To permute a 1D array A I know that you can run the following code:
import numpy as np
A = np.random.permutation(A)
I have a 2D array and want to apply exactly the same permutation for every row of the array. Is there any way you can specify the numpy to do that for you?
Generate random permutations for the number of columns in A and index into the columns of A, like so -
A[:,np.random.permutation(A.shape[1])]
Sample run -
In [100]: A
Out[100]:
array([[3, 5, 7, 4, 7],
[2, 5, 2, 0, 3],
[1, 4, 3, 8, 8]])
In [101]: A[:,np.random.permutation(A.shape[1])]
Out[101]:
array([[7, 5, 7, 4, 3],
[3, 5, 2, 0, 2],
[8, 4, 3, 8, 1]])
Actually you do not need to do this, from the documentation:
If x is a multi-dimensional array, it is only shuffled along its first index.
So, taking Divakar's array:
a = np.array([
[3, 5, 7, 4, 7],
[2, 5, 2, 0, 3],
[1, 4, 3, 8, 8]
])
you can just do: np.random.permutation(a) and get something like:
array([[2, 5, 2, 0, 3],
[3, 5, 7, 4, 7],
[1, 4, 3, 8, 8]])
P.S. if you need to perform column permutations - just do np.random.permutation(a.T).T. Similar things apply to multi-dim arrays.
It depends what you mean on every row.
If you want to permute all values (regardless of row and column), reshape your array to 1d, permute, reshape back to 2d.
If you want to permutate each row but not shuffle the elements among the different columns you need to loop trough the one axis and call permutation.
for i in range(len(A)):
A[i] = np.random.permutation(A[i])
It can probably done shorter somehow but that is how it can be done.
