+from _collections_abc import Set as _Set, Sequence as _Sequence, Mapping as _Mapping
+from itertools import accumulate as _accumulate
+from bisect import bisect as _bisect
+ "SystemRandom", "weighted_choice"]
+ def weighted_choice(self, data, amount=1):
+ """An iterator of random elements with the chances defined by relative
+ weights.
+
+ If argument is a mapping then generates random keys with the
+ probability that is proportional to the value mapped from this key.
+
+ If argument is a sequence then generates random indices with the
+ probability that is proportional to value at this index.
+ """
+ if isinstance(data, _Mapping):
+ indices = list(data.keys())
+ weights = data.values()
+ else:
+ indices = None
+ weights = data
+ cumulative_dist = list(_accumulate(weights))
+ total_sum = cumulative_dist[-1]
+ if any(w < 0 for w in weights):
+ raise ValueError("All weights must be non-negative")
+ if not total_sum:
+ raise ValueError("At least one weight must be greater than zero")
+ del weights
+
+ # Fast path for weighted_choice()
+ u = _bisect(cumulative_dist, self.random() * total_sum)
+ if indices is not None:
+ u = indices[u]
+ k = 1.0 / total_sum
+ cumulative_dist = [k * s for s in cumulative_dist]
+ results = []
+ if indices is None:
+ for i in range(0,amount):
+ u = self.random()
+ results.append(_bisect(cumulative_dist, u))
+ return results
+ else:
+ for i in range(0,amount):
+ u = self.random()
+ results.append(indices[_bisect(cumulative_dist, u)])
+ return results
+weighted_choice = _inst.weighted_choice