python - Vectorized KL divergence calculation between all pairs of rows of a matrix

Question

I would like to find out the KL divergence between all pairs of rows of a matrix. To explain, let's assume there is a matrix V of shape N x K. Now I want to create a matrix L of dimension N x N, where each element L[i,j] = KL(V[i,:],V[j,:]). So far I have used the following scipy.stats.entropy to compute

upper_triangle = [entropy(V[i,:],V[j,:]) for (i,j) in itertools.combinations(range(N,2)]
lower_triangle = [entropy(V[j,:],V[i,:]) for (i,j) in itertools.combinations(range(N,2)]

L = np.zeroes((N,N))

L[np.triu_indices(N,k = 1)] = upper_triangle
L[np.tril_indices(N,k = -1)] = lower_triangle

Is there a cleverer way?

Answer 1

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