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Merge p3yk branch with the trunk up to revision 45595. This breaks a fair

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number of tests, all because of the codecs/_multibytecodecs issue described
here (it's not a Py3K issue, just something Py3K discovers):
http://mail.python.org/pipermail/python-dev/2006-April/064051.html

Hye-Shik Chang promised to look for a fix, so no need to fix it here. The
tests that are expected to break are:

test_codecencodings_cn
test_codecencodings_hk
test_codecencodings_jp
test_codecencodings_kr
test_codecencodings_tw
test_codecs
test_multibytecodec

This merge fixes an actual test failure (test_weakref) in this branch,
though, so I believe merging is the right thing to do anyway.
This commit is contained in:
Thomas Wouters 2006-04-21 10:40:58 +00:00
parent 9ada3d6e29
commit 49fd7fa443
640 changed files with 52240 additions and 18408 deletions
Download patch file Download diff file Expand all files Collapse all files

34
Lib/random.py
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@ -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 -------------------