Clever solution sought: Summing values from an array into a vector component given in a second array

I have two arrays -- an array of indices and an array of values, both are K x M. The index array has values between 1:M. Now I want to obtain the vector whose jth component is sum( valArray( indArray == j ) ).

Can you think of way to do this that beats the simple for loop solution? I have a couple of bsxfun solutions to do the ( indArray == j ) in one shot, but those don't beat the for loop. I also have a way to do this using a sparse array, but that doesn't beat the for loop either.

Here's code to set up the problem and find the answer using a for loop:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%NOTE: set M and K to be much larger for the real problem
M = 10;
K = 5; 

indArray = randi(M,[K M]);
valArray = rand([K M]);

result = zeros(M,1);
for ii = 1 : M * K
   temp = indArray(ii);
   result(temp) = result(temp) + valArray(ii);
end

Thanks for taking a look.

Answer 1

[Peter Perkins]

I think you are looking for accumarray.

[Matthew Masarik]

I hadn't seen that function. Thanks!

Answer 2

[Roger Stafford]

You might try using accumarray:

 A = accumarray(indArray(:),valArray(:),[M,1]);

Answer 3

[per isakson]

Yes, accumarray beats the loop for large arrays (R2013a)

    M = 2e3;
    K = 5e3; 
    ix  = randi(M,[K,M]);
    val = rand([K,M]);
    tic
    out = zeros(M,1);
    for ii = 1 : M * K
       out(ix(ii)) = out(ix(ii)) + val(ii);
    end
    toc
    tic
    acc = accumarray( ix(:), val(:), [], @sum );
    toc
    assert( all( abs(out-acc)<1e-6 ), 'A:B:C', 'Results differ' )

returns

    Elapsed time is 0.330848 seconds.
    Elapsed time is 0.179041 seconds.
&nbsp

However

    M = 100;
    K = 40;

returns

    Elapsed time is 0.000125 seconds.
    Elapsed time is 0.000258 seconds.