traverseda
/
cpython
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Rename weighted_choices() to just choices()

This commit is contained in:
Raymond Hettinger 2016-09-07 00:08:44 -07:00
parent c98b26a6ac
commit 28aa4a0684
4 changed files with 32 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
Doc/library/random.rst
View File

@ -124,7 +124,7 @@ Functions for sequences:
Return a random element from the non-empty sequence *seq*. If *seq* is empty,
raises :exc:`IndexError`.
.. function:: weighted_choices(k, population, weights=None, *, cum_weights=None)
.. function:: choices(k, population, weights=None, *, cum_weights=None)
Return a *k* sized list of elements chosen from the *population* with replacement.
If the *population* is empty, raises :exc:`IndexError`.

6
Lib/random.py
View File

@ -51,7 +51,7 @@ __all__ = ["Random","seed","random","uniform","randint","choice","sample",
"randrange","shuffle","normalvariate","lognormvariate",
"expovariate","vonmisesvariate","gammavariate","triangular",
"gauss","betavariate","paretovariate","weibullvariate",
"getstate","setstate", "getrandbits", "weighted_choices",
"getstate","setstate", "getrandbits", "choices",
"SystemRandom"]
NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
@ -337,7 +337,7 @@ class Random(_random.Random):
result[i] = population[j]
return result
def weighted_choices(self, k, population, weights=None, *, cum_weights=None):
def choices(self, k, population, weights=None, *, cum_weights=None):
"""Return a k sized list of population elements chosen with replacement.
If the relative weights or cumulative weights are not specified,
@ -749,7 +749,7 @@ choice = _inst.choice
randrange = _inst.randrange
sample = _inst.sample
shuffle = _inst.shuffle
weighted_choices = _inst.weighted_choices
choices = _inst.choices
normalvariate = _inst.normalvariate
lognormvariate = _inst.lognormvariate
expovariate = _inst.expovariate

52
Lib/test/test_random.py
View File

@ -142,8 +142,8 @@ class TestBasicOps:
def test_sample_on_dicts(self):
self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2)
def test_weighted_choices(self):
weighted_choices = self.gen.weighted_choices
def test_choices(self):
choices = self.gen.choices
data = ['red', 'green', 'blue', 'yellow']
str_data = 'abcd'
range_data = range(4)
@ -151,10 +151,10 @@ class TestBasicOps:
# basic functionality
for sample in [
weighted_choices(5, data),
weighted_choices(5, data, range(4)),
weighted_choices(k=5, population=data, weights=range(4)),
weighted_choices(k=5, population=data, cum_weights=range(4)),
choices(5, data),
choices(5, data, range(4)),
choices(k=5, population=data, weights=range(4)),
choices(k=5, population=data, cum_weights=range(4)),
]:
self.assertEqual(len(sample), 5)
self.assertEqual(type(sample), list)
@ -162,52 +162,52 @@ class TestBasicOps:
# test argument handling
with self.assertRaises(TypeError): # missing arguments
weighted_choices(2)
choices(2)
self.assertEqual(weighted_choices(0, data), []) # k == 0
self.assertEqual(weighted_choices(-1, data), []) # negative k behaves like ``[0] * -1``
self.assertEqual(choices(0, data), []) # k == 0
self.assertEqual(choices(-1, data), []) # negative k behaves like ``[0] * -1``
with self.assertRaises(TypeError):
weighted_choices(2.5, data) # k is a float
choices(2.5, data) # k is a float
self.assertTrue(set(weighted_choices(5, str_data)) <= set(str_data)) # population is a string sequence
self.assertTrue(set(weighted_choices(5, range_data)) <= set(range_data)) # population is a range
self.assertTrue(set(choices(5, str_data)) <= set(str_data)) # population is a string sequence
self.assertTrue(set(choices(5, range_data)) <= set(range_data)) # population is a range
with self.assertRaises(TypeError):
weighted_choices(2.5, set_data) # population is not a sequence
choices(2.5, set_data) # population is not a sequence
self.assertTrue(set(weighted_choices(5, data, None)) <= set(data)) # weights is None
self.assertTrue(set(weighted_choices(5, data, weights=None)) <= set(data))
self.assertTrue(set(choices(5, data, None)) <= set(data)) # weights is None
self.assertTrue(set(choices(5, data, weights=None)) <= set(data))
with self.assertRaises(ValueError):
weighted_choices(5, data, [1,2]) # len(weights) != len(population)
choices(5, data, [1,2]) # len(weights) != len(population)
with self.assertRaises(IndexError):
weighted_choices(5, data, [0]*4) # weights sum to zero
choices(5, data, [0]*4) # weights sum to zero
with self.assertRaises(TypeError):
weighted_choices(5, data, 10) # non-iterable weights
choices(5, data, 10) # non-iterable weights
with self.assertRaises(TypeError):
weighted_choices(5, data, [None]*4) # non-numeric weights
choices(5, data, [None]*4) # non-numeric weights
for weights in [
[15, 10, 25, 30], # integer weights
[15.1, 10.2, 25.2, 30.3], # float weights
[Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional weights
[True, False, True, False] # booleans (include / exclude)
]:
self.assertTrue(set(weighted_choices(5, data, weights)) <= set(data))
self.assertTrue(set(choices(5, data, weights)) <= set(data))
with self.assertRaises(ValueError):
weighted_choices(5, data, cum_weights=[1,2]) # len(weights) != len(population)
choices(5, data, cum_weights=[1,2]) # len(weights) != len(population)
with self.assertRaises(IndexError):
weighted_choices(5, data, cum_weights=[0]*4) # cum_weights sum to zero
choices(5, data, cum_weights=[0]*4) # cum_weights sum to zero
with self.assertRaises(TypeError):
weighted_choices(5, data, cum_weights=10) # non-iterable cum_weights
choices(5, data, cum_weights=10) # non-iterable cum_weights
with self.assertRaises(TypeError):
weighted_choices(5, data, cum_weights=[None]*4) # non-numeric cum_weights
choices(5, data, cum_weights=[None]*4) # non-numeric cum_weights
with self.assertRaises(TypeError):
weighted_choices(5, data, range(4), cum_weights=range(4)) # both weights and cum_weights
choices(5, data, range(4), cum_weights=range(4)) # both weights and cum_weights
for weights in [
[15, 10, 25, 30], # integer cum_weights
[15.1, 10.2, 25.2, 30.3], # float cum_weights
[Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional cum_weights
]:
self.assertTrue(set(weighted_choices(5, data, cum_weights=weights)) <= set(data))
self.assertTrue(set(choices(5, data, cum_weights=weights)) <= set(data))
def test_gauss(self):
# Ensure that the seed() method initializes all the hidden state. In

2
Misc/NEWS
View File

@ -101,7 +101,7 @@ Library
- Issue #27691: Fix ssl module's parsing of GEN_RID subject alternative name
fields in X.509 certs.
- Issue #18844: Add random.weighted_choices().
- Issue #18844: Add random.choices().
- Issue #25761: Improved error reporting about truncated pickle data in
C implementation of unpickler. UnpicklingError is now raised instead of