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3081d59f92
The default seed is time.time(). Multiplied by 256 before truncating so that fractional seconds are used. This way, two successive calls to random.seed() are much more likely to produce different sequences.
298 lines
11 KiB
Python
298 lines
11 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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from math import log, exp, sqrt, pi
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from sets import Set
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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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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_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_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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class MersenneTwister_TestBasicOps(TestBasicOps):
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gen = random.Random()
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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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_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)):
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sum += cof[i] / (z+i)
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z += 0.5
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return (z+g)**z / exp(z+g) * sqrt(2*pi) * sum
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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 xrange(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)
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g.random = x[:].pop; g.normalvariate(0.0, 1.0)
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g.random = x[:].pop; g.gauss(0.0, 1.0)
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g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
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g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
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g.random = x[:].pop; g.gammavariate(0.01, 1.0)
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g.random = x[:].pop; g.gammavariate(1.0, 1.0)
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g.random = x[:].pop; g.gammavariate(200.0, 1.0)
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g.random = x[:].pop; g.betavariate(3.0, 3.0)
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def test_avg_std(self):
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# Use integration to test distribution average and standard deviation.
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# Only works for distributions which do not consume variates in pairs
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g = random.Random()
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N = 5000
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x = [i/float(N) for i in xrange(1,N)]
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for variate, args, mu, sigmasqrd in [
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(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
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(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
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(g.paretovariate, (5.0,), 5.0/(5.0-1),
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5.0/((5.0-1)**2*(5.0-2))),
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(g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0),
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gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]:
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g.random = x[:].pop
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y = []
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for i in xrange(len(x)):
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try:
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y.append(variate(*args))
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except IndexError:
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pass
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s1 = s2 = 0
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for e in y:
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s1 += e
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s2 += (e - mu) ** 2
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N = len(y)
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self.assertAlmostEqual(s1/N, mu, 2)
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self.assertAlmostEqual(s2/(N-1), sigmasqrd, 2)
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class TestModule(unittest.TestCase):
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def testMagicConstants(self):
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self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141)
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self.assertAlmostEqual(random.TWOPI, 6.28318530718)
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self.assertAlmostEqual(random.LOG4, 1.38629436111989)
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self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627)
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def test__all__(self):
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# tests validity but not completeness of the __all__ list
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self.failUnless(Set(random.__all__) <= Set(dir(random)))
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def test_main(verbose=None):
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testclasses = (WichmannHill_TestBasicOps,
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MersenneTwister_TestBasicOps,
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TestDistributions,
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TestModule)
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test_support.run_unittest(*testclasses)
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# verify reference counting
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import sys
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if verbose and hasattr(sys, "gettotalrefcount"):
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counts = [None] * 5
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for i in xrange(len(counts)):
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test_support.run_unittest(*testclasses)
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counts[i] = sys.gettotalrefcount()
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print counts
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if __name__ == "__main__":
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test_main(verbose=True)
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