937984 ];\r\nv=CodyJams(m);\r\nvexp=[4131 7344 7803 8397 8667 13872 14553 14742 14928 15408 17631 20925 21816 23598 23949 25872 26208 31344 37200 38784 41952 42576 7440174 8148762 8601471 8837667 9290376 9664353 9920232 10865016 11468628 11783556 12387168 12885804 13187610 13226976 13443489 14309541 14486688 15291504 15711408 16516224 17181072 17583480 17635968 17924652 19079388 19315584 20388672 20948544 22021632 22908096 23444640 23514624 23899536 25439184 25754112 27184896 29362176 30544128 31259520 31352832 31866048 33918912 34338816 36246528 39149568 40725504 41679360 41803776 42488064 45225216 45785088 48328704 52199424 54300672 55572480 55738368 56650752 60300288 61046784 64438272 69599232 72400896 74096640 74317824 75534336 80400384 81395712 85917696 92798976 96534528 98795520 100712448 107200512 108527616 131727360 134283264 142934016 144703488 ];\r\nassert(isequal(vexp,v))\r\n%%\r\nm=[47652765 63537020 67915215 69349602 72816921 73024614 82232592 90005040 90280239 90553620 92466136 94414812 97089228 97366152 103779192 109643456 111106431 111164235 120006720 120373652 125886416 135665742 136205178 138372256 138890175 141176913 142430418 148141908 148218980 151625514 165274734 180887656 181606904 185186900 186382386 188235884 189907224 191652879 197100096 200131203 202167352 220366312 228636354 230018664 233064210 248509848 252322995 253203684 255537172 262800128 266522562 266752464 266841604 276629997 284197221 289714236 292147407 298051953 304848472 306691552 310752280 315722328 323249112 326482563 331581687 336430660 337604912 341668035 343831008 350075772 355363416 355669952 368839996 377457843 378929628 380744748 386285648 389529876 394930203 397402604 398376315 398670747 400335744 403277814 417169314 420741072 420963104 429730527 430998816 431599908 435310084 437438880 442108916 446485650 455557380 456522042 456986763 458441344 458579964 462594018 466767696 473333373 473409816 498541914 500694354 502642542 502680435 503277124 507659664 526573604 531168420 531512730 531560996 533780992 537703752 554512398 556085664 556225752 559312521 560988096 572974036 575466544 576554121 582854961 583251840 595314200 603269448 608696056 609315684 609830100 611439952 616792024 622311657 630908808 631111164 631213088 640087608 662978055 664722552 666189831 667592472 668044893 670190056 670240580 670949520 676510731 678398706 680890563 683321925 683396070 686473173 698833314 701501745 704706354 706326441 708683640 712094160 717827469 719130264 721789842 730229028 732531111 739232499 739349864 740488395 741447552 743207979 745750028 747639918 768738828 777139948 804359264 813106800 829748876 841211744 853450144 883970740 888253108 890726524 894599360 902014308 904531608 907854084 911095900 911194760 915297564 931777752 935335660 939608472 941768588 949458880 957103292 958840352 962386456 973638704 976708148 985643332 987317860 990943972 996853224 ];\r\nv=CodyJams(m);\r\nvexp=[47652765 67915215 69349602 72816921 73024614 82232592 90005040 90280239 94414812 103779192 111106431 111164235 135665742 136205178 138890175 141176913 142430418 151625514 165274734 186382386 191652879 197100096 200131203 228636354 230018664 233064210 252322995 253203684 266522562 266752464 276629997 284197221 289714236 292147407 298051953 315722328 323249112 326482563 331581687 341668035 343831008 350075772 377457843 380744748 394930203 398376315 398670747 400335744 403277814 417169314 420741072 429730527 431599908 437438880 446485650 456522042 456986763 458579964 462594018 473333373 473409816 498541914 500694354 502642542 502680435 531512730 554512398 556085664 559312521 576554121 582854961 603269448 609830100 622311657 630908808 640087608 662978055 666189831 668044893 670949520 676510731 678398706 680890563 683321925 683396070 686473173 698833314 701501745 704706354 706326441 712094160 717827469 719130264 721789842 730229028 732531111 739232499 740488395 743207979 747639918 ];\r\nassert(isequal(vexp,v))\r\n%%\r\n% function GJam_IOW_2016a\r\n% % \r\n% fn='A-large-practice.in';\r\n% %fn='A-small-practice.in';\r\n% [data] = read_file(fn); % create cell array\r\n% \r\n% fidG = fopen('A-large-output.out', 'w');\r\n%  \r\n% tic\r\n% for i=1:size(data,2) % Cell array has N rows of cases\r\n%  v = Rd1A(data{i});\r\n%  m=data{i};\r\n%  \r\n%  fprintf(fidG,'Case #%i:',i);\r\n%  fprintf(fidG,' %i',v);fprintf(fidG,'\\n');\r\n%  fprintf('Case #%i:',i);\r\n%  fprintf(' %i',v);fprintf('\\n');\r\n%  \r\n% end\r\n% toc\r\n% \r\n% fclose(fidG);\r\n% end\r\n% \r\n% function v=Rd1A(m)\r\n%  L=length(m);\r\n%  v=zeros(1,L/2);\r\n%  for i=1:L/2\r\n%   vptr=find(m\u003e0,1,'first');\r\n%   v(i)=m(vptr);\r\n%   m(find(m==round(m(vptr)*4/3),1,'first'))=0;\r\n%   m(vptr)=0;\r\n%  end\r\n% end\r\n% \r\n% \r\n% function [d] = read_file(fn)\r\n% d={};\r\n% fid=fopen(fn);\r\n% fgetl(fid); % Total Count ignore\r\n% ptr=0;\r\n% while ~feof(fid)\r\n%  ptr=ptr+1;\r\n%  fgetl(fid); % Data set countIgnore\r\n%  v=str2num(fgetl(fid)); \r\n%  \r\n%  d{ptr}=v;\r\n%  \r\n% end % feof\r\n%  fclose(fid);\r\n% \r\n% end % read_file\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2016-03-13T05:45:39.000Z","updated_at":"2016-03-15T16:31:00.000Z","published_at":"2016-03-13T06:32:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is derived from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://code.google.com/codejam/contest/8274486/dashboard\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGJam March 2016 Annual I/O for Women Cody's Jam\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. This is a mix of the small and large data sets.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe GJam story goes that a store is having a 25% off sale and ordered original and sale price labels. Unfortunately the labels came commingled in increasing numeric value. Find the sale prices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m , Vector length N\u0026lt;=100 with values \u0026lt;=10^9.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e v , Vector containing the Sale price tags\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [m] [v]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[[15 20 60 75 80 100] creates v=[15 60 75]\\n[9 9 12 12 12 15 16 20] creates v=[9 9 12 15]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eGoogle Code Jam 2016 Open Qualifier: April 8, 2016\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eComplete Code Jam Input/Output included in Test Suite. The women's competition had 500 entrants. The qualifier winner was USA Stacy992 using Java. The top 60 show strength in USA, Russia, China, and South Korea under\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://code.google.com/codejam/contest/8274486/scoreboard?c=8274486#\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eContest Dashboard\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1668,"title":"Josephus Survivor","description":"The \u003chttp://en.wikipedia.org/wiki/Josephus_problem Josephus Challenge\u003e is to find the position that is the last remaining when every Kth item is removed from a list of N items. The removal wraps from the end to the start.\r\n\r\n*Input:* N, K  where N is the number of players and K is the removal period.\r\n\r\n*Output:* S  the last position remaining\r\n\r\n*Example:* N=4 K=2 produces the sequence\r\n\r\n1 2 3 4; 1 3 4; 1 3; 1\r\n\r\n*Comment:*\r\n\r\nThis is a replication of \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1092-decimation Decimation\u003e by James but has a different Historical story reference.","description_html":"\u003cp\u003eThe \u003ca href = \"http://en.wikipedia.org/wiki/Josephus_problem\"\u003eJosephus Challenge\u003c/a\u003e is to find the position that is the last remaining when every Kth item is removed from a list of N items. The removal wraps from the end to the start.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e N, K  where N is the number of players and K is the removal period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e S  the last position remaining\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e N=4 K=2 produces the sequence\u003c/p\u003e\u003cp\u003e1 2 3 4; 1 3 4; 1 3; 1\u003c/p\u003e\u003cp\u003e\u003cb\u003eComment:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis is a replication of \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\"\u003eDecimation\u003c/a\u003e by James but has a different Historical story reference.\u003c/p\u003e","function_template":"function survivor=solve_josephus(n,k)\r\n survivor=randi(n); \r\nend","test_suite":"%%\r\n% Fixed 6/21/13\r\nn=40;\r\nk=7;\r\ns_expect=24;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=4;\r\nk=2;\r\ns_expect=1;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=40;\r\nk=2;\r\ns_expect=17;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=37;\r\nk=6;\r\ns_expect=10;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=29;\r\nk=28;\r\ns_expect=3;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))\r\n%%\r\nn=101;\r\nk=1;\r\ns_expect=101;\r\nsurvivor=solve_josephus(n,k);\r\nassert(isequal(survivor,s_expect))","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":42,"test_suite_updated_at":"2013-06-21T13:42:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-06-21T04:41:00.000Z","updated_at":"2025-12-02T16:42:13.000Z","published_at":"2013-06-21T05:01:14.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Josephus_problem\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eJosephus Challenge\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is to find the position that is the last remaining when every Kth item is removed from a list of N items. The removal wraps from the end to the start.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e N, K where N is the number of players and K is the removal period.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e S the last position remaining\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e N=4 K=2 produces the sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 2 3 4; 1 3 4; 1 3; 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eComment:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a replication of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1092-decimation\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eDecimation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e by James but has a different Historical story reference.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":59045,"title":"8 : Find the Solving vector","description":"The Puzzle 8 is a variant of 15 ( Fifteen ). Fifteen is a slider puzzle, also a matlab function, with the goal being to create the matrix [1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 0] where 0 is a hole.\r\nGiven a matrix m [3,3] and a matrix vset [n,k] of n possible solutions of up to length k determine which solution is valid. Return a vector [1,k] that produces [1 2 3;4 5 6;7 8 0]\r\nMovement is of the Zero-0. 3-Up, 0-Down, 1-Left, 2-Right, 4 is Not used/SKIP\r\nSome potential solutions may cause faults so try/catch may be required\r\nThe deepest solutions of 8 is 31 single moves. Solving this will be the next 8 challenge.\r\n[867;254;301] and [647;850;321] have 29 solutions each of length 31\r\nThe Puzzle 8 cases are readily created using a 15 board.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 243px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 121.5px; transform-origin: 407px 121.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 101.5px 8px; transform-origin: 101.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Puzzle 8 is a variant of 15 ( \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/15_Puzzle\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eFifteen\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 252px 8px; transform-origin: 252px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e ). Fifteen is a slider puzzle, also a matlab function, with the goal being to create the matrix [1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 0] where 0 is a hole.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a matrix m [3,3] and a matrix vset [n,k] of n possible solutions of up to length k determine which solution is valid. Return a vector [1,k] that produces [1 2 3;4 5 6;7 8 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 241px 8px; transform-origin: 241px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMovement is of the Zero-0. 3-Up, 0-Down, 1-Left, 2-Right, 4 is Not used/SKIP\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 224px 8px; transform-origin: 224px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSome potential solutions may cause faults so try/catch may be required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 272.5px 8px; transform-origin: 272.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe deepest solutions of 8 is 31 single moves. Solving this will be the next 8 challenge.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 218.5px 8px; transform-origin: 218.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e[867;254;301] and [647;850;321] have 29 solutions each of length 31\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 180px 8px; transform-origin: 180px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Puzzle 8 cases are readily created using a 15 board.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = solve8(m,vset)\r\n% m is 3x3 matrix with 0:8 values\r\n% vset is multiple possible solutions to restore m to [1 2 3;4 5 6;7 8 0]\r\n% Movement is of the 0. 3-U, 0-D, 1-L, 2-R, 4 is Not used/SKIP\r\n% some possible solutions may move the 0 off the board so try/catch may be needed\r\n v=vset(1,:);\r\n for i=1:size(vset,1)\r\n  mf=Eight_SolveA(m,vset(i,:));\r\n  \r\n  %check for valid/return_vector\r\n  \r\n end % i\r\n \r\nend % solve8\r\n\r\n\r\nfunction m=Eight_SolveA(m,svec)\r\n%m [3,3]\r\n%svec [1,n] 3U 0D 1L 2R  Movement of the Zero/Hole, 4 is skip\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n  try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    return\r\n  end\r\n  m(zr,zc)=0;\r\n  catch\r\n   return;\r\n  end\r\n end % i  svec(i)\r\nend %Eight_SolveA\r\n","test_suite":"%%\r\nvalid=1;\r\nm=[1 2 3;4 5 6;7 0 8]; %2\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[0 4;2 4;1 1];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n \r\n%%\r\nvalid=1;\r\nm=[3 1 2;4 5 6;7 0 8]; %133201022313200\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[2 1 1 3 3 2 0 1 0 2 2 3 1 3 2;\r\n      1 3 3 2 0 3 0 1 0 2 2 3 1 3 2;\r\n      1 3 3 2 0 1 0 2 2 1 2 3 1 3 2;\r\n      1 3 3 2 0 1 0 2 2 3 1 3 2 0 0];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n \r\n%%\r\nvalid=1;\r\nm=[0 2 3;1 5 6;4 7 8]; %0022\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[2 0 0 1 2 4;\r\n      0 0 2 3 4 4;\r\n      0 0 2 2 4 4;\r\n      0 3 2 1 4 4];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n%%\r\nvalid=1;\r\nm=[2 3 0;1 5 6;4 7 8]; %110022\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[1 1 2 0 0 1 2 4;\r\n      1 1 0 0 2 3 3 3;\r\n      1 1 0 0 2 2 4 4;\r\n      1 1 0 3 2 1 2 1];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n \r\n%%\r\nvalid=1;\r\nm=[2 3 6;1 5 8;4 7 0]; %33110022\r\nfprintf('Challenge:\\n');\r\nfprintf('%i %i %i\\n',m');fprintf('\\n')\r\nvset=[3 3 1 1 2 0 0 1 2;\r\n      3 3 1 1 0 0 2 3 3;\r\n      3 3 1 1 0 3 2 1 2;\r\n      3 3 1 1 0 0 2 2 4;\r\n      3 3 1 1 0 0 2 3 3];\r\n\r\nsvec = solve8(m,vset);\r\n\r\nfprintf('Solution: ')\r\nfprintf('%i',svec);fprintf('\\n');\r\n\r\n [zr,zc]=find(m==0);\r\n for i=1:length(svec) \r\n try\r\n  switch svec(i)  % 1/3 time of if/elseif\r\n   case 3  %U\r\n    m(zr,zc)=m(zr-1,zc);\r\n    zr=zr-1;\r\n   case 0 %D\r\n    m(zr,zc)=m(zr+1,zc);\r\n    zr=zr+1;\r\n   case 1 %L\r\n    m(zr,zc)=m(zr,zc-1);\r\n    zc=zc-1;\r\n   case 2 %R\r\n    m(zr,zc)=m(zr,zc+1);\r\n    zc=zc+1;\r\n   otherwise %4\r\n    break;\r\n  end\r\n  m(zr,zc)=0;\r\n catch\r\n  valid=0;\r\n  break;\r\n end\r\n end % i  svec(i)\r\n \r\n if ~isequal(m,[1 2 3;4 5 6;7 8 0])\r\n  valid=0; %\r\n  fprintf('Invalid solution\\n')\r\n end\r\n fprintf('%i %i %i\\n',m');fprintf('\\n')\r\n \r\n assert(valid)\r\n\r\n ","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-10-02T21:40:49.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-10-02T19:13:59.000Z","updated_at":"2023-10-02T21:40:50.000Z","published_at":"2023-10-02T21:40:50.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Puzzle 8 is a variant of 15 ( \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/15_Puzzle\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eFifteen\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e ). Fifteen is a slider puzzle, also a matlab function, with the goal being to create the matrix [1 2 3 4;5 6 7 8;9 10 11 12;13 14 15 0] where 0 is a hole.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix m [3,3] and a matrix vset [n,k] of n possible solutions of up to length k determine which solution is valid. Return a vector [1,k] that produces [1 2 3;4 5 6;7 8 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMovement is of the Zero-0. 3-Up, 0-Down, 1-Left, 2-Right, 4 is Not used/SKIP\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSome potential solutions may cause faults so try/catch may be required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe deepest solutions of 8 is 31 single moves. Solving this will be the next 8 challenge.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[867;254;301] and [647;850;321] have 29 solutions each of length 31\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Puzzle 8 cases are readily created using a 15 board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44756,"title":"Lights Out 5 - 5x5, 10 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require ten moves to solve. For example, if\r\n\r\n board = [0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 0 1]\r\n\r\nan answer is:\r\n\r\n moves = [1 2 3 4 5 16 17 18 19 20]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves 5x5, 8 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44757-lights-out-6-5x5-13-moves 5x5, 13 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require ten moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 0 1]\u003c/pre\u003e\u003cp\u003ean answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 2 3 4 5 16 17 18 19 20]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\"\u003e5x5, 8 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44757-lights-out-6-5x5-13-moves\"\u003e5x5, 13 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_5(board) % 5x5 board, 10 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 0 1];\r\nmoves = lights_out_5(board); % [1 2 3 4 5 16 17 18 19 20]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 0 0 1  \r\n          0 0 1 0 1];\r\nmoves = lights_out_5(board); % [1 2 3 11 13 14 16 17 21 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 0 0  \r\n          0 1 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_5(board); % [1 2 3 4 6 7 8 11 12 16]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          0 0 1 1 0  \r\n          1 1 0 1 0  \r\n          1 1 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_5(board); % [3 6:7 11 13:15 19 22:23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          0 1 1 0 0  \r\n          0 0 1 0 0  \r\n          0 1 0 1 0  \r\n          1 0 1 1 0];\r\nmoves = lights_out_5(board); % [2 3 9 10 14 16 17 20 23 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          0 1 0 0 0  \r\n          0 0 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 1 0 1];\r\nmoves = lights_out_5(board); % [2 4 7 9 11 12 17 19 20 21]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 0 0 1 1  \r\n          1 0 1 0 0  \r\n          1 0 1 0 1  \r\n          1 0 0 1 0  \r\n          1 1 0 1 1];\r\nmoves = lights_out_5(board); % [1 4 6 12 14 15 18 21 23 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          0 0 0 1 1  \r\n          1 1 0 0 0  \r\n          1 0 0 1 0  \r\n          1 1 1 1 0];\r\nmoves = lights_out_5(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 0 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 1  \r\n          1 1 0 0 1];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 1 1 1 0  \r\n          0 1 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 1 0  \r\n          0 1 1 1 1];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          0 0 0 1 1  \r\n          1 0 1 0 0  \r\n          1 0 1 1 0  \r\n          0 1 1 0 0];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [1 0 0 1 1  \r\n          0 0 1 1 0  \r\n          0 1 0 0 0  \r\n          0 1 1 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 1 1 0 1  \r\n          0 0 0 1 1  \r\n          0 1 1 0 0  \r\n          1 1 1 1 0  \r\n          0 0 1 1 0];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 1 1];\r\nmoves = lights_out_5(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==10)","published":true,"deleted":false,"likes_count":3,"comments_count":8,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":18,"test_suite_updated_at":"2018-11-13T13:07:28.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T18:53:12.000Z","updated_at":"2025-11-29T13:41:10.000Z","published_at":"2018-11-12T15:53:32.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require ten moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 1 0 1  \\n          1 1 1 1 1  \\n          1 1 1 1 1  \\n          1 1 1 1 1  \\n          0 1 1 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ean answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 2 3 4 5 16 17 18 19 20]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 8 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44757-lights-out-6-5x5-13-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 13 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44759,"title":"Lights Out 7 - 5x5, x moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve, ranging from 1 to 25 buttons (indices). The one function you write has to solve all of them. An answer is provided for some of the test cases, though there are often multiple possible answers per test case—any correct answer will work.\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44757 5x5, 13 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44760-lights-out-8-5x5-light-only-solution-i 5x5, light-only solution? I\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve, ranging from 1 to 25 buttons (indices). The one function you write has to solve all of them. An answer is provided for some of the test cases, though there are often multiple possible answers per test case—any correct answer will work.\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44757\"\u003e5x5, 13 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44760-lights-out-8-5x5-light-only-solution-i\"\u003e5x5, light-only solution? I\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_7(board) % 5x5 board, any number of moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 0 0 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_7(board); % [13]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 1 0 0 0  \r\n          0 0 1 0 0  \r\n          0 0 0 1 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_7(board); % [7 13 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_7(board); % [7:9 12:14 17:19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_7(board); % [7 9 17 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_7(board); % [1 5 7 9 13 17 19 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0];\r\nmoves = lights_out_7(board); % [1:5 11:15 21:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_7(board); % [7 8 9 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 0 1  \r\n          0 0 1 1 0  \r\n          0 0 1 0 0  \r\n          0 0 1 1 1];\r\nmoves = lights_out_7(board); % [18:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_7(board); % [1:6 10:11 15:16 20:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1];\r\nmoves = lights_out_7(board); % [1:2 4:7 9:10 16:17 19:22 24:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 0 0  \r\n          1 0 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_7(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          0 1 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 1 0  \r\n          0 1 1 0 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          1 1 1 1 1  \r\n          1 1 0 1 0  \r\n          0 0 0 1 0  \r\n          0 0 0 0 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 1  \r\n          0 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 0 1 0];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 1 1 0 0  \r\n          0 1 0 0 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 1 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          0 0 1 1 1];\r\nmoves = lights_out_7(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-30T12:52:15.000Z","updated_at":"2025-11-29T15:18:21.000Z","published_at":"2018-12-03T13:24:38.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve, ranging from 1 to 25 buttons (indices). The one function you write has to solve all of them. An answer is provided for some of the test cases, though there are often multiple possible answers per test case—any correct answer will work.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44757\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 13 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44760-lights-out-8-5x5-light-only-solution-i\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46040,"title":"Solve a Weird Calculator puzzle","description":"The September 2012 issue of GAMES Magazine had a Weird Calculator puzzle by Erich Friedman. In this puzzle, the calculator has only four buttons: +13, -7, *2, and /3. The object is to add 1 to get from one number to another with the specified number of button presses. For example, if you were asked to get from 8 to 9 in two button presses, you could press *2 and -7. \r\n\r\nWrite a function to solve the Weird Calculator puzzle. The input will be the starting number a, the desired result b, and the number of button n presses. The output should be a string with the buttons separated by a comma and a space. For the example above, the output should be\r\n\r\n  *2, -7\r\n\r\nEnjoy!","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 217.433px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 108.717px; transform-origin: 407px 108.717px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 365.5px 7.91667px; transform-origin: 365.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe September 2012 issue of GAMES Magazine had a Weird Calculator puzzle by Erich Friedman. In this puzzle, the calculator has only four buttons: +13, -7,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: normal; font-weight: 400; color: rgb(0, 0, 0);\"\u003e×\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 234.133px 7.91667px; transform-origin: 234.133px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2, and /3. The object is to add 1 to get from one number to another with the specified number of button presses. For example, if you were asked to get from 8 to 9 in two button presses, you could press \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: normal; font-weight: 400; color: rgb(0, 0, 0);\"\u003e×\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27.6167px 7.91667px; transform-origin: 27.6167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2 and -7.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 280.583px 7.91667px; transform-origin: 280.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve the Weird Calculator puzzle. The input will be the starting number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.85px 7.91667px; transform-origin: 3.85px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003ea\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 59.5167px 7.91667px; transform-origin: 59.5167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the desired result \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.85px 7.91667px; transform-origin: 3.85px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eb\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.1667px 7.91667px; transform-origin: 29.1667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and the number n of button presses. The output should be a string with the buttons separated by a comma and a space. For the example above, the output should be\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 23.1px 7.91667px; transform-origin: 23.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e*2, -7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.45px 7.91667px; transform-origin: 19.45px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEnjoy!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function buttons = weirdCalc(a,b,n)\r\n  buttons = f(a,b,n);\r\nend","test_suite":"%%\r\na = 8;\r\nb = 9;\r\nn = 2;\r\nbtn_correct = '*2, -7';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 29;\r\nb = 30;\r\nn = 3;\r\nbtn_correct = '-7, -7, *2';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 54;\r\nb = 55;\r\nn = 4;\r\nbtn_correct = '/3, +13, *2, -7';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 4;\r\nb = 5;\r\nn = 5;\r\nbtn_correct = '*2, -7, *2, +13, /3';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 39;\r\nb = 40;\r\nn = 6;\r\nbtn_correct = '-7, *2, *2, +13, /3, -7';\r\nassert(isequal(weirdCalc(a,b,n),btn_correct))\r\n\r\n%%\r\na = 152;\r\nb = 153;\r\nn = 7;\r\nbtn = split(weirdCalc(a,b,n),', ');\r\nx = a;\r\nfor i = 1:n\r\n    x= str2num([num2str(x) btn{i}]); \r\nend\r\nassert(isequal(x,b))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2020-07-11T18:48:50.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-07-11T06:13:43.000Z","updated_at":"2020-07-30T13:30:20.000Z","published_at":"2020-07-11T14:06:38.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe September 2012 issue of GAMES Magazine had a Weird Calculator puzzle by Erich Friedman. In this puzzle, the calculator has only four buttons: +13, -7,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\times\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e2, and /3. The object is to add 1 to get from one number to another with the specified number of button presses. For example, if you were asked to get from 8 to 9 in two button presses, you could press \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\times\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e2 and -7.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve the Weird Calculator puzzle. The input will be the starting number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ea\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, the desired result \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and the number n of button presses. The output should be a string with the buttons separated by a comma and a space. For the example above, the output should be\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[*2, -7]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEnjoy!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":3033,"title":"Tic-Tac-Logic - Solution Checker","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/tic-tac-logic/rules Tic-Tac-Logic\u003e is a logic puzzle wherein a rectangular grid containing a certain set of pre-filled squares must be filled in completely to have no more than two consecutive X's or O's in any row or column. Also, the number of X's and O's must be the same in every row and column (equal to half the size of the row or column). Finally, all rows must be unique (compared to other rows) and all columns must be unique (compared to other columns).\r\n\r\nAn example puzzle from Conceptis is included here, wherein the first board represents the starting condition and the second board represents a properly solved board:\r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/3993.gif\u003e\u003e\r\n\r\n\u003c\u003chttp://www.conceptispuzzles.com/picture/11/3994.gif\u003e\u003e\r\n\r\nYou will be provided with a variety of completed boards. X's are represented by ones and O's by zeros. Write a function to test whether the board has been properly solved. Remember to check against all three criteria.","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/tic-tac-logic/rules\"\u003eTic-Tac-Logic\u003c/a\u003e is a logic puzzle wherein a rectangular grid containing a certain set of pre-filled squares must be filled in completely to have no more than two consecutive X's or O's in any row or column. Also, the number of X's and O's must be the same in every row and column (equal to half the size of the row or column). Finally, all rows must be unique (compared to other rows) and all columns must be unique (compared to other columns).\u003c/p\u003e\u003cp\u003eAn example puzzle from Conceptis is included here, wherein the first board represents the starting condition and the second board represents a properly solved board:\u003c/p\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/3993.gif\"\u003e\u003cimg src = \"http://www.conceptispuzzles.com/picture/11/3994.gif\"\u003e\u003cp\u003eYou will be provided with a variety of completed boards. X's are represented by ones and O's by zeros. Write a function to test whether the board has been properly solved. Remember to check against all three criteria.\u003c/p\u003e","function_template":"function [tf] = tic_tac_logic_check(board)\r\n\r\ntf = 1;\r\n[m,n] = size(board);\r\n\r\n%check up to only two consecutive\r\n\r\n%check same number in each row/column\r\n\r\n%check unique rows and columns\r\n\r\nend","test_suite":"%%\r\nboard = [0,1,1,0,0,1; 1,0,1,0,0,1; 0,1,0,1,1,0; 0,1,0,1,0,1; 1,0,1,0,1,0; 1,0,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,1,0,1,0,1; 0,1,0,1,0,1; 1,0,1,0,1,0; 1,0,1,0,1,0; 0,1,0,1,0,1; 1,0,1,0,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,1,0,0,1,0,1,1; 0,0,1,0,1,1,0,1; 1,0,1,1,0,1,0,0; 0,1,0,1,0,0,1,1; 1,1,0,0,1,1,0,0; 1,0,1,0,1,1,0,0; 0,0,1,1,0,0,1,1; 1,1,0,1,0,0,1,0];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,1,0,1,0; 1,1,0,0,1,1,0,1,0,0; 1,1,0,0,1,0,1,0,1,0; 0,0,1,1,0,1,0,1,0,1; 1,0,1,1,0,0,1,1,0,0; 1,1,0,0,1,1,0,0,1,0; 0,1,0,1,0,0,1,0,1,1; 0,0,1,0,1,0,1,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0; 0,1,1,0,1,0; 1,0,0,1,0,1; 1,0,0,1,1,0; 0,1,1,0,0,1; 0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,1,1,0,0,1; 1,0,1,0,0,1; 0,1,0,1,1,0; 0,1,1,0,0,1; 1,0,1,0,1,0; 1,0,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,1,0,0,1,1,0,1; 0,0,1,0,1,1,0,1; 1,0,1,1,0,1,0,0; 0,1,0,1,0,0,1,1; 1,1,0,0,1,1,0,0; 1,0,1,0,1,1,0,0; 0,0,1,1,0,0,1,1; 1,1,0,1,0,0,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,1,0; 1,1,0,1,0,0,1,0; 0,0,1,0,1,1,0,1; 0,1,0,1,0,1,0,1; 1,0,1,0,1,0,1,0; 0,0,1,0,1,0,1,1; 1,1,0,1,0,1,0,0; 1,1,0,0,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,0; 0,0,1,1,0,1,0,1; 1,1,0,1,0,0,1,0; 0,0,1,0,1,1,0,1; 0,1,0,1,0,1,1,0; 1,0,1,0,1,0,0,1; 0,1,0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,1,0; 1,1,0,1,0,0,1,0; 0,0,1,1,0,1,0,1; 0,1,0,1,0,1,0,1; 1,0,1,0,1,0,1,0; 0,0,1,0,1,0,1,1; 1,1,0,1,0,1,0,0; 1,1,0,0,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,1,1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,1,0,0; 1,0,0,1,0,1,0,0,1,1; 0,1,1,0,1,0,1,0,0,1; 1,0,1,0,0,1,0,1,1,0; 1,0,0,1,1,0,1,0,0,1; 0,1,0,1,0,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,1; 1,0,0,1,1,0,0,1,0,1; 0,0,1,1,0,1,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,0,0,1,1,1; 1,1,1,0,0,0; 1,0,1,0,1,0; 0,1,0,1,0,1; 1,1,0,0,0,1; 0,0,1,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,1,0,0,1,0,1,0,0,1; 1,0,0,1,0,1,0,1,1,0; 0,1,1,0,1,0,0,1,0,1; 0,0,1,1,0,1,1,0,1,0; 1,0,0,1,1,0,1,0,0,1; 1,1,0,0,1,0,0,1,1,0; 0,1,1,0,0,1,0,1,1,0; 0,0,1,1,0,1,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [0,1,1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,1,0,0; 1,0,0,1,0,1,0,0,1,1; 0,1,1,0,1,0,1,0,0,1; 1,0,1,0,0,1,0,1,1,0; 1,0,1,0,1,0,1,0,0,1; 0,1,0,1,0,1,0,1,1,0; 0,1,1,0,0,1,1,0,0,1; 1,0,0,1,1,0,0,1,0,1; 0,0,1,1,0,1,0,1,1,0];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [0,0,1,1,0,1,0,0,1,1; 1,1,0,0,1,1,0,1,0,0; 1,1,0,0,1,0,1,0,1,0; 0,0,1,1,0,1,0,1,0,1; 1,0,1,1,0,0,1,1,0,0; 1,1,0,0,1,1,0,0,1,0; 0,1,0,1,0,0,1,0,1,1; 0,0,1,0,1,0,1,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [1,1,0,0,1,0,1,0,0,1; 1,0,0,1,0,1,0,1,1,0; 0,1,1,0,1,0,0,1,0,1; 0,0,1,1,0,1,1,0,0,1; 1,0,0,1,1,0,1,0,0,1; 1,1,0,0,1,0,0,1,1,0; 0,1,1,0,0,1,0,1,1,0; 0,0,1,1,0,1,1,0,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,1,0,0,1,1,0,0; 0,0,1,1,0,1,0,1; 1,0,1,1,0,0,1,0; 0,1,0,0,1,0,1,1; 1,0,1,0,1,1,0,0; 0,1,0,0,1,0,1,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,1,0,0,1,1,0,0; 0,0,1,1,0,1,0,1; 1,0,1,1,0,0,1,0; 0,1,0,0,1,0,1,1; 1,0,1,0,1,1,0,0; 0,1,0,1,0,0,1,1];\r\nassert(isequal(tic_tac_logic_check(board),1))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0; 0,1,1,0,1,0; 1,0,0,1,0,1; 1,0,0,1,1,0; 0,1,1,0,1,1; 0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n\r\n%%\r\nboard = [1,0,1,0,1,0,1,0; 1,1,0,0,1,0,1,0; 0,0,1,1,0,1,0,1; 1,1,0,1,0,0,1,0; 1,0,1,0,0,1,0,1; 0,1,0,1,0,1,1,0; 1,0,1,0,1,0,0,1; 0,1,0,1,0,1,0,1];\r\nassert(isequal(tic_tac_logic_check(board),0))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":6,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":30,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-02-22T04:12:50.000Z","updated_at":"2025-11-01T18:08:07.000Z","published_at":"2015-02-22T04:12:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.gif\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.gif\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/tic-tac-logic/rules\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eTic-Tac-Logic\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle wherein a rectangular grid containing a certain set of pre-filled squares must be filled in completely to have no more than two consecutive X's or O's in any row or column. Also, the number of X's and O's must be the same in every row and column (equal to half the size of the row or column). Finally, all rows must be unique (compared to other rows) and all columns must be unique (compared to other columns).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn example puzzle from Conceptis is included here, wherein the first board represents the starting condition and the second board represents a properly solved board:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou will be provided with a variety of completed boards. X's are represented by ones and O's by zeros. Write a function to test whether the board has been properly solved. Remember to check against all three criteria.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"},{\"partUri\":\"/media/image1.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"},{\"partUri\":\"/media/image2.gif\",\"contentType\":\"image/gif\",\"content\":\"data:image/gif;base64,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\"}]}"},{"id":44755,"title":"Lights Out 4 - 5x5, 8 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require eight moves to solve. For example, if\r\n\r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0]\r\n\r\nthe answer is:\r\n\r\n moves = [2 4 6 10 16 20 22 24]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves 5x5, 6 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44756-lights-out-5-5x5-10-moves 5x5, 10 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require eight moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [2 4 6 10 16 20 22 24]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\"\u003e5x5, 6 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44756-lights-out-5-5x5-10-moves\"\u003e5x5, 10 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_4(board) % 5x5 board, 8 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_4(board); % [2 4 6 10 16 20 22 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_4(board); % [7 8 9 12 14 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 1 0 1 1  \r\n          1 0 0 0 1  \r\n          1 1 0 1 0  \r\n          0 0 1 1 0];\r\nmoves = lights_out_4(board); % [1 2 5 10 16 21 24 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_4(board); % [1 5 7 9 17 19 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 0 1 0 1  \r\n          0 0 1 0 0  \r\n          0 1 1 0 0  \r\n          0 1 0 0 0  \r\n          0 1 1 1 0];\r\nmoves = lights_out_4(board); % [4 5 8 12 14 19 22 23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_4(board); % [1 2 3 4 5 7 9 13]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 0 1 0 0  \r\n          1 1 1 0 0  \r\n          1 0 1 1 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_4(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 0 1 1  \r\n          1 1 1 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 0  \r\n          1 0 1 1 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          1 1 0 0 0  \r\n          0 1 1 0 1  \r\n          1 1 1 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 0 0 0  \r\n          1 1 0 1 0  \r\n          1 0 1 1 1  \r\n          1 1 1 1 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1  \r\n          1 0 1 0 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 1  \r\n          1 1 0 1 0  \r\n          0 1 0 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 1 1 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 0 0  \r\n          1 1 1 1 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [0 0 1 0 1  \r\n          0 1 0 0 1  \r\n          0 1 0 1 1  \r\n          1 0 1 1 0  \r\n          0 1 0 0 1];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          1 0 0 0 1  \r\n          0 0 1 1 1  \r\n          0 1 0 1 1  \r\n          1 0 0 1 0];\r\nmoves = lights_out_4(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==8)\r\n","published":true,"deleted":false,"likes_count":4,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":18,"test_suite_updated_at":"2018-11-09T14:19:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T18:36:00.000Z","updated_at":"2025-11-29T14:28:48.000Z","published_at":"2018-11-09T14:19:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require eight moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 1 0  \\n          1 0 0 0 1  \\n          0 0 0 0 0  \\n          1 0 0 0 1  \\n          0 1 0 1 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [2 4 6 10 16 20 22 24]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44756-lights-out-5-5x5-10-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 10 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44760,"title":"Lights Out 8 - 5x5, light-only solution? I","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state is a potential answer—i.e., if toggling only the starting lights are sufficient to solve the board.\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44759 5x5, any number of moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44761 5x5, light-only solution? II\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state is a potential answer—i.e., if toggling only the starting lights are sufficient to solve the board.\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44759\"\u003e5x5, any number of moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44761\"\u003e5x5, light-only solution? II\u003c/a\u003e\u003c/p\u003e","function_template":"function tf = lights_out_8(board) % 5x5 board, lights-only solution\r\n tf = 0;\r\nend","test_suite":"%% all true cases first\r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nassert(lights_out_8(board)); % [2 4 6 10 16 20 22 24]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 0 0 0 1];\r\nassert(lights_out_8(board)); % [1 5 7 9 17 19 21 25]\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nassert(lights_out_8(board)); % [2 6 8 12 14 18 20 24]\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1];\r\nassert(lights_out_8(board)); % [1:2 4:7 9:10 16:17 19:22 24:25]\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          0 0 1 0 0];\r\nassert(lights_out_8(board)); % [3 7 9 11 13 15 17 19 23]\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1];\r\nassert(lights_out_8(board)); % [1 3 5 11 13 15 21 23 25]\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0];\r\nassert(lights_out_8(board)); % [2:4 6:7 9:11 13 15:17 19:20 22:24]\r\n\r\n\r\n%% false cases start here\r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nassert(~lights_out_8(board)); % [1 2 3 4 5 7 9 13]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 0 0  \r\n          0 1 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 0 0 0];\r\nassert(~lights_out_8(board)); % [1 2 3 4 6 7 8 11 12 16]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          1 0 0 0 1];\r\nassert(~lights_out_8(board)); % on your own\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0];\r\nassert(~lights_out_8(board));\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1];\r\nassert(~lights_out_8(board));\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 1  \r\n          0 1 1 0 0];\r\nassert(~lights_out_8(board));\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0];\r\nassert(~lights_out_8(board));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":16,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":52,"created_at":"2018-10-30T14:01:47.000Z","updated_at":"2025-11-29T15:01:02.000Z","published_at":"2019-01-09T15:04:36.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state is a potential answer—i.e., if toggling only the starting lights are sufficient to solve the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44759\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, any number of moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44761\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45237,"title":"Queen's move - 02","description":"In continuation with the problem-45236 ... \r\nIn the previous problem, it was assumed that there were no other chess pieces on the board... Now lets assume there is a number of king present on the board whose location is given in an array *p* . Now, check for the validity of Queen's moves.\r\n\r\n# x={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'}\r\n# p={'Kd4','Ke5','Kh7','Ke7'}\r\n\r\noutput=[1,1,0,1,0,1,1,0,0,0]\r\n\r\nkindly see this problem for understanding\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\u003e\r\n","description_html":"\u003cp\u003eIn continuation with the problem-45236 ... \r\nIn the previous problem, it was assumed that there were no other chess pieces on the board... Now lets assume there is a number of king present on the board whose location is given in an array \u003cb\u003ep\u003c/b\u003e . Now, check for the validity of Queen's moves.\u003c/p\u003e\u003col\u003e\u003cli\u003ex={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'}\u003c/li\u003e\u003cli\u003ep={'Kd4','Ke5','Kh7','Ke7'}\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eoutput=[1,1,0,1,0,1,1,0,0,0]\u003c/p\u003e\u003cp\u003ekindly see this problem for understanding \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\u003c/a\u003e\u003c/p\u003e","function_template":"function z = Queen_move_3(x,p)\r\n  y = x;\r\nend","test_suite":"%%\r\nx={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'};\r\np={'Kd4','Ke5','Kh7','Ke7'};\r\ny_correct = [1,1,0,1,0,1,1,0,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qd1','Qd5','Qf1','Qa8','Qf7','Qb2','Qc7'};\r\np={'Kd4','Ke5','Kh7','Ke7'};\r\ny_correct = [1,0,1,0,1,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qh7','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7'};\r\np={'Kd4','Ke5','Kh7','Ke7'};\r\ny_correct = [0,0,0,0,0,0,0,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qh7','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7'};\r\np={'Ka8','Kb2','Kd7','Kf3','Kg6'};\r\ny_correct = [1,1,1,0,0,0,0,0,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n\r\n%%\r\nx={'Qh7','Qh1','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qg2','Qg7','Qf1','Qf7','Qf2','Qa2'};\r\np={'Ka8','Kb2','Kd7','Kf3','Kg6'};\r\ny_correct = [1,1,0,0,0,0,0,0,1,0,1,0,1,0];\r\nassert(isequal(Queen_move_3(x,p),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-12-20T23:00:44.000Z","updated_at":"2026-01-23T12:47:14.000Z","published_at":"2019-12-20T23:01:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn continuation with the problem-45236 ... In the previous problem, it was assumed that there were no other chess pieces on the board... Now lets assume there is a number of king present on the board whose location is given in an array\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . Now, check for the validity of Queen's moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ex={'Qd1','Qh1','Qh8','Qd5','Qg5','Qc5','Qa7','Qf2','Qe7','Qh7'}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ep={'Kd4','Ke5','Kh7','Ke7'}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eoutput=[1,1,0,1,0,1,1,0,0,0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ekindly see this problem for understanding\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45236-queen-s-move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44753,"title":"Lights Out 3 - 5x5, 6 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require six moves to solve. For example, if\r\n\r\n board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1]\r\n\r\nthe answer is:\r\n\r\n moves = [1 5 11 15 21 25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves 5x5, 4 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves 5x5, 8 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require six moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 5 11 15 21 25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\"\u003e5x5, 4 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\"\u003e5x5, 8 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_3(board) % 5x5 board, 6 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1];\r\nmoves = lights_out_3(board); % [1 5 11 15 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 0 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_3(board); % [4 9 10 16 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_3(board); % [7 8 9 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 1  \r\n          0 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 0 1 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_3(board); % [4 8 11 13 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 0  \r\n          0 0 0 1 0  \r\n          0 1 0 1 0];\r\nmoves = lights_out_3(board); % [7 8 12 14 15 21]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 1 1 0  \r\n          0 1 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_3(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 1 0 1  \r\n          0 1 0 0 1  \r\n          1 0 0 1 0  \r\n          1 1 0 1 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 1 1  \r\n          1 0 0 0 0  \r\n          1 0 0 0 1  \r\n          1 0 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 1  \r\n          0 0 0 1 0  \r\n          0 0 0 0 0  \r\n          1 0 1 1 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 0 0 0 0  \r\n          1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          1 0 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 1 1 1 1  \r\n          1 0 1 0 0  \r\n          1 1 0 0 1  \r\n          0 1 1 0 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 1 0 0  \r\n          0 0 0 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 0];\r\nmoves = lights_out_3(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":10,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":20,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T14:59:29.000Z","updated_at":"2025-11-29T15:04:22.000Z","published_at":"2018-11-05T13:04:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require six moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 1 0 1  \\n          1 0 1 0 1  \\n          0 0 0 0 0  \\n          1 0 1 0 1  \\n          1 0 1 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 5 11 15 21 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 4 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44755-lights-out-4-5x5-8-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 8 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":51251,"title":"Locate a family on a long street ","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 186.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 93.25px; transform-origin: 407px 93.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 374.983px 7.91667px; transform-origin: 374.983px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTwo children, Matilda and Labrun, live on a street in which—unlike many streets in the U.S.—the houses on one side are numbered consecutively, starting at 1. They notice that the sum of the numbers on the houses to the left of theirs equals the sum of the numbers to the right. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.75px; text-align: left; transform-origin: 384px 31.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 82.7083px 7.91667px; transform-origin: 82.7083px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that takes \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"nmax\" style=\"width: 27px; height: 20px;\" width=\"27\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 281.6px 7.91667px; transform-origin: 281.6px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the maximum number of houses on the street, and returns the largest possible number of houses and the number of the house where the family of Matilda and Labrun lives. For example, if \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"nmax\" style=\"width: 27px; height: 20px;\" width=\"27\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.8667px 7.91667px; transform-origin: 46.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 10, then the function should find that the street has 8 houses and Matilda and Labrun live in house 6 because 1+2+3+4+5 = 7+8 = 15.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 157.158px 7.91667px; transform-origin: 157.158px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHenry Dudeney posed a version of this problem in \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52.9083px 7.91667px; transform-origin: 52.9083px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eStrand Magazine\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 79.3417px 7.91667px; transform-origin: 79.3417px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in the early part of the 20\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5.83333px 7.91667px; transform-origin: 5.83333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 69.4917px 7.91667px; transform-origin: 69.4917px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e century. Although the connections might not be immediately clear, you might try Cody Problems \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/groups/25/problems/1215\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e1215\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.91667px; transform-origin: 3.88333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/8057\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e8057\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 17.5px 7.91667px; transform-origin: 17.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45253\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e45253\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2833px 7.91667px; transform-origin: 25.2833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e as well.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [x,y] = houseNumber(nmax)\r\n  % x = number of the house of Matilda and Labrun\r\n  % y = number of houses on the street\r\n  x = f1(nmax)\r\n  y = f2(nmax)\r\nend","test_suite":"%%\r\nnmax = 10;\r\nx_correct = 6;\r\ny_correct = 8;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 100;\r\nx_correct = 35;\r\ny_correct = 49;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%% Dudeney's problem\r\nnmax = 500;\r\nx_correct = 204;\r\ny_correct = 288;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 2000;\r\nx_correct = 1189;\r\ny_correct = 1681;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 10000;\r\nx_correct = 6930;\r\ny_correct = 9800;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 100000;\r\nx_correct = 40391;\r\ny_correct = 57121;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 500000;\r\nx_correct = 235416;\r\ny_correct = 332928;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\nassert(isequal(sum(1:x-1),sum(x+1:y)))\r\n\r\n%%\r\nnmax = 2e6;\r\nx_correct = 1372105;\r\ny_correct = 1940449;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\n\r\n%%\r\nnmax = 2e7;\r\nx_correct = 7997214;\r\ny_correct = 11309768;\r\n[x,y] = houseNumber(nmax);\r\nassert(isequal(x,x_correct) \u0026\u0026 isequal(y,y_correct))\r\n\r\n%%\r\nnmax = 1834432;\r\np_correct = 78376578048;\r\nds_correct = 48;\r\ndp_correct = 1866240;\r\n[x,y] = houseNumber(nmax);\r\nd = [num2str(x)-'0' num2str(y)-'0'];\r\nassert(isequal(x*y,p_correct) \u0026\u0026 isequal(sum(d),ds_correct) \u0026\u0026 isequal(prod(d),dp_correct))","published":true,"deleted":false,"likes_count":8,"comments_count":1,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":"2021-05-16T01:51:40.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2021-03-29T00:43:22.000Z","updated_at":"2026-03-22T23:36:57.000Z","published_at":"2021-03-29T00:53:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTwo children, Matilda and Labrun, live on a street in which—unlike many streets in the U.S.—the houses on one side are numbered consecutively, starting at 1. They notice that the sum of the numbers on the houses to the left of theirs equals the sum of the numbers to the right. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that takes \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"nmax\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en_{max}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, the maximum number of houses on the street, and returns the largest possible number of houses and the number of the house where the family of Matilda and Labrun lives. For example, if \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"nmax\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en_{max}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e = 10, then the function should find that the street has 8 houses and Matilda and Labrun live in house 6 because 1+2+3+4+5 = 7+8 = 15.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHenry Dudeney posed a version of this problem in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eStrand Magazine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e in the early part of the 20\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eth\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e century. Although the connections might not be immediately clear, you might try Cody Problems \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/groups/25/problems/1215\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e1215\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/8057\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e8057\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45253\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e45253\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e as well.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2358,"title":"Word Search Solver","description":"There are n words (minimum of three letters in each word) supplied with a given word search board. The answer will contain n rows where each row contains the row and column indices where the word starts followed by an integer indicating the direction of the word. The direction integer runs from 1 to 8 and starts at 12 o'clock, running clockwise. So, a word spelled to the right (normal fashion) would be indexed as a 3 and facing downward to the left (SW) would be a 6.\r\nThe first board is included here for reference:\r\n board = [\r\n  'xcupa'\r\n  'dyrng'\r\n  'osbaq'\r\n  'exbid'\r\n  'wgamv'\r\n ];\r\n\r\n words = {'aim'; 'bid'; 'cup'; 'doe'};\r\n\r\n loc_ans = [\r\n  3 4 5\r\n  4 3 3\r\n  1 2 3\r\n  2 1 5\r\n ];","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 450.933px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 225.467px; transform-origin: 407px 225.467px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 365px 8px; transform-origin: 365px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are n words (minimum of three letters in each word) supplied with a given word search board. The answer will contain n rows where each row contains the row and column indices where the word starts followed by an integer indicating the direction of the word. The direction integer runs from 1 to 8 and starts at 12 o'clock, running clockwise. So, a word spelled to the right (normal fashion) would be indexed as a 3 and facing downward to the left (SW) would be a 6.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 143.5px 8px; transform-origin: 143.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe first board is included here for reference:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 326.933px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 163.467px; transform-origin: 404px 163.467px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 40px 8.5px; tab-size: 4; transform-origin: 40px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e board = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'xcupa'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'dyrng'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'osbaq'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'exbid'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 36px 8.5px; tab-size: 4; transform-origin: 36px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e  \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 28px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 28px 8.5px; \"\u003e'wgamv'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e ];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 152px 8.5px; tab-size: 4; transform-origin: 152px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 40px 8.5px; transform-origin: 40px 8.5px; \"\u003e words = {\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'aim'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'bid'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'cup'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e; \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 20px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 20px 8.5px; \"\u003e'doe'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 8px 8.5px; transform-origin: 8px 8.5px; \"\u003e};\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 48px 8.5px; tab-size: 4; transform-origin: 48px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e loc_ans = [\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  3 4 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  4 3 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  1 2 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 28px 8.5px; tab-size: 4; transform-origin: 28px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e  2 1 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 12px 8.5px; tab-size: 4; transform-origin: 12px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e ];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function loc = WordSearch(board,words)\r\n loc = [-1 -1];\r\nend","test_suite":"%%\r\nboard = [\r\n 'xcupa';\r\n 'dyrng';\r\n 'osbaq';\r\n 'exbid';\r\n 'wgamv';\r\n];\r\nwords = {'aim'; 'bid'; 'cup'; 'doe'};\r\nloc_ans = [\r\n 3 4 5;\r\n 4 3 3;\r\n 1 2 3;\r\n 2 1 5;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))\r\n\r\n%%\r\nboard = [\r\n 'okeanpbirf';\r\n 'qicwnafehu';\r\n 'wniazcgame';\r\n 'egaxjelbiv';\r\n 'bnomelvmcr';\r\n];\r\nwords = {'fair'; 'game'; 'hall'; 'ice'; 'jack'; 'king'; 'lemon'};\r\nloc_ans = [\r\n 2 7 4;\r\n 3 7 3;\r\n 2 9 6;\r\n 3 3 1;\r\n 4 5 8;\r\n 1 2 5;\r\n 5 6 7;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))\r\n\r\n%%\r\nboard = [\r\n 'werojea';\r\n 'cafefls';\r\n 'apufrbw';\r\n 'hrleaan';\r\n 'aoltgbb';\r\n 'aoaevdr';\r\n 'mdzoece';\r\n];\r\nwords = {'able'; 'bare'; 'cafe'; 'door'; 'edge'; 'full'};\r\nloc_ans = [\r\n 4 6 1;\r\n 5 7 8;\r\n 2 1 3;\r\n 7 2 1;\r\n 7 7 8;\r\n 2 3 5;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))\r\n\r\n%%\r\nboard = [\r\n 'anmjwfpnyo';\r\n 'wasgijsaen';\r\n 'akigyqaekl';\r\n 'doorbellci';\r\n 'loiapucfdx';\r\n 'loepalirri';\r\n 'alzhheagle';\r\n 'mgxmsovnpr';\r\n 'aiqtbovgee';\r\n 'juyhctahnr';\r\n];\r\nwords = {'airplane'; 'board'; 'clasp'; 'doorbell'; 'eagle'; 'fiesty'; 'graph'; 'hatch'; 'igloo'; 'jigsaw'; 'key'; 'llama'};\r\nloc_ans = [\r\n 2 2 4;\r\n 9 5 2;\r\n 5 7 1;\r\n 4 1 3;\r\n 7 6 3;\r\n 5 8 6;\r\n 3 4 5;\r\n 10 8 7;\r\n 9 2 1;\r\n 2 6 7;\r\n 3 9 1;\r\n 5 1 5;\r\n];\r\nassert(isequal(WordSearch(board,words),loc_ans))","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":26769,"edited_by":223089,"edited_at":"2022-09-19T13:13:49.000Z","deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2022-09-19T13:13:50.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-06-11T13:58:10.000Z","updated_at":"2025-12-15T20:16:04.000Z","published_at":"2014-06-11T13:58:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are n words (minimum of three letters in each word) supplied with a given word search board. The answer will contain n rows where each row contains the row and column indices where the word starts followed by an integer indicating the direction of the word. The direction integer runs from 1 to 8 and starts at 12 o'clock, running clockwise. So, a word spelled to the right (normal fashion) would be indexed as a 3 and facing downward to the left (SW) would be a 6.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe first board is included here for reference:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [\\n  'xcupa'\\n  'dyrng'\\n  'osbaq'\\n  'exbid'\\n  'wgamv'\\n ];\\n\\n words = {'aim'; 'bid'; 'cup'; 'doe'};\\n\\n loc_ans = [\\n  3 4 5\\n  4 3 3\\n  1 2 3\\n  2 1 5\\n ];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44238,"title":"Mastermind III: Solve in 1","description":"\u003chttps://en.wikipedia.org/wiki/Mastermind_(board_game) Mastermind\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\r\n\r\n  Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\r\n[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v) \r\n\r\nwhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\r\n\r\nChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\r\n\r\nTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\r\n\r\nFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Mastermind_(board_game)\"\u003eMastermind\u003c/a\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eAnswer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.\r\n\u003c/pre\u003e\u003cp\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/p\u003e\u003cp\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/p\u003e\u003cp\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/p\u003e\u003cp\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/p\u003e\u003cp\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/p\u003e","function_template":"function [guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\r\n% mguess and mpegs are kx4 and kx2 and will not be empty\r\n% The player gets only one guess\r\n guess=[1 1 1 2];\r\nend","test_suite":"%%\r\nglobal m mpc mc mpc5c v\r\ntic\r\nv=1111:6666;\r\nvL=length(v);\r\nm=zeros(vL,4);\r\nfor i=1:vL\r\n  vp=v(i);\r\n  for k=4:-1:1\r\n   m(i,k)=mod(vp,10);\r\n   vp=floor(vp/10);\r\n  end\r\nend\r\nmdel=sum((m==0)+(m\u003e6),2)\u003e0;\r\nm(mdel,:)=[];\r\nv=m*[1000;100;10;1];\r\nmL=size(m,1);\r\n\r\nmpc=zeros(mL); % 0.030\r\nfor j=1:mL\r\nmpc(:,j)=sum(m==repmat(m(j,:),mL,1),2);\r\nend\r\n\r\nmch=zeros(mL,6); % 0.038\r\nfor i=1:mL\r\n  for k=1:6\r\n   mch(i,k)=nnz(m(i,:)==k);\r\n  end\r\nend\r\n\r\nmc=zeros(mL); % 0.06\r\nfor j=1:mL\r\n  mc(:,j)=sum(min(mch,repmat(mch(j,:),mL,1)),2);\r\nend\r\nmc=mc-mpc; % remove mpc part\r\nmpc5c=5*mpc+mc;\r\nfprintf('Initialization %.3f\\n',toc)\r\n% finished initilaiztion calculation in less than 0.2 sec\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 0\r\n3 6 5 6  2 1\r\n6 6 5 5  4 0]; % case 940\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  3 0\r\n6 5 4 5  4 0]; % case 900\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 2\r\n4 4 1 5  3 0\r\n1 4 5 6  1 3\r\n6 4 1 5  4 0]; % case 850\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  0 3\r\n6 3 2 1  4 0]; % case 816\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 2\r\n2 3 4 3  1 1\r\n2 1 3 5  1 2\r\n2 4 5 1  0 2\r\n6 1 2 3  4 0]; % case 750\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 1\r\n4 2 5 6  0 2\r\n6 5 1 5  1 1\r\n5 5 3 2  4 0]; % case 700\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  2 0\r\n3 6 3 6  0 1\r\n6 4 4 5  1 0\r\n5 3 5 5  4 0]; % case 650\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  0 2\r\n3 2 1 5  2 2\r\n3 5 1 2  1 3\r\n5 2 1 3  4 0]; % case 600\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  0 0\r\n6 6 6 6  4 0]; % case 947\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  1 2\r\n4 3 2 5  2 1\r\n4 4 2 3  3 0\r\n4 6 2 3  4 0]; % case 550\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 0\r\n3 3 4 5  1 3\r\n3 4 5 3  2 2\r\n4 3 5 3  4 0]; % case 500\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  0 1\r\n4 1 5 6  2 1\r\n4 5 5 1  1 1\r\n4 1 6 4  4 0]; % case 450\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  0 3\r\n1 4 4 5  0 2\r\n3 6 1 4  4 0]; % case 400\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 3\r\n1 1 4 3  0 4\r\n3 4 1 1  4 0]; % case 350\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  2 1\r\n1 4 1 5  0 1\r\n3 1 2 2  4 0]; % case 300\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  0 1\r\n2 3 4 3  2 0\r\n1 3 5 5  1 1\r\n2 6 5 3  1 1\r\n2 5 4 5  4 0]; % case 250\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 1\r\n1 3 1 4  1 0\r\n1 5 2 6  0 1\r\n2 2 4 4  1 1\r\n2 3 3 2  4 0]; % case 200\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 2\r\n1 1 2 3  1 2\r\n1 1 1 4  3 0\r\n2 1 1 4  4 0]; % case 150\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  1 0\r\n1 3 4 3  1 1\r\n3 2 5 3  0 2\r\n1 5 3 6  3 0\r\n1 5 3 5  4 0]; % case 100\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 1\r\n1 1 3 2  2 1\r\n4 1 1 5  0 1\r\n1 3 2 2  4 0]; % case 50\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n%%\r\nglobal m mpc mc mpc5c v\r\nmm=[1 2 1 2  2 0\r\n1 1 3 4  2 1\r\n1 4 2 5  1 0\r\n1 1 1 3  4 0]; % case 1\r\n[mguessn]=solve_mastermind(mm(1:end-1,1:end-2),mm(1:end-1,end-1:end),m,mpc,mc,mpc5c,v);\r\nif isequal(mguessn,mm(end,1:4))\r\n assert(isequal(1,1))\r\nelse\r\n fprintf('Invalid answer of %i %i %i %i\\n',mguessn)\r\n assert(isequal(1,0))\r\nend\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-06-18T19:30:49.000Z","updated_at":"2025-12-12T14:19:44.000Z","published_at":"2017-06-18T19:58:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Mastermind_(board_game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMastermind\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a code breaking logic puzzle. A pattern of 6 colors(values 1:6) of four positions (1111;1112;....6666) for a possible 6^4(1296) cases is generated. The solver plays a length 4 vector with values 1:6. Accuracy of the play is returned by a count of values in the right position and a count of values(excluding those in the right positions) common to the solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Answer:1233  Guess:3231 Response: 2,2  as x23x are right value/position, 3xx1 are right values.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[guess]=solve_mastermind(mguess,mpegs,m,mpc,mc,mpc5c,v)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere guess is a 1x4 vector, mguess is the kx4 matrix of prior guesses and is empty on first try, mpegs is kx2 giving right [value/position, values] for mguess, m is a 1296x4 array [1 1 1 1;...6 6 6 6] of all solutions, mpc is a 1296x1296 array of 0:4 for value/position solutions, mc is a 1296x1296 array of 0:4 for value solutions, mpc5c is state array of a combined mpc and pc of values 0:20, 5*mpc+mc, and v is integer value of solutions 1111 thru 6666.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eChallenge: A set of 25 cases will be provided that have optimal guesses and their scores. Solve the pattern in 1 guess.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTheory: Elimination of excluded possible guesses will leave only one. Some solutions require only three total guesses so the input size will vary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFuture: Four Mastermind challenges will be created, [Solve no limit, Solve in 8 or less, Solve in 1 given a guess pattern, Solve in 5 or less]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47518,"title":"Play Outside In with primes","description":"In the word game Outside In,* you start with a word, take the first and last letters, try to think of a word in which the two letters appear together in the interior, and repeat. For example, starting with ‘MATLAB’, you might proceed as follows:\r\nsombrero, dissolution, kidnap, pickpocket, helicopter, birthright, debtor, tawdry, katydid, backdrop, subpoena, anniversary, payback, napkin, mannequin, solemnity, psychosis, lopsided, balderdash, etc. \r\nYou can play alone or with others, and you could add other rules or objectives—e.g., all words must have six or more letters, a person who cannot think of a word is eliminated, players try to generate a list that uses all letters in the alphabet, etc.   \r\nThis game has one problem: without a convenient and stable word list, it does not translate easily to Cody. So instead of words, let’s use prime numbers. In particular, start with a two-digit seed, find the smallest prime for which the two digits appear together in the interior, create a new two-digit number from the first and last digits of the prime, and repeat. \r\nFor example, if the seed is 11, then the list would start 1117, 1171, 2111, 1213, 2131, 1217,… Because the last digits of primes are constrained, you will notice similarities in lists generated from different seeds. Solvers able to plot the numbers might enjoy seeing the patterns for longer lists graphically. \r\nWrite a function to play Outside In with primes: given a two-digit seed , generate a list of  primes following the rules above. \r\n\r\n*I devised the game and the name, but I would be surprised if someone had not already thought of it.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 420px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 210px; transform-origin: 407px 210px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.683px 8px; transform-origin: 368.683px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn the word game Outside In,* you start with a word, take the first and last letters, try to think of a word in which the two letters appear together in the interior, and repeat. For example, starting with ‘MATLAB’, you might proceed as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.39167px 8px; transform-origin: 7.39167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eso\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 10.5px 8px; transform-origin: 10.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003emb\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2833px 8px; transform-origin: 25.2833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003erero, dis\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eso\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.675px 8px; transform-origin: 25.675px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003elution, ki\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.55px 8px; transform-origin: 8.55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edn\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 20.6167px 8px; transform-origin: 20.6167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eap, pic\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ekp\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.9px 8px; transform-origin: 38.9px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eocket, helico\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.60833px 8px; transform-origin: 6.60833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ept\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4417px 8px; transform-origin: 19.4417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eer, birt\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7px 8px; transform-origin: 7px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ehr\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 22.95px 8px; transform-origin: 22.95px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eight, de\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.60833px 8px; transform-origin: 6.60833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ebt\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 20.6083px 8px; transform-origin: 20.6083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eor, taw\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7px 8px; transform-origin: 7px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edr\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.2583px 8px; transform-origin: 14.2583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ey, ka\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.225px 8px; transform-origin: 6.225px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ety\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 24.5083px 8px; transform-origin: 24.5083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003edid, bac\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ekd\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 21.3917px 8px; transform-origin: 21.3917px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003erop, su\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.55px 8px; transform-origin: 8.55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ebp\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.45px 8px; transform-origin: 19.45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eoena, anniver\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 8px; transform-origin: 7.78333px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003esa\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 13.0917px 8px; transform-origin: 13.0917px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ery, p\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 8px; transform-origin: 7.78333px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eay\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 26.45px 8px; transform-origin: 26.45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eback, na\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003epk\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.0583px 8px; transform-origin: 19.0583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ein, ma\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.55px 8px; transform-origin: 8.55px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003enn\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 33.85px 8px; transform-origin: 33.85px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eequin, sole\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 10.5px 8px; transform-origin: 10.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003emn\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14.2583px 8px; transform-origin: 14.2583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eity, p\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 7.78333px 8px; transform-origin: 7.78333px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003esy\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 29.175px 8px; transform-origin: 29.175px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003echosis, lo\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.16667px 8px; transform-origin: 8.16667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eps\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 24.9px 8px; transform-origin: 24.9px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eided, ba\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 6.21667px 8px; transform-origin: 6.21667px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eld\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38.5px 8px; transform-origin: 38.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eerdash, etc. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.05px 8px; transform-origin: 363.05px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can play alone or with others, and you could add other rules or objectives—e.g., all words must have six or more letters, a person who cannot think of a word is eliminated, players try to generate a list that uses all letters in the alphabet, etc. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.283px 8px; transform-origin: 373.283px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis game has one problem: without a convenient and stable word list, it does not translate easily to Cody. So instead of words, let’s use prime numbers. In particular, start with a two-digit seed, find the smallest prime for which the two digits appear together in the interior, create a new two-digit number from the first and last digits of the prime, and repeat. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 318.958px 8px; transform-origin: 318.958px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, if the seed is 11, then the list would start 1117, 1171, 2111, 1213, 2131, 1217,… Because \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45994-investigate-the-frequency-of-last-digits-of-prime-numbers\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ethe last digits of primes are constrained\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 306.775px 8px; transform-origin: 306.775px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, you will notice similarities in lists generated from different seeds. Solvers able to plot the numbers might enjoy seeing the patterns for longer lists graphically. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 215.742px 8px; transform-origin: 215.742px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to play Outside In with primes: given a two-digit seed \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 57.5583px 8px; transform-origin: 57.5583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, generate a list of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 82.8583px 8px; transform-origin: 82.8583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e primes following the rules above. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 311.567px 8px; transform-origin: 311.567px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e*I devised the game and the name, but I would be surprised if someone had not already thought of it.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = OutsideInPrimes(x,n)\r\n%  x = two-digit seed\r\n%  n = number of primes in the list\r\n\r\n  y = f(x,n);\r\nend","test_suite":"%%\r\nx = 11;\r\nn = 6;\r\ny_correct = [1117 1171 2111 1213 2131 1217];\r\nassert(isequal(OutsideInPrimes(x,n),y_correct))\r\n\r\n%%\r\nx = 52;\r\nn = 10;\r\ny_correct = [1523 2131 1213 2137 1277 1171 1117 2179 1291 2111];\r\nassert(isequal(OutsideInPrimes(x,n),y_correct))\r\n\r\n%%\r\nx = 88;\r\nn = 10;\r\ny_correct = [1889 1193 2131 1213 2137 1277 1171 1117 2179 1291];\r\nassert(isequal(OutsideInPrimes(x,n),y_correct))\r\n\r\n%%\r\nx = 66;\r\nn = 200;\r\ny = OutsideInPrimes(x,n);\r\nsum_correct = 1167614;\r\ny_correct_p = [10177 11171 11159 11903 11329 11909 11923 11351 11161 11173 11353 11369 11927 11177 11701 12113 11383 11393 11399];\r\nassert(isequal(sum(y),sum_correct))\r\nassert(isequal(y(149:167),y_correct_p))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-09-16T16:09:02.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-15T14:32:53.000Z","updated_at":"2025-11-29T23:35:09.000Z","published_at":"2020-11-15T15:09:15.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the word game Outside In,* you start with a word, take the first and last letters, try to think of a word in which the two letters appear together in the interior, and repeat. For example, starting with ‘MATLAB’, you might proceed as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eso\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003erero, dis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eso\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003elution, ki\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eap, pic\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ekp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eocket, helico\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ept\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eer, birt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ehr\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eight, de\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebt\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eor, taw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edr\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ey, ka\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ety\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003edid, bac\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ekd\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003erop, su\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ebp\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eoena, anniver\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esa\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ery, p\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eay\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eback, na\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003epk\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ein, ma\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eequin, sole\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eity, p\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003echosis, lo\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eps\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eided, ba\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eld\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eerdash, etc. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can play alone or with others, and you could add other rules or objectives—e.g., all words must have six or more letters, a person who cannot think of a word is eliminated, players try to generate a list that uses all letters in the alphabet, etc. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis game has one problem: without a convenient and stable word list, it does not translate easily to Cody. So instead of words, let’s use prime numbers. In particular, start with a two-digit seed, find the smallest prime for which the two digits appear together in the interior, create a new two-digit number from the first and last digits of the prime, and repeat. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if the seed is 11, then the list would start 1117, 1171, 2111, 1213, 2131, 1217,… Because \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45994-investigate-the-frequency-of-last-digits-of-prime-numbers\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe last digits of primes are constrained\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, you will notice similarities in lists generated from different seeds. Solvers able to plot the numbers might enjoy seeing the patterns for longer lists graphically. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to play Outside In with primes: given a two-digit seed \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, generate a list of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e primes following the rules above. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e*I devised the game and the name, but I would be surprised if someone had not already thought of it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44751,"title":"Lights Out 1 - 5x5, 3 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. Pressing any button on the board toggles that button as well as adjacent buttons vertically and horizontally, where present (and no wrapping here), to the opposite state (i.e., on to off and off to on). For example, pressing a corner button toggles three button states, an edge button four button states, and an interior button five button states (as can be visualized by the example below).\r\n\r\nThis first problem uses a 5x5 board and requires that your function return a column-major vector of button presses (indices) that will solve the board by turning off all lights. Since pressing any button twice will return to the same board state, only one move is needed (and allowed) per button. For example, if\r\n\r\n board = [0 1 0 0 0\r\n          1 1 1 0 1\r\n          0 1 0 1 1\r\n          1 0 0 0 1\r\n          1 1 0 0 0]\r\n\r\nthe answer is:\r\n\r\n moves = [5 7 23]\r\n\r\nIn this first problem, all boards have solutions of only three moves.\r\n\r\nNote: while brute-force solutions will solve some problems in this series, they will time out on later ones. Solving the first few problems by developing a robust solver function is encouraged.\r\n\r\nAs a potential starting point, you might check out one or more of the following resources by \u003chttp://www.mat.ucm.es/~vmunozve/lights-out.pdf Vicente Muñoz\u003e; \u003chttp://www.ijritcc.org/download/browse/Volume_5_Issues/August_17_Volume_5_Issue_8/1503304082_21-08-2017.pdf Chen, et al.\u003e; \u003chttp://vprusso.github.io/blog/2017/the-mathematics-of-lights-out/ Vincent Russo\u003e; \u003chttp://mathworld.wolfram.com/LightsOutPuzzle.html Margherita Barile (on wolfram.com)\u003e; \u003chttp://www.keithschwarz.com/interesting/code/?dir=lights-out Keith Schwarz\u003e; or \u003chttps://dc.ewu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/\u0026httpsredir=1\u0026article=1166\u0026context=theses Rebecca Meyer (a master's thesis)\u003e.\r\n\r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves 5x5, 4 moves\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"display: block; min-width: 0px; padding-top: 0px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eLights Out\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e is a logic game wherein all lights need to be turned off to complete each board. Pressing any button on the board toggles that button as well as adjacent buttons vertically and horizontally, where present (and no wrapping here), to the opposite state (i.e., on to off and off to on). For example, pressing a corner button toggles three button states, an edge button four button states, and an interior button five button states (as can be visualized by the example below).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eThis first problem uses a 5x5 board and requires that your function return a column-major vector of button presses (indices) that will solve the board by turning off all lights. Since pressing any button twice will return to the same board state, only one move is needed (and allowed) per button. For example, if\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-bottom: 10px; margin-left: 3px; margin-right: 3px; margin-top: 10px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e board = [0 1 0 0 0\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          1 1 1 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          0 1 0 1 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          1 0 0 0 1\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e          1 1 0 0 0]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 10px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003ethe answer is:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-bottom: 10px; margin-left: 3px; margin-right: 3px; margin-top: 10px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end: 1px solid rgb(233, 233, 233); border-inline-start: 1px solid rgb(233, 233, 233); border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-height: 18px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"border-inline-end: 0px none rgb(0, 0, 0); border-inline-start: 0px none rgb(0, 0, 0); border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-right: 45px; min-height: 0px; padding-left: 0px; tab-size: 4; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-right: 0px; \"\u003e moves = [5 7 23]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 10px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eIn this first problem, all boards have solutions of only three moves.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eNote: while brute-force solutions will solve some problems in this series, they will time out on later ones. Solving the first few problems by developing a robust solver function is encouraged.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eAs a potential starting point, you might check out one or more of the following resources by\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eVicente Muñoz\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eChen, et al.\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eVincent Russo\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMargherita Barile (on wolfram.com)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e;\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves/edit#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eKeith Schwarz\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e; or\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://dc.ewu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/\u0026amp;httpsredir=1\u0026amp;article=1166\u0026amp;context=theses\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eRebecca Meyer (a master's thesis)\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eNext:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e5x5, 4 moves\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function moves = lights_out_1(board) % 5x5 board, 3 moves\r\n moves = board;\r\nend","test_suite":"\r\n%% \r\n board = [0 1 0 0 0\r\n          1 1 1 0 1\r\n          0 1 0 1 1\r\n          1 0 0 0 1\r\n          1 1 0 0 0];\r\n %plot_board(board,'Input')\r\nmoves = lights_out_1(board); % should be [5 7 23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2);   %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 1];\r\nmoves = lights_out_1(board); %should be [1 13 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [1 0 0 0 0  \r\n          0 1 0 0 0  \r\n          1 1 0 0 0  \r\n          0 1 0 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_1(board); %should be [2 3 4]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 1 0 1  \r\n          0 1 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_1(board); %should be [7 12 17]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 1  \r\n          0 0 1 1 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_1(board); %you're on your own now\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 1 1 1 1  \r\n          0 0 1 0 0];\r\n plot_board(board,'Input',[])\r\nmoves = lights_out_1(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2);\r\n    plot_board(board,['Step ',num2str(i)],moves(i))\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==3)\r\n\r\n\r\nfunction plot_board(board,txt_title,sq_tap)\r\n sq_on = (board==1); % determine which squares are on\r\n pts = repmat(1:5,[5,1]); x = pts(1:end); % x coordinates for each square\r\n pts = fliplr(pts)'; y = pts(1:end); % y coordinates for each square\r\n figure; hold on\r\n fill([0,0,5,5],[0,5,5,0],'k','FaceAlpha',0.3) % background\r\n plot(x(sq_on)-0.5,y(sq_on)-0.5,'ys','MarkerSize',50, ...\r\n     'MarkerFaceColor','y') % on squares\r\n plot(x(~sq_on)-0.5,y(~sq_on)-0.5,'s','MarkerSize',50, ...\r\n     'Color',[0.9 0.95 1.0],'MarkerFaceColor',[0.9 0.95 1.0]) % off squares\r\n if ~isempty(sq_tap)\r\n  plot(x(sq_tap)-0.5,y(sq_tap)-0.5,'g*','MarkerSize',40, ...\r\n      'LineWidth',5) % tapped square\r\n end\r\n axis square\r\n set(gca,'XTick',0:5); set(gca,'YTick',0:5)\r\n title(txt_title,'FontSize',24)\r\nend","published":true,"deleted":false,"likes_count":5,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2021-04-13T00:54:00.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-25T19:39:19.000Z","updated_at":"2026-01-06T08:23:35.000Z","published_at":"2018-10-29T14:07:20.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. Pressing any button on the board toggles that button as well as adjacent buttons vertically and horizontally, where present (and no wrapping here), to the opposite state (i.e., on to off and off to on). For example, pressing a corner button toggles three button states, an edge button four button states, and an interior button five button states (as can be visualized by the example below).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis first problem uses a 5x5 board and requires that your function return a column-major vector of button presses (indices) that will solve the board by turning off all lights. Since pressing any button twice will return to the same board state, only one move is needed (and allowed) per button. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 0 0\\n          1 1 1 0 1\\n          0 1 0 1 1\\n          1 0 0 0 1\\n          1 1 0 0 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [5 7 23]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this first problem, all boards have solutions of only three moves.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote: while brute-force solutions will solve some problems in this series, they will time out on later ones. Solving the first few problems by developing a robust solver function is encouraged.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs a potential starting point, you might check out one or more of the following resources by\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVicente Muñoz\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eChen, et al.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVincent Russo\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMargherita Barile (on wolfram.com)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKeith Schwarz\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e; or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://dc.ewu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/\u0026amp;httpsredir=1\u0026amp;article=1166\u0026amp;context=theses\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eRebecca Meyer (a master's thesis)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNext:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44752-lights-out-2-5x5-4-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 4 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":46120,"title":"Solve the Challenger puzzle","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 442.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 221.167px; transform-origin: 407px 221.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 7.8px; transform-origin: 14px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://kingfeatures.com/features/puzzlesandgames/challenger/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eChallenger\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 321.267px 7.8px; transform-origin: 321.267px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e puzzle by Linus Maurer requires the solver to fill a 4x4 matrix of integers from 1 to 9 to match the given sums of the rows, columns, main diagonal, and anti-diagonal. Four of the numbers are given. Numbers can be repeated, and the solution is not necessarily unique. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 376.017px 7.8px; transform-origin: 376.017px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve the Challenger. The input will be a matrix resembling the game board. For example, if the input is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e   NaN  NaN  NaN  NaN  12\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     0    0    0    4  23\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     0    1    0    0  11\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     6    0    0    0  21\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     0    0    2    0  13\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96.25px 8.25px; transform-origin: 96.25px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     9   20   22   17  12\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.85px 7.8px; transform-origin: 379.85px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ethen the sums of the four columns are 9, 20, 22, and 17. The sums of the rows are 23, 11, 21, and 13, and the sums of the two diagonals are 12 and 12. The four starting numbers are 6, 1, 2, and 4, and zeros indicate the numbers to be determined. Ignore the NaNs. The output of the function should be the 4x4 matrix of numbers. In the example, a valid solution would be \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80.85px 8.25px; transform-origin: 80.85px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     1    9    9    4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80.85px 8.25px; transform-origin: 80.85px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     1    1    4    5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80.85px 8.25px; transform-origin: 80.85px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     6    3    7    5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.916667px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.916667px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.916667px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.916667px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 84.7px 8.25px; transform-origin: 84.7px 8.25px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e     1    7    2    3 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 7.8px; transform-origin: 0px 7.8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Challenger(x)\r\n  y = f(x);\r\nend","test_suite":"%%\r\nx = [NaN NaN NaN NaN 12; 0 0 0 4 23; 0 1 0 0 11; 6 0 0 0 21; 0 0 2 0 13; 9 20 22 17 12];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)\r\n\r\n%%\r\nx = [NaN NaN NaN NaN 25; 0 0 0 7 18; 0 9 0 0 29; 0 0 6 0 27; 9 0 0 0 27; 30 21 19 31 28];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)\r\n\r\n%%\r\nx = [NaN NaN NaN NaN 19; 0 0 0 2 16; 5 0 0 0 22; 0 0 4 0 27; 0 2 0 0 11; 19 16 19 22 15];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)\r\n\r\n%%\r\nx = [NaN NaN NaN NaN 20; 0 0 4 0 25; 0 6 0 0 22; 4 0 0 0 21; 0 0 0 4 25; 26 20 22 25 26];\r\nsum_row = x(6,1:4); sum_col = x(2:5,5); sum_d1 = x(6,5); sum_d2 = x(1,5);\r\ny = Challenger(x);\r\nassert(isequal(sum(y),sum_row) \u0026\u0026 isequal(sum(y,2),sum_col) \u0026\u0026 trace(y) == sum_d1 \u0026\u0026 trace(fliplr(y)) == sum_d2)","published":true,"deleted":false,"likes_count":3,"comments_count":3,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":27,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-08-08T15:02:54.000Z","updated_at":"2026-02-11T17:03:32.000Z","published_at":"2020-08-08T15:35:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://kingfeatures.com/features/puzzlesandgames/challenger/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eChallenger\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e puzzle by Linus Maurer requires the solver to fill a 4x4 matrix of integers from 1 to 9 to match the given sums of the rows, columns, main diagonal, and anti-diagonal. Four of the numbers are given. Numbers can be repeated, and the solution is not necessarily unique. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve the Challenger. The input will be a matrix resembling the game board. For example, if the input is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   NaN  NaN  NaN  NaN  12\\n     0    0    0    4  23\\n     0    1    0    0  11\\n     6    0    0    0  21\\n     0    0    2    0  13\\n     9   20   22   17  12]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethen the sums of the four columns are 9, 20, 22, and 17. The sums of the rows are 23, 11, 21, and 13, and the sums of the two diagonals are 12 and 12. The four starting numbers are 6, 1, 2, and 4, and zeros indicate the numbers to be determined. Ignore the NaNs. The output of the function should be the 4x4 matrix of numbers. In the example, a valid solution would be \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[     1    9    9    4\\n     1    1    4    5\\n     6    3    7    5\\n     1    7    2    3 ]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59586,"title":"List ways to make a sum in Killer Sudoku","description":"In Sudoku, the subject of several Cody problems, one tries to arrange the digits 1 through 9 in a 9x9 grid of 81 cells (or a 3x3 arrangement of nine 3x3 boxes) so that every row, column, and 3x3 box contains each digit exactly once. In the variant Killer Sudoku, digits are placed in cages that indicate the sum of the digits; digits cannot be repeated in cages. In the example below from Wikipedia, the cages are indicated by colors. \r\nSolving Killer Sudoku with few (or no) given digits requires ready ideas of how to make particular sums. A two-digit cage with a sum of 3 can contain only 1 and 2, and a two-digit cage with a sum of 4 can contain only 1 and 3 because 2 cannot be repeated. A three-digit cage with a sum of 21 can have some arrangement of [9 8 4], [9 7 5], or [8 7 6]. The three-digit cage summing to 17 in the central box of the example has seven possible sets, but if the box already has an 8 outside the 17-cage, then only four of those sets are possible: [9 7 1], [9 6 2], [9 5 3], [7 6 4].\r\nWrite a function to list the ways to make a sum with a given number of digits. If only two arguments are given, then all of the digits 1-9 are possible. If a third argument is given, then it indicates the digits that cannot be used. The output should be a matrix in which the digits decrease along the rows and the rows are sorted in decreasing order of the first digit, then the second digit, then the third, etc., as in the examples. List only one arrangement: in the last example above, only [9 7 1] is given, not [9 1 7], [7 9 1], [7 1 9], [1 9 7], and [1 7 9]. If the sum cannot be made, return [].\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 582.7px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 291.35px; transform-origin: 407px 291.35px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.95px 8px; transform-origin: 78.95px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn Sudoku, the subject of \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems?term=sudoku\u0026amp;submitsearch=\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eseveral Cody problems\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 223.65px 8px; transform-origin: 223.65px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, one tries to arrange the digits 1 through 9 in a 9x9 grid of 81 cells (or a 3x3 arrangement of nine 3x3 boxes) so that every row, column, and 3x3 box contains each digit exactly once. In the variant Killer Sudoku, digits are placed in cages that indicate the sum of the digits; digits cannot be repeated in cages. In the example below from \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Killer_sudoku\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eWikipedia\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 110.075px 8px; transform-origin: 110.075px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the cages are indicated by colors. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 372.65px 8px; transform-origin: 372.65px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSolving Killer Sudoku with few (or no) given digits requires ready ideas of how to make particular sums. A two-digit cage with a sum of 3 can contain only 1 and 2, and a two-digit cage with a sum of 4 can contain only 1 and 3 because 2 cannot be repeated. A three-digit cage with a sum of 21 can have some arrangement of [9 8 4], [9 7 5], or [8 7 6]. The three-digit cage summing to 17 in the central box of the example has seven possible sets, but if the box already has an 8 outside the 17-cage, then only four of those sets are possible: [9 7 1], [9 6 2], [9 5 3], [7 6 4].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.75px 8px; transform-origin: 383.75px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to list the ways to make a sum with a given number of digits. If only two arguments are given, then all of the digits 1-9 are possible. If a third argument is given, then it indicates the digits that cannot be used. The output should be a matrix in which the digits decrease along the rows and the rows are sorted in decreasing order of the first digit, then the second digit, then the third, etc., as in the examples. List only one arrangement: in the last example above, only [9 7 1] is given, not [9 1 7], [7 9 1], [7 1 9], [1 9 7], and [1 7 9]. If the sum cannot be made, return [].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 261.7px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 130.85px; text-align: left; transform-origin: 384px 130.85px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"256\" height=\"256\" style=\"vertical-align: baseline;width: 256px;height: 256px\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Killersudoku_color.svg/1024px-Killersudoku_color.svg.png\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = makesum(varargin)\r\n% Argument 1 is the target sum, and argument 2 is the number of digits to be used in the sum.\r\n% If given, argument 3 lists the digits to exclude from the sum.\r\n  y = sum(randi(9,n));\r\nend","test_suite":"%%\r\nx = randi(9);\r\nn = 1;\r\ny = makesum(x,n);\r\ny_correct = x;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 3;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 4;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 5;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [4 1; 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 6;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [5 1; 4 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 7;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [6 1; 5 2; 4 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 8;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [7 1; 6 2; 5 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 9;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [8 1; 7 2; 6 3; 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 10;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 1; 8 2; 7 3; 6 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 11;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 2; 8 3; 7 4; 6 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 12;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 3; 8 4; 7 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 13;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 4; 8 5; 7 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 14;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 5; 8 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 15;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 6; 8 7];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 16;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 7];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 2;\r\ny = makesum(x,n);\r\ny_correct = [9 8];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 2;\r\nd = [];\r\ny = makesum(x,n,d);\r\ny_correct = [9 8];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 2;\r\nd = [1 2];\r\ny = makesum(x,n,d);\r\ny_correct = [9 8];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 6;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [3 2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 7;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [4 2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 8;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [5 2 1; 4 3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 8;\r\nn = 3;\r\nd = 2;\r\ny = makesum(x,n,d);\r\ny_correct = [4 3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 9;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [6 2 1; 5 3 1; 4 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 10;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [7 2 1; 6 3 1; 5 4 1; 5 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 11;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [8 2 1; 7 3 1; 6 4 1; 6 3 2; 5 4 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 12;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 2 1; 8 3 1; 7 4 1; 7 3 2; 6 5 1; 6 4 2; 5 4 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 13;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 3 1; 8 4 1; 8 3 2; 7 5 1; 7 4 2; 6 5 2; 6 4 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 13;\r\nn = 3;\r\nd = [1 3];\r\ny = makesum(x,n,d);\r\ny_correct = [7 4 2; 6 5 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 14;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 4 1; 9 3 2; 8 5 1; 8 4 2; 7 6 1; 7 5 2; 7 4 3; 6 5 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 15;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 5 1; 9 4 2; 8 6 1; 8 5 2; 8 4 3; 7 6 2; 7 5 3; 6 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 16;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 6 1; 9 5 2; 9 4 3; 8 7 1; 8 6 2; 8 5 3; 7 6 3; 7 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 16;\r\nn = 3;\r\nd = 3;\r\ny = makesum(x,n,d);\r\ny_correct = [9 6 1; 9 5 2; 8 7 1; 8 6 2; 7 5 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 17;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 7 1; 9 6 2; 9 5 3; 8 7 2; 8 6 3; 8 5 4; 7 6 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 18;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 1; 9 7 2; 9 6 3; 9 5 4; 8 7 3; 8 6 4; 7 6 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 19;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 2; 9 7 3; 9 6 4; 8 7 4; 8 6 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 20;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 3; 9 7 4; 9 6 5; 8 7 5];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 21;\r\nn = 3;\r\ny = makesum(x,n);\r\ny_correct = [9 8 4; 9 7 5; 8 7 6];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nx = 21;\r\nn = 3;\r\nd = 7;\r\ny = makesum(x,n,d);\r\ny_correct = [9 8 4];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 10;\r\nn = 4;\r\ny = makesum(x,n);\r\ny_correct = [4 3 2 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 16;\r\nn = 4;\r\ny = makesum(x,n);\r\ny_correct = [9 4 2 1; 8 5 2 1; 8 4 3 1; 7 6 2 1; 7 5 3 1; 7 4 3 2; 6 5 4 1; 6 5 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 21;\r\nn = 4;\r\ny = makesum(x,n);\r\ny_correct = [9 8 3 1; 9 7 4 1; 9 7 3 2; 9 6 5 1; 9 6 4 2; 9 5 4 3; 8 7 5 1; 8 7 4 2; 8 6 5 2; 8 6 4 3; 7 6 5 3];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 21;\r\nn = 4;\r\nd = [1 3];\r\ny = makesum(x,n,d);\r\ny_correct = [9 6 4 2; 8 7 4 2; 8 6 5 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 40;\r\nn = 6;\r\ny = makesum(x,n);\r\nassert(isempty(y))\r\n\r\n%% \r\nx = 40;\r\nn = 7;\r\ny = makesum(x,n);\r\ny_correct = [9 8 7 6 5 4 1; 9 8 7 6 5 3 2];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nx = 45;\r\nn = 9;\r\ny = makesum(x,n);\r\ny_correct = 9:-1:1;\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('makesum.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-01-22T12:22:39.000Z","deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-22T05:25:55.000Z","updated_at":"2024-01-22T12:22:39.000Z","published_at":"2024-01-22T05:32:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn Sudoku, the subject of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems?term=sudoku\u0026amp;submitsearch=\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eseveral Cody problems\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, one tries to arrange the digits 1 through 9 in a 9x9 grid of 81 cells (or a 3x3 arrangement of nine 3x3 boxes) so that every row, column, and 3x3 box contains each digit exactly once. In the variant Killer Sudoku, digits are placed in cages that indicate the sum of the digits; digits cannot be repeated in cages. In the example below from \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Killer_sudoku\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eWikipedia\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, the cages are indicated by colors. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSolving Killer Sudoku with few (or no) given digits requires ready ideas of how to make particular sums. A two-digit cage with a sum of 3 can contain only 1 and 2, and a two-digit cage with a sum of 4 can contain only 1 and 3 because 2 cannot be repeated. A three-digit cage with a sum of 21 can have some arrangement of [9 8 4], [9 7 5], or [8 7 6]. The three-digit cage summing to 17 in the central box of the example has seven possible sets, but if the box already has an 8 outside the 17-cage, then only four of those sets are possible: [9 7 1], [9 6 2], [9 5 3], [7 6 4].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to list the ways to make a sum with a given number of digits. If only two arguments are given, then all of the digits 1-9 are possible. If a third argument is given, then it indicates the digits that cannot be used. The output should be a matrix in which the digits decrease along the rows and the rows are sorted in decreasing order of the first digit, then the second digit, then the third, etc., as in the examples. List only one arrangement: in the last example above, only [9 7 1] is given, not [9 1 7], [7 9 1], [7 1 9], [1 9 7], and [1 7 9]. If the sum cannot be made, return [].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"256\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"256\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Killersudoku_color.svg/1024px-Killersudoku_color.svg.png\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":47463,"title":"Slitherlink II: Gimmes","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 531.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 265.833px; transform-origin: 407px 265.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 87.8667px 7.91667px; transform-origin: 87.8667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink II: Gimmes\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 293.283px 7.91667px; transform-origin: 293.283px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363.683px 7.91667px; transform-origin: 363.683px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np1=trivial_solve(p,bsegs,s);\r\n\r\nif nnz(sum(p1,2)==10)\u003e3 % Possible final solution\r\n [sv,valid]=pcheck(s,p1,bsegs); \r\n if valid\r\n  %show_pfig(s,p1,c,emap,pmap,4)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n  return\r\n end\r\nend\r\n\r\n%No initial solve of p\r\n%Process p for standard beginning info\r\np=init(p,bsegs,s,c,emap,pmap);\r\n%show_pfig(s,p,c,emap,pmap,4)\r\n\r\n[sv,valid]=pcheck(s,p,bsegs); \r\nfprintf('sv  init solution\\n')\r\nfprintf('%i ',sv);fprintf('\\n') \r\n\r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=init(p,bsegs,s,c,emap,pmap)\r\n%Author Note: I found creating the complete set was time consuming\r\n% Standard Gimmes\r\n% https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\r\n% 0 Corners/Edge/Middle\r\n% 1 Corner\r\n% 2 Corner\r\n% 3 Corner\r\n% 0-3 Adjacent\r\n% 3-3 Adjacent\r\n% 0-3 Diagonal\r\n% 3-3 Diagonal\r\n% 3-1 Edge  add by raz as a Gimme\r\n\r\n [nr,nc]=size(s);\r\n %Example Zero processing\r\n [nr0,nc0]=find(s==0);\r\n idx0=find(s==0);\r\n for i=1:length(nr0)\r\n  bidx=idx0(i);\r\n  vb=bsegs(bidx,:);\r\n  for j=1:2:7\r\n   p(vb(j),vb(j+1))=0; % Clear p array segments around zeros valid for all 0s\r\n   p(vb(j+1),vb(j))=0;\r\n  end\r\n  \r\n  if nr0(i)==1 \u0026\u0026 nc0(i)==1 %TL0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(2,1:2); %bidx+1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(1+nr,3:4); %bidx+nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==1 \u0026\u0026 nc0(i)==nc %TR0\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==1 %BL\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==nr \u0026\u0026 nc0(i)==nc %BR\r\n   if nr\u003e1\r\n    vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n   if nc\u003e1\r\n    vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n   end\r\n    \r\n  elseif nr0(i)==1 %T non-corner\r\n   vbsegs=bsegs(bidx-nr,3:4); %bidx-nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,3:4); %bidx+nr, T\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   \r\n  elseif nr0(i)==nr %B non-corner\r\n   vbsegs=bsegs(bidx-nr,5:6); %bidx-nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+nr,5:6); %bidx+nr, B\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==1 %L non-corner\r\n   vbsegs=bsegs(bidx-1,1:2); %bidx-1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,1:2); %bidx+1, L\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n    \r\n  elseif nc0(i)==nc\r\n   vbsegs=bsegs(bidx-1,7:8); %bidx-1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n   vbsegs=bsegs(bidx+1,7:8); %bidx+1, R\r\n   p(vbsegs(1),vbsegs(2))=0;\r\n   p(vbsegs(2),vbsegs(1))=0;\r\n  end % if TL/TR/BL/BR/T/B/L/R\r\n  \r\n end %i  nr0 corners/edges/mid  s==0\r\n \r\n [nr1,nc1]=find(s==1); %One corner zeros\r\n idx1=find(s==1);\r\n for i=1:length(nr1)\r\n  %enter setting of p for 1s in corners\r\n end % nr1 corners\r\n \r\n [nr3,nc3]=find(s==3); %Three corners set corner segs to 5\r\n idx3=find(s==3);\r\n for i=1:length(nr3)\r\n  %enter setting of p for 1s in corners\r\n end % nr3 corners\r\n \r\n \r\n [nr2,nc2]=find(s==2);\r\n idx2=find(s==2);\r\n for i=1:length(nr2)\r\n  bidx=idx2(i);\r\n %enter setting of p for 1s in corners \r\n end %i  s==2 Corners\r\n \r\n \r\n% 0-3 Adjacent\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n %setting p for 03 adjacent cases\r\n  \r\n  %0-3mid sets4 segs, clears 4 segs\r\n  %0-3edge  sets 4 segs, clears 2 segs on edge\r\n  bidx=idx3(i);\r\n end % nr3 with adjacent 0; both can not be on edge or either in a corner\r\n\r\n\r\n% 3-3 Adjacent T3 not Possible. I3 or Ix possible\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  bidx=idx3(i);\r\n %setting p for 33 adjacent\r\n end % i nr3  3-3 adjacent\r\n\r\n\r\n% 0-3 Diagonal no 3 corners, edges-2/mid-4 allowed\r\n [nr3,nc3]=find(s==3); %3-0 adjacent set segs to 0/5\r\n idx3=find(s==3);\r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n end\r\n for i=1:length(nr3)\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==1,continue;end %corner detect of 3\r\n  if nr3(i)==1 \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==1,continue;end %corner detect\r\n  if nr3(i)==nr \u0026\u0026 nc3(i)==nc,continue;end %corner detect\r\n  \r\n  bidx=idx3(i);\r\n  %setting p for 03 diagonal\r\n \r\n end % i 0-3 diagonal\r\n\r\n\r\n% 3-3 Diagonal  Convolve to find locations [10;01],[01;10] find 6 \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[1 0;0 1],'same');\r\n  [nr3,nc3]=find(sc==6); \r\n  idx3=find(sc==6); \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i);  \r\n  %setting p for 33 diagonal\r\n end % i nr3 33 diagonal DR\r\n \r\n if nr==1 || nc==1  % No single row/col\r\n  nr3=[];\r\n else\r\n  sp=s;\r\n  sp(sp==5)=0;\r\n  sc=conv2(sp,[0 1;1 0],'same'); % conv puts 6 at TL of grid, want TR\r\n  [nr3,nc3]=find(sc==6); \r\n  nc3=nc3+1;\r\n  idx3=find(sc==6)+nr; \r\n end\r\n \r\n for i=1:length(nr3)\r\n  bidx=idx3(i); \r\n  %3-0 adjacent set segs to 0/5\r\n end % i nr3 33 diagonal DL\r\n \r\n \r\n if nr==1 || nc==1, return;end  % No single row/col\r\n %Slithering Starting Techniques misses the 13 edge Gimme     \r\n i=1; %Top Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, T set\r\n    p(vbsegs(3),vbsegs(4))=5;\r\n    p(vbsegs(4),vbsegs(3))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, BR CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n  i=1; %Top Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LB  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 31\r\n for j=1:nc-1\r\n  if s(i,j)==3 \u0026\u0026 s(i,j+1)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, TR CLR\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    p(vbsegs(7),vbsegs(8))=0;\r\n    p(vbsegs(8),vbsegs(7))=0;\r\n  end\r\n end\r\n \r\n i=nr; %Bot Edge 13\r\n for j=1:nc-1\r\n  if s(i,j)==1 \u0026\u0026 s(i,j+1)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT  clr\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+nr,:); %bidx, B set\r\n    p(vbsegs(5),vbsegs(6))=5;\r\n    p(vbsegs(6),vbsegs(5))=5;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(1),vbsegs(2))=0;\r\n    p(vbsegs(2),vbsegs(1))=0;\r\n  end\r\n end\r\n \r\n j=nc; %Right Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, LT clr\r\n    p(vbsegs(1),vbsegs(1))=0;\r\n    p(vbsegs(2),vbsegs(2))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, R set\r\n    p(vbsegs(7),vbsegs(8))=5;\r\n    p(vbsegs(8),vbsegs(7))=5;\r\n  end\r\n end\r\n \r\n \r\n  j=1; %Left Edge 31\r\n for i=1:nr-1\r\n  if s(i,j)==3 \u0026\u0026 s(i+1,j)==1\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, LB CLR\r\n    p(vbsegs(5),vbsegs(6))=0;\r\n    p(vbsegs(6),vbsegs(5))=0;\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n  end\r\n end\r\n \r\n j=1; %Left Edge 13\r\n for i=1:nr-1\r\n  if s(i,j)==1 \u0026\u0026 s(i+1,j)==3\r\n   bidx=i+(j-1)*nr;  \r\n    vbsegs=bsegs(bidx,:); %bidx, RT clr\r\n    p(vbsegs(7),vbsegs(7))=0;\r\n    p(vbsegs(8),vbsegs(8))=0;\r\n    p(vbsegs(3),vbsegs(4))=0;\r\n    p(vbsegs(4),vbsegs(3))=0;\r\n    vbsegs=bsegs(bidx+1,:); %bidx, L set\r\n    p(vbsegs(1),vbsegs(2))=5;\r\n    p(vbsegs(2),vbsegs(1))=5;\r\n  end\r\n end\r\n \r\nend % init  basic gimmes corners/3-3/33diag/0/03diag/03adj/13edge\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[5 3 5;3 0 3;5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 2;2 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5;5 0 5;5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 3 2;5 0 5 0 5;5 3 5 3 5]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns=[2 3 5 5 3 5;5 0 5 5 0 5;5 3 5 5 3 2]; %No evolve, init solves\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\npvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T17:23:06.000Z","updated_at":"2025-05-02T19:04:22.000Z","published_at":"2020-11-12T23:27:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink II: Gimmes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases where s is solved using only the Gimmes from Slitherlink Starting Techniques. The site is missing the Gimme case of adjacent 31 on an edge. Trivial cases may be presented and should be solved prior to processing the Gimmes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink I: Trivial, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":57810,"title":"List the two-bit primes","description":"Each year at Christmas, my father-in-law and his partner send me the Puzzle Mania section from the New York Times. This year’s edition had a puzzle by Mike Shenk called “Two Bits”, which asked solvers to find words whose first two letters could be shifted as a unit elsewhere in the word to form a new word. For example, the clue “??BID” had the answer RABID, with the accompanying word BRAID. The clue “??PREME” had the answer SUPREME, with the accompanying word PRESUME.\r\nIn a similar way, one can define two-bit primes: prime numbers whose first two digits can be shifted, as a unit, elsewhere in the number to form a new prime number. The first in the sequence is 113, with accompanying prime 311, followed by 131, with accompanying prime 113. Some two-bit primes can have multiple accompanying primes. For example, the two-bit prime 99611 has two accompanying primes: 69911 and 61991. Leading zeros are allowed: 1103 is a two-bit prime because 311 is also prime. \r\nRemember that the two-bit prime and its accompanying prime must differ. For example, the first two digits of 99991 can be moved one or two places to the right, but the resulting number (99991) is the same. Because 99199 is not prime, 99991 is not a two-bit prime. \r\nWrite a function to list the th two-bit and all its accompanying primes. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 321px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 160.5px; transform-origin: 407px 160.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 313.108px 8px; transform-origin: 313.108px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach year at Christmas, my father-in-law and his partner send me the Puzzle Mania section from the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 52.8417px 8px; transform-origin: 52.8417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eNew York Times.\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 17.1083px 8px; transform-origin: 17.1083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e This year’s edition had a puzzle by Mike Shenk called “Two Bits”, which asked solvers to find words whose first two letters could be shifted as a unit elsewhere in the word to form a new word. For example, the clue “??BID” had the answer RABID, with the accompanying word BRAID. The clue “??PREME” had the answer SUPREME, with the accompanying word PRESUME.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.058px 8px; transform-origin: 381.058px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIn a similar way, one can define two-bit primes: prime numbers whose first two digits can be shifted, as a unit, elsewhere in the number to form a new prime number. The first in the sequence is 113, with accompanying prime 311, followed by 131, with accompanying prime 113. Some two-bit primes can have multiple accompanying primes. For example, the two-bit prime 99611 has two accompanying primes: 69911 and 61991. Leading zeros are allowed: 1103 is a two-bit prime because 311 is also prime. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.875px 8px; transform-origin: 379.875px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eRemember that the two-bit prime and its accompanying prime must differ. For example, the first two digits of 99991 can be moved one or two places to the right, but the resulting number (99991) is the same. Because 99199 is not prime, 99991 is not a two-bit prime. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.375px 8px; transform-origin: 80.375px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to list the \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 133.808px 8px; transform-origin: 133.808px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eth two-bit and all its accompanying primes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,a] = twobitPrime(n)\r\n  y = isprime(n); a = primes(n);\r\nend","test_suite":"%%\r\nn = 1;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 113;\r\na_correct = 311;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 5;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 199;\r\na_correct = 919;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 31;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 977;\r\na_correct = 797;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 311;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 5119;\r\na_correct = 1951;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 500;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 7963;\r\na_correct = [6379 6793];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),sort(a_correct)))\r\n\r\n%%\r\nn = 873;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 13591;\r\na_correct = 59113;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 873;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 13591;\r\na_correct = 59113;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 1876;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 29009;\r\na_correct = [929 2909];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 5277;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 86069;\r\na_correct = [6869 8669];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 8153;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 134921;\r\na_correct = 492113;\r\nassert(isequal(y,y_correct))\r\nassert(isequal(a,a_correct))\r\n\r\n%%\r\nn = 10003;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 168617;\r\na_correct = [861167 861617];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 25000;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 433261;\r\na_correct = [324361 326143 343261];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nn = 39000;\r\n[y,a] = twobitPrime(n);\r\ny_correct = 697877;\r\na_correct = [787697 787769];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\n[y,a] = twobitPrime(twobitPrime(54));\r\ny_correct = 19853;\r\na_correct = [81953 85193];\r\nassert(isequal(y,y_correct))\r\nassert(isequal(sort(a),a_correct))\r\n\r\n%%\r\nfiletext = fileread('twobitPrime.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2023-03-19T00:22:09.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-03-19T00:21:05.000Z","updated_at":"2025-01-02T08:51:14.000Z","published_at":"2023-03-19T00:22:09.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach year at Christmas, my father-in-law and his partner send me the Puzzle Mania section from the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNew York Times.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e This year’s edition had a puzzle by Mike Shenk called “Two Bits”, which asked solvers to find words whose first two letters could be shifted as a unit elsewhere in the word to form a new word. For example, the clue “??BID” had the answer RABID, with the accompanying word BRAID. The clue “??PREME” had the answer SUPREME, with the accompanying word PRESUME.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a similar way, one can define two-bit primes: prime numbers whose first two digits can be shifted, as a unit, elsewhere in the number to form a new prime number. The first in the sequence is 113, with accompanying prime 311, followed by 131, with accompanying prime 113. Some two-bit primes can have multiple accompanying primes. For example, the two-bit prime 99611 has two accompanying primes: 69911 and 61991. Leading zeros are allowed: 1103 is a two-bit prime because 311 is also prime. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRemember that the two-bit prime and its accompanying prime must differ. For example, the first two digits of 99991 can be moved one or two places to the right, but the resulting number (99991) is the same. Because 99199 is not prime, 99991 is not a two-bit prime. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to list the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eth two-bit and all its accompanying primes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44752,"title":"Lights Out 2 - 5x5, 4 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require four moves to solve. For example, if\r\n\r\n board = [1 0 1 1 1  \r\n          1 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 1 1  \r\n          1 1 0 0 1]\r\n\r\nthe answer is:\r\n\r\n moves = [2 5 16 24]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves 5x5, 3 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves 5x5, 6 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require four moves to solve. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 1 1 1  \r\n          1 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 1 1  \r\n          1 1 0 0 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [2 5 16 24]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003e5x5, 3 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\"\u003e5x5, 6 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_2(board) % 5x5 board, 4 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 1 1 1  \r\n          1 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 1 1  \r\n          1 1 0 0 1];\r\nmoves = lights_out_2(board); % [2 5 16 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 1 0 0 0  \r\n          1 1 0 0 0  \r\n          0 1 0 0 0  \r\n          1 0 0 0 0];\r\nmoves = lights_out_2(board); % [1 2 3 4]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_2(board); % [7 9 17 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 0 1 1 0  \r\n          0 1 1 1 1  \r\n          0 1 1 1 1  \r\n          0 0 1 1 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_2(board); % [12 13 17 18]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_2(board); % [8 12 14 18]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 0 1  \r\n          0 0 1 1 1  \r\n          0 0 0 1 1  \r\n          0 1 0 0 1  \r\n          1 1 1 0 0];\r\nmoves = lights_out_2(board); % on your own now\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 0 0  \r\n          1 0 0 1 0  \r\n          0 1 1 1 1  \r\n          0 0 1 0 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 0 0  \r\n          0 0 0 1 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          0 1 0 1 0  \r\n          0 1 1 0 0  \r\n          1 0 1 0 0  \r\n          1 0 1 0 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 0 0 1 0  \r\n          0 1 0 0 0  \r\n          0 0 0 1 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_2(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==4)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":3,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":24,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-10-29T14:43:57.000Z","updated_at":"2025-11-29T15:31:35.000Z","published_at":"2018-10-31T12:25:12.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require four moves to solve. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 1 1 1  \\n          1 1 0 1 0  \\n          1 0 0 0 1  \\n          1 0 0 1 1  \\n          1 1 0 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [2 5 16 24]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 3 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44753-lights-out-3-5x5-6-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44761,"title":"Lights Out 9 - 5x5, light-only solution? II","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state contains a potential answer—i.e., if toggling any given subset of the starting lights is sufficient to solve the board.\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44760 5x5, light-only solution? I\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44764 5x5, with wrapping, 6 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state contains a potential answer—i.e., if toggling any given subset of the starting lights is sufficient to solve the board.\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44760\"\u003e5x5, light-only solution? I\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44764\"\u003e5x5, with wrapping, 6 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function tf = lights_out_9(board) % 5x5 board, lights-only subset solution\r\n tf = 0;\r\nend","test_suite":"%% all true cases first\r\n board = [0 1 0 0 0\r\n          1 1 1 0 1\r\n          0 1 0 1 1\r\n          1 0 0 0 1\r\n          1 1 0 0 0];\r\nassert(lights_out_9(board)); % [5 7 23]\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 1];\r\nassert(lights_out_9(board)); % [1 13 25]\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 1 1 1 1  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board)); % [9 15 19]\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 1 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 0 1 0];\r\nassert(lights_out_9(board)); % [7 9 17 19]\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 1 0  \r\n          1 0 0 0 1];\r\nassert(lights_out_9(board)); % [1 5 7 9 17 19 21 25]\r\n\r\n%% \r\n board = [0 0 1 1 0  \r\n          0 1 1 1 1  \r\n          0 1 1 1 1  \r\n          0 0 1 1 0  \r\n          0 0 0 0 0];\r\nassert(lights_out_9(board)); % [12 13 17 18]\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board)); % [8 12 14 18]\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 0 0  \r\n          1 0 0 1 0  \r\n          0 1 1 1 1  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board)); % [3 9 15 19]\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 1 0 1];\r\nassert(lights_out_9(board)); % [1 5 11 15 21 25]\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1  \r\n          0 1 1 1 0];\r\nassert(lights_out_9(board)); % [7 8 9 12 14 17 18 19]\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nassert(lights_out_9(board)); % [2 6 8 12 14 18 20 24]\r\n\r\n%% \r\n board = [0 1 1 1 1  \r\n          1 0 1 0 0  \r\n          1 1 1 0 0  \r\n          1 0 1 1 0  \r\n          0 1 0 0 0];\r\nassert(lights_out_9(board)); % on your own\r\n\r\n%% \r\n board = [1 1 1 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          1 1 1 1 1];\r\nassert(lights_out_9(board));\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1  \r\n          0 1 1 1 0];\r\nassert(lights_out_9(board));\r\n\r\n%% \r\n board = [0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0];\r\nassert(lights_out_9(board));\r\n\r\n\r\n%% false cases start here\r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nassert(~lights_out_9(board)); % [7 8 9 17 18 19]\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 1 0 1 1  \r\n          1 0 0 0 1  \r\n          1 1 0 1 0  \r\n          0 0 1 1 0];\r\nassert(~lights_out_9(board)); % [1 2 5 10 16 21 24 25]\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          1 1 0 1 0  \r\n          0 0 0 0 0  \r\n          0 1 0 0 1  \r\n          0 1 0 0 0];\r\nassert(~lights_out_9(board)); % [2 5:6 8:11 17:24]\r\n\r\n%% \r\n board = [1 0 1 0 0  \r\n          0 1 1 0 0  \r\n          1 0 1 0 0  \r\n          0 1 1 0 0  \r\n          0 0 0 0 0];\r\nassert(~lights_out_9(board)); % on your own\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          1 0 1 0 0  \r\n          1 1 1 1 0  \r\n          0 1 1 0 1  \r\n          0 1 0 1 0];\r\nassert(~lights_out_9(board));\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 0 1 0 0  \r\n          0 1 1 1 0  \r\n          0 0 1 0 0  \r\n          0 0 0 0 0];\r\nassert(lights_out_9(board));\r\n\r\n%% \r\n board = [1 0 1 1 0  \r\n          1 0 0 1 1  \r\n          0 0 0 0 0  \r\n          1 1 0 0 1  \r\n          0 1 1 0 1];\r\nassert(~lights_out_9(board));\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2019-05-02T12:12:00.000Z","rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-30T14:14:44.000Z","updated_at":"2025-11-29T15:23:35.000Z","published_at":"2019-04-22T15:59:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve. The function you write has to determine if the initial state contains a potential answer—i.e., if toggling any given subset of the starting lights is sufficient to solve the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44760\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? I\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44764\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, with wrapping, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44764,"title":"Lights Out 10 - 5x5, with wrapping, 6 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require six moves to solve. However, now wrapping of the lights occurs. For example, if \r\n\r\n board = [1 0 0 0 1\r\n          1 0 1 0 1\r\n          0 0 0 0 0\r\n          1 0 1 0 1\r\n          1 0 0 0 1]\r\n\r\nthe answer is:\r\n\r\n moves = [1 5 11 15 21 25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44761 5x5, light-only solution? II\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44765 5x5, wrapping, x moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require six moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 0 0 1\r\n          1 0 1 0 1\r\n          0 0 0 0 0\r\n          1 0 1 0 1\r\n          1 0 0 0 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 5 11 15 21 25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44761\"\u003e5x5, light-only solution? II\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44765\"\u003e5x5, wrapping, x moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_10(board) % 5x5 board, with wrapping, 6 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 0 0 1  \r\n          1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1  \r\n          1 0 0 0 1];\r\nmoves = lights_out_10(board); % [1 5 11 15 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          1 0 1 1 0  \r\n          1 1 0 1 1  \r\n          0 1 1 0 1  \r\n          0 0 1 1 0];\r\nmoves = lights_out_10(board); % [4 9 10 16 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_10(board); % [7 8 9 17 18 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 1 0 1  \r\n          1 1 1 0 0  \r\n          0 0 0 0 1  \r\n          1 0 1 0 1  \r\n          1 0 1 0 0];\r\nmoves = lights_out_10(board); % [4 8 11 13 17 22]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          1 0 1 0 0  \r\n          0 0 0 1 0  \r\n          0 1 0 1 1];\r\nmoves = lights_out_10(board); % [7 8 12 14 15 21]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 0 1  \r\n          0 1 1 1 1  \r\n          0 1 0 0 0  \r\n          0 0 1 0 0];\r\nmoves = lights_out_10(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 1 0 1  \r\n          1 1 0 0 0  \r\n          1 0 0 1 1  \r\n          1 1 0 1 0];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [1 0 0 0 0  \r\n          1 0 0 1 1  \r\n          0 0 0 0 1  \r\n          1 0 0 0 1  \r\n          1 0 1 0 1];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 0 0 1 0  \r\n          0 0 0 1 0  \r\n          0 0 0 0 1  \r\n          1 0 1 1 0  \r\n          1 1 1 1 0];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [1 0 1 0 0  \r\n          0 0 0 0 1  \r\n          1 1 0 0 1  \r\n          1 0 0 0 1  \r\n          1 1 1 0 1];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n\r\n%% \r\n board = [0 1 0 1 1  \r\n          0 0 0 0 1  \r\n          0 0 0 1 1  \r\n          0 1 1 0 0  \r\n          1 1 0 0 0];\r\nmoves = lights_out_10(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\nassert(numel(moves)==6)\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2019-04-30T11:31:05.000Z","rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-31T16:24:02.000Z","updated_at":"2025-11-29T15:08:07.000Z","published_at":"2019-04-22T16:00:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require six moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 0 0 1\\n          1 0 1 0 1\\n          0 0 0 0 0\\n          1 0 1 0 1\\n          1 0 0 0 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 5 11 15 21 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44761\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, light-only solution? II\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44765\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, wrapping, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44766,"title":"Lights Out 12 - 5x5, three stages, \u003c7 moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem is back to no wrapping and contains boards that each require six or fewer moves to solve. However, now lights are activated through three stages, rather than two, cycling from on1 (1) to on2 (2) to off (0). For example, if \r\n\r\n board = [1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 2 0  \r\n          0 0 2 2 2  \r\n          0 0 0 2 0];\r\n\r\nthe answer is:\r\n\r\n moves = [1 1 19]\r\n\r\nsince the first \"1\" will change the 1's in (1,1), (1,2), and (2,1) to 2's. The second \"1\" will then change those three values to zero, while the \"19\" will bump the five 2's to zero. Therefore, up to two moves are possible for each button (index).\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44765 5x5, wrapping, any number of moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44767 5x5, 3 stages, x moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem is back to no wrapping and contains boards that each require six or fewer moves to solve. However, now lights are activated through three stages, rather than two, cycling from on1 (1) to on2 (2) to off (0). For example, if\u003c/p\u003e\u003cpre\u003e board = [1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 2 0  \r\n          0 0 2 2 2  \r\n          0 0 0 2 0];\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 1 19]\u003c/pre\u003e\u003cp\u003esince the first \"1\" will change the 1's in (1,1), (1,2), and (2,1) to 2's. The second \"1\" will then change those three values to zero, while the \"19\" will bump the five 2's to zero. Therefore, up to two moves are possible for each button (index).\u003c/p\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44765\"\u003e5x5, wrapping, any number of moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44767\"\u003e5x5, 3 stages, x moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_12(board) % 5x5 board, three stages, \u003c7 moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 2 0  \r\n          0 0 2 2 2  \r\n          0 0 0 2 0];\r\nmoves = lights_out_12(board); % [1 1 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [2 2 0 2 2  \r\n          2 0 2 0 2  \r\n          0 2 2 2 0  \r\n          2 0 2 0 2  \r\n          2 2 0 2 2];\r\nmoves = lights_out_12(board); % [1 5 13 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 1 1 0 1  \r\n          0 1 0 1 1  \r\n          1 0 0 0 1  \r\n          1 1 0 0 0];\r\nmoves = lights_out_12(board); % [5 5 7 7 23 23]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [2 1 2 0 0  \r\n          2 0 2 0 0  \r\n          2 0 2 0 0  \r\n          2 0 2 0 0  \r\n          2 1 2 0 0];\r\nmoves = lights_out_12(board); % [6:10]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          2 0 0 0 0  \r\n          1 1 0 0 0  \r\n          2 0 0 0 0  \r\n          1 1 0 0 0];\r\nmoves = lights_out_12(board); % [1 1 3 3 5 5]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 2 0 2 0  \r\n          2 2 0 2 2  \r\n          0 0 2 0 0  \r\n          2 2 0 2 2  \r\n          0 2 0 2 0];\r\nmoves = lights_out_12(board); % [7 9 13 17 19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 2 0 2 0  \r\n          2 1 1 1 2  \r\n          2 0 1 0 2  \r\n          2 1 1 1 2  \r\n          0 2 0 2 0];\r\nmoves = lights_out_12(board); % [7:9 17:19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          0 2 1 2 0  \r\n          2 0 2 0 2  \r\n          0 2 1 2 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_12(board); % [8 13 13 18]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 2 0 2 1  \r\n          0 1 1 1 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_12(board); % [7 7 12 12 17 17]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 1 2  \r\n          0 0 2 2 1  \r\n          0 1 0 2 2  \r\n          0 1 1 2 2  \r\n          2 0 0 0 2];\r\nmoves = lights_out_12(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 2 1 0 0  \r\n          2 2 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0];\r\nmoves = lights_out_12(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 0 0 0  \r\n          2 1 1 0 0  \r\n          2 0 0 2 0  \r\n          0 1 1 2 0];\r\nmoves = lights_out_12(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),3); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-31T17:08:51.000Z","updated_at":"2025-11-04T21:21:50.000Z","published_at":"2019-05-02T12:25:05.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is back to no wrapping and contains boards that each require six or fewer moves to solve. However, now lights are activated through three stages, rather than two, cycling from on1 (1) to on2 (2) to off (0). For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 1 0 0 0  \\n          1 0 0 0 0  \\n          0 0 0 2 0  \\n          0 0 2 2 2  \\n          0 0 0 2 0];]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 1 19]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003esince the first \\\"1\\\" will change the 1's in (1,1), (1,2), and (2,1) to 2's. The second \\\"1\\\" will then change those three values to zero, while the \\\"19\\\" will bump the five 2's to zero. Therefore, up to two moves are possible for each button (index).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44765\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, wrapping, any number of moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44767\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 3 stages, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44776,"title":"Lights Out 15 - 5x5, broken buttons I","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains 5x5 boards that require any number of moves to solve. However, the game has a glitch now—each time you press a button, it doesn't toggle itself, only those lights that are adjacent. That is to say, all buttons toggle two, three, or four lights (indices), rather than the normal three, four, or five, respectively.\r\n\r\nFor example, if:\r\n\r\n board = [0 1 0 0 0  \r\n          1 0 0 1 0  \r\n          0 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 0]\r\n\r\nthe answer is:\r\n\r\n moves = [1 10 18]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44769 5x5, four stages, x moves\u003e — \r\nNext: [Check back later for new problems in the series.]","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains 5x5 boards that require any number of moves to solve. However, the game has a glitch now—each time you press a button, it doesn't toggle itself, only those lights that are adjacent. That is to say, all buttons toggle two, three, or four lights (indices), rather than the normal three, four, or five, respectively.\u003c/p\u003e\u003cp\u003eFor example, if:\u003c/p\u003e\u003cpre\u003e board = [0 1 0 0 0  \r\n          1 0 0 1 0  \r\n          0 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 0]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [1 10 18]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44769\"\u003e5x5, four stages, x moves\u003c/a\u003e — \r\nNext: [Check back later for new problems in the series.]\u003c/p\u003e","function_template":"function moves = lights_out_15(board) % 5x5 board, any number of moves, broken buttons I\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [0 1 0 0 0  \r\n          1 0 0 1 0  \r\n          0 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 0];\r\nmoves = lights_out_15(board); % [1 10 18]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 1 0 0  \r\n          0 1 0 1 0  \r\n          0 0 1 0 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_15(board); % [7 19]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_15(board); % [1 5 13 21 25]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          1 0 1 0 0  \r\n          1 0 1 0 0  \r\n          1 0 1 0 0  \r\n          1 1 1 0 0];\r\nmoves = lights_out_15(board); % [6:10]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 1 0 0  \r\n          1 0 1 1 1  \r\n          1 0 1 0 1  \r\n          1 0 0 1 1  \r\n          0 1 0 1 1];\r\nmoves = lights_out_15(board); % [2 5 7 11:13 19 21 24]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_15(board); % [2:4 6 10:11 15:16 20 22:24]\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_15(board); % on your own\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 1 0 1  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          1 0 1 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 1 0  \r\n          1 1 0 1 0  \r\n          0 1 0 1 0  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 1  \r\n          1 0 0 0 1  \r\n          1 0 0 0 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 1 0 0 0  \r\n          0 1 0 0 0  \r\n          0 0 1 1 1  \r\n          0 0 0 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          1 0 1 0 0  \r\n          1 1 0 0 0  \r\n          0 0 1 0 1  \r\n          0 1 0 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          1 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 0 1  \r\n          0 0 0 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 1 0  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          0 0 0 0 0  \r\n          0 0 0 1 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 0 0 0 0  \r\n          1 0 0 1 1  \r\n          1 0 0 0 0  \r\n          0 1 1 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          1 1 0 1 1  \r\n          0 1 1 0 1  \r\n          0 1 1 1 1  \r\n          1 1 0 1 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 1 0  \r\n          1 0 0 1 0  \r\n          0 1 0 1 0  \r\n          0 1 0 0 1  \r\n          0 1 0 0 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 0 0  \r\n          0 1 0 0 1  \r\n          0 0 0 0 0  \r\n          0 0 1 0 1  \r\n          0 0 1 1 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 0 0  \r\n          0 1 1 1 1  \r\n          0 0 1 0 0  \r\n          1 0 0 0 0  \r\n          0 1 0 0 0];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 0 0 0  \r\n          1 0 0 1 0  \r\n          1 1 0 1 0  \r\n          1 1 1 1 1  \r\n          0 0 0 1 1];\r\nmoves = lights_out_15(board);\r\nb1 = diag(ones(1,4),1) + diag(ones(1,4),-1); b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b3;b2,b1,b2,b3,b3;b3,b2,b1,b2,b3;b3,b3,b2,b1,b2;b3,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2018-11-06T13:17:12.000Z","updated_at":"2025-11-04T21:41:48.000Z","published_at":"2019-09-16T10:59:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains 5x5 boards that require any number of moves to solve. However, the game has a glitch now—each time you press a button, it doesn't toggle itself, only those lights that are adjacent. That is to say, all buttons toggle two, three, or four lights (indices), rather than the normal three, four, or five, respectively.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [0 1 0 0 0  \\n          1 0 0 1 0  \\n          0 0 1 0 1  \\n          0 1 0 1 0  \\n          1 0 1 0 0]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [1 10 18]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44769\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, four stages, x moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next: [Check back later for new problems in the series.]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2036,"title":"Santa's Cards","description":"This Challenge is inspired by the \u003chttp://www.kaggle.com/c/packing-santas-sleigh Packing Santa's Sleigh\u003e contest at kaggle that runs until January 26, 2014. Will Elfonso win another kaggle contest? The kaggle contest is a 3-D fitting/optimization.\r\n\r\nThis Challenge is to place Santa Cards in a minimum area given a board width(columns) and a set of N cards of (rows,columns). Cards can not be rotated.\r\n\r\n*Input*: Card_Array, W ; Card_Array(N,2) and W=width of M array\r\n\r\n*Output*: M ; An X by W array of values 0:N, 0 is unused space\r\n\r\n*Scoring*: Number of rows required to place all cards\r\n\r\n*Example*:\r\n\r\n[2 2;3 3;1 2], 5\r\n\r\nM\r\n\r\n  1 1 2 2 2\r\n  1 1 2 2 2\r\n  3 3 2 2 2\r\n\r\nScores a 3, number of rows\r\n\r\n*Contest Results:*\r\nAlfonso put in a tremendous 40,000 point reduction in the last days and appeared unsurpassable. However, a pair of Polish Professors in Mathematics and Computer Science, Marek and Cygan, submitted an astounding further 34,000 point improvement for the win. Alfonso won the Matlab category prize.","description_html":"\u003cp\u003eThis Challenge is inspired by the \u003ca href = \"http://www.kaggle.com/c/packing-santas-sleigh\"\u003ePacking Santa's Sleigh\u003c/a\u003e contest at kaggle that runs until January 26, 2014. Will Elfonso win another kaggle contest? The kaggle contest is a 3-D fitting/optimization.\u003c/p\u003e\u003cp\u003eThis Challenge is to place Santa Cards in a minimum area given a board width(columns) and a set of N cards of (rows,columns). Cards can not be rotated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput\u003c/b\u003e: Card_Array, W ; Card_Array(N,2) and W=width of M array\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput\u003c/b\u003e: M ; An X by W array of values 0:N, 0 is unused space\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring\u003c/b\u003e: Number of rows required to place all cards\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e[2 2;3 3;1 2], 5\u003c/p\u003e\u003cp\u003eM\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1 1 2 2 2\r\n1 1 2 2 2\r\n3 3 2 2 2\r\n\u003c/pre\u003e\u003cp\u003eScores a 3, number of rows\u003c/p\u003e\u003cp\u003e\u003cb\u003eContest Results:\u003c/b\u003e\r\nAlfonso put in a tremendous 40,000 point reduction in the last days and appeared unsurpassable. However, a pair of Polish Professors in Mathematics and Computer Science, Marek and Cygan, submitted an astounding further 34,000 point improvement for the win. Alfonso won the Matlab category prize.\u003c/p\u003e","function_template":"function m=cards(c,nc)\r\n % cards array row=card; [h,w]\r\n % fill array with values 1:size(c,1)\r\n % m is nc wide\r\n m=zeros(sum(c(:,1)),nc);\r\nend","test_suite":"assignin('caller','score',30);\r\n%%\r\nnc=8;\r\nc=[2 2;4 4;2 2;3 3;2 2;1 2;1 8;1 2;4 4;3 3;3 2];\r\nm=cards(c,nc)\r\n\r\nassert(size(m,2)==nc)\r\n\r\nfor i=1:size(c,1)\r\n mt=double(m==i);\r\n mtc=conv2(mt,ones(c(i,:)),'same');\r\n assert(nnz(mtc==c(i,1)*c(i,2))==1)\r\nend\r\n\r\n\r\nassignin('caller','score',size(m,1));\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-06T02:03:07.000Z","updated_at":"2014-01-29T00:17:00.000Z","published_at":"2013-12-06T02:19:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is inspired by the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/packing-santas-sleigh\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePacking Santa's Sleigh\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest at kaggle that runs until January 26, 2014. Will Elfonso win another kaggle contest? The kaggle contest is a 3-D fitting/optimization.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to place Santa Cards in a minimum area given a board width(columns) and a set of N cards of (rows,columns). Cards can not be rotated.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Card_Array, W ; Card_Array(N,2) and W=width of M array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: M ; An X by W array of values 0:N, 0 is unused space\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Number of rows required to place all cards\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[2 2;3 3;1 2], 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1 1 2 2 2\\n1 1 2 2 2\\n3 3 2 2 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eScores a 3, number of rows\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eContest Results:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Alfonso put in a tremendous 40,000 point reduction in the last days and appeared unsurpassable. However, a pair of Polish Professors in Mathematics and Computer Science, Marek and Cygan, submitted an astounding further 34,000 point improvement for the win. Alfonso won the Matlab category prize.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2026,"title":"Skyscrapers - Puzzle","description":"The Skyscraper puzzle challenge comes from \u003chttp://logicmastersindia.com/home/ Logic Masters India\u003e and \u003chttp://www.conceptispuzzles.com/ Games' Concept is Puzzles\u003e. \r\n\r\nCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\r\n\r\n*Input:* [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\r\n\r\n*Output:* M  an NxN matrix\r\n\r\n*Example:*\r\n\r\n  vr=[0 0 3 0 0]';\r\n  vL=[3 0 0 1 0]';\r\n  vd=[0 0 0 0 0];\r\n  vu=[5 2 0 0 0];\r\n\r\n  M\r\n         5     4     2     1     3\r\n         4     5     1     3     2\r\n         3     2     4     5     1\r\n         2     1     3     4     5\r\n         1     3     5     2     4\r\n\r\n*Algorithm Discussion:*\r\n\r\n  1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n  2) Calc Skyscraper count from Left and Right\r\n  3) Determine subset of SkyVectors possible for each Row and Column\r\n  4) Sort the Qty of 2*N possible solutions\r\n  5) Recursion from least to most valid SkyVectors\r\n  6) In recursion verify valid overlay or return\r\n","description_html":"\u003cp\u003eThe Skyscraper puzzle challenge comes from \u003ca href = \"http://logicmastersindia.com/home/\"\u003eLogic Masters India\u003c/a\u003e and \u003ca href = \"http://www.conceptispuzzles.com/\"\u003eGames' Concept is Puzzles\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e M  an NxN matrix\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003evr=[0 0 3 0 0]';\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eM\r\n       5     4     2     1     3\r\n       4     5     1     3     2\r\n       3     2     4     5     1\r\n       2     1     3     4     5\r\n       1     3     5     2     4\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm Discussion:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\r\n2) Calc Skyscraper count from Left and Right\r\n3) Determine subset of SkyVectors possible for each Row and Column\r\n4) Sort the Qty of 2*N possible solutions\r\n5) Recursion from least to most valid SkyVectors\r\n6) In recursion verify valid overlay or return\r\n\u003c/pre\u003e","function_template":"function m=solve_skyscrapers(vr,vL,vd,vu)\r\n m=[];\r\nend","test_suite":"%%\r\n%Games Feb 2014 #1\r\nvr=[0 0 1 0 5]'; %1\r\nvL=[0 4 4 0 0]';\r\nvd=[2 2 0 1 3];\r\nvu=[3 0 0 2 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd; % view down check\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view Left check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m); % view Up check\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #2\r\nvr=[0 4 0 2 0]'; %2\r\nvL=[5 1 0 0 0]';\r\nvd=[0 0 3 0 0];\r\nvu=[4 1 2 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #3\r\nvr=[5 2 2 0 0]'; %3\r\nvL=[0 3 0 3 4]';\r\nvd=[5 0 0 0 0];\r\nvu=[0 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #4\r\nvr=[0 0 4 5 0]'; %4\r\nvL=[0 0 0 0 0]';\r\nvd=[2 0 2 3 0];\r\nvu=[0 0 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n%%\r\n%Games Feb 2014 #5\r\nvr=[3 5 0 0 0]'; %5\r\nvL=[0 0 4 0 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[2 0 1 0 2];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\n\r\n%%\r\nvr=[0 0 3 0 0]'; %Games Feb 2014 #6\r\nvL=[3 0 0 1 0]';\r\nvd=[0 0 0 0 0];\r\nvu=[5 2 0 0 0];\r\n\r\ntic\r\nm=solve_skyscrapers(vr,vL,vd,vu)\r\ntoc\r\n\r\nnr=length(vr);\r\nnrsum=nr*(nr+1)/2;\r\nassert(nr*nrsum==sum(m(:)))\r\nassert(nr==size(m,1));\r\nassert(nr==size(m,2));\r\nassert(all(sum(m)==nrsum));\r\nassert(all(sum(m,2)==nrsum));\r\n\r\nmt=m; % view right check\r\nvz=vr;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nvz=vd;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\n\r\nmt=fliplr(m); % view right check\r\nvz=vL;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(k,1);\r\n for z=2:nr\r\n  if mt(k,z)\u003eshi\r\n   shi=mt(k,z);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)); % Assert check of valid count\r\n end % if\r\nend % k\r\n\r\nmt=flipud(m);\r\nvz=vu;\r\nfor k=1:nr\r\n if vz(k)\u003e0\r\n c=1;\r\n shi=mt(1,k);\r\n for z=2:nr\r\n  if mt(z,k)\u003eshi\r\n   shi=mt(z,k);\r\n   c=c+1;\r\n  end\r\n end % z\r\n c;\r\n vz(k);\r\n assert(c==vz(k)) % Assert check of valid count\r\n end % if\r\nend % k\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":6,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-29T19:42:36.000Z","updated_at":"2026-01-08T14:21:06.000Z","published_at":"2013-11-29T22:09:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Skyscraper puzzle challenge comes from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://logicmastersindia.com/home/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLogic Masters India\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames' Concept is Puzzles\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate an NxN matrix where each row and column contains 1:N given the constraints of View_Right, View_Left, View_Down, and View_Up. A View is the number of Skyscrapers visible the given edge location. A Zero value is No Information provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e [vr,vL,vd,vu] vectors of sizes (N,1),(N,1),(1,N),(1,N)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e M an NxN matrix\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[vr=[0 0 3 0 0]';\\nvL=[3 0 0 1 0]';\\nvd=[0 0 0 0 0];\\nvu=[5 2 0 0 0];\\n\\nM\\n       5     4     2     1     3\\n       4     5     1     3     2\\n       3     2     4     5     1\\n       2     1     3     4     5\\n       1     3     5     2     4]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm Discussion:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Create permutations H and V vectors of length N of values 1:N. (N=5) [12345;12354;...54321]\\n2) Calc Skyscraper count from Left and Right\\n3) Determine subset of SkyVectors possible for each Row and Column\\n4) Sort the Qty of 2*N possible solutions\\n5) Recursion from least to most valid SkyVectors\\n6) In recursion verify valid overlay or return]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2072,"title":"Packing Santa's Sleigh: First Layer","description":"This Challenge is inspired by the \u003chttp://www.kaggle.com/c/packing-santas-sleigh Packing Santa's Sleigh\u003e contest at kaggle that runs until January 26, 2014.\r\n\r\nThe Bag has a 1000 x 1000 base with the contest having 1,000,000 packages.\r\n\r\nThis Challenge is to place as many of the first N Santa Presents onto the 1000 x 1000 grid. The packages have been presorted to have Z as max, not provided, thus minimizing the X*Y cross section. Only the first 240 packages are provided as package 241 busts the bag bottom making an area greater than 1,000,000. Packages out of sequence cause a severe penalty thus all packages less than the highest package used must fit on the board.\r\n\r\n*Input*: Presents ; Presents(240,2)\r\n\r\n*Output*: L, xyTL; L(1000,1000) of values 0:n\u003c=240, 0 is unused space\r\n\r\n*Scoring*: Unused Area\r\n\r\n*Example*:\r\n\r\n[2 2;3 3;1 2] is Presents\r\n\r\nL\r\n\r\n  1 1 2 2 2 0  thru column 1000\r\n  1 1 2 2 2\r\n  3 3 2 2 2\r\n  0 0 0 0 0 0 rows 4 thru 1000 are zeros\r\n\r\nxyTL\r\n[1 1;1 3;3 1]\r\n\r\nScores 1,000,000 - 15= 999985\r\n\r\nBoxes 1:236 are possible, 97.5719% efficient pack in \u003c 1sec.\r\n\r\n*TestSuite Sample Code:*\r\n\r\nIn the TestSuite at the end is wrapper code for entering the kaggle contest.\r\nUpdate your SantaPack routine and execute the wrapper to see your contest score. The official presents.mat file needs to be downloaded. The wrapper has further discussion on efficient methods and current scores. \r\n","description_html":"\u003cp\u003eThis Challenge is inspired by the \u003ca href = \"http://www.kaggle.com/c/packing-santas-sleigh\"\u003ePacking Santa's Sleigh\u003c/a\u003e contest at kaggle that runs until January 26, 2014.\u003c/p\u003e\u003cp\u003eThe Bag has a 1000 x 1000 base with the contest having 1,000,000 packages.\u003c/p\u003e\u003cp\u003eThis Challenge is to place as many of the first N Santa Presents onto the 1000 x 1000 grid. The packages have been presorted to have Z as max, not provided, thus minimizing the X*Y cross section. Only the first 240 packages are provided as package 241 busts the bag bottom making an area greater than 1,000,000. Packages out of sequence cause a severe penalty thus all packages less than the highest package used must fit on the board.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput\u003c/b\u003e: Presents ; Presents(240,2)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput\u003c/b\u003e: L, xyTL; L(1000,1000) of values 0:n\u0026lt;=240, 0 is unused space\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring\u003c/b\u003e: Unused Area\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e[2 2;3 3;1 2] is Presents\u003c/p\u003e\u003cp\u003eL\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1 1 2 2 2 0  thru column 1000\r\n1 1 2 2 2\r\n3 3 2 2 2\r\n0 0 0 0 0 0 rows 4 thru 1000 are zeros\r\n\u003c/pre\u003e\u003cp\u003exyTL\r\n[1 1;1 3;3 1]\u003c/p\u003e\u003cp\u003eScores 1,000,000 - 15= 999985\u003c/p\u003e\u003cp\u003eBoxes 1:236 are possible, 97.5719% efficient pack in \u0026lt; 1sec.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTestSuite Sample Code:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the TestSuite at the end is wrapper code for entering the kaggle contest.\r\nUpdate your SantaPack routine and execute the wrapper to see your contest score. The official presents.mat file needs to be downloaded. The wrapper has further discussion on efficient methods and current scores.\u003c/p\u003e","function_template":"function  [L,xyTL]=SantaPack(p)\r\n% p is an Nx2\r\n% xyTL is Top Left position of pieces 1:n.\r\n% xyTL created for Speed vs find in a 1Kx1K array\r\n% Place 1:n of p onto L, a 1000x1000 array\r\n% Put p row onto L array. \r\n% If p(1,:) is [2 3]\r\n%[2 3] may be placed as [1 1 1;1 1 1] or [1 1; 1 1;1 1] for box 1\r\n% Note: big boxes typically pack to 95% and small boxes to \u003e98%\r\n\r\n L=zeros(1000);\r\n xyTL=p*0;\r\n\r\n% Placing first 16 pieces\r\n% No piece exceeds 250x250\r\n pxy=[1 251 501 751];\r\n piece=1;\r\n for i=1:4\r\n  for j=1:4\r\n% putting piece on layer\r\n   L(pxy(i):pxy(i)+p(piece,1)-1,pxy(j):pxy(j)+p(piece,2)-1)=piece; \r\n% TL Location of piece\r\n   xyTL(piece,:)=[pxy(i) pxy(j)]; \r\n   piece=piece+1;\r\n  end\r\n end\r\n %figure(3);imagesc(L)\r\n \r\nend % SantaPack","test_suite":"assignin('caller','score',100000);\r\n%%\r\nSanta_L1=[3     2;207    73;160    78;8     3;\r\n     9     9;\r\n   170   120;116    91;\r\n   206   142;28     8;\r\n     3     2;41    22;\r\n    31    11;28    20;\r\n    29    13;98    96;\r\n    26     4;34     9;\r\n     4     3;84    78;\r\n   219   114;28    22;\r\n   195   185;3     2;\r\n    31     9;104   101;\r\n    32    31;188   142;\r\n    45    18;8     2;\r\n    13    10;49    22;\r\n   172    72;28    17;\r\n    90    87;33     5;\r\n    32    23;16    14;\r\n    42    18;32     8;\r\n     7     5;6     6;\r\n   201    69;20    11;\r\n    30    18;211   120;\r\n   206    97;3     2;\r\n   124    92;48    43;\r\n     2     2;173   103;\r\n    26    12;8     7;\r\n     8     8;57    33;\r\n    21    20;24    15;\r\n    26    12;44    24;\r\n    30     8;\r\n    43    26;\r\n    23    23;\r\n     9     4;\r\n    16    13;\r\n    58    29;\r\n   133   125;\r\n     5     2;\r\n   197   117;\r\n    39    10;\r\n    31    11;\r\n    41    18;\r\n     6     3;\r\n    31     8;\r\n    40    32;\r\n    41    39;\r\n    36    30;\r\n     2     2;\r\n    25    24;\r\n     6     2;\r\n     3     2;\r\n     2     2;\r\n    85    70;\r\n    37    25;\r\n    24    20;\r\n    60    26;\r\n    29    14;\r\n    49    30;\r\n   153    75;\r\n     6     3;\r\n     7     3;\r\n   185   162;\r\n     4     2;\r\n     6     5;\r\n   176    99;\r\n     4     2;\r\n   219   154;\r\n    24    22;\r\n   148    87;\r\n    32     7;\r\n   143    98;\r\n    23    13;\r\n   150    65;\r\n     5     2;\r\n    53    41;\r\n    25    12;\r\n    36     6;\r\n    21    10;\r\n   211    79;\r\n   183   130;\r\n     6     3;\r\n    36    28;\r\n    32    16;\r\n    21    15;\r\n    27    26;\r\n    39    14;\r\n    36     7;\r\n    57    17;\r\n   214    90;\r\n    36     5;\r\n    27    16;\r\n    52    15;\r\n     8     6;\r\n     5     4;\r\n    52    37;\r\n     7     2;\r\n    92    79;\r\n    37    35;\r\n    33     5;\r\n     5     2;\r\n    52    10;\r\n    29     5;\r\n    44    18;\r\n     8     5;\r\n    16     8;\r\n   137   105;\r\n    78    74;\r\n     9     5;\r\n    39    29;\r\n    43    31;\r\n     6     3;\r\n     8     4;\r\n    26    19;\r\n    22     7;\r\n    30    15;\r\n   199   195;\r\n     7     7;\r\n     7     5;\r\n   134    81;\r\n   206   108;\r\n    54    29;\r\n    16     7;\r\n   116    99;\r\n    35    23;\r\n    31    17;\r\n    56    11;\r\n     7     3;\r\n    52     5;\r\n   102    99;\r\n     5     5;\r\n    35    17;\r\n     8     6;\r\n    51     7;\r\n    28    16;\r\n   107    83;\r\n    26     8;\r\n     8     6;\r\n   149    83;\r\n    45    29;\r\n    55    52;\r\n    27     6;\r\n    82    81;\r\n     9     5;\r\n    27    21;\r\n    19    10;\r\n    56    26;\r\n    19    14;\r\n    11     8;\r\n    47     7;\r\n    26    13;\r\n    36    19;\r\n    87    73;\r\n    14    10;223   100;2     2;33     5;198   135;38    15;19     8;211    95;9     6;21     7;175   145;22    16;7     5;7     4;9     8;42     5;3     3;2     2;3     2;5     2;30    24;29    29;27     9;168    72;6     4;22     7;9     6;10     6;19    16;7     2;43    14;138   115;138   130;39    20;9     4;27     7;26    22;169   144;8     8;41     9;50    26;62    10;33    19;7     2;121   112;102    93;109    88;9     8;40    40;25    19;31     8;55    23;41    11;6     2;8     3;128   114;40    16;7     6;5     2];\r\n[L,xyTL]=SantaPack(Santa_L1);\r\nptrxy=find(xyTL(:,1)\u003e0,1,'last');\r\n\r\npresents=Santa_L1;\r\nfor k=1:ptrxy\r\n  ptrxmin=xyTL(k,1);\r\n  ptrymin=xyTL(k,2);\r\n  assert(isequal(L(ptrxmin,ptrymin),k)) % Verify TL corner\r\n\r\n  if ptrxmin+presents(k,1)-1\u003e1000 || ptrymin+presents(k,2)-1\u003e1000\r\n% BR Corner verify for rotated only fit case\r\n    assert(isequal(L(ptrxmin+presents(k,2)-1,ptrymin+presents(k,1)-1),k))\r\n  elseif ptrxmin+presents(k,2)-1\u003e1000 || ptrymin+presents(k,1)-1\u003e1000\r\n% BR corner verify for non-rotated only fit case\r\n    assert(isequal(L(ptrxmin+presents(k,1)-1,ptrymin+presents(k,2)-1),k))\r\n  else % rot or non-rot case\r\n   v1=L(ptrxmin+presents(k,2)-1,ptrymin+presents(k,1)-1)==k;\r\n   v2=L(ptrxmin+presents(k,1)-1,ptrymin+presents(k,2)-1)==k;\r\n   assert(v1 || v2); % simple corner check\r\n  end\r\n% More robust checks may be implemented if needed\r\nend\r\n   \r\n\r\nA=Santa_L1(:,1).*Santa_L1(:,2);\r\nAs=sum(A(1:ptrxy))\r\nassignin('caller','score',min(100000,1000000-As));\r\n%%\r\n%{\r\nfunction SantaPack_Cody\r\n% www.kaggle.com Santa Packing Contest \r\n% 11/2013 thru January 2014\r\n% Given 1 Million Rectangularoid packages\r\n% Fit Packages into a Minimum Heigth Box with a base of 1000 x 1000\r\n% Rules allow presents out of order but this is virtually non-optimiziable\r\n% Presents out of order incur a penalty\r\n% Packing Construction Here:\r\n% All boxes dimension sorted [Mid, Min, Max]\r\n% Boxes 1:236 all have their tops on the same plane (97.5719% efficient pack)\r\n% Boxes 237:423 have their tops 250 lower in Z. Max Z of 1:236 is 250.\r\n% The very bottom layer, with box 1000000 has bottom box at Z=1\r\n% Note: Max dimension after box 700,000 is 70\r\n% This construction has min cross area per present, max dimension is placed on Z\r\n% Input is presents that have cumulative area \u003c= 1000000\r\n% The optimal score with perfect layer packing is \r\n% Layers 4098  zsum 996483  Score 1,992,966\r\n% Layers 4210, Score of 2,047,696 is possible with sequence layer packing\r\n% Kaggle Lead as of 12/21/2013 is 1,999,256. Unknown method.\r\n% Pack routine returns a 1000x1000 array with values 1:n, n\u003c=N\r\n% N is the Nth  box that fits in the 1,000,000 area limit\r\n% Next call uses n+1:N\r\n\r\nload presents  % available at kaggle site as a Mat file\r\nnumPresents = size(presents,1);\r\n\r\npresents(:,2:4)=sort(presents(:,2:4),2);\r\npresents(:,2:3)=fliplr(sort(presents(:,2:3),2)); % x\u003ey, z\u003ex\r\npresents=[presents presents(:,2).*presents(:,3)]; % Area of box tops\r\npresents=[presents cumsum(presents(:,end))]; % [presID x y z A Asum]\r\n\r\npe=0;\r\nLayer=0;\r\nzsum=0;\r\nz=-1;\r\npresentCoords=zeros(1000000,25);\r\n\r\ntic\r\nwhile pe\u003c1000000\r\n ps=pe+1; \r\n Asum=presents(ps:min(ps+5000,1000000),end); % valid for layer 1\r\n if pe\u003e0, Asum=Asum-presents(pe,end); end% remove prior layers sum\r\n ptr1M=find(Asum\u003c=1000000,1,'last');\r\n pe=ps+ptr1M-1;\r\n \r\n [L,xyTL]=SantaPack(presents(ps:pe,2:3)); % L has values 1 thru n, being ps thru ps+n-1\r\n % xyTL is Top Left position of pieces 1:n\r\n %figure(3);imagesc(L);\r\n\r\n pe=ps-1+find(xyTL(:,1)\u003e0,1,'last'); % find number of boxes placed\r\n zmax=max(presents(ps:pe,4));\r\n \r\n % Convert Layers to coordinates\r\n % Locate pieces in Layer and assign coordinate values\r\n % z axis values fixed in post processing to positives\r\n % Valid placement and sizes assumed\r\n for k=1:pe-ps+1\r\n  idx=k+ps-1; \r\n  ptrxmin=xyTL(k,1);\r\n  ptrymin=xyTL(k,2);\r\n  if ptrxmin+presents(idx,2)-1\u003c=1000 \u0026\u0026 ptrymin+presents(idx,3)-1\u003c=1000\r\n   if L(ptrxmin+presents(idx,2)-1,ptrymin+presents(idx,3)-1)==k\r\n    ptrxmax=ptrxmin+presents(idx,2)-1;\r\n    ptrymax=ptrymin+presents(idx,3)-1;\r\n   else\r\n    ptrxmax=ptrxmin+presents(idx,3)-1;\r\n    ptrymax=ptrymin+presents(idx,2)-1;\r\n   end\r\n  else % assumed placement if xmax(1)\u003e1000\r\n   ptrxmax=ptrxmin+presents(idx,3)-1;\r\n   ptrymax=ptrymin+presents(idx,2)-1;\r\n  end % if\r\n  \r\n % place this section inside SantaPack and output presentCoords vs L\r\n    presentCoords(idx,1) = idx;\r\n    presentCoords(idx,[2 8 14 20]) = ptrxmin;\r\n    presentCoords(idx,[5 11 17 23]) = ptrxmax;\r\n    presentCoords(idx,[3 6 15 18]) = ptrymin;\r\n    presentCoords(idx,[9 12 21 24]) = ptrymax;\r\n    presentCoords(idx,[4 7 10 13]) = z;\r\n    presentCoords(idx,[16 19 22 25]) = z - presents(idx,4) + 1;\r\n end % k\r\n \r\n z=z-zmax;\r\n Layer=Layer+1;\r\n zsum=zsum+zmax;\r\n fprintf('Layer %i Start %i  Final %i Zsum %i  Time %.2f\\n',Layer,ps,pe,zsum,toc) % Processing Status\r\n % Deep routine to 2M takes 30 minutes\r\n % Fast Placement takes 187 sec\r\n \r\nend  % pe\r\n\r\n% Offset Z coordinates \r\n% Bottom is 1 and very top is Positive\r\nzCoords = presentCoords(:,4:3:end);\r\nminZ = min(zCoords(:));\r\npresentCoords(:,4:3:end) = zCoords - minZ + 1;\r\n\r\n% Scoring function\r\n% Ideal order is the original order\r\npresentIDs = presents(:,1); %z\r\nidealOrder = presentIDs; \r\n\r\n% Determine the max z-coordinate; this is the max height of the box\r\nmaxZ = max(max(presentCoords(:,4:3:end)));\r\n\r\n% Go down the layers from top to bottom, reorder presents in numeric order\r\n% for each layer\r\nmaxZCoord = zeros(numPresents,2);\r\nfor i = 1:numPresents\r\n    maxZCoord(i,1) = presentCoords(i);\r\n    maxZCoord(i,2) = max(presentCoords(i,4:3:end));\r\nend\r\nmaxzCoordSorted = sortrows(maxZCoord,[-2 1]); %sort max z-coord for each present\r\nreOrder = maxzCoordSorted(:,1);\r\n\r\n% Compare the new order to the ideal order\r\norder = sum(abs(idealOrder - reOrder));\r\n\r\n% Compute metric\r\nfprintf('Metric %i MaxZ %i  Order Penalty %i\\n',2*maxZ + order,maxZ,order);\r\n\r\n% Creating a Submission File\r\nsubfile = 'submissionfile_SantaPack_Cody.csv';\r\nfileID = fopen(subfile, 'w');\r\nheaders = {'PresentId','x1','y1','z1','x2','y2','z2','x3','y3','z3','x4','y4','z4','x5','y5','z5','x6','y6','z6','x7','y7','z7','x8','y8','z8'};\r\nfprintf(fileID,'%s,',headers{1,1:end-1});\r\nfprintf(fileID,'%s\\n',headers{1,end});\r\nfprintf(fileID,'%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d\\n',presentCoords');\r\nfclose(fileID);\r\n\r\nend % SantaPack_Cody\r\n\r\nfunction  [L,xyTL]=SantaPack(p)\r\n% p is an Nx2\r\n% xyTL is Top Left position of pieces 1:n\r\n% Place 1:n of p onto L, a 1000x1000 array\r\n% Put p row onto L array. [2 3] may be placed [1 1 1;1 1 1] or [1 1; 1 1;1 1] for box 1\r\n% Note: big boxes typically pack to 95% and small boxes to \u003e98%\r\n\r\n L=zeros(1000);\r\n xyTL=p*0;\r\n% L(1:p(1,1),1:p(1,2))=1; % putting one piece per layer\r\n% return\r\n\r\n% Placing first 16 pieces\r\n% No piece exceeds 250x250\r\n pxy=[1 251 501 751];\r\n piece=1;\r\n for i=1:4\r\n  for j=1:4\r\n   L(pxy(i):pxy(i)+p(piece,1)-1,pxy(j):pxy(j)+p(piece,2)-1)=piece; % putting piece on layer\r\n   xyTL(piece,:)=[pxy(i) pxy(j)]; % Location of piece\r\n   piece=piece+1;\r\n  end\r\n end\r\n %figure(3);imagesc(L)\r\n \r\nend % SantaPack\r\n%}\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-12-22T04:12:10.000Z","updated_at":"2013-12-22T05:37:27.000Z","published_at":"2013-12-22T05:37:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is inspired by the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.kaggle.com/c/packing-santas-sleigh\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePacking Santa's Sleigh\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contest at kaggle that runs until January 26, 2014.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Bag has a 1000 x 1000 base with the contest having 1,000,000 packages.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to place as many of the first N Santa Presents onto the 1000 x 1000 grid. The packages have been presorted to have Z as max, not provided, thus minimizing the X*Y cross section. Only the first 240 packages are provided as package 241 busts the bag bottom making an area greater than 1,000,000. Packages out of sequence cause a severe penalty thus all packages less than the highest package used must fit on the board.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Presents ; Presents(240,2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: L, xyTL; L(1000,1000) of values 0:n\u0026lt;=240, 0 is unused space\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Unused Area\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[2 2;3 3;1 2] is Presents\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1 1 2 2 2 0  thru column 1000\\n1 1 2 2 2\\n3 3 2 2 2\\n0 0 0 0 0 0 rows 4 thru 1000 are zeros]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003exyTL [1 1;1 3;3 1]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eScores 1,000,000 - 15= 999985\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBoxes 1:236 are possible, 97.5719% efficient pack in \u0026lt; 1sec.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTestSuite Sample Code:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the TestSuite at the end is wrapper code for entering the kaggle contest. Update your SantaPack routine and execute the wrapper to see your contest score. The official presents.mat file needs to be downloaded. The wrapper has further discussion on efficient methods and current scores.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60785,"title":"Solve an easy binary puzzle","description":"A binary puzzle presents a square grid (or matrix) of cells in which each cell must be 0 or 1. The finished puzzle must follow these rules:\r\nIt may not have more than two 0s or 1s next to each other in any row or column. \r\nEach row and column must have an equal number of 0s and 1s. \r\nNo two rows and no two columns can be the same. \r\nWrite a function to solve easy binary puzzles—i.e., those that can be solved by applying only the first rule. Empty cells in the initial board will be marked by 9s. \r\nThe examples in the test suite and the one below come from binarypuzzle.com.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 487.112px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 243.55px; transform-origin: 407px 243.556px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA binary puzzle presents a square grid (or matrix) of cells in which each cell must be 0 or 1. The finished puzzle must follow these rules:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 61.3125px; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.6562px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIt may not have more than two 0s or 1s next to each other in any row or column. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach row and column must have an equal number of 0s and 1s. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eNo two rows and no two columns can be the same. \u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve easy binary puzzles—i.e., those that can be solved by applying only the first rule. Empty cells in the initial board will be marked by 9s. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe examples in the test suite and the one below come from binarypuzzle.com.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 272.8px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 136.4px; text-align: left; transform-origin: 384px 136.4px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAmkAAAELCAYAAACCpREeAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsIAAA7CARUoSoAAAJMoSURBVHhe7Z0HYBRF28f/19J7CL333qU3EQERC1VRLIgNARtFepEioIh0UECqSJEqTQGpIaH3HkhI771drrwzc3vhiLyCyt1uzPP7vjG3M3fvPjwzO/OfsjMqMwMEQRAEQRCEolBLfwmCIAiCIAgFQSKNIAiCIAhCgZBIIwiCIAiCUCAk0giCIAiCIBQIiTSCIAiCIAgFQiKNIAiCIAhCgZBIIwiCIAiCUCAk0giCIAiCIBQIiTSCIAiCIAgFQiKNIAiCIAhCgZBIIwiCIAiCUCAk0giCIAiCIBQIiTSCIAiCIAgFQiKNIAiCIAhCgajMDOkzQRAEoWAiIyMRGBgoXREEoVTq16+PGjVqSFf/nCcm0tLS0nDu3DmQ5iMI5VOnTh0EBARIV0Rh4ZdffkHv3r2lK4IglMq0adMwZswY6eqf88RE2okTJ9CuXTsYDAYphiAIpbJx40b06dNHuiIKC1u3bkXPnj2lK4IglMqMGTPwxRdfSFf/nCe2Jo1rPRJoBFE4MJlM0ieiMKFSqaRPD/L/4uVGqXYRhY/CVsbV6icjr2gkjSCKIDSSVjjZtm0bevToIV0B3t7eqF69Ory8vGA0GqVYx8MbKn7/mzdvIiYmBm5ubqhcuTKKFy8ua4fAaldYWJgITk5OqFixIkqWLAmtViubbbwB520l99Xt27eFHdyusmXLChvlsov7i0uChIQE3LlzB1lZWcJXVatWhU6nk3U5k0ajQXJysvAXX17l5+eHmjVrwsXFRdYyxvMyJydHlP3ExMR8H82aNQsjRowQn/8NdhNprq6uosD5+PjILty4E/nDwBfd8geTFzq+HsdaIOXAeu/U1FTcu3dP+Ij7qnTp0qKCk7vC5Q9nREQEMjIy8vPSw8NDVp/xfNTr9YiPj0dsbKyI43YVK1ZM9rzkgT+gUVFRwkZevrhtctrF4eWd52F4eLj4a4VEWuGkoEhr2rQpJk6cKISanOWMw+8/btw4sW6uSpUqGD58ODp27KgIu3766SdMmTIFtWvXxqpVq/LrMjnhdnEhxJ9DXt/+8MMPaNOmjex2cQ4cOIDZs2cL+95++22MHDnyiY0M/VO4X86fPy/Ez5kzZ4Svvv32W9FBkRNuF2+TJk+ejIMHD+a33YoXaVx5f/7552jUqJHsDynn559/xrx584QQeuutt8SDIXeh4/De3aBBg5CSkiLmsFu1aiV7j4XDhcaCBQuwefNm8TDwCs7Z2VlKlQ8uatevX481a9aIh2Pu3Llo0qSJlCove/fuFRVtdHQ0XnvtNQwZMkRKkQ/uI17RfvPNN+LFHisk0gonBUVahw4dsGLFClSqVEmKkRdely1ZsgR169bFnDlz0KlTJylFXrgw42KjRYsWoq1SCrxx520lHxkKDg5Gs2bNpBR5+e233/DZZ5/h6tWrGDt2LKZOnSqlyMulS5cwdOhQHD58GM8//zx27NihiHY8MzMTAwYMwJYtW564SONi4IkQGBhoZr12rixEYOLMzNSulCo/ixcvFnYVL15cfFYK7CEVNnHbTp48KcUqA/ZwCrv69esnxcgPE4/mSZMmmZn4MGs0GjPrWUkp8vPLL7+YK1euLHw2btw4KVZ+WEfA/Mwzzwi7rIGJNCmVKExs3br1gXxs27atmTVcUqr8DBw4UNhVu3ZtM2tApVj5sdb/Tz31lKhDlEJISIjZ09NT2Hbw4EEpVn5YZ8Bcs2ZNYdfw4cOlWPkJCgoyt2nTRtjVpUsXc0pKipQiLzExMWbWeRJtEreNBybSpNR/h90kKFeTXF0qBT5nzGH/ZvFZzjlsW3gPitvESU9PF3+VgtVnfFRNKXB/5ebmSld4YApPbviUhbVc2dooN9xHci85IAiCIP4+8o8TEgRBEARBEH+CRBpBEARBEIQCIZFGEARBEAShQEikEQRBEARBKBASaQRBEARBEAqERBpBEARBEIQCIZFGEARR5MhBVPA6zBn/Kd5/+22x0evbbw/Aux8Ox4y1R3ElTfqabKQifP132MPs2sLD518i8GgKDLLtnGRE+tVfEfTVx9jx3tvYxmwSYdAn2LPgV4RYDkCRESMyAr/BgU/exiZm1y9vj8GxX84hSUqVA33EMZxdMAE73x2A7VZ/vfMR9sxchYs35N6iyIzkm3sxl9kkyv6Ad/H52vO4mSIlKwhFirS8iGDsWbcEs7+ahmnTLOGrmXOxYlsgrirQiQTxZDEi5uw+rJTK/rTZC7DiRBzS8qRkgvgXZEecwq7pn2LYqImYMn0ufli1SuzIv2rVSixfOhtTJ47GyFELsPZwLOTaITEx6EcEz56CI8yuQBaOL1mHK5czYLJsKelYMi8hZNUk7Bk5DoenzkfgMotNIiyZh+NfjsP+z0fg6P6rsomi9NvbETx1Jo7OW4UTzK6jq1bg8rEQyLKLpDEBYT9NxYHho/HHl9MRuHzlfX/9uBjHp07CwZEj8fuq80jMkn7jYAzhR7Fz0ThMYTaJsr9yOZYcCEGEcrbdzEdRIs2cE4vrO+Zj1uQxGPHZUAwfM06cBcfDmFGf4pNhozHhqxXYFBQDmfKWIOyO/t5v2Dh7JD6Vyv644eMwY18kkpSzPy5RWEm5gRPrxmH0+KX4+VAIkh8yMpV55zh2Lx6D4TN+wIa7Bsv+6fbGlIW8WwdwcSVrxL+ejINjvsPty0nQsiQPFtz9feHiIkdzFY+ItfNwcNQ0nNp1AZms4XFisdwSfsKmjgVN/AUmSr5h35mH84cikc3iHEFW6FncWrUUp+Z8ycTQNJzemyCyypsFd/jBxd3J8Q18XgaSj8zH4fHjcXTDMSTHG4W/NCxwf/H81GSEIn7HPBwaPQHHDtxFpqN6AokXcGjHSixd/B0mT5iCeevOIFFKgtoZ/h5O0Clw2Eo5JulTcffgPIwf/DHGLTuIK/F/3iE9484R/DJrKN4fMR/rr2RAr4xDAwji32E2wJR4E6dPHMTvv27C4mkz8eO+i0iVkuHlDS8XDdTyn7tMFGoMSAncgC2rf8Ol/LrTFWUadUCnbt3QrVMDVPJzkeLTERu4CvOWnkVUngNUmjEZOcfnYc+7H2HLyEm48EcYclinhDfqcmKK2YXTy35FeIyZCR/2qKrd4VKpJWozf9V/tg1KldAIAcLFmv7MFlxd9StCHTRVnHJqG46+/yF++XwigtecRRbLJrkbdH38RVybuwD37lps4fmnK1cPFZ7rhgbd2qJCdf/74jZ6Jy7O3407d7LhkKY87Fcs/XIQPvzoM0xduR9nEqR4haMYkZYXchC/LZqPzRFSBMMtoBwq16yJmjXLo4Q31+OcLFY4l2Lm0hO4mmQ5yJQgCjXmLOQFz8Wg17qj8wt98en3h3E+v4vHUKn4/xPEv8MYhuNB53DoqnStdoJH1R74fOEW7Ny1C7t2LsbEvk1RxlVKT4vFvW0bcCJRz+SdvbEIQf5fHnijzYOsxd6UjcRdvyIuOhaWlQZqqCt1QNWpa/AK89frO9aj0+ft4OurA2+JtIhH3MVg3At23JC3rb8sHpSTDDEKe+23ZBiYMVy8Gj3ro+In8/DC7l14bddGPD/xdZT0teQq/6/+919w52II6xI4Cvm99HdRiEhLxs2LQdi515pVLPucGqLv5LXYHhSEoGNr8e37HVCey29Obgru/LQJQaHxsJwuSRCFGVZxGHKQmeGoiRKiSJJwARdDr+OKdKnyLI2q/T9F3wa+EONnLi3Rq+8zeKkBb1456cjKOIGzt/X2X16i0kHtXRb+NSqgeJkyCCjhDVdnjWNGWP4PZkMsQgOvIT3eLASHAcVRvOXzqN+vCpz5F1zKovp7Q1GuSmkxpcfJDbuH1CvXhWizN1qvYvCpUUb4q1ipAOEvWUWtPho5V08gXm8W/35ui/aZgajXrR1Kiy+URKn2vdC0ZwC0zKE8b9U4idi7UUh2xJSniz9KV6yBMsxfZZi//D2tuaZslCHSsm7j1vXTOGAt2RpX+PX6GP2fb4e63t7w9m+LHn16YkBba+VhhCnlIKs8kpAg51NMEE8EVp3pXOHp5QG1mvXW1TRyRtiBmCgkxcVJF6wf7O6BRg1rwcs6w8nwqFID5SuUla6AHH0Cbt3LZn+lCHuhKQa3LlPQb/9xfBQcjIE/DUPNp/wctr7rYZiN0Ui5ngM9+7fzhtKorQjvKnVQyubZVPvUR/HKnuCDj0J0JCUg827E/bVOdsS/3QA8ty8Y7zF/vb3vRzRs6iemEe0/6vl/SE9Fxr0wadSRo4JP7RrwLH1fZmj8SqBcvXrQaFViTEuFTKRFpyE72ZJuV6r1x7jFexHM/BV84AfMHVBHSlA2yhBpSdGICw+FdZBY5aRD/eYtUDFAimC4Vq6Bxg2rS1fsAVJF4WZ4BpLoDQKisKN2h67FZ1i6YR8OHz6Mwxsm4INnykiJBPFkMGWkIC/r/sSSq0tZVC2ng8a2FXD3hM7DS7pgDb4hD5GxScizd8uv0kDl5gePUmXgVYaFysXh6qaVcSTNBLM5FbmZxvxRMWNAMbiXLQ5P6VpgcoOznwY6J8tEmtqcjNykODhiWZrG1RNukr/8q1UQ/pKzb2fKzUFWWoLFEQwzSsCzjAecbB2mY+XNxx9qvoRDispNTIHBEfOdOg/4BpSyjKQxf5X1t87rKxtliLT0dOSk3X95WaMphZoV3OAqxpQlXN3g5FNCumCYjQiPSUZGpnRNEIUWDdS+VVhPuBXatGmDNs82Qe2yfJkyQTw5zCYmN1i9aUWjdoGHq+nBRsDTF14ePmJkSMAa3Lw8PRMs0rWjyMyF0WiWVXSIsTFbA1ycoHFxedBfKuarACY83ISrGEbm5zyHi0t9Rpbwl7yYYOJlLB9n6JzVD3YCNKxk+RSHm0ot/CgsNrAegO3PHAHzV7be0Tf9ZyhDpJkMrO64P0iqVnnD20MFJ1vrnD3hzBS4n3QJswo5OTkifwniP0VKBrJyC0cFQhRmVH/ed8ygRx4LVPosPChO+UVBhzFf5Zpste+fvlG0ePBf/ydfMEeZ81i7zVJ4mtDARdthj0QZIu1x4LnJgrw9K4IgiMKJSusMleb+Ymm9IRGJKWrxJl4+6WnIykjL38RWpVbBw4OvlZQiigy8pdFBYzMtp0rJhj4l3WbNFScV2Ql5MGRZ2yYXaJzcxdqwooZKrYVOZzuFmILc1Dzk2a5nNORAnZyMXJMpf7RR7eYKVeFYwy8Lynj0WOZCfb9Ym8xpSMswP7gPWk4GcpNZpSJdcvnt4uwMrdwb6RAEQRQC1H7+cPH2la6A7OxYXLqb+eBIRmIMMuPubyDlpHNGxdK+cC5yjSgTZ+picAvQiL2+uIs0KZFIvxeJB9a4m+ORHpULPvDNG1Ojzg+uJUvB35JapFC5ucGjWEnmN+maCdiUsBTk2S7Qy8lBeuRN2M6KuhT3gdP9YkkUQBkizcMTrl75E5kwGqNxPTwL2bbbzWQykZZgc0CaWoMyJX3hQUt3CIIgHk2pSihVorRl+wiGIT0FF/bvw838XZOzcTsoCCevxkvXGjhrK6F2JSe4Wl+sdxSertBo1ff1I6vv1W5ecHKgHSpNaRRr4AFn1sbw8QKtORRJl07grk0zlHVuL6JuJ4q3UMVIWskS8KhWGfdfvXAMTt6e0DJ/3Yf5y8U9f2sQh+DuB7cq1eFikHzBSA/+A9HX0vLfOM0Ou4jLBy/BYDQL8WFCKfiVZe34A29jOABvD3i4PFiY1Cyjbd6ZUQzKEGn+JVCsbPn83aXNeXpcPHka92x2BM4MuYIz565LV6wQmEuhRllP+LpJEQTxX8HFCTqNtZpjqFRQ61zgUjheRiKUilcdNKpWGw2sLXdWLKJ+mY2FGw4g8MoVXAlcgUXrj2N/mJQOb3iU74JmXKRJMXaDn7qREoHEa1cQy2yJO3cHGal6sT8ZR5WbgZxbpxB5kaWx9ISoNOjtfJatSuOHMu0awzNALdboaZCO5JM7cWHWJoQyG2JP7MTJOcsQHZEoxCQXcu6Vq6FYo4r853bHkBaPlOsWf0QGX0Aa8xe3g9ccKmQhN/ya8GXslWtIuB2LbJNlHZjd0BSHc81WKOtveSlATIQFr8b5VctxQeTbblxZvxZXjwL8HQdup8m9E8pWqwAfR4jvnARE3r2OK8wnV4Iu4kq47UGdZuRGX8OlUyyNp9+JQlymXb312ChDpLlVRoVqDdDM2i4ZspG4eR7W7T2BG5mZyEwLwq7tu7A6yOo01kvw7MAqHD+w54cgCjmsXBv1yM7OQiYv78npyMy1mes3mWDITEZyIktj6Vm5eTDYLgUgiMeiFJ7q0B4vPO2R3yHWp5/B6k9fROcWLdCi8zDMP3ArfysklKqFpm90RyN3Xf7IiN0wxCN733is79wGi5kty1+bg2unEsUmu/ze6rgriJr6Ala3boEfWrTGmpl/IOr+lm/2Qa2D7zOvoWK9uuL8UK4JNUnnET3vLfzEbFzx7Cs4+nMosnMsDalRWxNlW7ZDxQb8x/Yn4chK7OnK/dECq7q+gwunE8VaQq53dAhF9MoR2MnSlrboiLX9FuBaVp6dXwhxgUe5Nqg7oB7cWcbxe6mQhMSVo7CH5dvyFr2x75t9+RsjG5lXffq9iCq1ysMhYy231mHqoOfQgpf1Z9/HZyut2zozTHrEbRqOD19gaSy91XuzsPScfb31uChE4vijdoPW6NnJOnfJGq2cM9gwsT9eat0ardu+js+XHkKYteek80Cpl3ugefkAy07ZBFGYMWVBf+IbDOzVEa15ee85EYt/tzkfLTMG1xe/iR7PsDSW/szoDdh9V0ojiL+BS6NX0Pu9j9CrghbWwVpjLuscZGQgIzNXeolABbVzDTTq/iHGv1EZng5Z92uCKTuZFfUUpDNb0rNyoTdaRn54f8RkNiIvN9OSlpGKzNQcGBzRhrq1R8uRb6BOmzL5L08YDdnCX+mZ2eB9KdFf0jijWO+BaDCgK8o4qFU15aQjO4b7Q7KF+Uv4SgTWsdPzU0x4egwy49ORJwy1Lxq/aqgyaCLqti0OD1bAeP4ZWQc0R9iRjWxmBI8TZ6A2eR+tP2iHsmXt3gWwkJeJ1MQYZDBbMpi/Cm7Bwd86zc5kaSw9LSEV6fbewPkxUYhIU8O1eie8OOQjvFxKimJZmRF9BzcuXMCFi3cQmWw9AEoHTe3++GJIK9Qp5sAFCgRhNwwwJ97CmaBgXODl/do9RKfY1BCsksuKvolrl1gaSw+6HI5o2h+Q+CdoPVC920jMXLUKsz/qhgalPR4cJdP4oeYz72L8oh+walJPNPVwUAPK4G8H8pdPea1urdnviw7Rdc9P4+vVHHMqhw5urd5Fp4VL0W1sX5Ss7ioaTR64HSqtO5yf6ou63/yMF796F3Wqu+TbbneYv9SP8JfFTidodBqoHeEvlQaulbuj7fxN6D5jMCo0LitGbfP9xWxxrtkRVcctwUvLRqFJo+J4YCmdPVFroOW7Dj8OuvudGLlxlHsejbMvqj77GabMnY7Rr7ZFdd8/F3W3cs3Q/YOvsOjr4Xi7iR8KrPsjiEKL2cw3gpQuHoXDdxYl/kuoXf1Rof1reH/011i6djN2bNuKrVt52Ibt2zdg+Zzx+OTNtqhX2oELfjX+cOk4Cj02bcPbzJbX/yK8wezsPaQ1ShaTfmtv1D7wrP88mn46DS+s2IJ+O2zs2f4L+i6ahmfefxlVK/rkv5ThCPxavopnfub+2Ir+VnseEt7a+gt6LXkH1VyZ8JB+a1908KjRDnU/HI3nl6zFqzu23bdn+1b0XT4HXT95DfUaBjj0RRBU6oXPZv0slfW/Dtu+HYI36ylEYJifEIGBgWatVitGMnmoX7+++ciRI1Lq3yPj1lHz1uXfmadNGG8eP94SJkyeaV68/oD5fLRR+tbfY86cOcKugIAA8dlo/Gf/O0+akJAQYRO37cCBA1KsMhg2bJiwq1evXlKM/CQkJJhHjx5tVqlUZo1GYz527JiUIj9r1qwxV6xYUfhsxIgRUuxjYMox593YaV48+8v88v5XYcpPx81nY6XfPgZXrlwxt2/fXthlDRs3bpRSicIEa0AeyMe2bduaL126JKXKz8CBA4VdtWvXNu/YsUOKlZ/FixcLu5566imzXq+XYuWH1/+enp7CtoMHD0qx8rNt2zZzzZo1hV3Dhw+XYuUnKCjI3KZNG2FXly5dzCkpKVKKvMTExJh79Ogh2iRuGw+zZs2SUv8dyhlJs8G9ahu8/M4nGDP5S3z5pSVMnjASH77aEQ1KKtJkgvjnqJyhrd4dH34+Pr+8/1UY168VGhWXfksQBEH8ZyHFQxAEQRAEoUBIpBEEQRAEQSgQEmkEQRAEQRAKhEQaQRBEIUWlUkGnU85x3mppMzFul1ZBBytbbVGav2xtUZJd3F/cVxyNRjnbKNjaxf86OTn04Kv/C887a9l/0thVpFmdqQRsbeGflWJbQbuUhNLs4RTMOyXZWBjsIv5bGAwG5Obm8rf0FRG4PRyj0agou/LyLDuh889Kskuvv78fIv/8sO/IEXJyckQecniePuw7cgSed0osY7Z2PWlU7Ab8ddF/zYkTJ9CuXbt8Qxs0aIDly5ejSZMm4lpuFi9ejI8++ggBAQGYNGmS+KwE4uPjUbduXcTFxSE4OBjNmjWTUuRn7NixmD59Ol599VWsX79eipUX/jBMmzYNU6ZMET2806dPo2HDhlKqvGzevBkjR47E3bt3MWbMGGGnErh37x7eeustHDp0SIoBNm7ciD59+khXRGFh27Zt6NGjh3QFFC9eXBxj4+fnB9Njb7T35OEdAd5o8nYgJCQE3t7eaNy4MSpUqCC7Xfz+165dE3WFj48P2rdvDy8vLzHy8YSav7+N1a6EhAT8/vvvot3k7Wf58uVFvSaXXVZ4nXH27FmkpaWhZs2aaNq0qd1Gih4Xfv/Y2FiRj7zdLFWqlPCZi4uLrP7idvHj+nj7HRERkV/eZ82ahREjRojP/wa7iTTuwJ49e6Jq1aqyFzgOb6B27NgBd3d3PPPMM+JBVcIIAy90CxcuFEdRvP3226hTp44Y0pXbZ7znyf11/PhxYdMbb7yhiKFl7qc//vhDBP5wfPjhh6KMKYFTp05h7969SE5ORocOHfDiiy9KKfLBy3h0dLQQkHfu3JFiSaQVVgqKNIIglIniRRpBEMqFRFrhpKBI453O0qVLw8PDQ9a61zqSFhUVJTopzs7OoqPOR674yIJcnU6rXTExMUhMTBQdOz7qWLJkSfFZbrv4DAofTeN2+Pr6Cp/x9U1yjT5aBy5SU1OFz/jMBR8V5WWM2yWXvzh8hDE9PV3YxUeueJkvW7asKGty+YvD7eJ+ioyMFCOPVhQv0vhoEHciH4qU04EcXvD4CAzPWP5gurm5icDj5Sx0nOzsbGEbt4Pbxh8IPmIlp13cL7zQ8QeCVyTcLt4YuLq6St+QB24XtycrK0sEDreLB7nzkt+f5yUvY9xG7itPT08pVT543vG1LjwvretyOCTSCicFRRpfKjF48GAxmqwEkTZ37lz89ttvYtrunXfeQatWrRQh0rjffvjhB2EXX8ZRrlw5RYg0Pg07btw4sQbsiy++QJs2bUT9L7dICwwMxI8//ojw8HC88MILeP/99xUh0q5cuYJly5aJv3w6fdSoUaLNlFukcaG9YMECBAUF5duieJHGlXevXr3EfLZt4+BoeKHjD+P+/fuxfft2IRw7deokpju5c+V+GPj6JV558Ma9e/fuolLj6yXkqnC5XTzwnhQ/w4yvS+ANQL9+/VCsmOWwPLkeVC78eU/l4MGDOHDggIgbMGAA6tevL/JYzrzk4eTJk9i9ezeSkpLEdOfLL78s4uWs2HjFytdJbNq0Cbdv35ZiSaQVVgqKNF6P8UZLKVP+fPnB0qVLhXicPXs2OnfuLKXICxccXDTy9Xu8rVIKfFSoRo0aol7jdnH7lABftjFs2DBcvXoVo0ePFmuTlcCFCxfw8ccf48iRI+jWrZtoo5SwDIfn38CBA4U9XHxznpRI4w3IE+FhZ3cePnzYzBpORYRvv/1W2MXPyeSfmQh66PccHW7dupV/dufvv/8uzhR92PccHbgdn3/+ubCrZ8+eirErPj7ezHpP+Wd3Hj169KHfkyOsXr06/+xOft7dw74jR7h8+TKd3fkf4WFnd/L8VQq2Z3fu3LlTipWfJUuWCLv42Z15eXlSrPzcuXMn/+zOP/74Q4qVn+3btyvy7M7g4OAHzu5MTU2VUuQlNja28J3dyUcQrHuaKCFwW6zwz3wU7WHfc3Sw7QXwz3xE6GHfc3Tgdlh9xn2lFLv4yBC3xwq/ftj35AjW8s5RSvniweoj4r8Jq8elT8pCSXbZ2kJ2PRol2WJLQbuUYqc97bCbSONGW4f9lICtLfyzScY5bFtspzXlXFPyMKw+U4qvONxHtvYoyWfcX9aHVWk+U0plRhAEQTw+8m58QhAEQRAEQTwUEmkEQRAEQRAKhEQaQRAEQRCEAiGRRhAEQRAEoUBIpBEEQRRBskJP4OrGVTi9cCFOsXBy4SKc/mEtLgXeRrLs73zpEX5iD35idvFj8xau2oxd1zKRJ/v7L3lIv3cc66x2LVqM7efikCjfVqDQx1zGra2rcGYxz0NLOLV0BS7sv4A4y57fsmBOv43Q3zbhzJJFUvlidi1ehvM7jyE8QfqSjOTFX8Z1bhO3bdESnDsaivRMKVFBkEj7Bxizk3DvyhWx6/GVK1dxMyYD2Yp4yTATMXdvSnZdwY3QaCTlSkkEQRAMQ2o4IrYtxLEvR2Dn++9g65Ah2CLCYGx9fyB2jpiEg4v2I/x2JuTSatlhv+LHSR/jXWbXEB4+noTZR5KRK/NL03nxgdg2b8R9uwZ/hK9230F4jvQFB2LKjkPCkR8R+NU47Bk0ENs+suYjCx8OxPZPx2L/7I24dSkGjm0GspF0cgfOzJ6MvZ98gK2DWLmy2vXRe9g+eDj2zfgBF09EIUumdtOYdg03V47DTmbTLyxsHjwIu1adRXyy9AUFQSLt72KMwdkt0zCwZUu05KFtR/ScG4wrSVK6bOQh59oajHn9eYtdLHT/ZDY235WSCYIgchMQsX0qdr07BMd+PI6sVBP4roN8Fz0e1NBDH7gOZz57D9um/4KQZAcNXZnzkJd0BxfPB+P471vx/eQZWB90mzX3Eh5ucNOphY2OxpQahjtXT+P00T1YNXse5q86gfuaTAM3Jw00jjbMmI7EP5Zi3+B3cGDediTGGqX8s+Qjz1PjlV24POFDbB/xPS7fdZBMM+Uh88pGHPmkH3ZPXovI68kFyhf7SngwwmYPwrZBM3H6dDz0juoJZMUi5cY5hAcfwfmF3yL4++1IZ9F8p1IX6ODkooVagYqIRNrjYDbBaGSVV3YSrmxfjhULl+BAero4DzE9OR4JGbnIk6OHZzbDZDTAkJuOuPP7sGDqt9jFKjZhFwvxyWmy9VQIglAaZmSeWY9ry39EeKIlRsWaACf/0ihWvjxKlPGDq5NabJeuM4Yibs98nFgb4pg6xJCApH0T0bNrR3R8/hUMW3kK1++fVc1aKr6ZtvTZwWQf/wbj3miN1p1ewqCvt+KU7VQds0stg13Gu/twc/WPuHL5vgDSepeAfzmWj+VLwJ0JDh7nhGQkHfsJJ+cfRlKOWeStPTEmXsPtRVNw/WyWGL3jAk3j7gsfYVdJ+Hg5Cbs0MCLvwhIELT2Ie5FGu9sluP0Tjg5+AYvaPYvd45cj/LZFAAlhK23WrkRIpD0SVnyurcKE919A+6e74NXPv8NPJ7PuFyqtK1x1GlkeVMQGYf/MF9GuQyd0e/0TzNpyC3E2pd3V2QlaymGCIASxiDh2EreP50kVvwoa/7aoufBXvB4cjHcPzEfTbrWhYYm8OjPF3Eb86s24k6Z3wLSnCSbW2UxKzII+Lw9Gh7Taj4dZn4H0FNZJ1+fBYFKCYWZEBQXh1sG7FoHB/8+7GipM2oC+x1g+Bv+Mtq+2gAdTSNxadeZtJO9Zj0sRubDv0rlcZIQH4crGu8jVWwSaSV0aJd7/Fs+f4HZtQ7cxL8LLahf0SF+/GSGXQuGQpXPGbOhTk5Cl1yOHFTBeppUpyx6EmvDHIeUWzh77HUHBp3E5LAFpsi+qlciOR+TF33Ei6CTOXL2DeBnWRRAEUUhIPo/Em5cRy+ovXvGb3UvD89XhaPNcI5QoWRIeNfqg8RudULU2wAfPNEhDTvR+hIUwgSL+B+wJExpaZ7i6Ka/ZVGmc4OwiXSiC64i/dA0x8QA/uM+oLQa39kPQ7pV2KFee5WPJDmj2wUuo1c5P5BsftdInnUb4pSzk2XNU1BCNnKvHEJlkEgJICMh276Dhq6+gZhluV3NU7/UmGr/gKQYP+OSTKvcQIm/dQ7IjRmtVOmhcXKFjH7nfCov4IZH2WJhhUuKxOtwmBR0/RBCEgokORUZMlBBgvDbTeHrCv21b+HqIVIYO/g0bokz1UuI7KtaMGvMikBiWC7ufvqb1h+8zY/HT1r3Yu5eFpYPQvaGvlCgvLi0+wfgfJLtWTsLwZ72kFJlIvY2s2AhksI9iRMrDB54tnkaJYvcFrlOjJiheu4pYb8Ux6tOReiMSRnsuTctIRuadm8hhZlhbS6/GzVGsqmu+0NCVqoZqrepCo1VJo2kJSIlMQabt1La9qNIHrb7djPdZPr75y7d4pl9NYYPVVqVCIu1xKPs0Xhs0Fp999hk++7A3ujcpLiXIjG91NOg5xmLXxx/h3U4VUSK/wiUIgriPPiUJuempotLnDZPOtRR8KrrAZNMKqDx84eTtmz8NZMrLRXZUMsz2HkpTOcO5VH2079gZXbp0QZfnm6BaSVcpUV40AbXRoBWzidv1Ynu0qGSVPjKRkgxDZnr+1KXazQXulctyVX0fZx9o3PyESOPRZr0RuZFRLD/t16k3Z2chMykuX/WY4Qfv8j5wstW0Tjo4+5eG2mZ9UE58CvJSpAt74lkRxZ96GtVZPlbp2RkV6/iTSPtvwApT+c5467Op+Pbbb/Htgi8wuGsFKU1mfGuh8atTLHbNXYjZb9dDJWV0PgmCUBgGYx6MJkvTzhsmtcYHzp6mBxsBT29oPXxgnd0Tg/Ws8XX4REJKJnJkeRvrESSnISNX5mbdYASMljU3lnzUwMnDFaoHMxI6Nw84s0/8O2aTCcacHEuG2gmz2QiDwXaozhPOHhpo+dyiFS0rWT4BcFXxV1Yk25hdZkfvM5eSilxl7Jv1SEik/V1SM5CZq5RFaTYY05GWzRe2StcEQRCPwnb0hSPecqNmobBRMBt5087z8c/xcsMs4mVMurKgPCuVBD2NfxfWE3F4r/JxMCnULoIgFIFGzfeBsgxr8GbRZMqAPtOy5UY+aSkwpCfn7wPGdyVQ81Xz1I4qB/76LRM6HJ4tfJRMn5XLd4qyIZXlbarYZ05kHR+5cnKyZKidUKk00Ghsp4KzmF0mMfCXj4GVrKR4ZPN2lF1ya1ROOstKfuKhkEgjCIIoAjh5+cLZ3cvyVh0LhuxIpN7TQ2XTuJtTk6FPSc0XbiqdE1xK+kHNX4kjlIGXNzTuHvm6xpiVg6ywKKa6pQhOViLyMhLF2508L9VOWjiXKgmVHfdkUrm4ws0nIF8HqhCPtIg05Nm+FJCbC318FEzG+3u2OQf4QifzuxhKhkQaQRBEEUBVqizci5cU+1cJkZaRjsTgM8jI36TKhIRLZxBxM1J8x8y+pdWVhG8FF/bX8g2H4ayDxnbzSZWaCUVnuHDD5MTZCboCxwtwuxy6RYdvJbgVKw039pFbYsxIQ9qpE0hOuz8marh8CQk37oq3dDkaZ3d4VSvH/koR9sDDGx4VK0F33wykXryA5LC8fEGWG3sXN4NPwsBEGrfdDB94l/Zm4s6S7jBcnKApKFi1LM6e/vmHkEj7u7BuwoOPKIfF/TnSsXC7HmKD7HYRBKEM/OvAp0J1+LGPYjQtIwpp62bjxPEwpObmwhC3D1d/OYa7N8xiLykTvODk2w7lqjJhwn9vV8wwG/OQy+wQIT0betsFtiYDDFlpSM2S0lmaw/aVZffO01vtykSW3nbIio9IpiM9VUrPM9h/I15VDfhWrYxirkygsUtNXhyyDs7H0V8vIiGd5WPuBVxYuwc3jyeL0Tb+aoiTZ12Uru8OnT0zUlcCzjWbobiTWhL57N5/LMeFbbtxl+WbIfcmwn/bhAt7+AssFvFhUrVCqcpl4OuIToDZCBPLRwPLJ0NqBvQ5BV4cyM2EPk1K1xsUs7sVibS/i68nPFxtJ9BVULt4wsvRPYGCaD3h46Z74IQBtc4Fbt7SBUEQRRtVBZRp0xyVG1sadxV/Gy9yHy4Oeh4/tWqFFc8Owsltl5g4snzd7F0G3i/3QFVvVq9YouwHPxbqt4no0bUtWjFbWr0+B1tP2xyIHHcVx6e/hGfasLRWbfDirD9wJk5KszPZx7/GuDf4fVno/QWm7rGZv2MC7vSCd/FGJ0v6yyMWY2e4lGY3XFCubTNUezrAko9MhpkSzuPW2FewoR3Lx1av4I/VR5GWaxYNvEFXGm6tXkT9Cs75+6bZB3d4VGyH2i+XhBNTadw2de5tRC74GDtZvq1o9TJ2TdmMZGnBIxdxTl1fQuXaleGQnaNu/YQjg5/HQl7Wu3yI31ZdFf4RwaRHzuaR2PNSKyxn6cs++BqnztkuppMPEmmPhBWliEP4eelXmDBhAiaMmIsf9t+T0himXCQdXICvR7E0lj5x2S78EeYgCZ5yCxe3zbDYNfYLjP3pKsJs9ptJu34YG6dY7Jr01WxsupCBdGWUO4IgHI4WPi37oe6AvijpxhpvFmNGHrLvXsG9s2dx52IYUjPzxExBNnzh3eYNtPygPjx1DhiON+ciN+YSjh06hbPMlrPXIhCfbrMvQ14mUsMu4fw5lnb2NI5ei0OSg84MNyZcw6Ugfl8WroQgLNF2BMaM9MjruHbekn7s/C1EOuCMI13N7qj59huoWpG5hl3zFicv6gYimB13zt5ASmqOiMuFDu71eqDBsOcR4Gbvtz1V0AXUQ83PRqBKLcsWG9xThsR7iGX5dufsNcRFZwhxxuNNZXqh6dBnUbGS5TxPu5MRhvgzR3CLl/ULNxATkymiLfc2wxgfgqhL3E4WTl1DUoq9h0QfDxJpj8O9fVg5ewymTJmCKbPX4JegGCmBYcpDSvBPWDGHpbH0L+dtwZ4QB2Vu8lWcWT/OYtf0WZi3KwTR/Fh/iYy7p7F3icWuyWMmY9WZNKQqZAiXIAjHo/IohwqvTELX2aPQ+Ln6cHKWGkwpGFiTpanZCbWGzUKXCQNQu6zW2orZH7NZ2PA4OPQEGP4m4mMaxu1yiGXaAJTo8jE6fzMVLfu2hpeXJR95H5ybKuRtuadQ+YOp6PL1UDRt4OOYbNS6wLvZO2g/awE6DHwOxcq45otIS/li7vSrgZKvjkbHbyeh9dMV4eqovYF5mTFZDnPngdvC9bQ18Ddhuf9EOp9L5x8UAIm0x0IlTsl/LNSOXJ/G7/W4dqnlOQSeIAhF4RRQC9U+nIxnps1gjfwMdBk7Vgrj0OXLb9B55nR0mfguGjQr4bidEdRe8GryBiZOGY+xzJa/DuMwuU8DVHPQUg6nOn3w1siH2fHnMPHd7mgRIP3Qzmi8KqBsr7HoOPUrPPv1TCaqrfnIwsTp6DyL5e2UYXiqYzV4Sr9xDF4o+dxHaDuF2/A1s4WVK6td47lonIlnp45D+7514eXiwEapzNOoO2QKXrDa8n9CVx4+6YPqVRQij8xPiMDAQLNWq7WKVHP9+vXNR44ckVLlZ86cOcKugIAA8dloNEopj4L1jS4tNQ97ra25QYMGjwyN3p5hXnTmcf+3zeaQkBBhE7ftwIEDUuxjEnXUvGtih4faUTA0advF/MWueHNcnvTbx2DYsGHCrl69ekkx8pOQkGAePXq0WaVSmTUajfnYsWNSivysWbPGXLFiReGzESNGSLHyc+XKFXP79u2FXdawceNGKZUoTGzduvWBfGzbtq350qVLUqr8DBw4UNhVu3Zt844dO6RY+Vm8eLGw66mnnjLr9XopVn54/e/p6SlsO3jwoBQrP9u2bTPXrFlT2DV8+HApVn6CgoLMbdq0EXZ16dLFnJKSIqXIS0xMjLlHjx6iTeK28TBr1iwp9d9BI2mPhCn92gMwbfleHD9+/JHh6OLP8G4DB7m1ZEt0Hr3roXYUDIf3bsXkzv4o5rCuMUEQBEEQ/wYSaY+DWgdnFze4u7s/OrjwfXSk39kblQZa58e0y80VzlqVw5aXEARBEATx7yCRRhAEQRAEoUBIpBEEQRAEQSgQEmkEQRAEQRAKhEQaQRBEIUUljoNTzkpTqy1Ks4v4eyg1/4qiXXYTaUp0JEepmUw8HkrOP6XbRvz3MBqN8PR07C5Yf4VGY3lrKi8vDzq7HhT59+AvT3G0Wq2i7PLz88t/Nr29lXWGn8HAt3sFXF1dxV8l4OLiIsoWR61WK8Zn3A6TycS3NJNinhwqvg+H9PlfceLECbRr1y4/Y+vXr4/ly5ejadOm4lpuFi9ejI8++gjFihXDxIkTMWTIEClFXuLi4lCvXj3xNygoCM2bN5dS5Idvzjh9+nS8+uqrWL9+vRQrLzk5OeIEha+++ko8pKdPn0bDhg2lVHnZsGEDvvjiC4SFhWHMmDGYNm2alCIv3J633noLhw8flmKAjRs3ok+fPtIVUVjYtm0bevToIV0BZcqUQb9+/cRfJbBu3TrxTPr6+uL5559HkyZNpBR54dsQbd68GeXKlcPQoUMVI9QSEhLwzTffiMPZBw0ahOrVq0sp8sLbon379iElJQUdOnTASy+9JKXIy61bt8QzEBUVhapVq2LgwIFCuMlNamqqaCNv3LghxQCzZs3CiBEjpKt/jt1EWoUKFTB16lRx6KwS+OGHHzBjxgx4eHiIjOUijTfycsMb0J49e4qHYc2aNYrxFy8WXAhxod21a1csWLBAEaMxsbGxmDdvHn7++WeRf/zBUEpHYO3atZg/f76oeAcMGIBx48ZJKfJy/fp1jBo1CpcuXZJiSKQVVgqKNIIglIniRRofVuaCSCk9lqysLGRmZoqGnQ/furm5SSnywoduuQrn2cCnLZydnRUhhvjQLfcXH7lycnIStilB1PLpnezsbBE4Xl5ewmdKgPuK+4z7jpcxXv6VAPdZenp6/jQBh0Ra4aSgSOPPZo0aNRQz5ck7BElJSaKu4KNWShnhi4mJwZ07d8QzyWculDL9z0fQzp8/L57RunXrivpMCYSGhgqf8bqsdOnSqFixopQiL7wDzPOR6wyel7Vr1xZaQ254Pl67dk3oDCtPSqTZ7VgoChQoKDfQsVCFk4LHQlWrVk0clcMaU0WEvn37Crv8/f3NixYteuh35AgLFiwQdvFjoViD+tDvyBFu376dfywUPxbwYd+RI0ybNs3s5+cn7Pr8888f+h05Aj9qrEKFCsKuli1bmhMTEx/6PUeHe/fuCXu4XdbwpI6FsttIGu8RjBw5EsxwcS03fCrqxx9/FD285557Dp988kn+Ilc54XPrfC1CRkaGWJvQqFEjKUV+lixZgk2bNol85ev4lEB4eDjmzp2Lc+fOid7wd999J3qgSmDlypViXZper0f37t3x2WefSSnywtdJ8HV80dHRUgyNpBVWCo6ktWnTBsuWLROjaUrgvffeE/bUqlULX3/9tViXpgS4Tdy2Zs2aITg4WIqVn4iICDEaxEe6jx49KvJTCWzfvl2sr+V1B//LlwopAb7ekbfdgYGB6NKlC1inRREvNiQnJ+Odd97Bzp07xagoR/EjadWrVzefPHlSSpWfhQsXCru8vb3NM2fOlGLlh/cErAesnzp1SopVBuPGjRN29evXT4qRn8zMTPPgwYPzD1i/ePGilCI/TKSZS5YsKXw2atQoKVZ+wsLCzM2bNxd2WQONpBVOCo6ksQ6Uog5Yf/fdd4VdderUMbMGS4qVHzpg/e/BR6yY0BZ2KemAdSawzW3bthV2de3aVVEHrPfs2bNwHbDO13zxeVqlYF2Pw9cvceVtMpnEtdzwFwaspKWlSZ+UgXXdFx8ZUgq8TPHX1q0oyWd8fZB1raNSyheHr5NTyhpM4snC6nDpkzKw2sP/Ks024vHh9ZcS88+2XCnNPnvZYzeRprRMts1Y63AkUfjg5co6pa40eLmyijOllX0liUaCIAji8ZD/dT2CIAiCIAjiT5BIIwiCIAiCUCAk0giCIAiCIBQIiTSCIAiCIAgFQiKNIAiiCGNIjUBa2F0k32UhNAaZaXqxh4BcmLKTkRF1Dymhkk0spNyLQnpSFpTwypApJxkRzKa7IoQiJjUXejnfy8nLQGZM+IP+Cr2HtIR06JXw/lJuMhIjrf4KR0RcBvLkLWDIjo9EirXM8xAajtSYZOQo8J00EmkEQRBFFGN6JK589SLWtWuBpS1aYFG7T/HHxuuwbL7jaAzIi7uAK9+PwZa+z2BZyxZYxmxa1qIllj3dCxtGLsbZC3HIlvPlfNMdnNowFi8yu1rw0Ko1hqy7hhBZHGaCIeUW7myag52vd8XKVlZ/sdC2E9Z+/DVOHLyLjFw5HWbA3a2TMexFyV8tXkbfUXsQmiuHqjXDlBuHyL3f4/cPX8DKDqxcWf3VpgtWvz4Gf+w4h3hWwJTzbj6JtP8WuRewYfYEcXg8D2Pmb8ThGCmNeChRu7/E1OEWfw35dD5WHwjD/RMuCeK/iyHxCu5umorAVecQcS8OaXFxSAqXayQtCzkXlmNH7/7Y8+VK3Dl+G8kxcUhmNiXHxSL5ThDurZ2GQ/1ewfaVp5B0/4hEx5GTgPO/rMS8OStxjtkVx0NMlGwjaRmXt+HYkBex7fPZuHHwKhKirf5i+RhxCzGb5+LIgDexbcIvuJcj/ciBmHKTEH98Mb5bthnrz0r+igtHeKxMI2lpR3By5EBsev9LXNx+DgmhrFxJ/kqOvIa4gytxZkgvbB6zHFdDlbNNF4m0/wq5kTizbga+mjQFCxcuFOGbNbtxMl5KJwqQg/SLGzF3+peYONvir4Vz12Db8UhFTKkQhH1IRcxvC7CtUyesfelN7J28FrGsI8ePqHZmwcnFBVqd45uFnOizOD1hJq4fvYzkpBwhEvmhffwYdB7E59xkpF47hBtTvsTpU/fgqG2sc4LmYsp7ndCp68sYMGoJtl+wGTZT6+DC/KV29HntiUEI/Wkejm28joTYVHCNyH3Ec87qL3VeGnLCj+Ha6u8RuO46HDOgZkBm/H5898rLeL7rS3j5w+lYdSwK97dDd4Obi1YG4ZGEmwsW4NyyXxEdmQQ984Wtv/hfDWsTcqPv4t6yGTj90y6Eszgl4HhfEU+OzCiEBW/B+nUrsWTmdIybsQXXMqQ0hr+3B1x5SSQkcpF49wx+X78eqxfOxFfTJmNpsFlUcAJnb3i6acVDSxD/TXKQFXoWtw8cwLXjZxF5z1JhyNoQmBKRc+ZXnN93lz2hTCiyYHSujwoDp+GllSvR66uPUKuJixBuXEwaw/bi/NZgJMc6ZrTDEHUagfsO4MDh4zh/Jx6ZUrycJJ0+iOtbjiMjz+IvlcYTvq9NRpdlK9F35STUa1FJiBCeZo45gbAfN+BGYp4DOqBm5GWF4fSO7dh76BgCL8cgNVfuyUNWTiIP4trO44jKAvhJn0YEwLP1h+j4I/PX93PQpkcluLAEbqlTxl3c2bkbt06ni1/LDYm0wkz8WRye/yZe6z8AgyYuwt5bepseC/FnUnHz4PcY9dpreGvIJHy18SpSDXKu+CXkZs/BYKzetF+6KhrwEyh4Y8RLPpc5jpE6/x9jUgiijmxGChMc3CYVa5a8ur+DRuNHo9lbb6HxqAlo/n5PFNNZRj34aE3armDE3k5y0Kg3P4pI+qgIYhF16iLu3TKI0U+jygNODV9Dy1FfoO3At9DgrTHo+PGLqFzdVYheLbKQe3svblzNcdC0rML8ZTIg+dhGxEYliaUs3DTnWm1Q7YuJaPc289d7n6LdpCGoWNkfrIgJcq5cR+z+m8xz8kMirTDDK1uT3FVs4cJsNsneKBHK4cr1UIydsaIICTU3+NTvgqc++ADtP3gPrd/ohOIeLkL8yNWu5sRFIuJciGjYLXZUQNmX26JsBeuYdgmUaNsXlapbpqg46pCriI2Lc0gjqqvWDT3f/wAfMJ990K8LOlR3kVJkIucy0sJDkcoEl/CXWwA8O76BOtW5ZOPoUKxrR1RuU0E08Pw7BkMski8nwGD3Xrwazl710O2TTzGE++uDPni6Vhl4SqlyYDamI/zMZWQl54ryY4Q7fBq2QuUuJcXILMet/uuo+lRpeDpZOi3a9EhkhNxAnEiVFxJphRmtK9z9y8LX1xe+Pj7wdtNBQzn6F7AKxN0PJbm/fH3g4+0OZ7VK6p0TRRW/co2xYH1gERFqnvBv+So6LFmC7ku+x0tLP0a5AE9ZRVpeZjoyo9n9hUhjljg3hH8JT9aU3kfj7AOvmiyd1W/CTnMo0lNSHSLSnOu9hvfHL8ES5rMlc0fgo/YeUopMJMYiLy1RjJJxX2g9XOFZsxJUthWZbyk4+ZSBVU6aco3ICguD2e5Tjxq4+jbH6zPnYD7315JvMLhjHQRIqXJgNqaw8mWEgRUW7iITysOjeGX48blgK0Y/eFVzgjNTk9xDKibPclIjkaaAt8ioSS/MlGyJzmP34MSJEzhxbD+2j26BWnI+DYrHF3WeH4bvub9OHMcfe6fjlTK++RUZUXTxrvRMERJqVkzQZ2TDaLJ3w/3XmAwGGKSFXmbWPJoDSsLT1U1M5VnROGng4uUhRBpHhWRme46DpjttyMxGtqybfDFyc2HW5+WLapWTDs4BvlA9sP7YHRoXNzFSJAQ493FqGswmRy/vyGT+Mt5f9ysHZgOMrHyZjZIvNKzO9y/24OieysTKlyfrDPAyyL+XDoM+FXoFLEAkkVaY0bnDq2RV1KhRAzXqNEHDit5wt+0dEAXgFX1xlOf+qlEbDRtXRUlnbf4UClG0KXpCzcQabZkFx8PQ8MVnatGgWtHonOHqzYQIi7RabFlZ52CYyJF9vRW/v2QE/y/3E59BeWAkDW7QMaErXhywRMgE95fcDiuAWsuKl3WiU4I5z8XLF1rmsHxr2QclmE4i7b9CXhrSslmPQdYuS2HCgMy0LOSyRkphVQghI0VzRE0B2AqMvFyx1tb2uTTk5iAzKYHFW7+qYu3qA6qk6MD/3dK/nf+XCwmDoeBi/QzoMzPElGi+l4qqvzi2/3SjHmZDgb0AWTuQlZyAvBzrV9l/mb+sI7dyQiKNIAjCBhJqjkWt00EnzT2pYIIqPgKpWZkPnHpgyjMhNylbiDSOGcXg7OmW/zZekcLVFSpn5/zG26TXIycmXkzn5WNIhSE7Tbztz8WIWqeFzo9PiRbBJl+tg9aTCS6t5AtTIrLj45BqSZVQITc5C0ZJpJnhDa2LH1xkXn7IIZFGEARRgKIh1CyjBX/mYXH2w9nTBx5lLaaI0Y2880iMSHlgPzJ9agQST4kBD4t12krw9vV54OUCh/B//OXQQapipeHsEyDW0vLbGtOzkH7lNoy2C/QiQ5EZHSZG0jhqZx3cK1Zgfx1pKOfh93PkKKhK7QfPsk58dZAoXyqEIz3uFpJs9hSF+h4Sz2Yjm8VxUWRCcbj6lIW3AtbCkEgjCIJ4CP99oaaBs68HNOr7zYBZpYPG3Vts+OkonIuVQIV61di9rY1oFCI2/IbQq5LEyLuNyANrcTfWsj0Cb95NteqiZIkSDrVT4OMJjwJCR8f85e1ItairBa+y5eHNPgp/5cQh48APOHshXdpeKAMRvx5EyLEo0cDz72idysG/vp9Yc+VYvOHporMRGmponDzh4+JAkaZ1Q/n6teHmoxMvmmiRg+RTx3BrWwj7ZCH12HLcusjEG/uC2KbDrxy8atSU9a1UKyTS/ivoPODuooXGpuyrNDo4O7yrWVjQwt3DFU4PbMGhgc7Zjd72tCPhUXEIPHVZMSE8+q/PTfvvCbUspFzYg5OTJ+Pw5Ik4MHYlopMzJHHEqpG864j+ZRYOsPSDk+cgaP05pNh5B1S1T1UEtOkJX2drg2RG5sGVODv6c+xndvzx2WicWL7f5hgoLXy6NUexat4Oeekn7+YubF4yGZOZLZMnf491J202/jCbcHPbN5gx1pI+Z/0BnE2S0uxGMZRpXg8V6ujESJnGnI28a9twavJw7JvA8/VzHFi8B5FRBjEdnAcPOFV7FrVrOMPJ7trIhNy0i9j17UxMF/76CmsCb+K+S5IRd2ENpo6fxNK+xOTv9uDMPTvv7K/SwrNNbxQv45//IoUxNAh3Zo3Eb8zGw+OHYf/4lYiISRdpPLjVrYESz1RRRFtAIq0wY8xBVnI0IiMjERl6EyExGci2GfI2ZKUg9g5L4+nRMUjKMsLIS2CRxQQ980k890dkGG7dikSS3ub1cHMWUuPCcEekRyMmIRN6Jb79Vojp0ONzDBr/o2LCoVN34eL51/3l/5ZQS0dS8CYcmTQJuyZ9id++3oTolKz7Is1wG9G7l+IAS985aTwOLglCUq6d30bS+MG1eS+0eKW+aBT56IbGeAfROxZhP7Nj38LNuHPDIOwT21bV7I0mLzaDn59jmq/cyxuwfPokTGK2TJq7HlvP24o0I+789j0WSenjl2xHoAN2QPV+6jnUeqMT/Fz5YXd8GjgX6fu+x9Epk7B70g+4dSVajKrxNHXJtqj0fi9U8XHEm+xGZCefws/jRmEs99ekhdhy5i5SpFSwT4nXtmDB1MksbSImjd+EY7cfXB325GH/6tKdUL9/N5RnZYavdVQzO7IubcEJZuPuqd/izOEY6KWB22yfGqjy0suoXtfNEiEzJNIKM9En8Nu0rmjevDmat3kGL04PwnWbgYGkk5swux9LY+ltnn0J4/clIcF2cWmRIxlXds3Ge9xfzVujQ5fR2BCVfH+Bct5J7J4/AG1F+vN4/uMtuJ7m8J2Y/tNkZmWheJ2eigpexatK1v1//jtCjUkdlWX02DqowoUPF0Y88GfBun8nP57JUW+3OQc0QOMJE9CkdweUruAtGibbJ49XW2bP8gho/QIaTmLfa1wSDlvTzfxlMyP8l6gdtTm2V0NU7DMcTw/qgHLVS0LHbsr9ZRXb3F8m1wB4NXgBjYYOQat+NaB1WGvPfPC49+L+cojDfFDp3SFo9vnbqFavPFycLeXc6i/eDTGqfOBesxlqfjIZzfp2QimH+euvUYgZxD8iLwOpUTcsI2WRUYhJyRGn+1sx5mYiJdYykhZ64xYiUg2Qex9GeWG9vJRo3BX+ikRUdBIyDCbxoApYbzQrNR5RIv0GboYxAVe0hx4JG/4bQk0HlxJVUbpePVSUQvmHhAosVKrXHGXrloSL7RoKu+EETaVe6LL8B3T9cgjq9GyFCg0tdlSo1wDlW76EmoMm4enl6/DiK7Xg5cB5KI1fNdRqUg/1mC2PCs3rVkIpBw3AuFTuiKem/4wXvxmFBq88jcqNrP5iedi0Laq+w0Tc0kXoNqYbyjpECHFU0DoXR7XmrdDUxi8PD/VRr3lVlPK23bbYjugaocHYOeixaDKaDngOlZs3yPdXhcbtUfmVoWj+3Vr0GvcKqpeVfqMASKQRBEE8JoVfqPmgVLfP0TswEAMfET4I3IZXv+6OUq4OfMXNqyqq95+Al9f+hneOWm05hoG/r0efKW+iQQ1Ph6xDs8Wl9QhMXROIQGbLo8K2rz/CC45s4J1LoGz3wej2469484hN/h3ag9dmf46nmpeFYyfttHAv0RUjd/yGQzZ+eXg4jsCtn+PlBsWk3zoCLxRr8wae+W4z3jzAyhWzQ4Qju/HGj+PRrnM1eBfY51ZuSKQVZoo1QKv3l2DRokWPDEu+n4chrb3h6+gaTlF4okq7tzHpIf55WFg4+hlU9lDYE0vITuEWaiqotM7QeXjA6ZHBHToXnYOmo2xQO0Hr6v6gLe6u0DlpxNSUo+H+cnHzgAez41HB3cUJOke3qiottC5uBfzF8s5ZK0sDr2L2OLP7u0s++cvgzsqiQ0ZqbdFA48z85W7rLzdR1h1uymNAIq0w41ke1Tq8jUGDBj0yfDCgP56t5gb3Ip3jrihRow16PMQ/fw5v4c3udRDgTI8I8Wf+Wy8TEAShVKgFIgiC+AeQUCMIwt6QSCMIgviHkFAjCMKekEgjCIL4F5BQIwjCXpBIIwiC+JfIJdT4GYiOPAfxUVht4X/Vj7u5GKE4NBqNosqVFdtypTT77GUPPUUEQRBPACfP0jgUdFm6cgwGgwGurg4/wfL/Ym1AuV1KEmkuLpaN1bj40On4YUnKwNPTM79xd3dXzhl+RqNR5CHHycnhB37+X3je6fV66Qrw8vKSPskLf1PVZDLBbH7y+2qq2P/oE/lfPXHiBNq1a5efsQ0aNMCCBQvQpk0bcS03c+fOxaefforixYtj7NixGDp0qCKU+N27d8WJAPHx8Th48CCefvppKUV+RowYgW+++Qa9evXC5s2bpVh5SU5OxuzZszF9+nTRCBw5cgStWrWSUuXlp59+wrhx40Sejhw5EjNnzpRS5OXatWv46KOPcOjQISkG2LhxI/r06SNdOY6S9XqiXudh0tV/h+yEa6heLAMrZn8qxdiHbdu2oUePHtIVULlyZVHO6tatK8XIy/Dhw7Fr1y74+/uL+rZ3795SirzwZ3PKlCliE9V169YpRqiFh4eL/MzMzMTKlStFW6AEuC0//PADkpKS8Pbbb+OLL76QUuSF1/fTpk3DvXv30LhxYyxevFgRQi0mJka0l6dPn5ZigFmzZom4f4vdRFrVqlWxdu1axRQ6Lhi5MOMZygvcmDFjpBR5SUxMRM2aNZGQkICTJ0/iqaeeklLkhwsO/kD069dPVHJKICMjQxT8JUuWCJF27tw51K9fX0qVlxUrVohyFRsbqyiRFhoair59++LUqVNSDIm0J4mjBBqnoEjjHU0uOJQyapWXlydGYTjcLj5ypQS4Tdw27icljQzx5jc313JoJLdLKfnI23FrW67VakVQAny0iucj95uS8pLbw+3i9llRvEjjw39du3ZFlSpVxLXccPu4CueVRsOGDdGxY0dFPBB8ZGj16tXIycnBq6++igoVKkgp8sNH9njDXqNGDbz88stSrLxwf/3xxx+4deuWaKBee+01lC2rjDM8eBkLCgoSw/GNGjVC586dpRR54b28nTt3il6xFRJpTwZHCjROQZFGEIQyUbxIIwhCuZBI+/c4WqBxCoo03qkbP348atWqJcXIy8SJE7F//374+vrigw8+wAsvvCClyMuWLVvEMgnuJz4Kr5SRIX6GMJ9OzMrKwvz588UUnhJYv369mAlLSUkRgwd8FkoJcJ0xZ84c4Tc+g8KFEF/XJzdxcXGYNGkSLly4IMUUApHm7OwsRqz4GjAlwEderl+/LkbPeMVWp04dRaxJ4w8nH+HjQ6UtW7ZEsWKOPMfsr7l69SpCQkJQqlQpNG3aVIqVF75248qVK2JKkecfX4/m5+cnpcoLL2O3b98Wz0DFihXF+hclkJqairNnz4qpYisk0v4dcgg0TkGRxusMvhSBlzclwIXZ999/j2rVqol1wM8995yUIi98tuKtt94S/uLnRioFPrpdqVIlpKWlifVMTZo0kVLkhZczLjB4fcbXcE+dOlVKkZfz58/jww8/RHBwsJip27Nnj5QiL9aZMD5jYZ3yVLxI45UGX6OjlIXwvBfFF7XyaVi+oJUvIlUCfOEonxrja9O4WGvbtq2UIj987R4vaLwx5426EoiOjsaoUaNEpcsFN69wlbTucfLkyWJ9IS9jvMenBLjYfv3110UFZ4VE2j9HLoHGKSjS2rdvj4ULF4pOpxJ47733sGzZMvEiw4wZM/D8889LKfLCR8/4cW98ze/x48cV8+LAnTt3xGBGenq6ol4c+/XXX8W6Wv7SEW83v/76aylFXvi6bS58eFvJRdrPP/8Mb29vKVU++KDB4MGDxfNpXZP5pESa3RZl8QX6fDRNKVgfSv66upJGq7hGtq6Ns110qASekH5/ori5uYk1aHwUTQkjobbwET3eCeAoqezzqR0lVGT/BeQUaA/DXq/9/1OstnC7lFafEY8PFxpKrP+5TdZypTT77FXe7SbSeANqVZRKoKADlVKB2K7hU5K/OFYfKcVXnIJrHgteywmvNKzCUWk+U5qgLYwoTaARBPHfx24iTYkq3IqSbSMejVLzj9ulZNuUQtyVLYoK2elxkmX/HxJoBEHIgfx7UBAEUWTYv2k2Fk8ZoJjQ4alKSI8Lkax7OCTQCIKQCxJpBEE4jLo1K6HVU3UVE8qVCpAsezgk0AiCkBMSaQRBEA+BBBpBEHJDIo0gCKIAJNAIglACJNIIgiBsKBoCLRsJJ9bg4Oih2N6/P7aI8Ca2vfcZfl91GPdSpa/JRgrurZ2NXcyuzTx8PBHHjiTDINtL0wakX96OE1M+wvYBVn+x8N4Q7J67A7ejAXlfzTEi/dhM/D64PzYwuzb1H4kjm87i/kFw8pF1dg2WDu+P/syu/v0/xYi5xxCll89bOaGHcfbbYdjx3pv38/HtIfh18kpcvJHBclpZkEgjijSGpDu4e/O6OI3i+o17iE7KlrmyJeSkKAi07HtBOD95CPaNZMJn5gKcWLcOQSKsQdCy73B80ijsHT4Hpw9Gw3L0t6MxI+H4cgR/OxVHJdtOLPsZ165kwiTHw5lxHreWj8Ou4eNw+KvFOLHS6i8Wli1E4JRx+P3TT3F47yUkSj9xLGak3dyCoKmzcHzROgQzu46vW42rgXdw/4wRmYg9hp+XzMbk2euwjtm1bt1yrPz1GpINcmRkGO5u/Bp7PhuFQ1O/ZWWKlXdrPq5aiBNfTcIfw4bit41BiJXdcfchkUYUXdIjsP+rl9Hj6RZo0YKFdh/jy3VXZWqYCLkpCgLNnHwNd9eNx/4vV+DysbvgbSXf5ps3BHwnPXGiZWgQQpZNwO+zfsDFOwbHDBGZMpF38zdcWLYAR7+aiINj5yHkSoqwjW8P7e7vCxcXOZqrOISvmY8/xszEmX2XkZUNOLFYq7+4fZrESwjfOBeHxszH+YMRYF9xCFl3z+DGioUI/noi/pg8ndmXJLKKb1vtDj+4uDvJ0MCboM+4iUMrl2HRnOmYMn4svtl2DdFSKuAPPy8XaBy+baMeCbt+QOCoSTixLQjJyZZ81LBgLffa3DDE71qJY2Mm4eSey5B9MFmCRBpRtDBbdqzOjb2A4I1TMGXdZVyIShXnW6bGJSI5I0/6IlGUKBpTnAYkB27E1dX7kWSyNlJu8HzqWdR9+WU0fq4Jivm7smYWLDYDGYGrcXrxaSTnOUClGZORc3wB9nwwFFvHTMHFw/eQo5dEo4yYonbi9LJfER7HhQ+rPtQecK3SVvirUbenUbqUVggOLtbyzm3B1VU7Eeqg1j359DYc+3AItoycguCfzgtxKH+DbkRW4jGsGPo+Bn8+FhN+OIJr8SwjZYWV38xzuLBgE+7ezYILi1Gx/zqVboTqL7Fy/0IXVK7mAS1TbNx/2pB9uLJmA25EKqMtIJFGFCFScHXvXAxu3x4dX+iPdyatxYlomwbI2QXOOnokihoZCSFF4yUBYygig84j9LpFVJjUTtBW74km8zeiz9ateGXrQrR6pSncXfl4CGvI0mORtmMjbiXqHbROh6kdaYSF31/YIK5kwpSFxN27ER8TJ/371VBXfhpVp65CX+avV7euRafP28PXR8ekCReUiYi/fBL3gnPEt+3PfX/xWswBUvqxUcmbcw9iNiD35CZEhMaI6V9ew5v9G6Hcxwvw0jZW7ndsRrcpvVGilLPwIU9POXseEXvDFLE+jVokogiRi+Twizh27BgCT13G1YgsRVVshOPx8nRD++Y1/vsCjZNwASlMofHF5KIJ9SwNn9c/RdP6PhAnzTo3R60+z6BGA61onNSsSdNnBiHylh52X+et0kHtWx7FaldCyfLlUbyUL1xdNEKoyYXZEIfQwOtIj7dMixlQHCVadkP9VyuJUUg4lUa1d4eifNXSlmtGbmg4Uq9cF6LN3ui8AuBbuzxKMH8FlC0JN+Yv+aWRChqdL8rUqo3qzK7y5UvB38NF3hFRYw7unTiEtLg0kY9G9l+flu1R461W8BNf8ECplz9h5b4CPJki4nmnjgxD6sXrSBDp8kIijShS8OOR5Kz4CWXx/hsvFA2BxjBHRyEzPk6Uf665dB6e8G9YE85CoVnwrFwdxSqWFQ0Vb/CN+gSk3MuG0d4zP9oAuHWditf2n8BHJ0/ivZ+HodZT/g5b3/UwzMYopFzPgZ7923lDadRVgleVOihpSRaoveshoLIXXNln7ld1cjwy7kY4pHEv1uEddPvtJN5n/hrw249o2NRfjJDKO/qjhUeJ5zBq1wEcZXadPPkzJr3cFKWkVDkwGRORfCcd+gxLmTahDOuf1EKATUaqnGoioKEHXL2kfEQMshLCkKyAXjyJNKII4Y5yjbrjvU8+wSefDMVH73dDQ29XUbERxH+d3MxU6LPSRaXP2x6tcxl4lZMW4lhx94SWBWuU2ZCH3LhEmOze8quhcvGGW/ESrJEvAfdyxeDsKudImol16NKQm2XMHxUzBvjDvUwAWDt+H5MrnP3U0DlZfKo2p0DPhFq6JdWuqJ3d4RJg8ZdvxTJwYf5SwiSjSuMCr4DiKM7sKlGiHEp4yVzHmpjQTmF/9JJIU7PyVbwMfC2pFkxquPg6QcM6LDwfVUhGXlYcshTwlieJNKII4YEKzXrh0+++w3ffzcPCBUPQOcDTMtVDEP9xTCYjEx4WySEaIrULE2rmBxt2T1/oPHzE4mqOOJc/jyk0/teRZOXCZCxgm8NhEtHWAGfWiLu4PGiTygeuxXTQuFldxHxsynO4uNRnZsPI/KU8spCTZ5RRbEvYGqB2hdrFVUx95sOUkHuxEnBiBf9+Phoc0Dl5NCTSiCKKCdnpWdCbzA5vfwhCLh4s66o/l/28XBgN+vzRI05RfkKESM2HXxT0BfNVrgmS9hUUXW8plYI5whSbuYBsZBltyOFCV7pWECTSiCKKCSYSaEQRQqd1hpYFXuZ5xW80JCAnRf3gBrEZ6TClp+XvFahSq6Dx8GR/pYgiAx8vc4JGdf89RVUK69QlpzNZZksKsuLzYMiy/AJwgcbZI/9FAkIBqDRQ8+kSqQyrjGnQp6Y8uN7RrEF2chIMLNKaj2qdO3R8saHMkEgjCIIoAuj8/OHi5SNEGm+IDFlxSL6TCY2NSDMkRiMzPj5fmKh1LnAr7QN1kVMdTJypi8EtQCPeTOQu0iRHIv1eBJNlNpgTkB6di1yjJHx1/nAtUVJ6a5BQAiq1F1yLq6GVpjLV5nBkxBd4KUCThvRbecjLtOSjCQFw9igND+u8v4yQSCMIgigKlK4M9xJl8hdxG9NTkPj7HkTnq44sRJ8Iwt2rCUKYmMAEirYSilV2Eht9OhQvV2i06vsj3Wot1G7ecHKgHSpNaRRr4Alnd75Cib+3GIbESydwJ8aSzsk8sxvRtxLEqIwQtiWLw7N6lQdfLnAATt6eLK9sm3MN1C5KGNHzgrszyzvpiksOPkLl5eo46aHSeKNkrdJw9lKJN181SEbq9dMIP5WZX77ywnch9GQc0nIs260YnZlAK1+VSTX5IZFGFFFYT1llHS+wRVHbMBLEk8OzDorVqI2SzpZtGlTZscj8ZTaO/rQXIefPI+bIDzj703FEhFkaKhO84VyxKypVdLJ/Y2/Ogyk5FPGXziOa23LyNtKTc4UdHFVuGnKuByL8DEtj6XHhKWJrDHui0viibIcm8AxQ81VMzJZ0pJzcgQtfrccdZkPUka0I/nYZoiMtxzGZWM3hUaU6ijWuIH5vb/JSY5F02eKP8ONnkJqSK+zg9ZcKmcgJuyh8GX3+IuKuRyHLIcs7zDDmJSP8yiVcYPc+f/4krkal4P72vjnIjr+K4KCzLI2lX4lAgr1PedFoUapFV/gW95J8w8T16cO4PnsJLp1j/gvej/Nz5uFOeLSYyubpGia0/VvXEEdsyQ2JNKKIooGbrwdcNGrxUApUOtZr9qK3PYn/KCVRukMHVOvoKcq8EBaZ5xAyrAd+bt0aK7qOwIU/botv8pEjdWkm6N7ojoruOvs3FIYEZO2biJ+7tsMSbkv/Obh+JknsP8ZtVcddQeT0l7CmbWssa90Wa78+hMg48Uv7odbBp+NrqNSgHjyYEVxKaJIvImbhAKxnNq7s2g/HN95DFlMg3EajribKtmqHCvXFr+1O4pFV2NuN+6M1Vnd7DxdOJwmRwYWtDmGIWTUKv7K071s/i3WvL8L1zLwHXgixDwZkxu7F1y91QUd279at+2PKjtM2Z3fG4N7hKXitY1uW1gate8zB9gv23lVOBzTogaptGiCAOYf7SGu8i6RNo7GjDStrT7+AffNOIz3V4ju92g2l27ZBxdZKGEcjkUYUKbIQeWEPVkyejMmTJ2Lc6BU4nJBxfyGw4QZObpuN8SJ9Nr5dexpR2Qp83Ycg/iHuDfuizntDUa2yE/gJaFyoGfQ5yMrKQkZ2HizHdKphdquNMi9+hHb9K8LZIdvFm2DOSUVWXDoyuC05edAbLRtPi2A2IU+fzWxkaVkZyErLhbHAC3p2wbUdWo58C/XalYdGaxllNxpzJX/lIpf5i28UotK5ovgr76HhgK4oy7/kAEy5GciJ5/7gtuSIN9V5bWXxmQmGPMlOvt9XYoaUt/bHZMxBWnwckti9s5iC1RtY3kpp3Ftmox45LB+zsjKRlZCG7DwHZKSqChqOHIy6z9eDi9bSMTea85At+Y5X89wKldYJbl0+QsM3X0M1frK/AiCRRhQh0hEavAnfTpqESZO+xLRZGxGUnGkj0u7gwm8/YKpIH4txC47hXhaJNOI/hMYdpZ4bhudXrUaHwd3hU8ZTrD/jIwgi6IrB89n30GLRD3hxwsso5+4gxcFQaXTQuPC1X5bA78wbd2vg19Y0DVOYjrFMC9cW76DjgqXoNvZVlKzpLvzEG07+V+XkCZfmr6L+7A14cdo7qF3V2XGNqlorNl+19RfH6i8Ot1HL3zh10kLtoKxUqTTQOT/mUVDOLM8d4jAVdBVeQOu53+PlWYNQsWlxaJmB1nxUwxPOVbuh2qQf0eu7UWjSxOvx7HcAJNKIIgSrpVSqxyz0aqhZreageo0gHIbaxQ/+bV5Bs9HfoPfP2/D6rl14g4U3Wej/62b0/m482vdvhdKlXB1X/rX+cO04Br227MY7kj3/L7y5azde+aQtSjlqNkrtDc+6XdHkk6l4ceV2vL7H4isRft2GV5dMxTPvvoDK5b0dulDfv/Vr6LT5vi0P8xUPA3ZtR99l76K6KxN10m/thxbuxZ/BJ+u3YCe7965HhXWD8Xxdf+m39sYV7hVboN4HY9Dt+02i3Ofn455t6LdmNroMeQ21qvvDRSkKjUEijShC6OBZvAoa1KmDOo8MzdCsbil4aEmmEf9NXErVQLk2HVG1WzdUk0LVzu1RoXYZeNq/NX8QlQs0ZRqh0rPPoYaNPQ8L1bs9h8p1+YHi0m8dhNa3Mko3fwZVu9rY82xHVGpYGT4y7KflXKIqynTi/rCx5yGherfOqNyuJrylaT77ooLWpQzqPtMZXdm9uz0qdKyD8n6OzUiVW2kUb9QOVTrb+KlrR1RuURPFlPCmQAFIpBFFCB/U6fY5lp44gROPDL9ix3c9UNOLTvYkCIIg5IFEGlGEUEHDN+f09ITnI4MHPNx00NBAGkEQBCETJNIIgiAIgiAUCIk0giAIgiAIBUIijSAIopDCT83QaBy9yv//Yz3Fg/9Vq5XTvFht4XbpdMpZZ6rl+0BIKCkfub+seamkfOQ+si1jSvGZrV1PGhJpBEEQhRSz2QyjUTl7+XF7rH9NJkfsNvt4WH3EbcrNzRWflUBe3v0jkQwGfliXMuB+sual0vLRtowppezb2vWkUbH/4Sfyv8zfiGvXrl1+QWvQoAGWLl2K5s2bi2u5WbhwIYYMGYLixYtj/Pjx4rMSiIqKQqNGjRAXF4fAwEC0bNlSSpGf0aNHY8aMGXjllVfw888/S7HykpGRgVmzZmHq1KmihxcUFISmTZtKqfKyYcMG4bO7d++Kv9OnT5dS5CUkJATvvfce/vjjDykG2LhxI/r06SNdEYWFbdu2oUePHtIVUKtWLZG3VatWlVV88GeRCw5e5/NyVq5cObz++uto0aKFaBPs1YA9Cqtd27dvx/r160X9/8EHH6Bu3bpiRE2uRp6PvOj1epw7dw5z5swRn9999108/fTTcHNzk02wcX/xvDp16hTWrl2LyMhIdOrUCW+//TZcXV1lFWxOTk64cuUK1qxZg2vXrgmN8cknn8DX11dWgcvLUUJCAn744QfhN6uPeDs1YsQI8fnfYDeRxvea4ka2bt1adiXOhyEXLVqEsWPHolixYvjss8/Eg8ofFLkqD24Tv/etW7fEfjGJiYmi8HXp0kVkulx2cbhtvMLnYpYXvOeeew7Lly+Hi4uDNyYqALeL+4kL7u+++05UKFu3bkWbNm3y/SkX/P5cpHFhdu/ePQwdOhSTJ0+W3S7uI16hDR8+HMePH5diSaQVVgqKNN5wBgQEiIZdzlEFXs75/ePj45GWliYaVF7X8jel5az/rXbxeiMlJUVce3t7C9usgkQOrHYlJyeLwO3w8vISdvEpUDnt4vfOzMwUwoOLRw8PD1HG5G6XeHtttSs7O1uU+RIlSshul7UjwAdauH1WWxQv0rjzeONerVo1WXt4vNDxzD169Ch+//13uLu7i0a9WbNm4mGQq2KzVhC3b98WQoOfscZHhLhtfn5+svUMuL944JXtb7/9JkRkmTJlhJAsXbq0+I5cDwTPL94ABAcH49ixYyLu5ZdfRr169YQ/5WoMuL/4/c+fPy9GEXhjwEcQOnfuLKtdHGdnZ9Eb5rt7h4WFSbEk0gorBUUaQRDKRPEizdpwWcWI3PCGkgerCOF2KQEuEm39Y/WZ3BS0i2O7yFVOrHnJ4XnJRbgS4P7idvG/SslH7h+rv2zzk0Ra4aSgSOOd4e7du4u/SmDnzp24dOmSGK3q0KGDmFFRAmfOnMG+fftQqlQp9O/fX4y+KIGkpCSsWLFCjFj169cPlSpVklLkhXc4eUeYd4r5gAaf8lQCoaGh2L9/vxi1qlChAnr37i06onKTnp6OX3/9VSx1sfKkRBqvuJ8IgYGBZtaI81aAAgUKCg9MpElPLlGY2Lp16wP52LJlS/OdO3ekVPl57733hF3VqlUz7969W4qVn1WrVuX7S0kkJCSYvby8hG2nTp2SYuVny5Yt5qpVqwq7xo4dK8XKz9mzZ83NmzcXdnXp0kWKlZ+srCzzSy+9ZGYdc2EbD0ykSan/DruNpPGpMb72q3HjxuJabvjIAV/Uyuexufp+8803xSiD3PAXBwYPHix6LF9//bVi/MWLBffXpk2b0LZtW0ycOFER/uLrEViFC9YACHv4ols+3akE+OJkvq6QrzHp1asXPvroIylFXlgjLsrWzZs3pRgaSSusFBxJ43UuX2+rlBEr/hLDsmXLhD38pSM+yqcEeF324Ycf4qmnnhIvaCllVoCPvPAF8Hwkhi+V4KOPSoCPiH7xxRdiPSsfDeKjQkrg5MmTYn0tX77UtWtXsQ6Yr+WTGz6yN2jQINEGWJdQKX4kjTWcQvUqhSVLlgi7/P39zfPnz5di5ScxMdFcvHhxYZuSelKccePGCbv69esnxciPXq83jx492swEmlmj0ZgvXrwopcgPqzDM5cuXFz5TUu8zPDzc3L59e2GXNdBIWuGk4Ega60CZL126JKXKz8CBA4VdtWvXNu/YsUOKlZ/FixcLu5hIE3WIUggJCTF7enoK2w4ePCjFyg/rDJhr1qwp7GKiSIqVn6CgIHObNm2EXXwkLSUlRUqRl5iYGDPrPIk2idvGw5MaSbPbohm+Hodvl6AU+NsgHL5+iStd65omueGLzFk+iM98NE1JWH3G10soBd7jtF3rpSSf8Td8rOvjlOQz7iMljIISBEEQfw+7iTSr8FAa3C6l2kY8Gp53ShHYBVFy2aIyTxAEUfiQ//UzgiAIgiAI4k+QSCMIgiAIglAgJNIIgiAIgiAUCIk0giAIgiAIBUIijSAIogiTcX0vftu2GZs3s7D1GM7cToGcr+bkRF5E2MGduLplM64ym65u/gVXd+zHnfPhSJe+Ix9GGGIv4Raz6wq37ZctuHMzGdnynOInMCTfQcTh3bie7y8Wtv6KW6dvI0W+ExmB7EhEnzyI61t/wTWrXVt24Oaxi4hNlb4jI4aUOwiT7Lryy1bcPB+NLMuGBoqCRBpBEESRxAxDxCn8NPF1vNajj9jcuE/PCZi/8zZk0RyGeMQf24gT04dh+xs9sb5XH/zEbPqpT2+s7/kGtnw2Dcc2HEdUol5sRCUHhrQzuLZkGDYxu9Zx23r3wu4tN5GYI33BgZj1KUi9sANnvpuEnQP6YkO+v/owf/XCpiETcGDZ7wgPTUae9BvHoEf69eO48v1X2PPRG/i5J8s/q129emLTu59j34JfcPtKEnJl6g2Ysu/izs+TsJXZxPNxXe+e+GVJMGKTpC8oCEWLtNTAJVg0/lN8+ikPX2PBlmtQzs5rBEEQhQ0jcjMSEX33Lm6e2onNSydg5u5kJEqp0GqgUTt+Tz2zMRHJe7/B9lfexaHF+5EYZZGJvA3nwWyMQfqhpQge2A/bZ/+KiASjw0b7TFmJyIi8i8SrwbiwcBGO//A7+AAV3xFRDX4OtIr918GYcpB8fCX2f/gK9kxZg5i7mSxnueyW/MWEUm7wepz7eAC2jliBGzEOkt1mI7JDfsXRoT2wY9gihJ6JEvZw2yx2GaG/cQC3J7yNTR9+h3MXk2FwlOLWpyIrOhSJty/j2soFCF60FlyTcRHESj3UGvZJgdtJKlakmSKO4cdvp2Ds1LmYO5eHhVi+6xaJNIIgiH9MCi7v+gYDmzVD665v4oNvfsOdDJtWkok0LjocixlZIcdwfNwSREelwyjMUcPJ2R2eXl7wcneBi9RSGTPDETv3S5w4dA3JDlJpGcdmYN9rzbC4XTf8NuUnxEZaG3b2V62VwV/MY+H7cWv19zgflCP8xS3QOLnDg/vLyx0uGotw1JgiEfP7GgQtOoE0B+yvbUq+hbvfT8KVY/FIZ4YJG7QucJPscnXispbZa8pA5vE5CFx+BBHRDlJpt9bi8AfPYUHLDtg5YiHuXjL/KR8dn5OPRkEizYDMhOs4vmkTfl4xB7OnjMR3v8exKkVC5QsfDyflqkqCIAjFY0RuegIiExKQkJSKtBxHjUf9BXkRyD65HTeupoHrCH6qprFYJ9SYtgFvnDiBtzd/gybdA0QDyut/U9ZF3NxyAsmhjjnVw5QVj4zwBCQmJiEzO89hI3h/RXTwcdz87bqwhftE5V4OpcesRe8jJzDwxBq07NEErjyep6deRfzW9bgcnSv8az/0yIgIxpU1V5CVY8lHM/xR7O2v8NxxbtdmdB3eFW6WL0NjzkDyyl8QcvkesqQ4u5KbgqyoUCQmJCI9M1dM6StRlBVEQZonBzFXN2Na377oN/BzjPz+BMLSZFyNSRAE8V9EpVJUZzcv/i7Cju5AllTdm+GM4v3eRv2Bz6Ns7doo2XUAGgx4B2XcrQ2WGTkHzyA6NN7OosOKGip2Y35vPubjoHGfv+A24i5cRVSUGTp2ZdT4w6X1ILQf8CKqNaiNgNo90HLwy6jZ0luals2DPi4QoZeykWc5+9s+GGORc/UIwmJMYnpT5FXLt9DgrYFoUJfb1RV1+w9Awy5u4DOL3I+qjN8RcSMUKfa0y4rIRHV+2Zc/Hx8Pq70KgB+pY5RnwSpBEESRwAvVO7yLr1avxurVP+L7+YPxbDFPOEmpcpCbEIvYq4ms/reObNRAqfZ1UdxHXDDc4FunI8rVuy+U1LHXkZCYgEyRbl/cm36AtnNWoz/zWZ9vPkXDNh7CBtka+fSbyIi+/6aryt0XXq2fQ9lS95tzt+bNUbphZSHiOMa8VKRei4bRnm97ZiYi8/Z1ZDMzrL7xbNYepWp6iilFjnP5OqjVoTa0WpU0ChiDpMgUZDjitd2KL6HZ1JV4k+XjK0vGo92LVeTNx8dEQSJNDa2TF4r5eMPHOq+utcxfEwRBEE8CFxSr0gxd33gDb7zxNt778Hk09HLNb8zlIC87E9lx0gWr8c2edeHv5yGm66yodC5wr8BaCanFUiEcGclpcMSOCbqyzVHphTfQiPms4cCXUbGOq7yNe3IiDBkpYkCD26BxZ76pWk4k5ePqx+ID4Mw+8jbUlGtAdkQ4THr7Tdaas7KYTovNd4wZrC2v4Audt+VaoNPBNaB8fj5ycuJSoE+WLuyJb22U69wHjVk+1v/gVdRqXkrefHxMFCTSXFG6wRuYeTwQx0+cwIkTCzGoUUXkd6YIgiCIJ4gBGYlM6BhNsjZUZqMRRmneUiw19/WDq5PLA6N7Wicd3Hz8xYyVhUwYcvViWs2RGJNSoZd7HV8ek2cGY36eqbVaOHu7Q2UdrhJ4QOvqnu9Ds8kEI98EjA9X2gmzyYC8PFvZ7AMXLy3XZffRusDs7Q8XlpHWrDQxcWd2zLz1fVJSkJPl2I1J/ikKEmlqOLkVQ7natVFbhLoo7+0mFh8SBEEQ/1FUBd6qYw14QSmhVmugdXYRw0K2aUV9psXqiz/7QSfEG2/g//937MEj7sLyUaVzBvuv+KawjeU/8f9RkEgrSAZybHoLBEEQxH8Q1kY/MAqUlQGDMe+BUbK83FxkJMTCbLLKACZCNLypL4LwuUJpvlAIHZMJeTl5wjf3SYU+Mx18j13hIy6ExZCWPT2mFmL6PjnMLhOMtnYZsoHkOGQxY3m0sIaJyfxFa8SfULBIIwiCIP7r8BEyJ1/LZ76cXJUQguS0jAe2ZTDl5SEnxpAvRMwoBTcfD7Hmqsjh6Qm1m3u+rjHm5CIrPJo5SYrg5CTDkJkk3n7lAx1qnRZOJYozoWa/Jl/N8tHNu1j+wJgKcUiPSofe9qWAXD0MCXEwmcz5AzDO/j7QekkXxJ8gkUYQBFEk4euFtHhwL1YV1BoWL105Amcvf/hW0orGXTTc5ouIvxaF+zsw5SEr8jKig5kgYV/g5prdq8LH1y9/zy1HoWH+ur8uToL5S+1Ih/lVgKtfSfFv574wpqcj/exZpGfen3cy3biG5JC7+cdBaZxd4Vm1PPsrRdgDT294lK9os0TJjJQrN5AeeV896hMicOdsMIwGy0a3ZnjAq7Q3E9yWdIfBy73mgYLP1JCD8/ExIZFGEARRRDHzheT323YLLK5glD1xCiiFio2a5osfFZIRufoX3DwYIUSGOSoIIdtWICzTMljEm1ZV0wYoVab4A2+AOgLhr4I8zIf2RF0TPpUqwc/J4g+NPg4ZB+Yj8MBdpAk77uDqhr24eTRRCCYT85jOvQZK1vcUM4t2w6kEnGs2gT9T23yUj5tiOLAcF3cdQqT4QhRijmzDuV+zYWCG8+w2oRlKVioFH0fvAcPL+J/yzMH5+JgoWKS5wlljuwWHmvViXOEuXREEQRB/l1Rc/30RPuvaFV27dkH33lOxLS5NbHoq0J/G7gWDWBpP74+3Ju5DaLZ936FUeVaDd9u+KO1nWZrE20nj5Q04/2lf/MTsWNtnMALXXrhvI7xRsnsL+FVzt2kf7Edm8Hz88WFXrOC29B+P0/vSxH1546kyG5C8dDB2d++K1Sx948QVuGpRJHbEHeXaNkaV1t5iOlMNA0xRJ3B1zOvY3IXZ2PU1HFj2B5IyTcKfBk0AXJp0R71KTnCyq8M84V65PWp19YOWOUcIyIxLCJs3FDuFXX2w+8v1SLCZ/tS0exGValdhv3QAdzbjxPBXsIzn40uf4MC6azb5mIfcbeOwr29XrGHpaz6bj3MXHf3u8MNRkEgzw6jPRHJMDGJEiERCtu0r1rnITonEXZEWi5iEDOQ67GRWgiCI/wI5SLx7Cvv27WPhNxw4dAH3bOtZUwIirp/A7yJ9K7YfCkGqvetZlSvcGr2Adp92gwe75GKMj6alXjuBa8yOy4GXkJDA4/jEJ6B9dihaPt8Y/q6OkGjsnpEnEbp7H85zW46eQ1RUnrBF3N1sRs7NINw+sA8XWfrVQ+cQ74CNWV3qvoDab7+CCiV4jnJBZEburSDc+n0fLu0LRlx8hhBJfEMM5+rdUfezF1HKQ2PnBl8F5+INUXvYx6hQySK2+Yx1XsRV3PuN2xWIe7eThF0i3r8zGg7tikpVXB0jRJKvIWL/LzjH8unSwWCE3knNz0eV2QTDvXO4e5jbyfPyJGLilKEvFCTSshB5YRVGtmyBls2bo3nzQVh0NvT+2Z3mWzi78VN0FWkd0Lb/chy9R8etEwRB/B0eNmP3cMwwse86oqnSuldB1Y8mo9OXH6FO60p8yZAYJeINOr+/HhqoyrVElffH45kpH6NODU8HvjRw3wn8DxeK/KQDa+BCiItcnvb4vv2XOJVFqec/Q+epn6BxlwZwc7HYZbWDfzYVr41yfb9Ax6kfo1nr4o5p7HXu8Gs3GO2mTELLnm3g468V+Wi1i4szk0d5+D/3AVpNm4y2z1WFB7PdMfApTksGWW2xzUf+ogqP42l8wt/yTflRkEgzQZ8Zj9DQMITeu4d792KRkmN7mK0eOenxiBRp13H7ZizScu+PsxEEQRCPwg2l6z2LNwYOxMBHhsEY1Ls+Apwc0UyoAJ+maDj+K3SZ8SU6jP8Ybd4biDbMjjYD30WbIePQ/suv0Gnml2jVPAAOMUnCuUpX1H5nINoLW/5/aMtCi94dUEZ6U9XeaP1rosq736LLzGnoMGEY2r5vY8+Hn6LtpGno/M0ktO3ZAA4yyYLaHxX6TcQzX3+FjpPGoe2HLP+sdr3/MdpO+BIdZ85A5w9awN/dgXtvFG+O6q9/jA5WW/5P4PnY9o2uqFhOIfKIKcsnQmBgoFmr1VpEKAv169c3HzlyREp9HNLMt/6YaO5kFbKPCpVGm3+5miz99tHMmTNH/C4gIEB8NhqNUoq8hISECJu4bQcOHJBilcGwYcOEXb169ZJi5CchIcE8evRos0qlMms0GvOxY8ekFPlZs2aNuWLFisJnI0aMkGLl58qVK+b27dtbnhspbNy4UUolChNbt259IB/btm1rvnTpkpQqP0zcCbtq165t3rFjhxQrP4sXLxZ2PfXUU2a9Xi/Fyg+v/z09PYVtBw8elGLlZ9u2beaaNWsKu4YPHy7Fyk9QUJC5TZs2wq4uXbqYU1JSpBR5iYmJMffo0UO0Sdw2HmbNmiWl/jsUNJLmguLVX8Cn8+dj/rx5mPeXYTEWTnsZjUo6+gVsgiAIgiAIx6AgkaaDV+kmeH7IEAwZOhRD/zJ8iI/6NUMlX0e/t0sQBEEQBOEYFCTSCIIgCIIgCCsk0giCIAiCIBQIiTSCIAiCIAgFQiKNIAiikKLRaODmppwXqHQ6y+GHarUaLi4O2wDrkVht4f6y2qgEPDw8oJJOJHd3V855OtxfPA85Tk7KWfvt6uoq8pDD/yrFZ/wZ1NrpzC0Vf8VT+vyvOHHiBNq1aweDgW8HB9SvXx8LFy5EmzZtxLXczJ07F59++ikCAgIwduxYfPLJJ1KKvISGhqJZs2aIj4/HH3/8gQ4dOkgp8jNixAh888036N27NzZt2iTFyktycrKw6auvvhKVyNGjR9GyZUspVV7WrVuHcePGiTwdOXIkZs6cKaXIy7Vr1zBo0CAcPnxYigE2btyIPn36SFdEYWHbtm3o0aOHdAU0aNAAw4YNQ7Vq1WA0yrdvJBca/P68zO/atQsVK1YUZa5t27Ywme7vdikH/P68/po/fz4qV64s6o5SpUqJRv4JNX9/G6u/+LPJ26Xs7GxMmTJF+IuLSLnsssLr1SVLliAsLEzU/0OGDLGbCHlceH1/6dIloSsuXrwo2s1JkybB29tbVn9xuxISEkS7dOzYsfzyPmvWLNGG/lvsJtJ45bF06VI0b95cXMvNggULxJuhxYsXx/jx40WhUwKRkZFo3Lgx4uLiRAa3bt1aSpGfUaNGiUq3b9++2LBhgxQrLxkZGcKmadOmiYeDl7unnnpKSpUX7qPRo0fj7t27wne8MVACt2/fxvvvvy86AVZIpBVOCoo0T09PVKpUCV5eXvl1rxxYRQcv+7zDyUc8ypcvj2LFiolGS24xFB4ejujoaCHMSpYsibJly4r6Q267uE0RERHCjhIlSqBcuXJi5EouYcvt4iQlJeHevXtCPPr7+wvRze2Sy18cnnepqalCOKanpwtxVqVKFTHqJ2dHgNuVk5Mjyj73mxXFi7QKFSqIXavr1asne+XBw/bt27F69WpRmfFKrlu3brL3pDghISGYOnWqKHTvvvuu8CEfAucPsBxY7eL2rFq1CocOHRKjolzU+vn5iTS5fMbzKyUlBTt37hSNFbeVj1g1adJEfJY7L/lIFRdqXHC/8MILePPNN2W1i8N7v3xk74cffsDVq1elWBJphZWCIo0gCGWieJHm7OwsphblFBwc3kjywIcjExMTRUPv6+srghIa9rS0NNGo854A7xHwkT7eC5W7J5WVlSV6xLm5uWL4nfemuMDlyOUz3vPl5YtPeVp7LLynrqS85GUsLy8PPj4+ovzLaReHl3feG+ZljP+1QiKtcFJQpPGOU926dcWogpyjCbyc8/vzaSg+MsTr/Zo1a4ppRSXYxUc5+NQit6tRo0ZiBFLukTRuF6/HTp06JdrIhg0bonTp0rLaZSUmJgY3btwQMxd8wKV27drCLjnh9+f1K89H3gbwup/PQsk9wsft4nXr5cuXERsbm2+L4kUaQRDKhURa4aSgSGvRooVYb1unTh0pRl74OrQ1a9agevXqYo3V888/L6XIy7Jly8TaL96o79+/XzGL4bl45GtquRjia/nat28vpcgLn3ni671u3bol1m/z5SVK4OTJk2JJSXBwMDp27Ii1a9fmDx7ICRdn3E979uzJH5RSvEjjIy/8ukyZMrL3Cniv5dy5czh+/LgYreLTY3zNnNw9KX5v3jPgDwRX4p06dRJz7HIvHOW26fV64a8rV66IntSzzz4rRkflhNvFR/h4XvLA86979+5iHYfVn3LB7897eLwS4VPFvFeshPWF3C5exvhUbFRUlBRLIq2wUlCk8TqWCxD+4oAS+OCDD/D9998L0cgXUnft2lVKkZfly5eL5SR8jXRQUJAUKz/8meQjjrzO4PVtq1atpBR52b17N4YPHy7qNCWtr+X1PhdD/MWG5557Dlu3bpW9XeLwZTi8fPHn80mLNN6wPREKHrBer149RR1+zXqbwi7rAetKISwsLP+A9UOHDkmxyoAVMGFX7969pRj5SUpKMrNKQ9il0WjMrHMgpcgP69Up8oD1GzdumFljLuyyBibSpFSiMEEHrP8z6ID1vwcTG3TA+t+gUB6wzkc5eC9eiXDbiMIJzzu+zkqJZcu2XCmtjFGZJwiCKHzYreZmAlDWFwYKYrt4lX+WczGrLbZr+JTkL47VRzwvlQL3l23eKclntrYopXxxuM+UlIcEQRDE40Hda4IgCIIgCAVCIo0gCIIgCEKBkEgjCIIgCIJQICTSCIIgijCGlHtIvXMbibdZCIlCRmou5FxRac5KRFzEXYSE3BZHmt2+HYKQu+GITshEnvQdWchLQ0ZUKJKZXUnMLhHuhiIlLg16JSz5zIpGRKjVZ/cQkyizv6zkJCHhntWuMNyLSZfXX8YsZMXeQ/KdkPv5GMLyMTIR2Qrc5pVEGkEQRBHFmBaOS1O7Y2275ljavDkWtv0Ef2y8gRwp3bHkISvqLH5fPAqDe3dE6xbNxb5mzZu3QIsOPfHW8AX45Ww0UhzekBpgSLuB2xtmYGf/TljRpjl+YHaJ0L4z1gyaicD9t5CcaxB7L8hCTjSurngXvZ61+uwVjFxyBJFSsnzkIeSXCfj0BatdL6H3F3sQlitHN8AEY3Y0wnctxm/vv4Af27e4n4+tO2PVa6NwcMspxGbmydpJKQiJtL9DThwirp5C0PGjOHLkiCUcC8bpi6GIl6dWKxwYU5BwK1Dy2VEcC7yB6CxlPAi5EadxJtCal5dwIyJdUQ8oQdiLvPhLuLNxKk6svYSoyCRkJiUhNToeWel6GcRGJtLPLcPwPv0xcNpabAsORWxCkjg2KSkpAQn3TmP/+hn49LVX8dGPwQjLlH7mAEw3N+Lo4LexZdh83PwjBMkxzE/cVzyE30Lc9vk4PuBlbJ+xFSEp0o8cSfo9XPh9Hr789iBO3rb6LAaJqTmQ8913Q3YCYo8uwnfLt2HTRatdUZYRPjnUbOphnBwxEJs/nIZLuy4iKSLhfj7G3kLCkbU493Ef/DJmOa7eVc6QGom0xyIHKdEXcODHGRj7zkt47tn24vgOEZ55AS+/MQIzV+3DmdgMZCth2FtR5CHj1BrMH9pZ8lkndO42DTvuJUMvfUMuTInXsW1yL/TuLOVlp88x/edrsttFEPYjBTF752ILK++rX+iPPZPXIi4W0LIUflCSk4sLtDrHNwsZkWexdtxMrA+8hojkHDysiTTrUxB74wg2fzkZP50Mg+X0XntzDme/+R6nNwQhKS5DdOA0LHAP8cD9pjGmIyfyKm7NXYhzawORwOIcQdTRNZj+Iqu3uvTEG58txba7tiMFrnB20sDxu0kakBn3G2b3fB5dn+2OF9+fjtXHI23qVDe4OmtlEB6JuDFvAc6t2IOY6GTkMfXK89Gal5bPOdDHhiF82QycXrsT9xTSljveV4UQQ+h2zPvwbfQbvhA/BUcjxVaJ6eMReXEL5n/eF6+NWIjt17NpJIZ5QJ+RitT4BOabTVi0+Ef88Ju166uHXm+AUZZ9u8ww5GYhjfWcYm8cwm8/TsS0rREIzTctDwYjqWziv0wusu5dwJ0jR3Az+CJiIrJErKwNgSkB8Wd3YtH+MCYhJVwaovMHs7Dmp5/w0zcf441mrlIC6/ZF/I5FW4NwNcbe40SsHju1DdcPnURSHpc9rC1wqwi/nl/iJWZX3+9nolE7XzgxpcbFkCo5ECG79yHktvix3cmMvoEzu47gyIkzuBSSzHJWCZiRlx2O83t348DxYJy8Hoc02Xu9rJxEHMC1XScQnW3JRyNKwKvdEHRa9xNeXbkAbftUgQtL4G23U1YY7uzai1un0sSv5YZE2qMw3cXWmQvw067ziM/S5/fw+I7393e9Zw9zVhpubpiD1Wu345wCFx86FGMkDkx6A31at0L7Fz/DV5suIDpf+zjBidVqGllODEjGld3f4P0WLdDmuf4YOH0bLiXaSGonHbQa5Z1kQBBPEr6xMX8cecnnVZWcU2KCpBDcPPILrufXm2pUf2EABo0Zgf79+qHfsLH45L1eaKKzNlgGROw+iashSfadljWkImLvH0hJyBYizAQXeDTsjnpTvsBTzK767w1Du/FvomRpb2GHBnlIvXUZMafjxM/tDs9HRfYpzVDQXt4s4wxIOroJcdGJ4kUK7jLn2q1RbeQ4tH6tH+q+NRhtxg9BpSr+YEVMkHvlOuIO3IQDZ9X/LyTS/hI9zNd2Y/2Ry7hlrcnUlVC391fYeOgQDu35GYsG1kYxdylNH4tD+w5i23HHDMQrFnMuku5cxsVbtxASFoeUbKU8sQZkJNzDVWbX7buRiEqmiU2iqOEO34bPo/nQoeg4dDDaDeyKEp6uQoTI1d5nx0bh2rk7UOUbUAntX2qDZuWlSxRHjdZ98FINy/Si4M5VhMXFIUO6tAdmQxQiTkUjK8UyHWZAOQQ0fRo1avOJYY4G/s/0R7nqJcXoDDffFBmDjOs3YBmftC++tdqh5xdDMZTl5Ufv9kX7sm7CDnlRw9mrAV4cMRKfM7uGDn0Nz9YpCy8pVQ7MxnREnL2MrCS9KD9GeMCnYStU7lwivzy51XsNVZ4qAy9nS6dFmxGJ9JCbiLckywqJtL8iLwt3D2/CzYT7i8lLN+uEN4YPQe927dCuS1+8M+JT9K0cAG8pPfvqDVw+cgvp0nVRxcy6Usrs5FlGEQiiaOIBv+a90W7ePDw3bwG6LxiMssU8ZBVpGVlpiI1mAkcYwCxxaYCKxT3hK1ItOLv4oAwTaRpri2UORUxyqh3XpbF6whSHrEgjDMwu7h+jR3l4lq8AP8sXBGZTcXiXcYYL+wI3X5OdyH4T45B1acXqd0H/r+ZhHsvLhYsm4a3aPrKKIQsauPo+hVemzMRsZte8eV/hgw61UUxKlQOzMRmZ0SYYsi3XJia2PQMqw9c6bMYx+sKrqg5OHpZ8VCEOOWmRSFPAHiYk0v4CU24yrp2/jaw06zCaH6o2bIZnmrGclHCu8TKeb1oMxV2kiOwIpN67jQjpskiiLoam707E1BkzMIOFYa+0Rz3WQ5EfD1Rq1Q9fCLumY8qE/mjt5y4WTBNE0cMEfXoWjBZ1JBtGgxE5+UNPTO0UKwE/1wdHhTQ6Nby8PaHOb7FSkJmZDandtQO8M5cNg9GU30E3+XvCNcDnAbtUcIOTnwYaFmlp3DNgyEpx+BYmeekZyDEosWOcgew8Q74PZcFs5Fuj8T9CbJs1vnD2LwZPS6oFlQkuXl7QsHbKmo9GfSr0CpjvJJH2FxgNrIcXA+RaV2SqKyEgoBxK85y2kueBEhU1cMt/cuOQmh6FqKI8k6b2Qc3uA/DeF1/gCxaGv9oB9a0iVlbcULpuJ/QXdo3GuElvoK2/B4k0oojCGnWZBdpD0eigUT3YNKl1zvDw8oHDl7La3o8rRDWf+LRB5QlnLybSpMZdLkwsHxWYkwwFrptTa6H6Uz6qmEjzhpY1Bvnmsg9KsJ1E2l9gNhugZ12i/IzS+sLdxxc+0qWAKXBvFueUP1KUBb0+A1n26+IVOpLTs5GrtAcVRmQlpSGb9ZQVZxpBFDEeEF+s0tWbHnydwZibg5SkeJsF6ZYXt+yr2dj/um3lYDDAbCg4/5WC7CSDmEqz2ML+ywSmfe0i/ja2GWLUs3wssBcgE7lZSQnIY+39/Xxk5UsBColE2l9S4FHjDx8LDziNZaSruwe0tvPbhA1KlUAK7OERRBFEp9PBzdNa2zIVFh+BhMzMB14KyMszITExx0akBcDLyw3Wd7aePLyud4fOSZNf36vjU5EVnVjgZYU05CTkwZBrsd8ML+g8/exoF/G3UTtB68kEF2ujeZWvNiUgKy4WqZZUCRVykrNglESaGd7QufjB5f7KJtkgkfYIHlDSplzk6XMf3I+GVRrJiXHIzR85YyKOC7kCo6kEQRDKwjJa8CceFmdHPDx9UKYcqzmttzWcR0hE8gOL79NTwnHtNEuydqy0lVDax+eBRfxPGpW6JDwr6cB0mmjcNTmhSA8LefClgIw7SArLFJuY86bC6FkM7uVLO3yh/MNHFe090vg4/B+7HFjGVGpfeJVzgs7Nko8qhCMj/hYSbdW2OhSJZ7ORzeJ4PppQAi6+5eCtgHacRNpfoNa4wNNbBa31Pd28aKQmxD74Wq7KhPiEHOTkr0HzgZtbMfhRV4ogCEWjgbOPOzT56og1YiodNG5eDt3KwTmgJGrVrwFDvhnROLB+Hw5dlpbf62/i4v412BHLt8GQqFMXFUuWAGt37YZKWxKlmgawxtpyXx2iEBu0D1eOWMdgMhG5bQUibsfmd9y15crAq1ZNh69zdfLxgpvOdpZHAydXj/xdB+TDG+4uOhu71NA4ucObvw7rIFRaN5RvUAduvk4iH7Ust5JPHsWtX27lv3iScngZbl0MRzr7AtdlRv9y8KpZE8UtybJCIu0v0Gh9Ub6iC1zza6xQREffxm2bcVKz4SYuncxGUr4qLwUfv/Io47gyqHBUrIdcsDfFrtUFpo0dDrs/s+FPdhWcziaI/xSZSDm3C8ETJuCPCePw+6gViErKlEYYmBDJu4aojTOwn6Xvn/ANAteeRYrezu/meVdB5dY98FT+ul4zog6vwvxRn2Ass2PCJ6MwY8UBhEupXC5Vfa45alSzswRRe6BU167wLWaZ81IhD7nX9uLa6M+wl9l1cPjnOPD1r0hIyBZ1hgEu8K1eD6WaO0YaJV87jC0zmH+YLWO+mIUNN9Jstn6KwYW9SzCR+2/CVHw9/zfcyDU64C1LI3JSL2DH19PxpXTvVcdu2GyVkoS486vw5ejxwu4J3+zCqTA7b1il0sKjdW8UL+MvxDMv68awYNz5egT2jWfPwehP8fuEVYiMzRBpPLjVrYESz1SBEjYloPboL1A5e6Buo0Zw9bL2i/S4cfggNmy+JvWcchDBHoRN16OQYC39JSuiWO0aTKoRVtxcnKC1UUMq9tC4uMm99YUarh4u0KltBaQGWmc3etuT+A+TgcRTm3F0yhTsmTIN++dsQUxq1n2RZghBzL5lOMjSd02ZiEM/BCEx185Nu8YPxZv3xsjXG6KkFAXjXZzd9T2mMzumLNmK/Tfvv0jgVKcvhrzUDDV87dx8sQ6bS71+aNT7GZRgOo3vxqDRxyMt8EccYnbtm/09blzOQh5zj7CuzDOo1uN5VHZQ5Z9w6TesHcv8w2z5avYK7AnNsNkhPwHXj23EAu6/KUwQfbUDl5lI4/lsX0zISTmNjRPGYqK49xJsPxd6/7gvpCLx+nYsnjFV2D1l0mYEhjy4OuzJowFKdUKDt55HBX+NGD1TMzuyr2xH8NQp2DtjLs4djYNeGg7N9q2Fqi/3RI06jhxP/v+QSPsrNK7wadMX7Ut55y8EzYwIxLZZH2PAhx/iw3cGYPCkjQhOvH8gcPGGddCiY4X84yWKJKYEnFkxBRO4jz78AMMX7URw/po9A/RZR7Fs+Id4T6RPwbI9Vxx0WHI67p74GbPEfd/HwPe/w964tPt7Ghku4ei6cZJdI/HF3CMIy8yfYCGI/wC8U2IZQbZ2TvhKDf4M8MAfU+vKDfG9Bzox9sOlWH28MHYChr7yDBpW8sHDduzReFVAzXYv46OJ49GvUYkHNru1H5VRe/BgtBncExVrlYSWtffWGoH7hYszo1tJ+DTqgIajPkXjnvUffPvfjoicfMwW/M+zBvaEWfa4yoLb5RDDvFFxwBA0H/4OqjeoCBdWwKxHRPHb826IQeULj9otUfuzyWjW52mUdJzD/hISaX+JFijbCe9/8BJa+ltdlY7km/uxfulSLP3xZ+y8kIo861Nbshle6t0LL1RTwiCpjJiScXXbD1jEfbT0e2z84zzu5K/ZM8Ggv4WjG1ZjmZS+9VgIHHOUbRYiL+7DKnHfH7Bi1e+4lHZfYPMzR28GbZXs+g5zfzqD6Bz7TxAQhOPQwaV0DZRt3BiVbUIlm2CNq9K4Dco3KA0Xh5xnq4OuYg+MWc7qjamf4L2+7dGyaWM0ZnY0btwUTdv2xhtDvsQ3y9Zhdp8aKO7IKrbks2g6ZSFe/nYUGvbviMotbPzVthNqvDcK7ZZsQLchnVDOgW8DuhavhDrtrD76q9AGbVtWRTGtIwS3ClqXkqjVluXfQ22xDU3QuE0NlPFxUGZqG6DeqDnosfhLNHv3BVRt0/R+PjbvhGqvf4oWc9ei59g+qFZG+o0SMD8hAgMDzVqt1jqla65fv775yJEjUqr8zJkzR9gVEBAgPhuNRinlMci7aP519lDzSy2qm0u5Wf59DwRtSXPlFs+Z35y/z3w6XvrNYxISEiJs4v87Bw4ckGKVwbBhw4RdvXr1kmIek7yb5tUvljcXs/XR/w3lzM+P3WG+K/30USQkJJhHjx5tVqlUZo1GYz527JiU8jjEmI8tHWCu81A7CgYns2vzOeYTCbnSbx/NmjVrzBUrVhS/HzFihBQrP1euXDG3b9/+gX/fxo0bpVSiMLF169YH8rFt27bmS5cuSamPg8lsMujNednZjxdyDewXj8/AgQOFXbVr1zbv2LFDiv2bmPLM+txscza7//2Qa87N+zuWPMjixYuFXU899ZRZr9dLsX8Xg9mQm1PAR+xa//d8ZAuv/z09PYVtBw8elGIfD5OB+SnfP48IOXpm/eOzbds2c82aNYVdw4cPl2IfE5Ph8e3KZnYZH997QUFB5jZt2gi7unTpYk5JSZFS/g4ms1H/kHxkZf1vqIIHiImJMffo0UO0Sdw2HmbNmiWl/jtoJO1x0NbD85/PxOK5kzFqcD/0eK4jnn76aUvo0huvfzgKk75bgYVDOqOJnIeUKQWVB8o1fxbPWX30l6EzWtS071ta93GGX4VGD7HhYeEFdG9XBb5O9IgQ/yVUUGl00Lq4PF5w0jhg9KUAKi10Ti5wYfe/H5zgZLuwVRY00Dg5F/ARu9bJ4COGSsP8lO+fRwRnnXhr0SGoNI9vlwuzy+btYsegEidY/CkfbfbEUxLUAj02rijV7FV8POsnbNl9AKzXYwl7N2Ht/E/wRvOSUMC+d8pAUwodxizDaquP/jIsw7j+zRz0qrMPanUZivkPtaNg2IyNs15ADU/r/isEQRAE4VhIpBEEQRAEQSgQEmkEQRAEQRAKhEQaQRAEQRCEAiGRRhAEQRAEoUBIpBEEQRRSNBoNXO+fWyc7WumgY755Kn97Tyk4O1v24uL+0umUs9W4u7t7/mHjbm6Oecf9ceB5x/OQ4+SknDNYeFnnecjhf7n/lADPO2vZf9Ko+D4c0ud/xYkTJ9CuXTsYDJatQevXr48FCxagbdu24lpuvvvuO3z22WcICAjAmDFj8PHHH+cXQjm5c+cOWrRogfj4eBw4cAAdO3aUUuRn+PDhmD17Nnr16oXNmzdLsfKSmJgobJoxY4bIv8OHD6N169ZSqrysXbsW48ePR2hoKEaMGIFZs2ZJKfJy9epVfPTRR8JXVjZu3Ig+ffpIV0RhYdu2bejRo4d0BTRr1gzTp09HrVq1+J6XUqw88Pvzcv/zzz+jWrVqop599tlnpVT54HatXr0aY8eORb169URdxhtVqziSC24Xr/+7deuGzMxM/PTTT6K9lNsuzr59+/DVV1/h9u3beP/990W9Jrdd/P5nz57Fl19+iVOnTqF9+/ZYvHgxvLy8pG/IB2+/R48ejd9//x1Go+UIM17/8+fh32I3kdawYUMsW7YMTZo0EddywzOTN1TFixfHpEmTMGjQIClFXuLi4kTFwf8GBQWhefPmUor88EqNNwCvvvoq1q9fL8XKS25urqg8+IPKRRp/WBs1aiSlyguv/L/44gtR8XLfTZ06VUqRFy4aBw4cKLYWsUIirXBSUKT5+fmJ+sPX1ze/cZALk8mEixcvIjw8HB4eHkI4li5dWsTLCb//rVu3cPPmTTESw+0qVaqUGIl5Qs3f34YLDp5fUVFRuHDhgrCxdu3aqFChghjpk8suKzExMbh27RoyMjJQvnx5YZvcI5A8vxISEnDlyhUkJyeLARde9/NRPzn9xduhrKwsXLp0CbGxsfm2KF6k1axZU4gh3tOTu8DxB2LFihWi0eSVGhdrb7/9tnCuXLZxm/i9eQPKG8ukpCQsX75c+FDOyoPDbeP5yAXaypUr0blzZ8yfP18M51rtlgN+b/5wcvG/ZMkSkX989IqXMbnt4vAG9Ntvv0VERATee+89jBo1Sla7OPz+vIGaMGECgoODpVgSaYWVgiKN5681yA0v59bA4c+nUuwqKBSVYJtS7eIUtE2JdnF7rEFuuF3WYEXxIs3T01P0WIoVKyZ7D49n4t27d3Hjxg0xv165cmXRO+AF7wn98/823CZ+bz59x3ufer1e2FSpUiUxFF/w4XUk3Dbeg+L+4sO43t7eoifFh5WtdssBvzcfSQsLCxP5yalbt67orcttFycyMlJMD2RnZ4seMe+oyGkXhwv+1NRUMeXJBa4VEmmFk61bt6Jnz57SFUEQSmXmzJkYOXKkdPXPsZtI4/AGSu5GimO1wWqHtVGV07aCNtkip3jkcNseJhJ5vBJ8xrG1QQl2cfj9rTZY4+S0i2O9f0EbSKQVTjZt2oS+fftKVwRBKJUpU6Zg3Lhx0tU/54mJtMDAQLHoUc4RIIIgHo8NGzZQY18I4esdd+3aJV0RBKFU+AttjRs3lq7+OU9MpB07dkwxb3ISBPHXrFu3Dq+99pp0RRAEQSiRJybS+FsNv/76K42kEUQh4JlnnhFrMwmCIAjl8sREGkEQBEEQBPHkoBMHCIIgCIIgFAiJNIIgCIIgCAVCIo0gCIIgCEKBkEgjCIIgCIJQICTSCIIgCIIgFAiJNIIgCIIgCAVCIo0gCIIgCEKBkEgjCIIgCIJQICTSCIIgCIIgFAiJNIIgCIIgCAVCIo0gCIIgCEKBkEgjCIIgCIJQICTSCIIgCIIgFAfwPz+8iHNCxa1VAAAAAElFTkSuQmCC\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = binary1(b)\r\n  y = bitxor(b);\r\nend","test_suite":"%%\r\nb = [1 9 9 0 9 9; 9 9 0 0 9 1; 9 0 0 9 9 1; 9 9 9 9 9 9; 0 0 9 1 9 9; 9 1 9 9 0 0];\r\ny = binary1(b);\r\ny_correct = [1 0 1 0 1 0; 0 1 0 0 1 1; 1 0 0 1 0 1; 0 1 1 0 1 0; 0 0 1 1 0 1; 1 1 0 1 0 0];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [9 9 0 9 9 1; 1 9 0 0 9 9; 9 1 9 9 9 9; 9 9 9 9 1 9; 9 9 1 9 9 0; 0 0 9 1 9 9];\r\ny = binary1(b);\r\ny_correct = [1 0 0 1 0 1; 1 1 0 0 1 0; 0 1 1 0 0 1; 1 0 0 1 1 0; 0 1 1 0 1 0; 0 0 1 1 0 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [9 9 9 9 9 9 9 0; 9 0 0 9 9 1 9 9; 9 0 9 9 9 1 9 0; 9 9 1 9 9 9 9 9; 0 0 9 1 9 9 1 9; 9 9 9 9 1 9 9 9; 1 1 9 9 9 0 9 1; 9 1 9 9 9 9 9 1];\r\ny = binary1(b);\r\ny_correct = [0 1 1 0 1 0 1 0; 1 0 0 1 0 1 0 1; 1 0 0 1 0 1 1 0; 0 1 1 0 1 0 0 1; 0 0 1 1 0 1 1 0; 1 0 0 1 1 0 1 0; 1 1 0 0 1 0 0 1; 0 1 1 0 0 1 0 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [1 9 1 9 9 1 9 0; 1 9 0 9 0 9 9 9; 9 9 9 1 9 9 9 1; 0 9 9 9 9 9 1 1; 9 9 1 9 9 9 9 9; 9 9 9 0 9 9 9 1; 0 9 9 9 1 1 9 1; 0 9 9 9 1 9 9 9];\r\ny = binary1(b);\r\ny_correct = [1 0 1 0 1 1 0 0; 1 1 0 1 0 0 1 0; 0 0 1 1 0 1 0 1; 0 1 0 0 1 0 1 1; 1 0 1 1 0 0 1 0; 1 0 1 0 0 1 0 1; 0 1 0 0 1 1 0 1; 0 1 0 1 1 0 1 0];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [9 9 1 9 9 9 9 0 9 9 0 9; 9 9 9 1 9 0 9 9 1 9 9 1; 9 9 0 9 0 9 9 9 9 0 9 1; 1 9 0 9 9 9 0 0 9 9 9 9; 9 1 9 9 9 9 9 9 9 9 9 1; 9 9 9 9 1 9 9 9 9 0 9 9; 9 9 9 9 9 9 1 9 0 0 9 9; 9 9 0 9 9 9 9 1 9 9 1 9; 1 9 0 0 9 9 9 9 9 0 9 9; 9 9 9 9 9 1 9 9 9 0 9 0; 9 0 9 9 9 9 0 9 9 9 1 9; 0 0 9 1 9 1 9 1 9 1 9 9];\r\ny = binary1(b);\r\ny_correct = [0 1 1 0 1 1 0 0 1 1 0 0; 0 0 1 1 0 0 1 0 1 1 0 1; 1 0 0 1 0 0 1 1 0 0 1 1; 1 1 0 0 1 1 0 0 1 0 1 0; 0 1 1 0 0 1 0 1 0 1 0 1; 1 0 0 1 1 0 1 0 1 0 0 1; 0 1 1 0 1 0 1 1 0 0 1 0; 1 0 0 1 0 1 0 1 0 1 1 0; 1 1 0 0 1 0 1 0 1 0 0 1; 0 1 1 0 0 1 1 0 1 0 1 0; 1 0 0 1 1 0 0 1 0 1 1 0; 0 0 1 1 0 1 0 1 0 1 0 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nb = [0 9 0 9 1 1 9 1 9 9 0 9; 0 9 9 1 1 9 9 9 0 1 9 9; 9 9 9 9 9 9 9 9 9 1 9 0; 9 9 9 9 1 9 9 0 9 9 9 0; 9 1 1 9 9 9 1 9 9 0 9 9; 0 9 9 1 9 1 9 9 9 9 9 9; 9 9 1 9 9 9 9 9 9 1 9 0; 9 9 9 0 9 1 9 9 0 9 9 9; 1 9 9 0 9 9 1 9 9 1 9 9; 1 9 1 9 9 0 9 9 0 9 9 1; 9 9 9 9 9 0 9 9 9 9 9 1; 1 9 1 1 9 9 9 9 0 0 9 9];\r\ny = binary1(b);\r\ny_correct = [0 1 0 0 1 1 0 1 1 0 0 1;0 1 0 1 1 0 1 0 0 1 0 1;1 0 1 0 0 1 0 1 0 1 1 0;1 0 0 1 1 0 1 0 1 0 1 0;0 1 1 0 1 0 1 0 1 0 0 1;0 1 0 1 0 1 0 1 0 1 1 0;1 0 1 1 0 0 1 0 1 1 0 0;0 1 0 0 1 1 0 1 0 0 1 1;1 0 1 0 0 1 1 0 1 1 0 0;1 0 1 1 0 0 1 0 0 1 0 1;0 1 0 0 1 0 0 1 1 0 1 1;1 0 1 1 0 1 0 1 0 0 1 0];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('binary1.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext, 'regexp') || contains(filetext, 'str2num'); \r\nassert(~illegal)\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2025-01-14T12:13:56.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2025-01-14T12:13:49.000Z","updated_at":"2026-01-25T13:59:44.000Z","published_at":"2025-01-14T12:13:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA binary puzzle presents a square grid (or matrix) of cells in which each cell must be 0 or 1. The finished puzzle must follow these rules:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt may not have more than two 0s or 1s next to each other in any row or column. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach row and column must have an equal number of 0s and 1s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNo two rows and no two columns can be the same. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve easy binary puzzles—i.e., those that can be solved by applying only the first rule. Empty cells in the initial board will be marked by 9s. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe examples in the test suite and the one below come from binarypuzzle.com.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"267\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"617\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1978,"title":"Sokoban: Puzzle 10.45","description":"The \u003chttp://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 Sokoban Site\u003e has many puzzles to solve.  This Challenge is to solve puzzle 10.45.  The link may place the Cody enthusiast at 10.55. \u003chttp://en.wikipedia.org/wiki/Sokoban wiki Sokoban reference\u003e. \r\n\r\nThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\r\n\r\nSokoban can not jump blocks or move diagonally.\r\n\r\nThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).  \r\n\r\nSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\r\n\r\n*Input:* Map, [nr,nc] of  Sokoban characters [0,1,2,3,4,5,7]\r\n\r\n*Output:* Moves, Vector of [-1 +1 -nr +nr] values\r\n\r\n*Scoring:* Sum of Moves and Pushes\r\n\r\n*Examples:* \r\n\r\nMap\r\n\r\n  11111111\r\n  11111111 Moves=[5]  push right for a 5 row array\r\n  11042311\r\n  11111111\r\n  11111111\r\n\r\n*Test Suite Visualization:* A visualization option is provided.\r\n\r\n*Algorithms:* Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid. \r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138\"\u003eSokoban Site\u003c/a\u003e has many puzzles to solve.  This Challenge is to solve puzzle 10.45.  The link may place the Cody enthusiast at 10.55. \u003ca href = \"http://en.wikipedia.org/wiki/Sokoban\"\u003ewiki Sokoban reference\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/p\u003e\u003cp\u003eSokoban can not jump blocks or move diagonally.\u003c/p\u003e\u003cp\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/p\u003e\u003cp\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Map, [nr,nc] of  Sokoban characters [0,1,2,3,4,5,7]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Sum of Moves and Pushes\u003c/p\u003e\u003cp\u003e\u003cb\u003eExamples:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMap\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e11111111\r\n11111111 Moves=[5]  push right for a 5 row array\r\n11042311\r\n11111111\r\n11111111\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eTest Suite Visualization:\u003c/b\u003e A visualization option is provided.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAlgorithms:\u003c/b\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/p\u003e","function_template":"function moves=solve_Sokoban(m)\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n\r\n moves=[];\r\nend","test_suite":"assignin('caller','score',200);\r\n%%\r\nvisualize=0;\r\nif visualize\r\n figure(1); % Start\r\n map=[.5 .5 .5;0 0 0;.5 .5 .5;0 1 0;0 0 1;\r\n    1 0 0;1 1 0;0 0 0;1 0 1;.5 .5 .5];\r\n colormap(map);\r\n figure(2); % Move map\r\n% -1 0 1 2 3 4 5 6 7 8\r\n% -1 color limit, 8 color limit\r\n% 0 Empty; 1 Wall; 2 Block; 3 Pedestal;\r\n% 4 Sokoban; 5 Block \u0026 Pedestal;6 Nothing; 7 Soko \u0026 Pedestal\r\n colormap(map)\r\nend\r\n\r\n%Sokoban map http://www.game-sokoban.com/index.php?mode=level\u0026lid=16138 \r\n%Puzzle 45 \r\nsmap=[0 0 0 0 0 0;0 3 2 2 4 0;3 3 2 0 2 0;3 5 0 0 1 1];\r\n[nr,nc]=size(smap);\r\nm=ones(nr+4,nc+4);\r\nm(3:end-2,3:end-2)=smap;\r\n\r\nif visualize\r\n im=m;\r\n mend=size(map,1)-2;\r\n im(1)=-1;im(end)=mend;\r\n figure(1);imagesc(im)\r\n m\r\nend\r\n\r\ntic\r\nmoves=solve_Sokoban(m);\r\ntoc\r\n\r\n% Check Solution\r\n valid=1;\r\n ptr=find(m==4);\r\n pushes=0;\r\n if isempty(ptr),ptr=find(m==7);end\r\n for i=1:length(moves)\r\n  mv=moves(i);\r\n  mvptr=m(ptr+mv);\r\n  mvptr2=m(ptr+2*mv);\r\n  if mvptr==1 % Illegal run into wall\r\n   valid=0;\r\n   break;\r\n  end\r\n  if (mvptr2==5 || mvptr2==2 || mvptr2==1) \u0026\u0026 (mvptr==5 || mvptr==2) % Illegal double block push\r\n   valid=0;\r\n   break;\r\n  end\r\n  if mvptr==0 || mvptr==3\r\n   m(ptr)=m(ptr)-4;\r\n   m(ptr+mv)=m(ptr+mv)+4;\r\n   ptr=ptr+mv;\r\n  elseif mvptr==2 || mvptr==5\r\n   m(ptr)=m(ptr)-4;\r\n   m(ptr+2*mv)=m(ptr+2*mv)+2;\r\n   m(ptr+mv)=m(ptr+mv)-2+4;\r\n   ptr=ptr+mv;\r\n   pushes=pushes+1;\r\n  end\r\n end\r\n \r\n fprintf('Moves %i  Pushes %i\\n',length(moves),pushes)\r\n valid=valid \u0026\u0026  nnz(m==3)==0 \u0026\u0026 nnz(m==7)==0;\r\n assert(valid)\r\n\r\nif visualize \u0026\u0026 valid\r\n % display moves\r\n figure(2);imagesc(im)\r\n pause(0.2)\r\n ptr=find(im==4);\r\n if isempty(ptr),ptr=find(im==7);end\r\n for i=1:length(moves)\r\n  mv=moves(i);\r\n  mvptr=im(ptr+mv);\r\n  if mvptr==0 || mvptr==3\r\n   im(ptr)=im(ptr)-4;\r\n   im(ptr+mv)=im(ptr+mv)+4;\r\n   ptr=ptr+mv;\r\n  elseif mvptr==2 || mvptr==5\r\n   im(ptr)=im(ptr)-4;\r\n   im(ptr+2*mv)=im(ptr+2*mv)+2;\r\n   im(ptr+mv)=im(ptr+mv)-2+4;\r\n   ptr=ptr+mv;\r\n  end\r\n  \r\n  figure(2);imagesc(im)\r\n  pause(0.2)\r\n end\r\n \r\nend % vis and valid\r\n\r\n\r\nmovs=length(moves);\r\nassignin('caller','score',min(200,max(0,movs+pushes)));","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2013-11-11T01:51:09.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-10T23:21:50.000Z","updated_at":"2025-12-03T12:16:08.000Z","published_at":"2013-11-11T01:51:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.game-sokoban.com/index.php?mode=level\u0026amp;lid=16138\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Site\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e has many puzzles to solve. This Challenge is to solve puzzle 10.45. The link may place the Cody enthusiast at 10.55.\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Sokoban\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ewiki Sokoban reference\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basic rules are to places the Blocks on the Pedestals. Blocks can only be pushed, never pulled. A connected Pair of blocks can not be moved along their long axis. A 2x2 square of blocks is immoveable. A Wall can not be moved.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban can not jump blocks or move diagonally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map will be double ringed by a Wall(1). Map definitions: Empty(0) Block(2) Pedestal(3) Sokoban(4) Block on Pedestal(5) Sokoban on Pedestal(7).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSokoban Movement is a numeric vector L(-nr) U(-1) R(nr) D(+1).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map, [nr,nc] of Sokoban characters [0,1,2,3,4,5,7]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Moves, Vector of [-1 +1 -nr +nr] values\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Sum of Moves and Pushes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[11111111\\n11111111 Moves=[5]  push right for a 5 row array\\n11042311\\n11111111\\n11111111]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTest Suite Visualization:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A visualization option is provided.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithms:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Recursive routines that check all possible pushes can solve small Sokoban puzzles. Routines that limit their depth can find minimal Push solutions at the cost of time. Identification of Locked conditions is important to avoid being stuck in recursion. Pairs of blocks on a wall or too many blocks on a wall are unsolveable conditions to avoid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2004,"title":"BattleShip - Petty Officer (Level 2)","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships Games Magazine Battleships\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\r\n\r\nThe Seaman Level is the simplest and can be solved by direct evolution of current condition. The Petty Officer Level requires just a pinch more effort to solve.\r\n\r\nMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\r\n\r\nShips have no diagonal or UDLR adjacency.  The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\r\n\r\nThe map is ringed by zeros to make m a 12x12 array.\r\n\r\n*Input:* m,r,c;  m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\r\n\r\n*Output:* b; A binary 12x12 array\r\n\r\n*Example:*\r\n\r\n  r=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\n  c=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n  \r\n  m              b\r\n  000000000000  000000000000\r\n  077757777770  000011000000\r\n  077777777770  000000000000\r\n  077777777770  000100010000\r\n  077777777770  000100010000\r\n  077777777770  010000010000\r\n  077777777770  010000010010\r\n  027777777760  010000000010\r\n  077777777770  000101000010\r\n  077777777770  000000000000\r\n  077777477770  010001100100\r\n  000000000000  000000000000\r\n\r\n*Algorithm:* \r\n\r\n  1) Initialize processing array based upon input matrix.\r\n  2) Implement a cycling check of driven array changes\r\n  3) Quick Test of Change every single Unknown serially\r\n  4) Evolve and check if complete solution created","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 795.667px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 397.833px; transform-origin: 407px 397.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003ca target='_blank' href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eGames Magazine Battleships\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 291.35px 7.91667px; transform-origin: 291.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 360.45px 7.91667px; transform-origin: 360.45px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Seaman Level is the simplest and can be solved by direct evolution of current condition. The Petty Officer Level requires just a pinch more effort to solve.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.1px 7.91667px; transform-origin: 382.1px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.267px 7.91667px; transform-origin: 382.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 165.183px 7.91667px; transform-origin: 165.183px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 220.5px 7.91667px; transform-origin: 220.5px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 74.3px 7.91667px; transform-origin: 74.3px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e b; A binary 12x12 array\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 326.933px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 163.467px; transform-origin: 404px 163.467px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 111.65px 7.91667px; transform-origin: 111.65px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003er=[0 2 0 2 2 2 3 2 3 0 4 0]';\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 107.8px 7.91667px; transform-origin: 107.8px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ec=[0 4 0 3 1 3 1 4 0 1 3 0];\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 7.91667px; transform-origin: 0px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 61.6px 7.91667px; transform-origin: 61.6px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 57.75px 7.91667px; transform-origin: 57.75px 7.91667px; \"\u003em              \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 3.85px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 3.85px 7.91667px; \"\u003eb\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e000000000000  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077757777770  000011000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000100010000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000100010000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  010000010000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  010000010010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e027777777760  010000000010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000101000010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777777770  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e077777477770  010001100100\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100.1px 7.91667px; transform-origin: 100.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e000000000000  000000000000\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 35.3833px 7.91667px; transform-origin: 35.3833px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eAlgorithm:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 211.75px 7.91667px; transform-origin: 211.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 53.9px 7.91667px; transform-origin: 53.9px 7.91667px; \"\u003e1) Initialize \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 157.85px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 157.85px 7.91667px; \"\u003eprocessing array based upon input matrix.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 200.2px 7.91667px; transform-origin: 200.2px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 50.05px 7.91667px; transform-origin: 50.05px 7.91667px; \"\u003e2) Implement \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 150.15px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 150.15px 7.91667px; \"\u003ea cycling check of driven array changes\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 204.05px 7.91667px; transform-origin: 204.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 34.65px 7.91667px; transform-origin: 34.65px 7.91667px; \"\u003e3) Quick \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 169.4px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 169.4px 7.91667px; \"\u003eTest of Change every single Unknown serially\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 184.8px 7.91667px; transform-origin: 184.8px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 38.5px 7.91667px; transform-origin: 38.5px 7.91667px; \"\u003e4) Evolve \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 146.3px 7.91667px; text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 146.3px 7.91667px; \"\u003eand check if complete solution created\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function b=solve_battleship(m,r,c)\r\n% WSUDLRMX 0W 1S 2U 3D 4L 5R 6M 7X\r\n% Surround 10x10 with ring of zeros\r\n% r : RowSum Vector [12,1]\r\n% c : ColSum Vector [1,12]\r\n b=zeros(12);\r\nend","test_suite":"%%\r\nglobal valid\r\nfiletext = fileread('solve_battleship.m');\r\nvalid=isempty(strfind(filetext, '(exist(fullfile(cd'));\r\nassert(valid,'overwrite assert forbidden')\r\n%%\r\nglobal valid\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n% Games August 2013 2-Petty Officer %\r\nr=[0 0 1 4 1 3 3 3 3 2 0 0]';\r\nc=[0 2 3 2 0 5 0 4 0 2 2 0];\r\nm(5,4)=3;\r\nm(6,11)=3;\r\nm(9,8)=0;\r\n\r\ntic\r\nb=0;\r\nif valid\r\n b=solve_battleship(m,r,c);\r\nend\r\ntoc\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n\r\n%%\r\nglobal valid\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 2-Petty %\r\nr=[0 2 2 2 3 2 0 0 7 0 2 0]';\r\nc=[0 2 5 1 4 1 4 0 2 1 0 0];\r\nm(3,3)=3;\r\nm(5,7)=1;\r\nm(9,4)=0;\r\n\r\ntic\r\nb=0;\r\nif valid\r\n b=solve_battleship(m,r,c);\r\nend\r\ntoc\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n\r\n%%\r\nglobal valid\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 2-Petty\r\nr=[0 5 1 4 1 0 5 1 2 1 0 0]';\r\nc=[0 2 3 3 2 0 5 0 3 1 1 0];\r\nm(9,2)=1;\r\nm(2,7)=0;\r\nm(3,9)=1;\r\n\r\n\r\ntic\r\nb=0;\r\nif valid\r\n b=solve_battleship(m,r,c);\r\nend\r\ntoc\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2020-10-01T19:08:44.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2013-11-17T22:45:53.000Z","updated_at":"2025-12-10T03:22:09.000Z","published_at":"2013-11-17T23:06:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames Magazine Battleships\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Seaman Level is the simplest and can be solved by direct evolution of current condition. The Petty Officer Level requires just a pinch more effort to solve.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e b; A binary 12x12 array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[r=[0 2 0 2 2 2 3 2 3 0 4 0]';\\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\\n\\nm              b\\n000000000000  000000000000\\n077757777770  000011000000\\n077777777770  000000000000\\n077777777770  000100010000\\n077777777770  000100010000\\n077777777770  010000010000\\n077777777770  010000010010\\n027777777760  010000000010\\n077777777770  000101000010\\n077777777770  000000000000\\n077777477770  010001100100\\n000000000000  000000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Initialize processing array based upon input matrix.\\n2) Implement a cycling check of driven array changes\\n3) Quick Test of Change every single Unknown serially\\n4) Evolve and check if complete solution created]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2005,"title":"BattleShip - Seaman (1) thru Admiral(6) :  CPU Time Scoring(msec)","description":"\u003chttp://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships Games Magazine Battleships\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\r\n\r\nThis Challenge is to complete three full sets of Battleship in minimal time.\r\n\r\nMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\r\n\r\nShips have no diagonal or UDLR adjacency.  The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\r\n\r\nThe map is ringed by zeros to make m a 12x12 array.\r\n\r\n*Input:* m,r,c;  m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\r\n\r\n*Output:* b; A binary 12x12 array\r\n\r\n*Scoring:* Total Time (msec)\r\n\r\n*Example:*\r\n\r\n  r=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\n  c=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n  \r\n  m              b\r\n  000000000000  000000000000\r\n  077757777770  000011000000\r\n  077777777770  000000000000\r\n  077777777770  000100010000\r\n  077777777770  000100010000\r\n  077777777770  010000010000\r\n  077777777770  010000010010\r\n  027777777760  010000000010\r\n  077777777770  000101000010\r\n  077777777770  000000000000\r\n  077777477770  010001100100\r\n  000000000000  000000000000\r\n\r\n*Algorithm:* \r\n\r\n  1) Initialize processing array based upon input matrix.\r\n  2) Implement a cycling check of driven array changes\r\n  3) Quick Test of Change every single Unknown serially\r\n  4) Evolve and check if complete solution created\r\n  5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs","description_html":"\u003cp\u003e\u003ca href = \"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\"\u003eGames Magazine Battleships\u003c/a\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/p\u003e\u003cp\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/p\u003e\u003cp\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/p\u003e\u003cp\u003eShips have no diagonal or UDLR adjacency.  The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/p\u003e\u003cp\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e m,r,c;  m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e b; A binary 12x12 array\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Total Time (msec)\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003er=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003em              b\r\n000000000000  000000000000\r\n077757777770  000011000000\r\n077777777770  000000000000\r\n077777777770  000100010000\r\n077777777770  000100010000\r\n077777777770  010000010000\r\n077777777770  010000010010\r\n027777777760  010000000010\r\n077777777770  000101000010\r\n077777777770  000000000000\r\n077777477770  010001100100\r\n000000000000  000000000000\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003eAlgorithm:\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e1) Initialize processing array based upon input matrix.\r\n2) Implement a cycling check of driven array changes\r\n3) Quick Test of Change every single Unknown serially\r\n4) Evolve and check if complete solution created\r\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs\r\n\u003c/pre\u003e","function_template":"function b=solve_battleship(m,r,c)\r\n% WSUDLRMX 0W 1S 2U 3D 4L 5R 6M 7X\r\n% Surround 10x10 with ring of zeros\r\n% r : RowSum Vector [12,1]\r\n% c : ColSum Vector [1,12]\r\n b=zeros(12);\r\nend","test_suite":"assignin('caller','score',2000);\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 1-Seaman\r\nr=[0 2 2 3 1 1 1 1 2 2 5 0]';\r\nc=[0 1 0 1 1 2 6 0 5 0 4 0];\r\nm(2,2)=1;\r\nm(2,6)=1;\r\nm(4,9)=3;\r\n\r\n%tz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\n%tt=tz+cputime-time0\r\ntt=cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 2-Petty Officer\r\nr=[0 0 1 4 1 3 3 3 3 2 0 0]';\r\nc=[0 2 3 2 0 5 0 4 0 2 2 0];\r\nm(5,4)=3;\r\nm(6,11)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 3-Ensign\r\nr=[0 3 0 4 1 0 0 1 2 1 8 0]';\r\nc=[0 5 1 1 3 1 1 1 1 3 3 0];\r\nm(4,7)=1;\r\nm(4,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 4-Captain\r\nr=[0 1 2 2 2 2 5 0 5 0 1 0]';\r\nc=[0 5 0 0 0 2 1 4 2 1 5 0];\r\nm(4,8)=0;\r\nm(7,10)=4;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 5-Commodore\r\nr=[0 1 1 5 0 3 1 3 2 1 3 0]';\r\nc=[0 2 2 1 0 2 1 6 0 5 1 0];\r\nm(6,4)=1;\r\nm(6,8)=0;\r\nm(7,10)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% Games August 2013 6-Admiral\r\nr=[0 5 1 4 2 3 1 1 0 3 0 0]';\r\nc=[0 4 0 1 2 4 2 1 1 5 0 0];\r\nm(5,2)=1;\r\nm(10,7)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 1-Seaman\r\nr=[0 1 1 1 1 2 3 3 3 1 4 0]';\r\nc=[0 3 2 0 1 6 0 3 1 4 0 0];\r\nm(2,3)=1;\r\nm(8,5)=1;\r\nm(7,8)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 2-Petty\r\nr=[0 2 2 2 3 2 0 0 7 0 2 0]';\r\nc=[0 2 5 1 4 1 4 0 2 1 0 0];\r\nm(3,3)=3;\r\nm(5,7)=1;\r\nm(9,4)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 3-Ensign\r\nr=[0 3 0 0 2 4 3 2 1 4 1 0]';\r\nc=[0 2 2 5 2 3 0 3 0 2 1 0];\r\nm(7,2)=1;\r\nm(7,4)=3;\r\nm(9,8)=0;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 4-Captain\r\nr=[0 2 0 2 2 2 3 2 3 0 4 0]';\r\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\r\nm(8,2)=2;\r\nm(2,5)=5;\r\nm(11,7)=4;\r\nm(8,11)=6;\r\n\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 5-Commodore\r\nr=[0 3 2 3 1 1 1 3 3 2 1 0]';\r\nc=[0 1 2 4 1 4 1 1 0 5 1 0];\r\nm(2,10)=5;\r\nm(8,4)=6;\r\nm(8,6)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% December 2013 6-Admiral\r\nr=[0 5 1 0 3 0 1 5 2 3 0 0]';\r\nc=[0 0 4 2 5 2 1 2 1 1 2 0];\r\nm(2,10)=0;\r\nm(8,7)=0;\r\nm(10,5)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 1-Seaman\r\nr=[0 1 1 2 4 1 0 2 2 5 2 0]';\r\nc=[0 1 1 1 1 4 0 7 0 2 3 0];\r\nm(2,8)=0;\r\nm(8,3)=1;\r\nm(9,6)=0;\r\nm(5,11)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 2-Petty\r\nr=[0 5 1 4 1 0 5 1 2 1 0 0]';\r\nc=[0 2 3 3 2 0 5 0 3 1 1 0];\r\nm(9,2)=1;\r\nm(2,7)=0;\r\nm(3,9)=1;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 3-Ensign\r\nr=[0 3 0 2 3 1 1 2 2 2 4 0]';\r\nc=[0 1 1 0 6 1 4 0 3 1 3 0];\r\nm(4,3)=0;\r\nm(5,6)=4;\r\nm(7,9)=6;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 4-Captain\r\nr=[0 0 6 0 2 2 4 1 3 2 0 0]';\r\nc=[0 3 1 3 1 2 2 2 2 0 4 0];\r\nm(5,2)=0;\r\nm(9,4)=0;\r\nm(3,5)=4;\r\nm(6,11)=2;\r\nm(8,11)=3;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 5-Commodore %\r\nr=[0 5 2 1 1 7 1 2 0 0 1 0]';\r\nc=[0 2 3 1 2 1 3 1 2 0 5 0];\r\nm(8,2)=1;\r\nm(5,11)=2;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\nm=zeros(12);\r\nm(2:end-1,2:end-1)=7;\r\n\r\n% September 2013 6-Admiral % Solved with with Bship HV .10 \r\n% solved recur .023\r\nr=[0 0 2 4 1 4 1 0 2 0 6 0]';\r\nc=[0 3 1 3 1 3 2 1 2 1 3 0];\r\nm(3,2)=0;\r\nm(4,5)=4;\r\nm(9,9)=5;\r\n\r\ntz=tt; % anti-cheat\r\ntic\r\ntime0=cputime;\r\nb=solve_battleship(m,r,c);\r\ntt=tz+cputime-time0\r\n%toc\r\n\r\n\r\nb(b\u003e1)=0;\r\nb(b\u003c0)=0;\r\n\r\nbr=sum(b,2);\r\nbc=sum(b);\r\n\r\nassert(isequal(r,br))\r\nassert(isequal(c,bc))\r\n\r\n% find battleship,cruisers,destroyers,subs\r\n% conv2 to locate pieces\r\n% bsh,bsv\r\n% ch,cv,dh,dv,s\r\n mconvsub=conv2(b,[2 2 2;2 1 2;2 2 2],'same');\r\n subs_ptr=find(mconvsub==1); % Isolated valid subs\r\n assert(size(subs_ptr,1)==4)\r\n % Qty of subs_ptr must be 4\r\n mconvBH=conv2(b,[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n mconvBV=conv2(b',[5 5 5 5 5 5;5 1 1 1 1 5;5 5 5 5 5 5],'same');\r\n BS_ptr=[find(mconvBH==4);find(mconvBV==4)];\r\n assert(size(BS_ptr,1)==1)\r\n % Qty of BS_ptr must be 1\r\n mconvCH=conv2(b,[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n mconvCV=conv2(b',[5 5 5 5 5;5 1 1 1 5;5 5 5 5 5],'same');\r\n CS_ptr=[find(mconvCH==3);find(mconvCV==3)];\r\n assert(size(CS_ptr,1)==2)\r\n % Qty of CS_ptr must be 2\r\n mconvDH=conv2(b,[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n mconvDV=conv2(b',[5 5 5 5;5 1 1 5;5 5 5 5],'same');\r\n DS_ptr=[find(mconvDH==2);find(mconvDV==2)];\r\n assert(size(DS_ptr,1)==3)\r\n % Qty of DS_ptr must be 3\r\ntoc\r\n%%\r\nglobal tt\r\ntt\r\nassignin('caller','score',min(2000,floor(1000*tt)));","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":3,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-11-17T23:26:01.000Z","updated_at":"2013-11-18T00:27:11.000Z","published_at":"2013-11-18T00:27:11.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.conceptispuzzles.com/index.aspx?uri=puzzle/battleships\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eGames Magazine Battleships\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic puzzle to find the Fleet given some map information and the number of Ship cells in every column and row. The fleet is made of a Battleship(4), two Cruisers(3), three Destroyers(2), and four Submarines(1). Thus the total filled cells is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to complete three full sets of Battleship in minimal time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMap information contains Water(0), Subs(1), Middle of a ship(6), Unknown(7), and the Aft(rear) of a ship. Ship going Up(2), Down(3), Left(4), and Right(5).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShips have no diagonal or UDLR adjacency. The best way in Seaman to deal with Midship segments is to determine where it can not go to determine an orientation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe map is ringed by zeros to make m a 12x12 array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e m,r,c; m 12x12 of map values, r(12,1) of row sums, c(1,12) of col sums\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e b; A binary 12x12 array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Total Time (msec)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[r=[0 2 0 2 2 2 3 2 3 0 4 0]';\\nc=[0 4 0 3 1 3 1 4 0 1 3 0];\\n\\nm              b\\n000000000000  000000000000\\n077757777770  000011000000\\n077777777770  000000000000\\n077777777770  000100010000\\n077777777770  000100010000\\n077777777770  010000010000\\n077777777770  010000010010\\n027777777760  010000000010\\n077777777770  000101000010\\n077777777770  000000000000\\n077777477770  010001100100\\n000000000000  000000000000]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eAlgorithm:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[1) Initialize processing array based upon input matrix.\\n2) Implement a cycling check of driven array changes\\n3) Quick Test of Change every single Unknown serially\\n4) Evolve and check if complete solution created\\n5) Robustly recursively check all potential Battleships, Cruisers, Destroyers, Subs]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"term":"tag:\"puzzle\"","current_player_id":null,"fields":[{"name":"page","type":"integer","callback":null,"default":1,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"per_page","type":"integer","callback":null,"default":50,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"sort","type":"string","callback":null,"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":null,"prepend":true},{"name":"body","type":"text","callback":null,"default":"*:*","directive":null,"facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":false},{"name":"group","type":"string","callback":null,"default":null,"directive":"group","facet":true,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"difficulty_rating_bin","type":"string","callback":null,"default":null,"directive":"difficulty_rating_bin","facet":true,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"id","type":"integer","callback":null,"default":null,"directive":"id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"tag","type":"string","callback":null,"default":null,"directive":"tag","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"product","type":"string","callback":null,"default":null,"directive":"product","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"created_at","type":"timeframe","callback":{},"default":null,"directive":"created_at","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"profile_id","type":"integer","callback":null,"default":null,"directive":"author_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"created_by","type":"string","callback":null,"default":null,"directive":"author","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"player_id","type":"integer","callback":null,"default":null,"directive":"solver_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"player","type":"string","callback":null,"default":null,"directive":"solver","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"solvers_count","type":"integer","callback":null,"default":null,"directive":"solvers_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"comments_count","type":"integer","callback":null,"default":null,"directive":"comments_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"likes_count","type":"integer","callback":null,"default":null,"directive":"likes_count","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"leader_id","type":"integer","callback":null,"default":null,"directive":"leader_id","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true},{"name":"leading_solution","type":"integer","callback":null,"default":null,"directive":"leading_solution","facet":null,"facet_method":"and","operator":null,"param":"term","static":null,"prepend":true}],"filters":[{"name":"asset_type","type":"string","callback":null,"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":null,"static":"\"cody:problem\"","prepend":true},{"name":"profile_id","type":"integer","callback":{},"default":null,"directive":null,"facet":null,"facet_method":"and","operator":null,"param":"author_id","static":null,"prepend":true}],"query":{"params":{"per_page":50,"term":"tag:\"puzzle\"","current_player":null,"sort":"map(difficulty_value,0,0,999) asc"},"parser":"MathWorks::Search::Solr::QueryParser","directives":{"term":{"directives":{"tag":[["tag:\"puzzle\"","","\"","puzzle","\""]]}}},"facets":{"#\u003cMathWorks::Search::Field:0x00007f24a36676e0\u003e":null,"#\u003cMathWorks::Search::Field:0x00007f24a3667640\u003e":null},"filters":{"#\u003cMathWorks::Search::Field:0x00007f24a3666d80\u003e":"\"cody:problem\""},"fields":{"#\u003cMathWorks::Search::Field:0x00007f24a3667960\u003e":1,"#\u003cMathWorks::Search::Field:0x00007f24a36678c0\u003e":50,"#\u003cMathWorks::Search::Field:0x00007f24a3667820\u003e":"map(difficulty_value,0,0,999) asc","#\u003cMathWorks::Search::Field:0x00007f24a3667780\u003e":"tag:\"puzzle\""},"user_query":{"#\u003cMathWorks::Search::Field:0x00007f24a3667780\u003e":"tag:\"puzzle\""},"queried_facets":{}},"query_backend":{"connection":{"configuration":{"index_url":"http://index-op-v2/solr/","query_url":"http://search-op-v2/solr/","direct_access_index_urls":["http://index-op-v2/solr/"],"direct_access_query_urls":["http://search-op-v2/solr/"],"timeout":10,"vhost":"search","exchange":"search.topic","heartbeat":30,"pre_index_mode":false,"host":"rabbitmq-eks","port":5672,"username":"cody-search","password":"78X075ddcV44","virtual_host":"search","indexer":"amqp","http_logging":"true","core":"cody"},"query_connection":{"uri":"http://search-op-v2/solr/cody/","proxy":null,"connection":{"parallel_manager":null,"headers":{"User-Agent":"Faraday v1.0.1"},"params":{},"options":{"params_encoder":"Faraday::FlatParamsEncoder","proxy":null,"bind":null,"timeout":null,"open_timeout":null,"read_timeout":null,"write_timeout":null,"boundary":null,"oauth":null,"context":null,"on_data":null},"ssl":{"verify":true,"ca_file":null,"ca_path":null,"verify_mode":null,"cert_store":null,"client_cert":null,"client_key":null,"certificate":null,"private_key":null,"verify_depth":null,"version":null,"min_version":null,"max_version":null},"default_parallel_manager":null,"builder":{"adapter":{"name":"Faraday::Adapter::NetHttp","args":[],"block":null},"handlers":[{"name":"Faraday::Response::RaiseError","args":[],"block":null}],"app":{"app":{"ssl_cert_store":{"verify_callback":null,"error":null,"error_string":null,"chain":null,"time":null},"app":{},"connection_options":{},"config_block":null}}},"url_prefix":"http://search-op-v2/solr/cody/","manual_proxy":false,"proxy":null},"update_format":"RSolr::JSON::Generator","update_path":"update","options":{"url":"http://search-op-v2/solr/cody"}}},"query":{"params":{"per_page":50,"term":"tag:\"puzzle\"","current_player":null,"sort":"map(difficulty_value,0,0,999) asc"},"parser":"MathWorks::Search::Solr::QueryParser","directives":{"term":{"directives":{"tag":[["tag:\"puzzle\"","","\"","puzzle","\""]]}}},"facets":{"#\u003cMathWorks::Search::Field:0x00007f24a36676e0\u003e":null,"#\u003cMathWorks::Search::Field:0x00007f24a3667640\u003e":null},"filters":{"#\u003cMathWorks::Search::Field:0x00007f24a3666d80\u003e":"\"cody:problem\""},"fields":{"#\u003cMathWorks::Search::Field:0x00007f24a3667960\u003e":1,"#\u003cMathWorks::Search::Field:0x00007f24a36678c0\u003e":50,"#\u003cMathWorks::Search::Field:0x00007f24a3667820\u003e":"map(difficulty_value,0,0,999) asc","#\u003cMathWorks::Search::Field:0x00007f24a3667780\u003e":"tag:\"puzzle\""},"user_query":{"#\u003cMathWorks::Search::Field:0x00007f24a3667780\u003e":"tag:\"puzzle\""},"queried_facets":{}},"options":{"fields":["id","difficulty_rating"]},"join":" "},"results":[{"id":2052,"difficulty_rating":"easy"},{"id":50018,"difficulty_rating":"easy"},{"id":2027,"difficulty_rating":"easy"},{"id":50392,"difficulty_rating":"easy"},{"id":50008,"difficulty_rating":"easy"},{"id":2770,"difficulty_rating":"easy"},{"id":50232,"difficulty_rating":"easy"},{"id":1154,"difficulty_rating":"easy"},{"id":2359,"difficulty_rating":"easy"},{"id":50078,"difficulty_rating":"easy"},{"id":60536,"difficulty_rating":"easy"},{"id":3042,"difficulty_rating":"easy-medium"},{"id":44757,"difficulty_rating":"easy-medium"},{"id":45329,"difficulty_rating":"easy-medium"},{"id":44709,"difficulty_rating":"easy-medium"},{"id":60551,"difficulty_rating":"easy-medium"},{"id":45330,"difficulty_rating":"easy-medium"},{"id":45247,"difficulty_rating":"easy-medium"},{"id":42769,"difficulty_rating":"easy-medium"},{"id":1668,"difficulty_rating":"medium"},{"id":59045,"difficulty_rating":"medium"},{"id":44756,"difficulty_rating":"medium"},{"id":44759,"difficulty_rating":"medium"},{"id":46040,"difficulty_rating":"medium"},{"id":3033,"difficulty_rating":"medium"},{"id":44755,"difficulty_rating":"medium"},{"id":44760,"difficulty_rating":"medium"},{"id":45237,"difficulty_rating":"medium"},{"id":44753,"difficulty_rating":"medium"},{"id":51251,"difficulty_rating":"medium"},{"id":2358,"difficulty_rating":"medium"},{"id":44238,"difficulty_rating":"medium"},{"id":47518,"difficulty_rating":"medium"},{"id":44751,"difficulty_rating":"medium"},{"id":46120,"difficulty_rating":"medium"},{"id":59586,"difficulty_rating":"medium"},{"id":47463,"difficulty_rating":"medium"},{"id":57810,"difficulty_rating":"medium"},{"id":44752,"difficulty_rating":"medium"},{"id":44761,"difficulty_rating":"medium"},{"id":44764,"difficulty_rating":"medium"},{"id":44766,"difficulty_rating":"medium"},{"id":44776,"difficulty_rating":"medium"},{"id":2036,"difficulty_rating":"medium"},{"id":2026,"difficulty_rating":"medium"},{"id":2072,"difficulty_rating":"medium"},{"id":60785,"difficulty_rating":"medium"},{"id":1978,"difficulty_rating":"medium"},{"id":2004,"difficulty_rating":"medium"},{"id":2005,"difficulty_rating":"medium"}]}}