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.
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.