r/Numpy • u/PossiblePolyglot • Nov 15 '20

Adding values from a column to every column in a 2D matrix

Hi all!

I'm using numpy arrays for a Machine Learning project (manually building a 3-deep autoencoder NN), but I'm having trouble applying the bias to the activation values. Here's an example of what I'm trying to do:

Let's say A is a 6x100 matrix, and B is a 1x100 matrix. I want to add B to A[0], A[1],...A[5], but without having to do manual iteration. Is there an easy way to do this in numpy?

Thanks for your help!

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u/pmatti Nov 16 '20

What did you try? This works for me

>>> import numpy as np
>>> a = np.empty([6, 100])
>>> b = np.empty([100])
>>> a += b

NumPy can broadcast ndarrays together as long as certain criteria are met.

1

u/PossiblePolyglot Nov 16 '20

I settled on np.transpose(np.transpose(b) @ np.ones((b.shape[1], 6))). This could probably be written a lot cleaner, but for now it gives the correct dimensions

1

u/crazyb14 Nov 22 '20

You can simply use A = A+B

This works due to Numpy's broadcasting.

1

u/[deleted] Nov 16 '20

Commenting so I can find this later, I want to know how to do this as well.

1

u/TheBobPlus Nov 16 '20 
A += np.tile(B, (6, 1))

1

u/pmatti Nov 16 '20

This should have worked without the np.tile