Problem 44694. Monte Carlo integration: area of a polygon

Created by David Verrelli

Solve Later

{"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.png"},{"relationshipType":"http://schemas.mathworks.com/matlab/code/2013/relationships/image","targetMode":"","relationshipId":"rId2","target":"/media/image2.png"},{"relationshipType":"http://schemas.mathworks.com/matlab/code/2013/relationships/image","targetMode":"","relationshipId":"rId3","target":"/media/image3.png"}],"contentType":"application/vnd.mathworks.matlab.code.document+xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<w:document xmlns:w=\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\"><w:body><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>The area of a polygon (or any plane shape) can be evaluated by</w:t></w:r><w:r><w:t> </w:t></w:r><w:hyperlink w:docLocation=\"https://www.mathworks.com/matlabcentral/cody/problems/179\"><w:r><w:t>Monte Carlo integration</w:t></w:r></w:hyperlink><w:r><w:t>. The process involves 'random' sampling of (x,y) points in the xy plane, and testing whether they fall inside or outside the shape. That is illustrated schematically below for a circle.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId1\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>^</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:rPr><w:b/></w:rPr><w:t>Steps:</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"ListParagraph\"/><w:numPr><w:numId w:val=\"2\"/></w:numPr></w:pPr><w:r><w:t>Define a 2D region within which to sample points. In the present problem you must choose the rectangular box (aligned to the axes) that bounds the specified polygon.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"ListParagraph\"/><w:numPr><w:numId w:val=\"2\"/></w:numPr></w:pPr><w:r><w:t>By a 'random' process generate coordinates of a point within the bounding box. In the present problem a basic scheme using the uniform pseudorandom distribution available in MATLAB must be employed. </w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:i/></w:rPr><w:t>Other schemes in use include quasi-random sampling (for greater uniformity of coverage) and stratified sampling (with more attention near the edges of the polygon).</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"ListParagraph\"/><w:numPr><w:numId w:val=\"2\"/></w:numPr></w:pPr><w:r><w:t>Determine</w:t></w:r><w:r><w:t> </w:t></w:r><w:hyperlink w:docLocation=\"https://www.mathworks.com/matlabcentral/cody/problems/198\"><w:r><w:t>whether the sampled point is inside or outside the polygon</w:t></w:r></w:hyperlink><w:r><w:t>. </w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:i/></w:rPr><w:t>Due to working in double precision, it is extremely unlikely to sample a point falling exactly on the edge of the polygon, and consequently if it does happen it doesn't really matter whether it's counted as inside or outside or null.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"ListParagraph\"/><w:numPr><w:numId w:val=\"2\"/></w:numPr></w:pPr><w:r><w:t>Repeat steps 2–3</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>N</w:t></w:r><w:r><w:t> times.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"ListParagraph\"/><w:numPr><w:numId w:val=\"2\"/></w:numPr></w:pPr><w:r><w:t>Based upon the proportion of sampled points falling within the polygon, report the approximate area of the polygon.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Inputs to your function will be</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>N</w:t></w:r><w:r><w:t>, the number of points to sample, and</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>polygonX</w:t></w:r><w:r><w:t> &amp;</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>polygonY</w:t></w:r><w:r><w:t>, which together constitute</w:t></w:r><w:r><w:t> </w:t></w:r><w:hyperlink w:docLocation=\"https://www.mathworks.com/matlabcentral/cody/problems/194\"><w:r><w:t>an ordered list of polygon vertices</w:t></w:r></w:hyperlink><w:r><w:t>. </w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>N</w:t></w:r><w:r><w:t> will always be a positive integer. </w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>polygonX</w:t></w:r><w:r><w:t> &amp;</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>polygonY</w:t></w:r><w:r><w:t> will always describe a single, continuous outline; however, the polygon may be self-intersecting.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>For polygons that are self-intersecting, you must find the area of the enclosed point set. In the case of the cross-quadrilateral, it is treated as two simple triangles (cf. three triangles in the first figure below), and the clockwise/counterclockwise ordering of points is irrelevant. A self-intersecting pentagram (left &amp; middle of the second figure below) will therefore have the same area as a concave decagon (right).</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId2\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId3\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>^</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>EXAMPLE</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"code\"/></w:pPr><w:r><w:t><![CDATA[ % Input\n N = 1000\n polygonX = [1 0 -1 0]\n polygonY = [0 1 0 -1]\n % Output\n A = 2.036]]></w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Explanation: the above polygon is a square of side-length √2 centred on the origin, but rotated by 45°. The exact area is hence 2. As the value of</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>N</w:t></w:r><w:r><w:t> is moderate, the estimate by Monte Carlo integration is moderately accurate (an error of 0.036 in this example, corresponding to 509 of the 1000 sampled points falling within the polygon). Of course, if the code were run again then a slightly different value of</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>A</w:t></w:r><w:r><w:t> would probably be returned, such as</w:t></w:r><w:r><w:t> </w:t></w:r><w:r><w:rPr><w:rFonts w:cs=\"monospace\"/></w:rPr><w:t>A</w:t></w:r><w:r><w:t>=1.992 (corresponding to 498 of the 1000 sampled points falling within the polygon), due to the random qualities of the technique.</w:t></w:r></w:p></w:body></w:document>"},{"partUri":"/matlab/output.xml","contentType":"text/xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\" ?><embeddedOutputs><metaData><evaluationState>manual</evaluationState><layoutState>code</layoutState><outputStatus>ready</outputStatus></metaData><outputArray type=\"array\"/><regionArray type=\"array\"/></embeddedOutputs>"},{"partUri":"/media/image1.png","contentType":"image/png","content":"data:image/png;base64,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"},{"partUri":"/media/image2.png","contentType":"image/png","content":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAADwCAYAAACgyrkbAAAXbElEQVR42u2dB7hU1fHAx4ogGgREUBFs2KKoaBKNBRVFYyxR/McSaxSNBVsM9kBiAf1bMPYWbLHFqEETW2zYe+9SooioCCigaIy5457jnbdv777dt7t3b/n9vm++T9nde8+dOWfeuWfOmREBAAAAAAAAAACAvNAxkAVRAwDETfdAPgzk5UB6oA4AiJNdAvnWyVjUAQBxMsI4IJUtUAkAxMXNRQ7orUAWQS0AEAevOMczyTih4agFABrNwoF85ZzOIYHc6f57biAroB4AaCRrmFnPZoGsFMgX7v/vQj0A0EhsBMyH4EeYf9sJFQFAo/DO5mPzbx0CecP9+3uBdEZNANAIbnKO5qGif9/SzILORE0A0AhedU7mwhKf3eg++zqQ/qgKAOqJjYAdWuLznoHMdJ8/Esh8qAwA6sXq5jVr84jvHGG+sx8qA4B6YSNgS0V8Z4FAnnPfmR7IkqgNAOrB76V1BKwU6wfyjfvuZagNAOqBX2R+uILvXuK++99ANkR1AFAr/gzYRRV8t2sg09z3NW/QQqgPANqLOhAfATuswt/sI+Ga0VGoEADay2pSff4fDcPf737zWSDLokYAaA9DjAPqWcXv1jAzp5tRIwC0h5MlDK1Xy2jjvLZFlQBQLTc4BzK+Hb/tFMhE9/t3pFBRAwCgYl52DuTidv5+RzMLGok6AaBStP7XPOc8htVwnb+7a+i1VkWtAFAJq0p9KmAsF8hsd517USsAVMLOxgH1qvFax5tr7YpqAaAtaomAFaMpPV5z15saSBfUCwDlqCUCVopNpHBGTK85BvUCQDlecs7ikjpe8xp3zf8Esi4qBoBS2AjY4XW8ruYT+tRd92kp5BECAGiBjYANqvO1DzbXPghVA0AxOxknsXSdrz1/II+7a+tsqAfqBgDLSVK/CFgpBkhhHUjvcRXqBgDL9RJWuWgU50uYPXFzVA4Anhedc7i0gfdYPJAp7j5vSqHSKgDkHI2AfekcwxENvtfuEq41HYvqAWAV4xS2jOF+97l7zQlkedQPkG9+YRzQMjHcb2Uz47od9QPkmxOdM5gR4z1PMU5ve0wAkF/+4hzBozHeU7MlvuvuOzmQRTEDQD7xEbC4q5sONrOg0zEDQP6wEbAjm3D/WyTMnrg65gDIF/3MLGSrJty/dyCfS1gKej5MApAfbASsWQUFf2vasBcmAcgPJ0j8EbBSr4EvuHZonfklMAtAPrjODfzHmtyOn0qYPfEizAKQD/zM47IEtOVK15ZvAtkA0wBkG81O+IU0LwJWTLdAPnbtedG9mgFARllZwsXfwQlp0/6mTcMwEUB2sWWUl01ImzQM/6hr0yyJ52waADQBXzxwZsLatWYgX7m2XY+ZALLJtW6QP57Atp1tZmfbYCqA7PG8G+CXJ7BtiwXynmvf24EsgrkAsoONgB2V0DYOMbOgkzAZQHawEbCtE9zOO1wb9cBsP8wGkA12MA6od4LbuaKZqd2N2QCygY2AJf0E+snGWQ7BdADpx0fAnkhBWxcO5HXX3g8C+QHmA0g3z7kBfUVK2jvIzILOwnwA6cVGwI5OUbtvcG3+OpC1MSNAOllJ0rnJr6cU8hZpu58MZH5MCZA+tjcOaLmUtX2YafuvMSVA+jjODeDPJH05mPX10a9fTQ9kScwJkC6uMa8xaWR9KSQtS9MiOgA4nnWD98oUP8PF7hk0jetATAqQDnThdo4bvL9N8XNo4vpp7jleCWQhTAuQfFaUcBH3Zyl/lr3NsxyNaQGSz3Zm0PZJ+bPoAvr97ll0VtcX8wIkm2MlvRGwUmg5Z5898RbMC5BsrpZ0R8BKMcrM6n6OiQGSi4+A/TlDz9QpkAnuuSYFsihmBkgeNgJ2TMaezeY3+iOmBkgeK0h2ImCluN0927xAVsPcAMnCRsD6ZvD59FzbbPd8D0o2FtkBMsNwyVYErBTHGie7GyYHSA5XuYH5VIafUbMnvuqe88NAumB2gGTwlBuYV2X8Obc0s6CzMXtT6RzI4EAOF9blco2+cn3uBuXwHDzv9RJmT+yP+WOjk/sDcGogjzr9+z8GN6Ke/NLXdITtcvC8vQKZ5Z5XBwLZExtDx0C2kMLWh/FSiEB+GyGkTskx25qOsEJOntlmT9yfLlAXtET2wEBGBvKwFApGlnI26vy1qOQD5t/WR3355XeuE8zO0WxAn/NxMyB60Q2qRksg7RLImECeCeQ/EQ7nk0BuCmRo0R+4FyU95Z+ggfgI2NM5e+51zaC5hm7QJou7V/RR7XQ4lg3M9/dBtfnmaclHBKwU55mBsDldoazD+TrC4Uw3DmeNCmfRY81vO6Lq/GLPgA3P6SCb4p7/zUA65LgvqCMYVIHD+bTI4SxQ5X26BTLXXeschmC+WV5IV7Gb0cFxOXruRSp0ODOcw9H9OgPa4XCKOUrCnN2rMATzTR4jYKW41+lgbob10ME4nEfMLKRYZtbZ4Vh0z9nb7j73M/wgjxGwUqwsYUnqf2bU4cyR6LD4OPcKXm+HU8wW5r67MPxgrOsMz6AK+YMZHDuksP0LO4czws3oohzOXPe5dzgLxtjGm10bpsR8X0go/gzY1ajiu0XYd5w+/i2Fs0ppcjizIxzOF010OJalJVxnOoXuBnk7A1YJg83AHZ2wtml9s42crdLgcIo50bVP9w/1pqtBX8nXGbBK+auEh1XXamI75ncOxDuczyMczpfG4WwkydxXs4CbVWp7x9HFQPmZEAErhR7LmOn0Ml7iS9BW7HA+q9DhdEqBTm3GzW3pYqAcI0TAojjaDJi9m+xw5rnPR0hhzadTCvV5p3uWCfQ18PzZdYpnUUUrdN3keQnPNnWvwzXnMw5HX0M+inA4WkxRw+ajnMNJeykh3ez6jXu24+la4HlSiICV40dm4Fwco8PpnDE9nmZmckvRrcAPDr+oeSzqiORypyN1RBtU6HB0B7HuJJ4W4XD0Ws9k2OFYOhjHewPdCTx9zIDYHnVE0tUMoJekdUhbD2MOdQ7nwwodzmI50t+uRg8D6U7g2dp0jJVRR1kOMrrS2eLugVwayFsSnWJUNzReFsgeUtiAl1fukzDTALXY4Hv8iWTdtLYA6ohE1yz0tPzcMs7GR3euDGRPYZOdp58UTrx/6/obwPdc4TrG86iiBT2kcEjyAglriJWSSVI4R7e3e52F1pwl4dmzbqgDLD4f8nU514OG13cO5E+BvGL+YhfLe1LYruDz2GxGFyqL7saeLvnNtAllsBGw43L27FojXheNdevBu2VmODr7ucTNhnq63y7mHJF+rjltFqErRbKfUPECygzCNKedqIbeNTicUuxsfvN7ulIkPssCaV6gFduYQbRSxp5t2UD2cs6knMOZYBxOtSV5xkl4JouUoq1ZT6i5BmXw55x0cTDt53K6OyfSlsOZaBxOrWFxXXT2qTDupju14jIJk9d3Qh1QTJojYN0qdDiT6uhwSnGSkFq0FEtImInxPNQBpXjCdZBrU9DWrhU6nMnuO/r6FUdqEc1G+Lq791QpVAkFkcMkjBSuhjqgGI2AzZLknkxewjgcXRj+JsLhaHIrXVgeKs3LZbSphGH7s+la3/Wt15w+HkIdUIreZhDvmECHE1Xu9/0EOJxS/EXCNKPr5LxvbWLstRtDDUrR7DNgXZzDGSPl64t/LG3XF08CelRjhmuzhp7znGzLO+Np7hUVoBU2AhbHGTBdG7H1xaMczicpcTjl1j1UDshpv9IjLPOcDkYxzCAKHwF7oUHXX7zI4USV+52eYodTjM56njTPtWQO+9VwCVOPLM8wgyjqfQZMz/xUUl/8U+Nw1pDsncBfz8zursxZn1IHPEGyVVUWGoCNgJ3QzmssUqHDmSGNqy+eVC6UMAQ9MEf9yq4rktwOImlPBKxShzMzhw6nGF3v+sDpQ0/WL5ST575Nws2f5JaCSGzVz34R3+lgHI4mSp9bxuGMk7D6Jh2vwJ5GR8fk4Hn7mFfPkzA/lMNnQbQRsOL64nMiHM6sIoezIOqM5F9OZ6rLvhl/1pESVvboiemhHJdKuIt4WCB/kzBpVLHoq5YuWJ/uZk6Lor6KWdO8qt6Y4efUP2I+P9KtmB1KoQvPGnU6VArnpaLOUuk0WkPJo6WQrqMzqquJ/ze63Sqjz7iDecbBmBwUXfjcSMJyv59LZfXFO6K6uqL69KHpyRmdQd4jVLwAKSTFOjCQ66VwMvvbNkRD8JzebjxZzp6om0f9QeHfYer8oJu+BpgZzmcRTmae+3yEFBaZdzSfkcUvPm4z9shSeoozJSzrRMWLDFNcX/zjCh1O8ZT/SPPqRQQrPnqb1+CHMvKqonvDPhEqXuTC4XwU4XA07Kn7dEZFOJxifJrMF1Fx7Aw3dtsjA89j9zptgHmz4XB0B7HuJJ4mldUXrzZK9Zi7zvWoPHYWdI5f9a/147uk/Hkedc/yLKZNv8P5sEKHs1iN9/RnwNit2hw00uizJ56f4udYx/TRgzBrOtB9OEOdw5kag8MpZhlzn50wR9MYa2z945Q+gz9wO0vYK5ZYVjAO54MIh/Nf43A0f04jc8hsZe67KuZpGhot8kEEtX3azs9prie/oH4h5kwOyyfM4RRzhBABSwoHmj5xSIrbvhambB59q3A4mvtYcyD3aGJ7/RmwlzBd09F9XI+Z15ilU9R2v5D+KGaMlz5SWX1x63CWSlD7fdTiBkyZCOxh1WtT0uafmH6+JyZsLL0rdDhaRsZX30xyKgJfteFkTJsYxph+tEUK2jvWtVX3pXXAfPVlWSlU1Gyr+ua7xuH0SsmzLW3avzOmTgwa5Xzf2eWthA9qXTz3ienOxHS1010qK/c7URpbXzwOthQiYEnll9LygHBS8YnsdPvAipitfR68EoczKQMOp5jDhQhYkvmHhFkqk1ieSDexvu3aeA/mqq/Dmey+s5ekvzZVFJe4Z32ZbpFIVpLCifKklrTZQtjE2iZdpWV98W8iHM6/c+BwinlEiIAlnZGmj/4iYW272bXrfWbQIUsUOZyocr+ar1YjWVmovtleiIAlH12AfsP8kUzKEQddhvDbBUbm2UBdnMPR0GW5+uK6zT0r5X7rQS+jmyGoI9HY4zJnJKRNJ0pYoGDZPBnjBxU6nE9wOGUZZHS1GupIPDeZAd+/yW1ZwM3GtD23ZV3xeshNz0eNasPhTMfhVIWPgGm2xIVQRypmrDOdzcZLc7MnbicZrnjRUSor9/upcTia0mJ++mhV+AjYK6giNRxp+v++TWzHnRJukkx9GtlK64sXOxzK/dbGeMl+cbysoX3+ObPE0L0JbdDsDpmpeKGOZ4ZE16Z6UAqJ1DcVzpjUG584fASqSBWbSJg98YIm3P+P5tV9qbQrc7S0LPf7sBRCepsJxfAaSU+j9/9DHaljrIQVa9eL8b66VuhTzGRi75h60DeNMgkHx8PmxgH9EHWkDk1YN93Z7+kYlyOGmH4zMCvK1GRcrxsnxF/kxjNMiIClnX2NMzgipns+IBlNXqc5diaaQbEt/auhXCxhziJIJ/MZh6AVcJdp8P3WMA7v0CwqdCXzfqmnfzejjzWMh52eb0IVqUadwlcx2fJcd5/ZUtgMnEl0PcJXBpgTyMb0sYbwqRABywpnmplJo94ctLKuj1ZfmnWF9jcDRHd+DqCP1RUiYNmik4TLF+9IYV9dvdnP9Jn+eVCqJrn2NYY+dlNNqA82AoZes8FOxqaNmNU+5a79ZN4Gik/GpCWKV6Gf1YXDnE517YAIWHYYJ+EG3nqOlQGSjOMfTWGwU6jPhdKXflYzFzl9voYqMoWWh5ot9U+PepmEh71zuTlYp5f+fJjmn+1FX6sJHwG7GVVkjhPMbOWXdbieJveb4653Tp4Vq2lS/QE4zV/cjb7WbvwZsJGoInMs7Ga2at+pUnu4/FAJK/nmvmrKwca7P++8M1THUkIELOvYw6rn1nCd+Ywzux+1FjjCDCCtod0ZlVQFZ8DywXUSHm1ap53X2Jg/VqWxVQLuk8bse8gqfkr9lZuuQ3Znun7joIbQ25OszzuxKUK0tBU2jcddQq6gSrlQiIDlhUPMGBla5W/1gPg899tTUGXp99OLjIJvEeoSVcJDTl9/RRWZR2c9T0iYTbRHFb8dbl7hlkOV0U7ocuOErhLyQrcFEbB8MUDCIg5jq3BcvmLwOFRYHk3EdINxQhdIBpJkN4geUt89IpAOLpAwlF5JhomtpfGHWzOFLpDdYZR2DiopyWZGR2uijtygZa2mSFgBpa0F5dvcdyfyRlE5ukX8fjPAKDXcGr8oqbvKiYDli1+ZsVGukkUf88p2PGqrDk1L8HCFis7zVPx1VJFL/iVhnq3lI77jt7hkouJFM9Ct58+Yd97foJLveVDCiCHkj34SHuwuVU7ZVrygVlwNaLG2V40T+jUq+Q6fafIPqCK3nGreELYr+szmFBqIqmpDp49vSLiXIe9RHxsB25XukVt0rdSH2CdLIdWq5x6hVHddsZU29OjBz3Osi4HGAa1F18g1Nsx+mnk98wdYD0dF9cNW2tCFtcE51YONgHFsBW41Y2J1CRPb6wJ1F9RTX9aVQnJ7n+R+vRzq4E/u+d+kO4AUomBzXZ/Q6Ng0CU8TQANYW1pW2sibE/IF7P5GVwDHMeZVzMuPUEvjsEnup7r33rzwoXvuU+kG4NDNqK8b5/MCKmk820qYZiAvSe67m062O10Aiv4o+8XnA1FHPOwi4XZzLeS2dMafdxPjgNbG/FCE7v85RTieEys7S8tKG1l2Qj6XtjpdskcCJIS9Jay0oZsWs3r25XwhAgaQSGzaSl2Ey2KlDZ8lgAgYQAI50jihxyV7lTb8Hg9y+wIklFOME9KNWVkpQUsEDCAlnGEG692SjSMLNgLWHxMDJBfNJX2xGbC6ZpL2Shu/ESJgAKlBc+Bea5zQ1ZLuvLj+DNhbmBYgHWiljRuNE7pC0ltpw0fAbsWsAOlBd4XaShvnpvQ5OAMGkFI0EvaAcUIjUtZ+GwHbA3MCpA9NWTneDOThKWo7Z8AAMoBW2nhWwiT3B6ek3QcJETCATLCktKy0sX8K2nyehIdtASDlLCNhRQGdVSS9uoQvRHcbpgPIBssFMknCShvbJbitU6Vl5QMAyAArm8Gt2RW3TmAbu0m4AP0rTAaQLdYM5BMJS5lskrD2bWwc0DqYCyB7aAWBz9wgnyXJqrRxoGuXJlzriKkAssmGgXzuBrvWXv9hQtrlI2DvYCKAbDNIwnI/mvxr1QS06T7XntsxD0D22UEKUTEd9O9JofJkM/GL5KdjGoB8MESSUe6nq4QL0HtiFoD8sI+ElTa0CkUzKm1sZBzQupgEIF8cKi0rbXSN+f42AtYJcwDkjxOME3oikMVivPcYIQIGkHtOM07oESmk9oiDe909/44JAPLNmcYJ3SPxVNr4QIiAAYAUcklfYpyQ5mZuZKUNImAA0AKtqnGdcQzXSOMqbfzU3GcAqgcARStt3GScw5XSmEobQ4UIGACUQCtt3Gmc0JgG3ONcd+13UTcAFKMn0x80Tmhkna9/j7vuOFQNAKVYPJCnjBM6to7XnuKuOQo1A0AUXSSstKFyVB2uaSNge6FiACiHVtp4TcJKGwfUeD0bAVsP9QJAW2iljQkSVtrYrYZrHSBEwACgSrTSxmQJK21s387rnOOuMQGVAkA19JOWlTa2acc1fATsDtQJANWyViDTJay0sWmVv3/f/XY0qgSA9vBjaVlpY/0Kf9dFwgXovVEjALQXjWbNds5khlRW12tD44DWR4UAUAtbBvKlcygfBbJaG9/fX8JwfmfUBwC1smMgX0tllTbOdt+biNoAoF5oXXef5L5cpY273XfuRGUAUE/2da9WvtJGzxLfec99fgbqAoB6M0zCReYXpWWlDRsB2wdVAUAjOElKV9rYQIiAAUAMnC6tK20QAQOA2DhLWlbauMD99yRUAwCNpjjJvQ/V/xPVAEAcaGmfW40T+tbNjAAAYkGLHN5lHNB+qAQA4kQTj10VyAPSMjQPAAAAAAAAAAAAAAAA9eR/oeqV3auxALsAAAAASUVORK5CYII="},{"partUri":"/media/image3.png","contentType":"image/png","content":"data:image/png;base64,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"}]}

The area of a polygon (or any plane shape) can be evaluated by <a href = "https://www.mathworks.com/matlabcentral/cody/problems/179">Monte Carlo integration</a>. The process involves 'random' sampling of (x,y) points in the xy plane, and testing whether they fall inside or outside the shape. That is illustrated schematically below for a circle.<img src = "https://upload.wikimedia.org/wikipedia/commons/thumb/2/20/MonteCarloIntegrationCircle.svg/247px-MonteCarloIntegrationCircle.svg.png">^Steps:<ol><li>Define a 2D region within which to sample points. In the present problem you must choose the rectangular box (aligned to the axes) that bounds the specified polygon.</li><li>By a 'random' process generate coordinates of a point within the bounding box. In the present problem a basic scheme using the uniform pseudorandom distribution available in MATLAB must be employed. Other schemes in use include quasi-random sampling (for greater uniformity of coverage) and stratified sampling (with more attention near the edges of the polygon).</li><li>Determine <a href = "https://www.mathworks.com/matlabcentral/cody/problems/198">whether the sampled point is inside or outside the polygon</a>. Due to working in double precision, it is extremely unlikely to sample a point falling exactly on the edge of the polygon, and consequently if it does happen it doesn't really matter whether it's counted as inside or outside or null.</li><li>Repeat steps 2–3 <tt>N</tt> times.</li><li>Based upon the proportion of sampled points falling within the polygon, report the approximate area of the polygon.</li></ol>Inputs to your function will be <tt>N</tt>, the number of points to sample, and <tt>polygonX</tt> & <tt>polygonY</tt>, which together constitute <a href = "https://www.mathworks.com/matlabcentral/cody/problems/194">an ordered list of polygon vertices</a>. <tt>N</tt> will always be a positive integer. <tt>polygonX</tt> & <tt>polygonY</tt> will always describe a single, continuous outline; however, the polygon may be self-intersecting.For polygons that are self-intersecting, you must find the area of the enclosed point set. In the case of the cross-quadrilateral, it is treated as two simple triangles (cf. three triangles in the first figure below), and the clockwise/counterclockwise ordering of points is irrelevant. A self-intersecting pentagram (left & middle of the second figure below) will therefore have the same area as a concave decagon (right).<img src = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/Complex_polygon.svg/288px-Complex_polygon.svg.png"><img src = "https://upload.wikimedia.org/wikipedia/commons/thumb/8/80/Pentagram_interpretations.svg/320px-Pentagram_interpretations.svg.png">^EXAMPLE<pre> % Input
 N = 1000
 polygonX = [1 0 -1 0]
 polygonY = [0 1 0 -1]
 % Output
 A = 2.036</pre>Explanation: the above polygon is a square of side-length √2 centred on the origin, but rotated by 45°. The exact area is hence 2. As the value of <tt>N</tt> is moderate, the estimate by Monte Carlo integration is moderately accurate (an error of 0.036 in this example, corresponding to 509 of the 1000 sampled points falling within the polygon). 
Of course, if the code were run again then a slightly different value of <tt>A</tt> would probably be returned, such as <tt>A</tt>=1.992 (corresponding to 498 of the 1000 sampled points falling within the polygon), due to the random qualities of the technique.

The area of a polygon (or any plane shape) can be evaluated by <https://www.mathworks.com/matlabcentral/cody/problems/179 Monte Carlo integration>. The process involves 'random' sampling of (x,y) points in the xy plane, and testing whether they fall inside or outside the shape. That is illustrated schematically below for a circle.

<<https://upload.wikimedia.org/wikipedia/commons/thumb/2/20/MonteCarloIntegrationCircle.svg/247px-MonteCarloIntegrationCircle.svg.png>>

*Steps:*

# Define a 2D region within which to sample points. In the present problem you must choose the rectangular box (aligned to the axes) that bounds the specified polygon. 
# By a 'random' process generate coordinates of a point within the bounding box. In the present problem a basic scheme using the uniform pseudorandom distribution available in MATLAB must be employed. _Other schemes in use include quasi-random sampling (for greater uniformity of coverage) and stratified sampling (with more attention near the edges of the polygon)._
# Determine <https://www.mathworks.com/matlabcentral/cody/problems/198 whether the sampled point is inside or outside the polygon>. _Due to working in double precision, it is extremely unlikely to sample a point falling exactly on the edge of the polygon, and consequently if it does happen it doesn't really matter whether it's counted as inside or outside or null._ 
# Repeat steps 2–3 |N| times. 
# Based upon the proportion of sampled points falling within the polygon, report the approximate area of the polygon.

For polygons that are self-intersecting, you must find the area of the enclosed point set. In the case of the cross-quadrilateral, it is treated as two simple triangles (cf. three triangles in the first figure below), and the clockwise/counterclockwise ordering of points is irrelevant.  A self-intersecting pentagram (left & middle of the second figure below) will therefore have the same area as a concave decagon (right).

<<https://upload.wikimedia.org/wikipedia/commons/thumb/0/0f/Complex_polygon.svg/288px-Complex_polygon.svg.png>>

<<https://upload.wikimedia.org/wikipedia/commons/thumb/8/80/Pentagram_interpretations.svg/320px-Pentagram_interpretations.svg.png>>

EXAMPLE

% Input
 N = 1000
 polygonX = [1 0 -1  0]
 polygonY = [0 1  0 -1]
 % Output
 A = 2.036

Explanation:  the above polygon is a square of side-length √2 centred on the origin, but rotated by 45°.  The exact area is hence 2.  As the value of |N| is moderate, the estimate by Monte Carlo integration is moderately accurate (an error of 0.036 in this example, corresponding to 509 of the 1000 sampled points falling within the polygon).  
Of course, if the code were run again then a slightly different value of |A| would probably be returned, such as |A|=1.992 (corresponding to 498 of the 1000 sampled points falling within the polygon), due to the random qualities of the technique.

Solve

Solution Stats

23 Solutions
5 Solvers

Last Solution submitted on Nov 14, 2022

Last 200 Solutions

Problem Comments

1 Comment

1 Comment

Rafael S.T. Vieira on 14 Nov 2022 at 2:35

Monte Carlo Integration improves its results as we increase the number of samples, and the test suite should probably consider this. Step 4 of the algorithm, according to the problem's description, claims the process needs to be repeated 2N or 3N times, but the test suite requires that we test it only N times.

For instance, in test #4, the Axis Aligned Bounding Box has area 4 and our N=1. Therefore, for a single random point, the resulting area will be 0 or 4. And as such, the test suite only tests for 0 and 4 as valid areas. However, suppose we repeated this process three times, as the problem's description states. In that case, it's improbable that we would get three 0s or three 4s. In 75% of cases, one or two results would be different, [0 0 4] or [4 0 4], resulting in an area of 4/3 or 8/3. Curiously, by repeating this process twice, we might reach the correct value of 2 for [0 4] and [4 0].

Solution Comments

Solution 1576522

1 Comment

1 Comment

David Verrelli on 24 Jun 2019

An elegant solution.

Problem Recent Solvers5

Problem Tags

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Problem 44694. Monte Carlo integration: area of a polygon

Solution Stats

Last 200 Solutions

Problem Comments

Solution Comments

Problem Recent Solvers5

Suggested Problems

More from this Author32

Problem Tags

Community Treasure Hunt

Problem 44694. Monte Carlo integration: area of a polygon

Solution Stats

Last 200 Solutions

Problem Comments

Solution Comments

Problem Recent Solvers5

Suggested Problems

More from this Author32

Problem Tags

Community Treasure Hunt

Players