Weighted random choice

Weighted random choice makes you able to select a random value out of a set of values using a distribution specified though a set of weights. So, given a list we want to pick randomly some elements from it but we need that the chances to pick a specific element is defined using a weight. In the following code we have a function that implements the weighted random choice mechanism and an example of how to use it:

from numpy import cumsum, sort, sum, searchsorted
from numpy.random import rand
from pylab import hist,show,xticks

def weighted_pick(weights,n_picks):
 """
  Weighted random selection
  returns n_picks random indexes.
  the chance to pick the index i 
  is give by the weight weights[i].
 """
 t = cumsum(weights)
 s = sum(weights)
 return searchsorted(t,rand(n_picks)*s)

# weights, don't have to sum up to one
w = [0.1, 0.2, 0.5, 0.5, 1.0, 1.1, 2.0]

# picking 10000 times
picked_list = weighted_pick(w,10000)

# plotting the histogram
hist(picked_list,bins=len(w),normed=1,alpha=.8,color='red')
show()

The code above plots the distribution of the selected indexes:

We can observe that the chance to pick the element i is proportional to the weight w[i].