How to apply numpy random.choice to a matrix of probability values (Vectorized solution)

Question

The problem I have is as follows

I have a 1-D list of integers (or np.array) with 3 values

l = [0,1,2]

I have a 2-D list of probabi

Answer 1

Here's one way.

Here's the array of probabilities:

In [161]: p
Out[161]: 
array([[ 0.8 ,  0.1 ,  0.1 ],
       [ 0.3 ,  0.3 ,  0.4 ],
       [ 0.25,  0.5 ,  0.25]])

c holds the cumulative distributions:

In [162]: c = p.cumsum(axis=1)

Generate a set of uniformly distributed samples...

In [163]: u = np.random.rand(len(c), 1)

...and then see where they "fit" in c:

In [164]: choices = (u < c).argmax(axis=1)

In [165]: choices
Out[165]: array([1, 2, 2])