I have a piece of code in Matlab that I want to convert into Python/numpy.
I have a matrix ind which has the dimensions (32768, 24). I have another matrix X which has the dimensions (98304, 6). When I perform the operation
result = X(ind)
the shape of the matrix is (32768, 24).
but in numpy when I perform the same shape
result = X[ind]
I get the shape of the result matrix as (32768, 24, 6).
I would greatly appreciate it if someone can help me with why I can these two different results and how can I fix them. I would want to get the shape (32768, 24) for the result matrix in numpy as well
In Octave, if I define:
>> X=diag([1,2,3,4])
X =
Diagonal Matrix
1 0 0 0
0 2 0 0
0 0 3 0
0 0 0 4
>> idx = [6 7;10 11]
idx =
6 7
10 11
then the indexing selects a block:
>> X(idx)
ans =
2 0
0 3
The numpy equivalent is
In [312]: X=np.diag([1,2,3,4])
In [313]: X
Out[313]:
array([[1, 0, 0, 0],
[0, 2, 0, 0],
[0, 0, 3, 0],
[0, 0, 0, 4]])
In [314]: idx = np.array([[5,6],[9,10]]) # shifted for 0 base indexing
In [315]: np.unravel_index(idx,(4,4)) # raveled to unraveled conversion
Out[315]:
(array([[1, 1],
[2, 2]]),
array([[1, 2],
[1, 2]]))
In [316]: X[_] # this indexes with a tuple of arrays
Out[316]:
array([[2, 0],
[0, 3]])
another way:
In [318]: X.flat[idx]
Out[318]:
array([[2, 0],
[0, 3]])