One trick I have used often is generating a random array and using argsort
to get unique indices as the required unique numbers. Thus, we could do –
def random_choice_noreplace(m,n, axis=-1):
# m, n are the number of rows, cols of output
return np.random.rand(m,n).argsort(axis=axis)
Sample runs –
In [98]: random_choice_noreplace(3,7)
Out[98]:
array([[0, 4, 3, 2, 6, 5, 1],
[5, 1, 4, 6, 0, 2, 3],
[6, 1, 0, 4, 5, 3, 2]])
In [99]: random_choice_noreplace(5,7, axis=0) # unique nums along cols
Out[99]:
array([[0, 2, 4, 4, 1, 0, 2],
[1, 4, 3, 2, 4, 1, 3],
[3, 1, 1, 3, 2, 3, 0],
[2, 3, 0, 0, 0, 2, 4],
[4, 0, 2, 1, 3, 4, 1]])
Runtime test –
# Original approach
def loopy_app(m,n):
a = (np.random.choice(n,size=n,replace=False) for _ in range(m))
return np.vstack(a)
Timings –
In [108]: %timeit loopy_app(1000,100)
10 loops, best of 3: 20.6 ms per loop
In [109]: %timeit random_choice_noreplace(1000,100)
100 loops, best of 3: 3.66 ms per loop