cpython/Lib/test/test_random.py

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#!/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)
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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
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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,
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TestDistributions,
TestModule):
suite.addTest(unittest.makeSuite(testclass))
test_support.run_suite(suite)
if __name__ == "__main__":
test_main()