Converting bsxfun with @times to numpy

Question

This is the code I have in Octave:

sum(bsxfun(@times, X*Y, X), 2)

The bsxfun part of the code produces element-wise multiplication so I thought that numpy.multiply(X*Y, X) would do the trick but I got an exception. When I did a bit of research I found that element-wise multiplication won't work on Python arrays (specifically if X and Y are of type "numpy.ndarray"). So I was wondering if anyone can explain this a bit more -- i.e. would type casting to a different type of object work? The Octave code works so I know I don't have a linear algebra mistake. I'm assuming that bsxfun and numpy.multiply are not actually equivalent but I'm not sure why so any explanations would be great.

I was able to find a website! that gives Octave to Matlab function conversions but it didn't seem to be help in my case.

Answer 1

bsxfun in Matlab stand for binary singleton expansion, in numpy it's called broadcasting and should happen automatically. The solution will depend on the dimensions of your X, i.e. is it a row or column vector but this answer shows one way to do it:

How to multiply numpy 2D array with numpy 1D array?

I think that the issue here is that broadcasting requires one of the dimensions to be 1 and, unlike Matlab, numpy seems to differentiate between a 1 dimensional 2 element vector and a 2 dimensional 2 element, i.e. the difference between a matrix of shape (2,) and of shape (2,1), you need the latter for broadcasting to happen.

Answer 2

For those who don't know Numpy, I think it's worth pointing out that the equivalent of Octave's (and Matlab's) * operator (matrix multiplication) is numpy.dot (and, debatably, numpy.outer). Numpy's * operator is similar to bsxfun(@times,...) in Octave, which is itself a generalization of .*.

In Octave, when applying bsxfun, there are implicit singleton dimensions to the right of the "true" size of the operands; that is, an n1 x n2 x n3 array can be considered as n1 x n2 x n3 x 1 x 1 x 1 x.... In Numpy, the implicit singleton dimensions are to the left; so an m1 x m2 x m3 can be considered as ... x 1 x 1 x m1 x m2 x m3. This matters when considering operand sizes: in Octave, bsxfun(@times,a,b) will work if a is 2 x 3 x 4 and b is 2 x 3. In Numpy one could not multiply two such arrays, but one could multiply a 2 x 3 x 4 and a 3 x 4 array.

Finally, bsxfun(@times, X*Y, X) in Octave will probably look something like numpy.dot(X,Y) * X. There are still some gotchas: for instance, if you're expecting an outer product (that is, in Octave X is a column vector, Y a row vector), you could look at using numpy.outer instead, or be careful about the shape of X and Y.