What is the best way to compute the distance/proximity matrix for very large sparse vectors? For example you are given the following design matrix, where each row is 68771 dimensional sparse vector.
I am trying to create a function called calc(f,a,b) where x is an equation with the variable f and I want to put this code within the function.
For a set of observations: [a1,a2,a3,a4,a5] their pairwise distances d=[[0,a12,a13,a14,a15] [a21,0,a23,a24,a25]
As a C++ programmer, I\'m used to access vectors in C++ style: for (i=0; i<max_x; i++) { for (j=0; j<max_y; j++) {
BACKGROUND The issue I\'m working with is as follows: Within the context of an experiment I am designing for my research, I produce a large number of large (length 4M) arrays which are somewhat spa
I have a bilinear surface (surface defined by lines between the 4 vertices) in 3D.I want to map this surface to the unit square.I know how to do this mapping and I know how to calculate the x,y,z coor
I have two 2-D arrays with the same first axis dimensions. In python, I would like to convolve the two matrices along the second axis only. I would like to get C below without computing the convolutio
I have a .mat file in which I put data previously processed. When I perform dict = scipy.io.loadmat(\'training_data.mat\')
I have some data for which I have a set of numerically determined model curves. Now I would like to find the one w开发者_开发百科ith least square deviation, I only need to vary one parameter, which is
Let\'s say we have a particularly simple function like import scipy as sp def func(x, y): return x + y This function evidently works for several builtin python datatypes of x and y like string, lis
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