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8f8ec92de8
requires them. Disable executable bits and shebang lines in test and benchmark files in order to prevent using a random system python, and in source files of modules which don't provide command line interface. Fixed shebang lines in the unittestgui and checkpip scripts.
759 lines
31 KiB
Python
759 lines
31 KiB
Python
import unittest
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import unittest.mock
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import random
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import time
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import pickle
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import warnings
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from functools import partial
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from math import log, exp, pi, fsum, sin
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from test import support
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class TestBasicOps:
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# Superclass with tests common to all generators.
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# Subclasses must arrange for self.gen to retrieve the Random instance
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# to be tested.
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def randomlist(self, n):
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"""Helper function to make a list of random numbers"""
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return [self.gen.random() for i in range(n)]
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def test_autoseed(self):
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self.gen.seed()
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state1 = self.gen.getstate()
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time.sleep(0.1)
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self.gen.seed() # diffent seeds at different times
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state2 = self.gen.getstate()
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self.assertNotEqual(state1, state2)
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def test_saverestore(self):
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N = 1000
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self.gen.seed()
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state = self.gen.getstate()
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randseq = self.randomlist(N)
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self.gen.setstate(state) # should regenerate the same sequence
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self.assertEqual(randseq, self.randomlist(N))
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def test_seedargs(self):
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# Seed value with a negative hash.
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class MySeed(object):
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def __hash__(self):
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return -1729
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for arg in [None, 0, 0, 1, 1, -1, -1, 10**20, -(10**20),
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3.14, 1+2j, 'a', tuple('abc'), MySeed()]:
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self.gen.seed(arg)
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for arg in [list(range(3)), dict(one=1)]:
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self.assertRaises(TypeError, self.gen.seed, arg)
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self.assertRaises(TypeError, self.gen.seed, 1, 2, 3, 4)
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self.assertRaises(TypeError, type(self.gen), [])
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@unittest.mock.patch('random._urandom') # os.urandom
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def test_seed_when_randomness_source_not_found(self, urandom_mock):
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# Random.seed() uses time.time() when an operating system specific
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# randomness source is not found. To test this on machines were it
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# exists, run the above test, test_seedargs(), again after mocking
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# os.urandom() so that it raises the exception expected when the
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# randomness source is not available.
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urandom_mock.side_effect = NotImplementedError
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self.test_seedargs()
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def test_shuffle(self):
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shuffle = self.gen.shuffle
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lst = []
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shuffle(lst)
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self.assertEqual(lst, [])
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lst = [37]
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shuffle(lst)
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self.assertEqual(lst, [37])
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seqs = [list(range(n)) for n in range(10)]
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shuffled_seqs = [list(range(n)) for n in range(10)]
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for shuffled_seq in shuffled_seqs:
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shuffle(shuffled_seq)
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for (seq, shuffled_seq) in zip(seqs, shuffled_seqs):
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self.assertEqual(len(seq), len(shuffled_seq))
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self.assertEqual(set(seq), set(shuffled_seq))
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# The above tests all would pass if the shuffle was a
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# no-op. The following non-deterministic test covers that. It
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# asserts that the shuffled sequence of 1000 distinct elements
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# must be different from the original one. Although there is
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# mathematically a non-zero probability that this could
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# actually happen in a genuinely random shuffle, it is
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# completely negligible, given that the number of possible
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# permutations of 1000 objects is 1000! (factorial of 1000),
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# which is considerably larger than the number of atoms in the
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# universe...
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lst = list(range(1000))
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shuffled_lst = list(range(1000))
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shuffle(shuffled_lst)
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self.assertTrue(lst != shuffled_lst)
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shuffle(lst)
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self.assertTrue(lst != shuffled_lst)
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def test_choice(self):
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choice = self.gen.choice
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with self.assertRaises(IndexError):
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choice([])
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self.assertEqual(choice([50]), 50)
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self.assertIn(choice([25, 75]), [25, 75])
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def test_sample(self):
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# For the entire allowable range of 0 <= k <= N, validate that
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# the sample is of the correct length and contains only unique items
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N = 100
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population = range(N)
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for k in range(N+1):
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s = self.gen.sample(population, k)
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self.assertEqual(len(s), k)
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uniq = set(s)
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self.assertEqual(len(uniq), k)
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self.assertTrue(uniq <= set(population))
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self.assertEqual(self.gen.sample([], 0), []) # test edge case N==k==0
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# Exception raised if size of sample exceeds that of population
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self.assertRaises(ValueError, self.gen.sample, population, N+1)
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def test_sample_distribution(self):
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# For the entire allowable range of 0 <= k <= N, validate that
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# sample generates all possible permutations
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n = 5
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pop = range(n)
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trials = 10000 # large num prevents false negatives without slowing normal case
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def factorial(n):
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if n == 0:
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return 1
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return n * factorial(n - 1)
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for k in range(n):
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expected = factorial(n) // factorial(n-k)
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perms = {}
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for i in range(trials):
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perms[tuple(self.gen.sample(pop, k))] = None
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if len(perms) == expected:
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break
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else:
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self.fail()
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def test_sample_inputs(self):
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# SF bug #801342 -- population can be any iterable defining __len__()
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self.gen.sample(set(range(20)), 2)
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self.gen.sample(range(20), 2)
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self.gen.sample(range(20), 2)
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self.gen.sample(str('abcdefghijklmnopqrst'), 2)
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self.gen.sample(tuple('abcdefghijklmnopqrst'), 2)
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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_gauss(self):
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# Ensure that the seed() method initializes all the hidden state. In
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# particular, through 2.2.1 it failed to reset a piece of state used
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# by (and only by) the .gauss() method.
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for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
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self.gen.seed(seed)
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x1 = self.gen.random()
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y1 = self.gen.gauss(0, 1)
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self.gen.seed(seed)
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x2 = self.gen.random()
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y2 = self.gen.gauss(0, 1)
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self.assertEqual(x1, x2)
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self.assertEqual(y1, y2)
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def test_pickling(self):
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state = pickle.dumps(self.gen)
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origseq = [self.gen.random() for i in range(10)]
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newgen = pickle.loads(state)
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restoredseq = [newgen.random() for i in range(10)]
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self.assertEqual(origseq, restoredseq)
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def test_bug_1727780(self):
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# verify that version-2-pickles can be loaded
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# fine, whether they are created on 32-bit or 64-bit
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# platforms, and that version-3-pickles load fine.
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files = [("randv2_32.pck", 780),
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("randv2_64.pck", 866),
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("randv3.pck", 343)]
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for file, value in files:
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f = open(support.findfile(file),"rb")
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r = pickle.load(f)
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f.close()
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self.assertEqual(int(r.random()*1000), value)
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def test_bug_9025(self):
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# Had problem with an uneven distribution in int(n*random())
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# Verify the fix by checking that distributions fall within expectations.
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n = 100000
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randrange = self.gen.randrange
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k = sum(randrange(6755399441055744) % 3 == 2 for i in range(n))
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self.assertTrue(0.30 < k/n < .37, (k/n))
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try:
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random.SystemRandom().random()
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except NotImplementedError:
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SystemRandom_available = False
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else:
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SystemRandom_available = True
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@unittest.skipUnless(SystemRandom_available, "random.SystemRandom not available")
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class SystemRandom_TestBasicOps(TestBasicOps, unittest.TestCase):
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gen = random.SystemRandom()
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def test_autoseed(self):
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# Doesn't need to do anything except not fail
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self.gen.seed()
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def test_saverestore(self):
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self.assertRaises(NotImplementedError, self.gen.getstate)
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self.assertRaises(NotImplementedError, self.gen.setstate, None)
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def test_seedargs(self):
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# Doesn't need to do anything except not fail
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self.gen.seed(100)
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def test_gauss(self):
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self.gen.gauss_next = None
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self.gen.seed(100)
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self.assertEqual(self.gen.gauss_next, None)
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def test_pickling(self):
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self.assertRaises(NotImplementedError, pickle.dumps, self.gen)
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def test_53_bits_per_float(self):
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# This should pass whenever a C double has 53 bit precision.
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span = 2 ** 53
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cum = 0
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for i in range(100):
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cum |= int(self.gen.random() * span)
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self.assertEqual(cum, span-1)
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def test_bigrand(self):
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# The randrange routine should build-up the required number of bits
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# in stages so that all bit positions are active.
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span = 2 ** 500
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cum = 0
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for i in range(100):
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r = self.gen.randrange(span)
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self.assertTrue(0 <= r < span)
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cum |= r
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self.assertEqual(cum, span-1)
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def test_bigrand_ranges(self):
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for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
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start = self.gen.randrange(2 ** (i-2))
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stop = self.gen.randrange(2 ** i)
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if stop <= start:
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continue
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self.assertTrue(start <= self.gen.randrange(start, stop) < stop)
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def test_rangelimits(self):
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for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
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self.assertEqual(set(range(start,stop)),
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set([self.gen.randrange(start,stop) for i in range(100)]))
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def test_randrange_nonunit_step(self):
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rint = self.gen.randrange(0, 10, 2)
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self.assertIn(rint, (0, 2, 4, 6, 8))
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rint = self.gen.randrange(0, 2, 2)
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self.assertEqual(rint, 0)
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def test_randrange_errors(self):
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raises = partial(self.assertRaises, ValueError, self.gen.randrange)
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# Empty range
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raises(3, 3)
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raises(-721)
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raises(0, 100, -12)
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# Non-integer start/stop
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raises(3.14159)
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raises(0, 2.71828)
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# Zero and non-integer step
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raises(0, 42, 0)
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raises(0, 42, 3.14159)
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def test_genrandbits(self):
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# Verify ranges
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for k in range(1, 1000):
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self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k)
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# Verify all bits active
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getbits = self.gen.getrandbits
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for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]:
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cum = 0
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for i in range(100):
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cum |= getbits(span)
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self.assertEqual(cum, 2**span-1)
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# Verify argument checking
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self.assertRaises(TypeError, self.gen.getrandbits)
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self.assertRaises(TypeError, self.gen.getrandbits, 1, 2)
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self.assertRaises(ValueError, self.gen.getrandbits, 0)
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self.assertRaises(ValueError, self.gen.getrandbits, -1)
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self.assertRaises(TypeError, self.gen.getrandbits, 10.1)
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def test_randbelow_logic(self, _log=log, int=int):
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# check bitcount transition points: 2**i and 2**(i+1)-1
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# show that: k = int(1.001 + _log(n, 2))
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# is equal to or one greater than the number of bits in n
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for i in range(1, 1000):
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n = 1 << i # check an exact power of two
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numbits = i+1
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k = int(1.00001 + _log(n, 2))
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self.assertEqual(k, numbits)
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self.assertEqual(n, 2**(k-1))
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n += n - 1 # check 1 below the next power of two
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k = int(1.00001 + _log(n, 2))
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self.assertIn(k, [numbits, numbits+1])
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self.assertTrue(2**k > n > 2**(k-2))
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n -= n >> 15 # check a little farther below the next power of two
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k = int(1.00001 + _log(n, 2))
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self.assertEqual(k, numbits) # note the stronger assertion
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self.assertTrue(2**k > n > 2**(k-1)) # note the stronger assertion
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class MersenneTwister_TestBasicOps(TestBasicOps, unittest.TestCase):
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gen = random.Random()
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def test_guaranteed_stable(self):
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# These sequences are guaranteed to stay the same across versions of python
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self.gen.seed(3456147, version=1)
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self.assertEqual([self.gen.random().hex() for i in range(4)],
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['0x1.ac362300d90d2p-1', '0x1.9d16f74365005p-1',
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'0x1.1ebb4352e4c4dp-1', '0x1.1a7422abf9c11p-1'])
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self.gen.seed("the quick brown fox", version=2)
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self.assertEqual([self.gen.random().hex() for i in range(4)],
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['0x1.1239ddfb11b7cp-3', '0x1.b3cbb5c51b120p-4',
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'0x1.8c4f55116b60fp-1', '0x1.63eb525174a27p-1'])
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def test_setstate_first_arg(self):
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self.assertRaises(ValueError, self.gen.setstate, (1, None, None))
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def test_setstate_middle_arg(self):
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# Wrong type, s/b tuple
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self.assertRaises(TypeError, self.gen.setstate, (2, None, None))
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# Wrong length, s/b 625
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self.assertRaises(ValueError, self.gen.setstate, (2, (1,2,3), None))
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# Wrong type, s/b tuple of 625 ints
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self.assertRaises(TypeError, self.gen.setstate, (2, ('a',)*625, None))
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# Last element s/b an int also
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self.assertRaises(TypeError, self.gen.setstate, (2, (0,)*624+('a',), None))
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# Little trick to make "tuple(x % (2**32) for x in internalstate)"
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# raise ValueError. I cannot think of a simple way to achieve this, so
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# I am opting for using a generator as the middle argument of setstate
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# which attempts to cast a NaN to integer.
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state_values = self.gen.getstate()[1]
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state_values = list(state_values)
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state_values[-1] = float('nan')
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state = (int(x) for x in state_values)
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self.assertRaises(TypeError, self.gen.setstate, (2, state, None))
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def test_referenceImplementation(self):
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# Compare the python implementation with results from the original
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# code. Create 2000 53-bit precision random floats. Compare only
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# the last ten entries to show that the independent implementations
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# are tracking. Here is the main() function needed to create the
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# list of expected random numbers:
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# void main(void){
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# int i;
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# unsigned long init[4]={61731, 24903, 614, 42143}, length=4;
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# init_by_array(init, length);
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# for (i=0; i<2000; i++) {
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# printf("%.15f ", genrand_res53());
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# if (i%5==4) printf("\n");
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# }
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# }
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expected = [0.45839803073713259,
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0.86057815201978782,
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0.92848331726782152,
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0.35932681119782461,
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0.081823493762449573,
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0.14332226470169329,
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0.084297823823520024,
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0.53814864671831453,
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0.089215024911993401,
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0.78486196105372907]
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self.gen.seed(61731 + (24903<<32) + (614<<64) + (42143<<96))
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actual = self.randomlist(2000)[-10:]
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for a, e in zip(actual, expected):
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self.assertAlmostEqual(a,e,places=14)
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def test_strong_reference_implementation(self):
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# Like test_referenceImplementation, but checks for exact bit-level
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# equality. This should pass on any box where C double contains
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# at least 53 bits of precision (the underlying algorithm suffers
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# no rounding errors -- all results are exact).
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from math import ldexp
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expected = [0x0eab3258d2231f,
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0x1b89db315277a5,
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0x1db622a5518016,
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0x0b7f9af0d575bf,
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0x029e4c4db82240,
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0x04961892f5d673,
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0x02b291598e4589,
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0x11388382c15694,
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0x02dad977c9e1fe,
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0x191d96d4d334c6]
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self.gen.seed(61731 + (24903<<32) + (614<<64) + (42143<<96))
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actual = self.randomlist(2000)[-10:]
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for a, e in zip(actual, expected):
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self.assertEqual(int(ldexp(a, 53)), e)
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def test_long_seed(self):
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# This is most interesting to run in debug mode, just to make sure
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# nothing blows up. Under the covers, a dynamically resized array
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# is allocated, consuming space proportional to the number of bits
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# in the seed. Unfortunately, that's a quadratic-time algorithm,
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# so don't make this horribly big.
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seed = (1 << (10000 * 8)) - 1 # about 10K bytes
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self.gen.seed(seed)
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def test_53_bits_per_float(self):
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# This should pass whenever a C double has 53 bit precision.
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span = 2 ** 53
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cum = 0
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for i in range(100):
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cum |= int(self.gen.random() * span)
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self.assertEqual(cum, span-1)
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def test_bigrand(self):
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# The randrange routine should build-up the required number of bits
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# in stages so that all bit positions are active.
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span = 2 ** 500
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cum = 0
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for i in range(100):
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r = self.gen.randrange(span)
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self.assertTrue(0 <= r < span)
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cum |= r
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self.assertEqual(cum, span-1)
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def test_bigrand_ranges(self):
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for i in [40,80, 160, 200, 211, 250, 375, 512, 550]:
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start = self.gen.randrange(2 ** (i-2))
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stop = self.gen.randrange(2 ** i)
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if stop <= start:
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continue
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self.assertTrue(start <= self.gen.randrange(start, stop) < stop)
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def test_rangelimits(self):
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for start, stop in [(-2,0), (-(2**60)-2,-(2**60)), (2**60,2**60+2)]:
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self.assertEqual(set(range(start,stop)),
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set([self.gen.randrange(start,stop) for i in range(100)]))
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|
|
|
def test_genrandbits(self):
|
|
# Verify cross-platform repeatability
|
|
self.gen.seed(1234567)
|
|
self.assertEqual(self.gen.getrandbits(100),
|
|
97904845777343510404718956115)
|
|
# Verify ranges
|
|
for k in range(1, 1000):
|
|
self.assertTrue(0 <= self.gen.getrandbits(k) < 2**k)
|
|
|
|
# Verify all bits active
|
|
getbits = self.gen.getrandbits
|
|
for span in [1, 2, 3, 4, 31, 32, 32, 52, 53, 54, 119, 127, 128, 129]:
|
|
cum = 0
|
|
for i in range(100):
|
|
cum |= getbits(span)
|
|
self.assertEqual(cum, 2**span-1)
|
|
|
|
# Verify argument checking
|
|
self.assertRaises(TypeError, self.gen.getrandbits)
|
|
self.assertRaises(TypeError, self.gen.getrandbits, 'a')
|
|
self.assertRaises(TypeError, self.gen.getrandbits, 1, 2)
|
|
self.assertRaises(ValueError, self.gen.getrandbits, 0)
|
|
self.assertRaises(ValueError, self.gen.getrandbits, -1)
|
|
|
|
def test_randbelow_logic(self, _log=log, int=int):
|
|
# check bitcount transition points: 2**i and 2**(i+1)-1
|
|
# show that: k = int(1.001 + _log(n, 2))
|
|
# is equal to or one greater than the number of bits in n
|
|
for i in range(1, 1000):
|
|
n = 1 << i # check an exact power of two
|
|
numbits = i+1
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertEqual(k, numbits)
|
|
self.assertEqual(n, 2**(k-1))
|
|
|
|
n += n - 1 # check 1 below the next power of two
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertIn(k, [numbits, numbits+1])
|
|
self.assertTrue(2**k > n > 2**(k-2))
|
|
|
|
n -= n >> 15 # check a little farther below the next power of two
|
|
k = int(1.00001 + _log(n, 2))
|
|
self.assertEqual(k, numbits) # note the stronger assertion
|
|
self.assertTrue(2**k > n > 2**(k-1)) # note the stronger assertion
|
|
|
|
@unittest.mock.patch('random.Random.random')
|
|
def test_randbelow_overriden_random(self, random_mock):
|
|
# Random._randbelow() can only use random() when the built-in one
|
|
# has been overridden but no new getrandbits() method was supplied.
|
|
random_mock.side_effect = random.SystemRandom().random
|
|
maxsize = 1<<random.BPF
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("ignore", UserWarning)
|
|
# Population range too large (n >= maxsize)
|
|
self.gen._randbelow(maxsize+1, maxsize = maxsize)
|
|
self.gen._randbelow(5640, maxsize = maxsize)
|
|
|
|
# This might be going too far to test a single line, but because of our
|
|
# noble aim of achieving 100% test coverage we need to write a case in
|
|
# which the following line in Random._randbelow() gets executed:
|
|
#
|
|
# rem = maxsize % n
|
|
# limit = (maxsize - rem) / maxsize
|
|
# r = random()
|
|
# while r >= limit:
|
|
# r = random() # <== *This line* <==<
|
|
#
|
|
# Therefore, to guarantee that the while loop is executed at least
|
|
# once, we need to mock random() so that it returns a number greater
|
|
# than 'limit' the first time it gets called.
|
|
|
|
n = 42
|
|
epsilon = 0.01
|
|
limit = (maxsize - (maxsize % n)) / maxsize
|
|
random_mock.side_effect = [limit + epsilon, limit - epsilon]
|
|
self.gen._randbelow(n, maxsize = maxsize)
|
|
|
|
def test_randrange_bug_1590891(self):
|
|
start = 1000000000000
|
|
stop = -100000000000000000000
|
|
step = -200
|
|
x = self.gen.randrange(start, stop, step)
|
|
self.assertTrue(stop < x <= start)
|
|
self.assertEqual((x+stop)%step, 0)
|
|
|
|
def gamma(z, sqrt2pi=(2.0*pi)**0.5):
|
|
# Reflection to right half of complex plane
|
|
if z < 0.5:
|
|
return pi / sin(pi*z) / gamma(1.0-z)
|
|
# Lanczos approximation with g=7
|
|
az = z + (7.0 - 0.5)
|
|
return az ** (z-0.5) / exp(az) * sqrt2pi * fsum([
|
|
0.9999999999995183,
|
|
676.5203681218835 / z,
|
|
-1259.139216722289 / (z+1.0),
|
|
771.3234287757674 / (z+2.0),
|
|
-176.6150291498386 / (z+3.0),
|
|
12.50734324009056 / (z+4.0),
|
|
-0.1385710331296526 / (z+5.0),
|
|
0.9934937113930748e-05 / (z+6.0),
|
|
0.1659470187408462e-06 / (z+7.0),
|
|
])
|
|
|
|
class TestDistributions(unittest.TestCase):
|
|
def test_zeroinputs(self):
|
|
# Verify that distributions can handle a series of zero inputs'
|
|
g = random.Random()
|
|
x = [g.random() for i in range(50)] + [0.0]*5
|
|
g.random = x[:].pop; g.uniform(1,10)
|
|
g.random = x[:].pop; g.paretovariate(1.0)
|
|
g.random = x[:].pop; g.expovariate(1.0)
|
|
g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
|
|
g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0)
|
|
g.random = x[:].pop; g.normalvariate(0.0, 1.0)
|
|
g.random = x[:].pop; g.gauss(0.0, 1.0)
|
|
g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
|
|
g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
|
|
g.random = x[:].pop; g.gammavariate(0.01, 1.0)
|
|
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
|
|
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
|
|
g.random = x[:].pop; g.betavariate(3.0, 3.0)
|
|
g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
|
|
|
|
def test_avg_std(self):
|
|
# Use integration to test distribution average and standard deviation.
|
|
# Only works for distributions which do not consume variates in pairs
|
|
g = random.Random()
|
|
N = 5000
|
|
x = [i/float(N) for i in range(1,N)]
|
|
for variate, args, mu, sigmasqrd in [
|
|
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
|
|
(g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
|
|
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
|
|
(g.vonmisesvariate, (1.23, 0), pi, pi**2/3),
|
|
(g.paretovariate, (5.0,), 5.0/(5.0-1),
|
|
5.0/((5.0-1)**2*(5.0-2))),
|
|
(g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0),
|
|
gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]:
|
|
g.random = x[:].pop
|
|
y = []
|
|
for i in range(len(x)):
|
|
try:
|
|
y.append(variate(*args))
|
|
except IndexError:
|
|
pass
|
|
s1 = s2 = 0
|
|
for e in y:
|
|
s1 += e
|
|
s2 += (e - mu) ** 2
|
|
N = len(y)
|
|
self.assertAlmostEqual(s1/N, mu, places=2,
|
|
msg='%s%r' % (variate.__name__, args))
|
|
self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2,
|
|
msg='%s%r' % (variate.__name__, args))
|
|
|
|
def test_constant(self):
|
|
g = random.Random()
|
|
N = 100
|
|
for variate, args, expected in [
|
|
(g.uniform, (10.0, 10.0), 10.0),
|
|
(g.triangular, (10.0, 10.0), 10.0),
|
|
#(g.triangular, (10.0, 10.0, 10.0), 10.0),
|
|
(g.expovariate, (float('inf'),), 0.0),
|
|
(g.vonmisesvariate, (3.0, float('inf')), 3.0),
|
|
(g.gauss, (10.0, 0.0), 10.0),
|
|
(g.lognormvariate, (0.0, 0.0), 1.0),
|
|
(g.lognormvariate, (-float('inf'), 0.0), 0.0),
|
|
(g.normalvariate, (10.0, 0.0), 10.0),
|
|
(g.paretovariate, (float('inf'),), 1.0),
|
|
(g.weibullvariate, (10.0, float('inf')), 10.0),
|
|
(g.weibullvariate, (0.0, 10.0), 0.0),
|
|
]:
|
|
for i in range(N):
|
|
self.assertEqual(variate(*args), expected)
|
|
|
|
def test_von_mises_range(self):
|
|
# Issue 17149: von mises variates were not consistently in the
|
|
# range [0, 2*PI].
|
|
g = random.Random()
|
|
N = 100
|
|
for mu in 0.0, 0.1, 3.1, 6.2:
|
|
for kappa in 0.0, 2.3, 500.0:
|
|
for _ in range(N):
|
|
sample = g.vonmisesvariate(mu, kappa)
|
|
self.assertTrue(
|
|
0 <= sample <= random.TWOPI,
|
|
msg=("vonmisesvariate({}, {}) produced a result {} out"
|
|
" of range [0, 2*pi]").format(mu, kappa, sample))
|
|
|
|
def test_von_mises_large_kappa(self):
|
|
# Issue #17141: vonmisesvariate() was hang for large kappas
|
|
random.vonmisesvariate(0, 1e15)
|
|
random.vonmisesvariate(0, 1e100)
|
|
|
|
def test_gammavariate_errors(self):
|
|
# Both alpha and beta must be > 0.0
|
|
self.assertRaises(ValueError, random.gammavariate, -1, 3)
|
|
self.assertRaises(ValueError, random.gammavariate, 0, 2)
|
|
self.assertRaises(ValueError, random.gammavariate, 2, 0)
|
|
self.assertRaises(ValueError, random.gammavariate, 1, -3)
|
|
|
|
@unittest.mock.patch('random.Random.random')
|
|
def test_gammavariate_full_code_coverage(self, random_mock):
|
|
# There are three different possibilities in the current implementation
|
|
# of random.gammavariate(), depending on the value of 'alpha'. What we
|
|
# are going to do here is to fix the values returned by random() to
|
|
# generate test cases that provide 100% line coverage of the method.
|
|
|
|
# #1: alpha > 1.0: we want the first random number to be outside the
|
|
# [1e-7, .9999999] range, so that the continue statement executes
|
|
# once. The values of u1 and u2 will be 0.5 and 0.3, respectively.
|
|
random_mock.side_effect = [1e-8, 0.5, 0.3]
|
|
returned_value = random.gammavariate(1.1, 2.3)
|
|
self.assertAlmostEqual(returned_value, 2.53)
|
|
|
|
# #2: alpha == 1: first random number less than 1e-7 to that the body
|
|
# of the while loop executes once. Then random.random() returns 0.45,
|
|
# which causes while to stop looping and the algorithm to terminate.
|
|
random_mock.side_effect = [1e-8, 0.45]
|
|
returned_value = random.gammavariate(1.0, 3.14)
|
|
self.assertAlmostEqual(returned_value, 2.507314166123803)
|
|
|
|
# #3: 0 < alpha < 1. This is the most complex region of code to cover,
|
|
# as there are multiple if-else statements. Let's take a look at the
|
|
# source code, and determine the values that we need accordingly:
|
|
#
|
|
# while 1:
|
|
# u = random()
|
|
# b = (_e + alpha)/_e
|
|
# p = b*u
|
|
# if p <= 1.0: # <=== (A)
|
|
# x = p ** (1.0/alpha)
|
|
# else: # <=== (B)
|
|
# x = -_log((b-p)/alpha)
|
|
# u1 = random()
|
|
# if p > 1.0: # <=== (C)
|
|
# if u1 <= x ** (alpha - 1.0): # <=== (D)
|
|
# break
|
|
# elif u1 <= _exp(-x): # <=== (E)
|
|
# break
|
|
# return x * beta
|
|
#
|
|
# First, we want (A) to be True. For that we need that:
|
|
# b*random() <= 1.0
|
|
# r1 = random() <= 1.0 / b
|
|
#
|
|
# We now get to the second if-else branch, and here, since p <= 1.0,
|
|
# (C) is False and we take the elif branch, (E). For it to be True,
|
|
# so that the break is executed, we need that:
|
|
# r2 = random() <= _exp(-x)
|
|
# r2 <= _exp(-(p ** (1.0/alpha)))
|
|
# r2 <= _exp(-((b*r1) ** (1.0/alpha)))
|
|
|
|
_e = random._e
|
|
_exp = random._exp
|
|
_log = random._log
|
|
alpha = 0.35
|
|
beta = 1.45
|
|
b = (_e + alpha)/_e
|
|
epsilon = 0.01
|
|
|
|
r1 = 0.8859296441566 # 1.0 / b
|
|
r2 = 0.3678794411714 # _exp(-((b*r1) ** (1.0/alpha)))
|
|
|
|
# These four "random" values result in the following trace:
|
|
# (A) True, (E) False --> [next iteration of while]
|
|
# (A) True, (E) True --> [while loop breaks]
|
|
random_mock.side_effect = [r1, r2 + epsilon, r1, r2]
|
|
returned_value = random.gammavariate(alpha, beta)
|
|
self.assertAlmostEqual(returned_value, 1.4499999999997544)
|
|
|
|
# Let's now make (A) be False. If this is the case, when we get to the
|
|
# second if-else 'p' is greater than 1, so (C) evaluates to True. We
|
|
# now encounter a second if statement, (D), which in order to execute
|
|
# must satisfy the following condition:
|
|
# r2 <= x ** (alpha - 1.0)
|
|
# r2 <= (-_log((b-p)/alpha)) ** (alpha - 1.0)
|
|
# r2 <= (-_log((b-(b*r1))/alpha)) ** (alpha - 1.0)
|
|
r1 = 0.8959296441566 # (1.0 / b) + epsilon -- so that (A) is False
|
|
r2 = 0.9445400408898141
|
|
|
|
# And these four values result in the following trace:
|
|
# (B) and (C) True, (D) False --> [next iteration of while]
|
|
# (B) and (C) True, (D) True [while loop breaks]
|
|
random_mock.side_effect = [r1, r2 + epsilon, r1, r2]
|
|
returned_value = random.gammavariate(alpha, beta)
|
|
self.assertAlmostEqual(returned_value, 1.5830349561760781)
|
|
|
|
@unittest.mock.patch('random.Random.gammavariate')
|
|
def test_betavariate_return_zero(self, gammavariate_mock):
|
|
# betavariate() returns zero when the Gamma distribution
|
|
# that it uses internally returns this same value.
|
|
gammavariate_mock.return_value = 0.0
|
|
self.assertEqual(0.0, random.betavariate(2.71828, 3.14159))
|
|
|
|
class TestModule(unittest.TestCase):
|
|
def testMagicConstants(self):
|
|
self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141)
|
|
self.assertAlmostEqual(random.TWOPI, 6.28318530718)
|
|
self.assertAlmostEqual(random.LOG4, 1.38629436111989)
|
|
self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627)
|
|
|
|
def test__all__(self):
|
|
# tests validity but not completeness of the __all__ list
|
|
self.assertTrue(set(random.__all__) <= set(dir(random)))
|
|
|
|
def test_random_subclass_with_kwargs(self):
|
|
# SF bug #1486663 -- this used to erroneously raise a TypeError
|
|
class Subclass(random.Random):
|
|
def __init__(self, newarg=None):
|
|
random.Random.__init__(self)
|
|
Subclass(newarg=1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|