user3080953 user3080953 - 3 years ago 102
Python Question

Numpy match indexing dimensions

Problem



I have two numpy arrays,
A
and
indices
.

A
has dimensions m x n x 10000.
indices
has dimensions m x n x 5 (output from
argpartition(A, 5)[:,:,:5]
).
I would like to get a m x n x 5 array containing the elements of
A
corresponding to
indices
.

Attempts



indices = np.array([[[5,4,3,2,1],[1,1,1,1,1],[1,1,1,1,1]],
[500,400,300,200,100],[100,100,100,100,100],[100,100,100,100,100]])
A = np.reshape(range(2 * 3 * 10000), (2,3,10000))

A[...,indices] # gives an array of size (2,3,2,3,5). I want a subset of these values
np.take(A, indices) # shape is right, but it flattens the array first
np.choose(indices, A) # fails because of shape mismatch.


Motivation



I'm trying to get the 5 largest values of
A[i,j]
for each
i<m
,
j<n
in sorted order using
np.argpartition
because the arrays can get fairly large.

Answer Source

You can use advanced-indexing -

m,n = A.shape[:2]
out = A[np.arange(m)[:,None,None],np.arange(n)[:,None],indices]

Sample run -

In [330]: A
Out[330]: 
array([[[38, 21, 61, 74, 35, 29, 44, 46, 43, 38],
        [22, 44, 89, 48, 97, 75, 50, 16, 28, 78],
        [72, 90, 48, 88, 64, 30, 62, 89, 46, 20]],

       [[81, 57, 18, 71, 43, 40, 57, 14, 89, 15],
        [93, 47, 17, 24, 22, 87, 34, 29, 66, 20],
        [95, 27, 76, 85, 52, 89, 69, 92, 14, 13]]])

In [331]: indices
Out[331]: 
array([[[7, 8, 1],
        [7, 4, 7],
        [4, 8, 4]],

       [[0, 7, 4],
        [5, 3, 1],
        [1, 4, 0]]])

In [332]: m,n = A.shape[:2]

In [333]: A[np.arange(m)[:,None,None],np.arange(n)[:,None],indices]
Out[333]: 
array([[[46, 43, 21],
        [16, 97, 16],
        [64, 46, 64]],

       [[81, 14, 43],
        [87, 24, 47],
        [27, 52, 95]]])

For getting those indices corresponding to the max 5 elements along the last axis, we would use argpartition, like so -

indices = np.argpartition(-A,5,axis=-1)[...,:5]

To keep the order from highest to lowest, use range(5) instead of 5.

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