537 lines
20 KiB
Python
537 lines
20 KiB
Python
#!/usr/bin/env python
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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, sqrt, pi
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from test import test_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 xrange(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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for arg in [None, 0, 0L, 1, 1L, -1, -1L, 10**20, -(10**20),
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3.14, 1+2j, 'a', tuple('abc')]:
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self.gen.seed(arg)
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for arg in [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)
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self.assertRaises(TypeError, type(self.gen), [])
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def test_jumpahead(self):
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self.gen.seed()
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state1 = self.gen.getstate()
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self.gen.jumpahead(100)
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state2 = self.gen.getstate() # s/b distinct from state1
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self.assertNotEqual(state1, state2)
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self.gen.jumpahead(100)
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state3 = self.gen.getstate() # s/b distinct from state2
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self.assertNotEqual(state2, state3)
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self.assertRaises(TypeError, self.gen.jumpahead) # needs an arg
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self.assertRaises(TypeError, self.gen.jumpahead, "ick") # wrong type
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self.assertRaises(TypeError, self.gen.jumpahead, 2.3) # wrong type
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self.assertRaises(TypeError, self.gen.jumpahead, 2, 3) # too many
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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 = xrange(N)
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for k in xrange(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.failUnless(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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return reduce(int.__mul__, xrange(1, n), 1)
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for k in xrange(n):
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expected = factorial(n) // factorial(n-k)
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perms = {}
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for i in xrange(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(xrange(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.gen.sample(dict.fromkeys('abcdefghijklmnopqrst'), 2)
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# SF bug #1460340 -- random.sample can raise KeyError
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a = dict.fromkeys(range(10)+range(10,100,2)+range(100,110))
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self.gen.sample(a, 3)
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# A followup to bug #1460340: sampling from a dict could return
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# a subset of its keys or of its values, depending on the size of
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# the subset requested.
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N = 30
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d = dict((i, complex(i, i)) for i in xrange(N))
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for k in xrange(N+1):
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samp = self.gen.sample(d, k)
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# Verify that we got ints back (keys); the values are complex.
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for x in samp:
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self.assert_(type(x) is int)
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samp.sort()
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self.assertEqual(samp, range(N))
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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 xrange(10)]
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newgen = pickle.loads(state)
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restoredseq = [newgen.random() for i in xrange(10)]
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self.assertEqual(origseq, restoredseq)
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class WichmannHill_TestBasicOps(TestBasicOps):
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gen = random.WichmannHill()
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def test_setstate_first_arg(self):
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self.assertRaises(ValueError, self.gen.setstate, (2, None, None))
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def test_strong_jumpahead(self):
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# tests that jumpahead(n) semantics correspond to n calls to random()
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N = 1000
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s = self.gen.getstate()
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self.gen.jumpahead(N)
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r1 = self.gen.random()
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# now do it the slow way
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self.gen.setstate(s)
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for i in xrange(N):
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self.gen.random()
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r2 = self.gen.random()
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self.assertEqual(r1, r2)
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def test_gauss_with_whseed(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.whseed(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.whseed(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_bigrand(self):
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# Verify warnings are raised when randrange is too large for random()
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oldfilters = warnings.filters[:]
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warnings.filterwarnings("error", "Underlying random")
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self.assertRaises(UserWarning, self.gen.randrange, 2**60)
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warnings.filters[:] = oldfilters
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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_jumpahead(self):
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# Doesn't need to do anything except not fail
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self.gen.jumpahead(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 xrange(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 xrange(100):
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r = self.gen.randrange(span)
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self.assert_(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.assert_(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 xrange(100)]))
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def test_genrandbits(self):
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# Verify ranges
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for k in xrange(1, 1000):
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self.assert_(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 xrange(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 xrange(1, 1000):
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n = 1L << 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.assert_(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.assert_(k in [numbits, numbits+1])
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self.assert_(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.assert_(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_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(61731L + (24903L<<32) + (614L<<64) + (42143L<<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 = [0x0eab3258d2231fL,
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0x1b89db315277a5L,
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0x1db622a5518016L,
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0x0b7f9af0d575bfL,
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0x029e4c4db82240L,
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0x04961892f5d673L,
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0x02b291598e4589L,
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0x11388382c15694L,
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0x02dad977c9e1feL,
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0x191d96d4d334c6L]
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self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<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(long(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 = (1L << (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 xrange(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 xrange(100):
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r = self.gen.randrange(span)
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self.assert_(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.assert_(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 xrange(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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97904845777343510404718956115L)
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# Verify ranges
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for k in xrange(1, 1000):
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self.assert_(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 xrange(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 xrange(1, 1000):
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n = 1L << 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.assert_(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.assert_(k in [numbits, numbits+1])
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self.assert_(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.assert_(2**k > n > 2**(k-1)) # note the stronger assertion
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_gammacoeff = (0.9999999999995183, 676.5203681218835, -1259.139216722289,
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771.3234287757674, -176.6150291498386, 12.50734324009056,
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-0.1385710331296526, 0.9934937113930748e-05, 0.1659470187408462e-06)
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def gamma(z, cof=_gammacoeff, g=7):
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z -= 1.0
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sum = cof[0]
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for i in xrange(1,len(cof)):
|
|
sum += cof[i] / (z+i)
|
|
z += 0.5
|
|
return (z+g)**z / exp(z+g) * sqrt(2*pi) * sum
|
|
|
|
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 xrange(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.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)
|
|
|
|
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 xrange(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.expovariate, (1.5,), 1/1.5, 1/1.5**2),
|
|
(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 xrange(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, 2)
|
|
self.assertAlmostEqual(s2/(N-1), sigmasqrd, 2)
|
|
|
|
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.failUnless(set(random.__all__) <= set(dir(random)))
|
|
|
|
def test_main(verbose=None):
|
|
testclasses = [WichmannHill_TestBasicOps,
|
|
MersenneTwister_TestBasicOps,
|
|
TestDistributions,
|
|
TestModule]
|
|
|
|
try:
|
|
random.SystemRandom().random()
|
|
except NotImplementedError:
|
|
pass
|
|
else:
|
|
testclasses.append(SystemRandom_TestBasicOps)
|
|
|
|
test_support.run_unittest(*testclasses)
|
|
|
|
# verify reference counting
|
|
import sys
|
|
if verbose and hasattr(sys, "gettotalrefcount"):
|
|
counts = [None] * 5
|
|
for i in xrange(len(counts)):
|
|
test_support.run_unittest(*testclasses)
|
|
counts[i] = sys.gettotalrefcount()
|
|
print counts
|
|
|
|
if __name__ == "__main__":
|
|
test_main(verbose=True)
|