| #!/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() |