I have an array of element probabilities, let's say [0.1, 0.2, 0.5, 0.2]. The array sums up to 1.0.

Using plain Python or numpy, I want to draw elements proportional to their probability: the first element about 10% of the time, second 20%, third 50% etc. The "draw" should return index of the element drawn.

I came up with this:

def draw(probs):cumsum = numpy.cumsum(probs / sum(probs)) # sum up to 1.0, just in casereturn len(numpy.where(numpy.random.rand() >= cumsum)[0])

It works, but it's too convoluted, there must be a better way. Thanks.

import numpy as np
def random_pick(choices, probs):'''>>> a = ['Hit', 'Out']>>> b = [.3, .7]>>> random_pick(a,b)'''cutoffs = np.cumsum(probs)idx = cutoffs.searchsorted(np.random.uniform(0, cutoffs[-1]))return choices[idx]

How it works:

In [22]: import numpy as np
In [23]: probs = [0.1, 0.2, 0.5, 0.2]

Compute the cumulative sum:

In [24]: cutoffs = np.cumsum(probs)
In [25]: cutoffs
Out[25]: array([ 0.1,  0.3,  0.8,  1. ])

Compute a uniformly distributed random number in the half-open interval [0, cutoffs[-1]):

In [26]: np.random.uniform(0, cutoffs[-1])
Out[26]: 0.9723114393023948

Use searchsorted to find the index where the random number would be inserted into cutoffs:

In [27]: cutoffs.searchsorted(0.9723114393023948)
Out[27]: 3

Return choices[idx], where idx is that index.