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Take Tim's advice and have random.sample() support only sequences and sets.

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This commit is contained in:
Raymond Hettinger 2008-01-14 01:00:53 +00:00
parent 28de64fd0f
commit 1acde190b2
4 changed files with 22 additions and 49 deletions
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43
Lib/random.py
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@ -267,7 +267,7 @@ class Random(_random.Random):
x[i], x[j] = x[j], x[i]
def sample(self, population, k):
"""Chooses k unique random elements from a population sequence.
"""Chooses k unique random elements from a population sequence or set.
Returns a new list containing elements from the population while
leaving the original population unchanged. The resulting list is
@ -284,15 +284,6 @@ class Random(_random.Random):
large population: sample(range(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.
@ -303,37 +294,35 @@ class Random(_random.Random):
# preferred since the list takes less space than the
# set and it doesn't suffer from frequent reselections.
if isinstance(population, (set, frozenset)):
population = tuple(population)
if not hasattr(population, '__getitem__') or hasattr(population, 'keys'):
raise TypeError("Population must be a sequence or set. For dicts, use dict.keys().")
random = self.random
n = len(population)
if not 0 <= k <= n:
raise ValueError("sample larger than population")
random = self.random
raise ValueError("Sample larger than population")
_int = int
result = [None] * k
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 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.
if n <= setsize:
# An n-length list is smaller than a k-length set
pool = list(population)
for i in range(k): # invariant: non-selected at [0,n-i)
j = _int(random() * (n-i))
result[i] = pool[j]
pool[j] = pool[n-i-1] # move non-selected item into vacancy
else:
try:
selected = set()
selected_add = selected.add
for i in range(k):
selected = set()
selected_add = selected.add
for i in range(k):
j = _int(random() * n)
while j in selected:
j = _int(random() * n)
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)
selected_add(j)
result[i] = population[j]
return result
## -------------------- real-valued distributions -------------------