raise ValueError('The number of weights does not match the population')
bisect = _bisect.bisect
total = cum_weights[-1]
- return [population[bisect(cum_weights, random() * total)] for i in range(k)]
+ hi = len(cum_weights) - 1
+ return [population[bisect(cum_weights, random() * total, 0, hi)]
+ for i in range(k)]
## -------------------- real-valued distributions -------------------
with self.assertRaises(IndexError):
choices([], cum_weights=[], k=5)
+ def test_choices_subnormal(self):
+ # Subnormal weights would occassionally trigger an IndexError
+ # in choices() when the value returned by random() was large
+ # enough to make `random() * total` round up to the total.
+ # See https://bugs.python.org/msg275594 for more detail.
+ choices = self.gen.choices
+ choices(population=[1, 2], weights=[1e-323, 1e-323], k=5000)
+
def test_gauss(self):
# Ensure that the seed() method initializes all the hidden state. In
# particular, through 2.2.1 it failed to reset a piece of state used