diff options
Diffstat (limited to 'Lib/random.py')
| -rw-r--r-- | Lib/random.py | 34 |
1 files changed, 23 insertions, 11 deletions
diff --git a/Lib/random.py b/Lib/random.py index b4ad2b3..465f477 100644 --- a/Lib/random.py +++ b/Lib/random.py @@ -285,6 +285,15 @@ class Random(_random.Random): large population: sample(xrange(10000000), 60) """ + # XXX Although the documentation says `population` is "a sequence", + # XXX attempts are made to cater to any iterable with a __len__ + # XXX method. This has had mixed success. Examples from both + # XXX sides: sets work fine, and should become officially supported; + # XXX dicts are much harder, and have failed in various subtle + # XXX ways across attempts. Support for mapping types should probably + # XXX be dropped (and users should pass mapping.keys() or .values() + # XXX explicitly). + # Sampling without replacement entails tracking either potential # selections (the pool) in a list or previous selections in a set. @@ -304,7 +313,9 @@ class Random(_random.Random): setsize = 21 # size of a small set minus size of an empty list if k > 5: setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets - if n <= setsize: # is an n-length list smaller than a k-length set + if n <= setsize or hasattr(population, "keys"): + # An n-length list is smaller than a k-length set, or this is a + # mapping type so the other algorithm wouldn't work. pool = list(population) for i in xrange(k): # invariant: non-selected at [0,n-i) j = _int(random() * (n-i)) @@ -312,17 +323,18 @@ class Random(_random.Random): pool[j] = pool[n-i-1] # move non-selected item into vacancy else: try: - n > 0 and (population[0], population[n//2], population[n-1]) - except (TypeError, KeyError): # handle non-sequence iterables - population = tuple(population) - selected = set() - selected_add = selected.add - for i in xrange(k): - j = _int(random() * n) - while j in selected: + selected = set() + selected_add = selected.add + for i in xrange(k): j = _int(random() * n) - selected_add(j) - result[i] = population[j] + while j in selected: + j = _int(random() * n) + selected_add(j) + result[i] = population[j] + except (TypeError, KeyError): # handle (at least) sets + if isinstance(population, list): + raise + return self.sample(tuple(population), k) return result ## -------------------- real-valued distributions ------------------- |