def test(self, weights, num_samples=10000):
plot.figure(self.i)
self.i+=1
weights = weights/float(weights.sum())
d = distribution(weights)
sample = d.sample(num_samples)
frequencies = numpy.array([sample.count(i) for i in xrange(weights.size)])
plot.plot(numpy.arange(weights.size), weights/float(sum(weights)))
plot.plot(numpy.arange(weights.size), frequencies/float(sum(frequencies)), "b*")
def better_resample(X, weights):
return numpy.array(distribution(weights).sample(X.shape[0], X))
