#!/usr/bin/env python import unittest import random import time from math import log, exp, sqrt, pi from test import test_support class TestBasicOps(unittest.TestCase): # Superclass with tests common to all generators. # Subclasses must arrange for self.gen to retrieve the Random instance # to be tested. def randomlist(self, n): """Helper function to make a list of random numbers""" return [self.gen.random() for i in xrange(n)] def test_autoseed(self): self.gen.seed() state1 = self.gen.getstate() time.sleep(1) self.gen.seed() # diffent seeds at different times state2 = self.gen.getstate() self.assertNotEqual(state1, state2) def test_saverestore(self): N = 1000 self.gen.seed() state = self.gen.getstate() randseq = self.randomlist(N) self.gen.setstate(state) # should regenerate the same sequence self.assertEqual(randseq, self.randomlist(N)) def test_seedargs(self): for arg in [None, 0, 0L, 1, 1L, -1, -1L, 10**20, -(10**20), 3.14, 1+2j, 'a', tuple('abc')]: self.gen.seed(arg) for arg in [range(3), dict(one=1)]: self.assertRaises(TypeError, self.gen.seed, arg) def test_jumpahead(self): self.gen.seed() state1 = self.gen.getstate() self.gen.jumpahead(100) state2 = self.gen.getstate() # s/b distinct from state1 self.assertNotEqual(state1, state2) self.gen.jumpahead(100) state3 = self.gen.getstate() # s/b distinct from state2 self.assertNotEqual(state2, state3) self.assertRaises(TypeError, self.gen.jumpahead) # needs an arg self.assertRaises(TypeError, self.gen.jumpahead, "ick") # wrong type self.assertRaises(TypeError, self.gen.jumpahead, 2.3) # wrong type self.assertRaises(TypeError, self.gen.jumpahead, 2, 3) # too many def test_sample(self): # For the entire allowable range of 0 <= k <= N, validate that # the sample is of the correct length and contains only unique items N = 100 population = xrange(N) for k in xrange(N+1): s = self.gen.sample(population, k) self.assertEqual(len(s), k) uniq = dict.fromkeys(s) self.assertEqual(len(uniq), k) self.failIf(None in uniq) self.assertEqual(self.gen.sample([], 0), []) # test edge case N==k==0 def test_gauss(self): # Ensure that the seed() method initializes all the hidden state. In # particular, through 2.2.1 it failed to reset a piece of state used # by (and only by) the .gauss() method. for seed in 1, 12, 123, 1234, 12345, 123456, 654321: self.gen.seed(seed) x1 = self.gen.random() y1 = self.gen.gauss(0, 1) self.gen.seed(seed) x2 = self.gen.random() y2 = self.gen.gauss(0, 1) self.assertEqual(x1, x2) self.assertEqual(y1, y2) class WichmannHill_TestBasicOps(TestBasicOps): gen = random.WichmannHill() def test_strong_jumpahead(self): # tests that jumpahead(n) semantics correspond to n calls to random() N = 1000 s = self.gen.getstate() self.gen.jumpahead(N) r1 = self.gen.random() # now do it the slow way self.gen.setstate(s) for i in xrange(N): self.gen.random() r2 = self.gen.random() self.assertEqual(r1, r2) def test_gauss_with_whseed(self): # Ensure that the seed() method initializes all the hidden state. In # particular, through 2.2.1 it failed to reset a piece of state used # by (and only by) the .gauss() method. for seed in 1, 12, 123, 1234, 12345, 123456, 654321: self.gen.whseed(seed) x1 = self.gen.random() y1 = self.gen.gauss(0, 1) self.gen.whseed(seed) x2 = self.gen.random() y2 = self.gen.gauss(0, 1) self.assertEqual(x1, x2) self.assertEqual(y1, y2) class MersenneTwister_TestBasicOps(TestBasicOps): gen = random.Random() def test_referenceImplementation(self): # Compare the python implementation with results from the original # code. Create 2000 53-bit precision random floats. Compare only # the last ten entries to show that the independent implementations # are tracking. Here is the main() function needed to create the # list of expected random numbers: # void main(void){ # int i; # unsigned long init[4]={61731, 24903, 614, 42143}, length=4; # init_by_array(init, length); # for (i=0; i<2000; i++) { # printf("%.15f ", genrand_res53()); # if (i%5==4) printf("\n"); # } # } expected = [0.45839803073713259, 0.86057815201978782, 0.92848331726782152, 0.35932681119782461, 0.081823493762449573, 0.14332226470169329, 0.084297823823520024, 0.53814864671831453, 0.089215024911993401, 0.78486196105372907] self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96)) actual = self.randomlist(2000)[-10:] for a, e in zip(actual, expected): self.assertAlmostEqual(a,e,places=14) def test_strong_reference_implementation(self): # Like test_referenceImplementation, but checks for exact bit-level # equality. This should pass on any box where C double contains # at least 53 bits of precision (the underlying algorithm suffers # no rounding errors -- all results are exact). from math import ldexp expected = [0x0eab3258d2231fL, 0x1b89db315277a5L, 0x1db622a5518016L, 0x0b7f9af0d575bfL, 0x029e4c4db82240L, 0x04961892f5d673L, 0x02b291598e4589L, 0x11388382c15694L, 0x02dad977c9e1feL, 0x191d96d4d334c6L] self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96)) actual = self.randomlist(2000)[-10:] for a, e in zip(actual, expected): self.assertEqual(long(ldexp(a, 53)), e) def test_long_seed(self): # This is most interesting to run in debug mode, just to make sure # nothing blows up. Under the covers, a dynamically resized array # is allocated, consuming space proportional to the number of bits # in the seed. Unfortunately, that's a quadratic-time algorithm, # so don't make this horribly big. seed = (1L << (10000 * 8)) - 1 # about 10K bytes self.gen.seed(seed) _gammacoeff = (0.9999999999995183, 676.5203681218835, -1259.139216722289, 771.3234287757674, -176.6150291498386, 12.50734324009056, -0.1385710331296526, 0.9934937113930748e-05, 0.1659470187408462e-06) def gamma(z, cof=_gammacoeff, g=7): z -= 1.0 sum = cof[0] 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 defined = dict.fromkeys(dir(random)) for entry in random.__all__: self.failUnless(entry in defined) def test_main(): suite = unittest.TestSuite() for testclass in (WichmannHill_TestBasicOps, MersenneTwister_TestBasicOps, TestDistributions, TestModule): suite.addTest(unittest.makeSuite(testclass)) test_support.run_suite(suite) if __name__ == "__main__": test_main()