mirror of https://github.com/python/cpython
570 lines
22 KiB
Python
570 lines
22 KiB
Python
#!/usr/bin/env python3
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import unittest
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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 math import log, exp, pi, fsum, sin
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from test import support
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class TestBasicOps(unittest.TestCase):
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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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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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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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class SystemRandom_TestBasicOps(TestBasicOps):
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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)
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stop = self.gen.randrange(2 ** (i-2))
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if stop <= start:
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return
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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):
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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):
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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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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)
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stop = self.gen.randrange(2 ** (i-2))
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if stop <= start:
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return
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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):
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# Verify cross-platform repeatability
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self.gen.seed(1234567)
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self.assertEqual(self.gen.getrandbits(100),
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97904845777343510404718956115)
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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, 'a')
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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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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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def test_randrange_bug_1590891(self):
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start = 1000000000000
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stop = -100000000000000000000
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step = -200
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x = self.gen.randrange(start, stop, step)
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self.assertTrue(stop < x <= start)
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self.assertEqual((x+stop)%step, 0)
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def gamma(z, sqrt2pi=(2.0*pi)**0.5):
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# Reflection to right half of complex plane
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if z < 0.5:
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return pi / sin(pi*z) / gamma(1.0-z)
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# Lanczos approximation with g=7
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az = z + (7.0 - 0.5)
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return az ** (z-0.5) / exp(az) * sqrt2pi * fsum([
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0.9999999999995183,
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676.5203681218835 / z,
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-1259.139216722289 / (z+1.0),
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771.3234287757674 / (z+2.0),
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-176.6150291498386 / (z+3.0),
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12.50734324009056 / (z+4.0),
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-0.1385710331296526 / (z+5.0),
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0.9934937113930748e-05 / (z+6.0),
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0.1659470187408462e-06 / (z+7.0),
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])
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class TestDistributions(unittest.TestCase):
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def test_zeroinputs(self):
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# Verify that distributions can handle a series of zero inputs'
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g = random.Random()
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x = [g.random() for i in range(50)] + [0.0]*5
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g.random = x[:].pop; g.uniform(1,10)
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g.random = x[:].pop; g.paretovariate(1.0)
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g.random = x[:].pop; g.expovariate(1.0)
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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)
|
|
|
|
|
|
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)
|
|
|
|
|
|
def test_main(verbose=None):
|
|
testclasses = [MersenneTwister_TestBasicOps,
|
|
TestDistributions,
|
|
TestModule]
|
|
|
|
try:
|
|
random.SystemRandom().random()
|
|
except NotImplementedError:
|
|
pass
|
|
else:
|
|
testclasses.append(SystemRandom_TestBasicOps)
|
|
|
|
support.run_unittest(*testclasses)
|
|
|
|
# verify reference counting
|
|
import sys
|
|
if verbose and hasattr(sys, "gettotalrefcount"):
|
|
counts = [None] * 5
|
|
for i in range(len(counts)):
|
|
support.run_unittest(*testclasses)
|
|
counts[i] = sys.gettotalrefcount()
|
|
print(counts)
|
|
|
|
if __name__ == "__main__":
|
|
test_main(verbose=True)
|