Using Numpy Broadcast to achieve array/Matrix subtraction without looping over the indices

Question

I was trying to perform mean clustering on my dataset. The dataset X is of the dimension (182,108,130). The mean is calculated using np.mean(X, axis = 1). The mean is of dimension (182,130). Now I'd like to subtract the mean from the dataset. How do I achieve it without a for loop?

I have tried to directly subtract X - mean but returned error

`operands could not be broadcast together with shapes (182,108,130) (130,182) `

I'd expect the mean being subtracted in each block.

Suraj Shourie · Accepted Answer · 2023-11-07 22:20:16Z

0

You need to use keepdims=True while calculating mean. Example code below:

x = np.random.random((180,108,130)) # random init
x_mean = x.mean(axis=1, keepdims=True) # shape (180, 1, 130)

x - x_mean

Another option (if you don't want to use keepdims=True is to reshape the array by adding a third axis something like this 9which essentially does the same thing as keepdims=True:

x_mean.reshape(x.shape[0], 1, x.shape[2])

edited Nov 7, 2023 at 22:20

answered Nov 7, 2023 at 21:50

Suraj Shourie

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