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]*.

thanks for sharing..

ReplyDeleteCool stuff, thanks.

ReplyDeleteI'm learning Python list & dict math methods, found your article very helpful & concise.

What if you wanted the weights to add up to 1, how does that change the code?

That would be helpful so as to extend your Matplotlib code to produce boxplots for each indice on a single chart.

Thanks in advance for your help.

Cheers

Hi :) you can use a list of weights that add to 1.

DeleteThis will convert a list with weights which do not add up to 1, to a list of weights which do.

DeleteIt uses a list comprehension, which might look scary at first, but they are very fast and useful!

weights = [1, 2, 3, 4, 5]

sumOfWeights = sum(weights)

probabilities = [(i/float(sumOfWeights)) for i in weights]

>>> probabilities

[0.06666666666666667,

0.13333333333333333,

0.2,

0.26666666666666666,

0.3333333333333333]

>>> sum(probabilities)

1

The list comprehension can be translated to a normal loop, for better understanding:

[(i/float(sumOfWeights)) for i in weights]

l = []

for i in weights:

l.append(i/float(sumOfWeights))