Chris Toczycki wrote:
I am looking to find a solution to a problem and believe that Numpy may help. I'd appreciate some advice and/or direction and I'll pursue from there.
The problem is that I am looking for a most efficient solution where I have to select 25 solutions where the highest pair-covariance value is the lowest of all possible 25 solution combinations. There are possible 95 solutions to choose from and I have a 95 x 95 matrix of pair-covariance values.
I'll give you a brief sketch: * Write a function that will extract the upper-triangle (including the diagonal) into a flattened array. a[triu(ones((len(a),len(a)))).astype(bool)] is the one-liner. * Write a function that takes an index from this flattened array to the pair of (i,j) indices that correspond to the same element in the original matrix. * Flatten your covariance matrix with the first function, use argsort() to find the indices into the flattened array in ascending order, extract the first 25, and then use the second function to get the (i,j) pair that you are looking for. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco