I have an MxN array called A which stores the data I want. I have another M x N2 array B which stores array indices, and N2<N. Each row of B stores the indices of the elements I want to get from A for that row. For example, the following code works for me:
A_reduced = np.zeros((M,N2))
for i in range(M):
A_reduced[i,:] = A[i,B[i,:]]
Are there any 'vectorized' ways to extract the desired elements from A based on B instead of looping through each row?
In [203]: A = np.arange(12).reshape(3,4)
In [204]: B = np.array([[0,2],[1,3],[3,0]])
Your row iteration:
In [207]: A_reduced = np.zeros((3,2),int)
In [208]: for i in range(3):
...: A_reduced[i,:] = A[i, B[i,:]]
...:
In [209]: A_reduced
Out[209]:
array([[ 0, 2],
[ 5, 7],
[11, 8]])
A 'vectorized' version:
In [210]: A[np.arange(3)[:,None], B]
Out[210]:
array([[ 0, 2],
[ 5, 7],
[11, 8]])
and streamlined with a newish function:
In [212]: np.take_along_axis(A,B,axis=1)
Out[212]:
array([[ 0, 2],
[ 5, 7],
[11, 8]])
You can exploit array indexing and use reshape:
# set up M=N=4, N2=2
a = np.arange(16).reshape(4,4)
b = np.array([[1,2],[0,1],[2,3],[1,3]])
row_idx = np.repeat(np.arange(b.shape[0]),b.shape[1])
col_idx = b.ravel()
# output:
a[row_idx, col_idx].reshape(b.shape)
Output:
array([[ 1, 2],
[ 4, 5],
[10, 11],
[13, 15]])
Update: Another similar solution
row_idx = np.repeat(np.arange(b.shape[0]),b.shape[1]).reshape(b.shape)
# output
a[row_idx,b]
[0, 0, 0]and B is[1, 2]. Would you expect A_reduced to now be[0, 0]A_reduced[i,:] = [A[i,B[i,:]] for i in range(M)]. I think numpy has a vectorize class but that might overcomplicate: docs.scipy.org/doc/numpy/reference/generated/…