Rename weighted_choices() to just choices()
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@ -124,7 +124,7 @@ Functions for sequences:
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Return a random element from the non-empty sequence *seq*. If *seq* is empty,
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raises :exc:`IndexError`.
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.. function:: weighted_choices(k, population, weights=None, *, cum_weights=None)
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.. function:: choices(k, population, weights=None, *, cum_weights=None)
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Return a *k* sized list of elements chosen from the *population* with replacement.
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If the *population* is empty, raises :exc:`IndexError`.
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@ -51,7 +51,7 @@ __all__ = ["Random","seed","random","uniform","randint","choice","sample",
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"randrange","shuffle","normalvariate","lognormvariate",
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"expovariate","vonmisesvariate","gammavariate","triangular",
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"gauss","betavariate","paretovariate","weibullvariate",
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"getstate","setstate", "getrandbits", "weighted_choices",
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"getstate","setstate", "getrandbits", "choices",
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"SystemRandom"]
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NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
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@ -337,7 +337,7 @@ class Random(_random.Random):
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result[i] = population[j]
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return result
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def weighted_choices(self, k, population, weights=None, *, cum_weights=None):
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def choices(self, k, population, weights=None, *, cum_weights=None):
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"""Return a k sized list of population elements chosen with replacement.
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If the relative weights or cumulative weights are not specified,
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@ -749,7 +749,7 @@ choice = _inst.choice
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randrange = _inst.randrange
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sample = _inst.sample
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shuffle = _inst.shuffle
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weighted_choices = _inst.weighted_choices
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choices = _inst.choices
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normalvariate = _inst.normalvariate
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lognormvariate = _inst.lognormvariate
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expovariate = _inst.expovariate
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@ -142,8 +142,8 @@ class TestBasicOps:
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def test_sample_on_dicts(self):
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self.assertRaises(TypeError, self.gen.sample, dict.fromkeys('abcdef'), 2)
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def test_weighted_choices(self):
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weighted_choices = self.gen.weighted_choices
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def test_choices(self):
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choices = self.gen.choices
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data = ['red', 'green', 'blue', 'yellow']
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str_data = 'abcd'
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range_data = range(4)
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@ -151,63 +151,63 @@ class TestBasicOps:
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# basic functionality
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for sample in [
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weighted_choices(5, data),
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weighted_choices(5, data, range(4)),
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weighted_choices(k=5, population=data, weights=range(4)),
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weighted_choices(k=5, population=data, cum_weights=range(4)),
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choices(5, data),
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choices(5, data, range(4)),
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choices(k=5, population=data, weights=range(4)),
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choices(k=5, population=data, cum_weights=range(4)),
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]:
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self.assertEqual(len(sample), 5)
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self.assertEqual(type(sample), list)
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self.assertTrue(set(sample) <= set(data))
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# test argument handling
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with self.assertRaises(TypeError): # missing arguments
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weighted_choices(2)
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with self.assertRaises(TypeError): # missing arguments
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choices(2)
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self.assertEqual(weighted_choices(0, data), []) # k == 0
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self.assertEqual(weighted_choices(-1, data), []) # negative k behaves like ``[0] * -1``
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self.assertEqual(choices(0, data), []) # k == 0
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self.assertEqual(choices(-1, data), []) # negative k behaves like ``[0] * -1``
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with self.assertRaises(TypeError):
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weighted_choices(2.5, data) # k is a float
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choices(2.5, data) # k is a float
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self.assertTrue(set(weighted_choices(5, str_data)) <= set(str_data)) # population is a string sequence
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self.assertTrue(set(weighted_choices(5, range_data)) <= set(range_data)) # population is a range
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self.assertTrue(set(choices(5, str_data)) <= set(str_data)) # population is a string sequence
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self.assertTrue(set(choices(5, range_data)) <= set(range_data)) # population is a range
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with self.assertRaises(TypeError):
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weighted_choices(2.5, set_data) # population is not a sequence
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choices(2.5, set_data) # population is not a sequence
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self.assertTrue(set(weighted_choices(5, data, None)) <= set(data)) # weights is None
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self.assertTrue(set(weighted_choices(5, data, weights=None)) <= set(data))
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self.assertTrue(set(choices(5, data, None)) <= set(data)) # weights is None
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self.assertTrue(set(choices(5, data, weights=None)) <= set(data))
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with self.assertRaises(ValueError):
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weighted_choices(5, data, [1,2]) # len(weights) != len(population)
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choices(5, data, [1,2]) # len(weights) != len(population)
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with self.assertRaises(IndexError):
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weighted_choices(5, data, [0]*4) # weights sum to zero
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choices(5, data, [0]*4) # weights sum to zero
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with self.assertRaises(TypeError):
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weighted_choices(5, data, 10) # non-iterable weights
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choices(5, data, 10) # non-iterable weights
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with self.assertRaises(TypeError):
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weighted_choices(5, data, [None]*4) # non-numeric weights
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choices(5, data, [None]*4) # non-numeric weights
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for weights in [
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[15, 10, 25, 30], # integer weights
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[15.1, 10.2, 25.2, 30.3], # float weights
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[Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional weights
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[True, False, True, False] # booleans (include / exclude)
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]:
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self.assertTrue(set(weighted_choices(5, data, weights)) <= set(data))
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self.assertTrue(set(choices(5, data, weights)) <= set(data))
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with self.assertRaises(ValueError):
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weighted_choices(5, data, cum_weights=[1,2]) # len(weights) != len(population)
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choices(5, data, cum_weights=[1,2]) # len(weights) != len(population)
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with self.assertRaises(IndexError):
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weighted_choices(5, data, cum_weights=[0]*4) # cum_weights sum to zero
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choices(5, data, cum_weights=[0]*4) # cum_weights sum to zero
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with self.assertRaises(TypeError):
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weighted_choices(5, data, cum_weights=10) # non-iterable cum_weights
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choices(5, data, cum_weights=10) # non-iterable cum_weights
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with self.assertRaises(TypeError):
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weighted_choices(5, data, cum_weights=[None]*4) # non-numeric cum_weights
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choices(5, data, cum_weights=[None]*4) # non-numeric cum_weights
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with self.assertRaises(TypeError):
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weighted_choices(5, data, range(4), cum_weights=range(4)) # both weights and cum_weights
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choices(5, data, range(4), cum_weights=range(4)) # both weights and cum_weights
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for weights in [
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[15, 10, 25, 30], # integer cum_weights
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[15.1, 10.2, 25.2, 30.3], # float cum_weights
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[Fraction(1, 3), Fraction(2, 6), Fraction(3, 6), Fraction(4, 6)], # fractional cum_weights
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]:
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self.assertTrue(set(weighted_choices(5, data, cum_weights=weights)) <= set(data))
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self.assertTrue(set(choices(5, data, cum_weights=weights)) <= set(data))
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def test_gauss(self):
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# Ensure that the seed() method initializes all the hidden state. In
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@ -101,7 +101,7 @@ Library
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- Issue #27691: Fix ssl module's parsing of GEN_RID subject alternative name
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fields in X.509 certs.
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- Issue #18844: Add random.weighted_choices().
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- Issue #18844: Add random.choices().
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- Issue #25761: Improved error reporting about truncated pickle data in
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C implementation of unpickler. UnpicklingError is now raised instead of
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