Is there a difference between the input paramaters of numpy.random.choice and random.choice?
Why does numpy.random.choice not work the same as random.choice? When I do this :
>>> random.choice([(1,2),(4,3)])
(1, 2)
It works.
But when I do this:
>>> np.random.choice([(1,2), (3,4)])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "mtrand.pyx", line 1393, in mtrand.RandomState.choice
(numpy/random/mtrand/mtrand.c:15450)
ValueError: a must be 1-dimensional
How do I achieve the same behavior as random.choice() in numpy.random.choice()?
Answer
Well np.random.choice as noted in the docs, expects a 1D array and your input when expressed as an array would be 2D. So, it won't work simply like that.
To make it work, we can feed in the length of the input and let it select one index, which when indexed into the input would be the equivalent one from random.choice, as shown below -
out = a[np.random.choice(len(a))] # a is input
Sample run -
In [74]: a = [(1,2),(4,3),(6,9)]
In [75]: a[np.random.choice(len(a))]
Out[75]: (6, 9)
In [76]: a[np.random.choice(len(a))]
Out[76]: (1, 2)
Alternatively, we can convert the input to a 1D array of object dtype and that would allow us to directly use np.random.choice, as shown below -
In [131]: a0 = np.empty(len(a),dtype=object)
In [132]: a0[:] = a
In [133]: a0.shape
Out[133]: (3,) # 1D array
In [134]: np.random.choice(a0)
Out[134]: (6, 9)
In [135]: np.random.choice(a0)
Out[135]: (4, 3)