A weighted version of random.choice

Question

I needed to write a weighted version of random.choice (each element in the list has a different probability for being selected). This is what I came up with:

Answer 1

If you don't mind using numpy, you can use numpy.random.choice.

For example:

import numpy

items  = [["item1", 0.2], ["item2", 0.3], ["item3", 0.45], ["item4", 0.05]
elems = [i[0] for i in items]
probs = [i[1] for i in items]

trials = 1000
results = [0] * len(items)
for i in range(trials):
    res = numpy.random.choice(items, p=probs)  #This is where the item is selected!
    results[items.index(res)] += 1
results = [r / float(trials) for r in results]
print "item\texpected\tactual"
for i in range(len(probs)):
    print "%s\t%0.4f\t%0.4f" % (items[i], probs[i], results[i])

If you know how many selections you need to make in advance, you can do it without a loop like this:

numpy.random.choice(items, trials, p=probs)