Python standardized function for axis-specific outer product-CodePudding

I have two matrices X and V with respective shape (K, M) and (K,L) The first dimension represents the batch_size (each row is a singular element from the batch). I wish to do the outer product only on dimensions 1 on both of these matrices such that the output has the shape (K, M, L).

I have of course tried to use the NumPy np.outer function but it flattens the arrays and the result is of final shape (K * M, K * L)

Is there a function in one of Python's standard libraries that can do the operation I want to do ?

An example of what I want, but it is not efficient :

W = np.zeros(K,M,L)
for i in range(X.shape[0]):
    W[i,:,:] = np.outer(X[i,:], V[i,:])

Edit: Found an answer already on StackOverflow!

CodePudding user response：

For my specific problem, adding an extra dimension and letting Python do the broadcasting was sufficient.

My working code:

W = X[:,:,None]*V[:,None,:]

Thanks for this answer for helping me out.