mirror of https://github.com/python/cpython
268 lines
10 KiB
Python
268 lines
10 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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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(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 = dict.fromkeys(s)
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self.assertEqual(len(uniq), k)
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self.failIf(None in uniq)
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self.assertEqual(self.gen.sample([], 0), []) # test edge case N==k==0
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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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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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defined = dict.fromkeys(dir(random))
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for entry in random.__all__:
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self.failUnless(entry in defined)
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def test_main():
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suite = unittest.TestSuite()
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for testclass in (WichmannHill_TestBasicOps,
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MersenneTwister_TestBasicOps,
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TestDistributions,
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TestModule):
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suite.addTest(unittest.makeSuite(testclass))
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test_support.run_suite(suite)
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if __name__ == "__main__":
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test_main()
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