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SF bug #690377: Dud footnote reference in distutils docs
author Raymond Hettinger <python@rcn.com>
date Fri, 21 Feb 2003 02:47:09 +0000
parents 107d988901a0
children c5f18db1c966
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 #!/usr/bin/env python
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2
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3 import unittest
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4 import random
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5 import time
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6 from math import log, exp, sqrt, pi
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7 from sets import Set
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8 from test import test_support
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9
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10 class TestBasicOps(unittest.TestCase):
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11 # Superclass with tests common to all generators.
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12 # Subclasses must arrange for self.gen to retrieve the Random instance
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13 # to be tested.
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14
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15 def randomlist(self, n):
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16 """Helper function to make a list of random numbers"""
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17 return [self.gen.random() for i in xrange(n)]
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18
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19 def test_autoseed(self):
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20 self.gen.seed()
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21 state1 = self.gen.getstate()
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22 time.sleep(1.1)
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23 self.gen.seed() # diffent seeds at different times
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24 state2 = self.gen.getstate()
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25 self.assertNotEqual(state1, state2)
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26
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27 def test_saverestore(self):
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28 N = 1000
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29 self.gen.seed()
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30 state = self.gen.getstate()
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31 randseq = self.randomlist(N)
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32 self.gen.setstate(state) # should regenerate the same sequence
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33 self.assertEqual(randseq, self.randomlist(N))
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34
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35 def test_seedargs(self):
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36 for arg in [None, 0, 0L, 1, 1L, -1, -1L, 10**20, -(10**20),
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37 3.14, 1+2j, 'a', tuple('abc')]:
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38 self.gen.seed(arg)
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39 for arg in [range(3), dict(one=1)]:
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40 self.assertRaises(TypeError, self.gen.seed, arg)
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41
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42 def test_jumpahead(self):
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43 self.gen.seed()
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44 state1 = self.gen.getstate()
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45 self.gen.jumpahead(100)
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46 state2 = self.gen.getstate() # s/b distinct from state1
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47 self.assertNotEqual(state1, state2)
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48 self.gen.jumpahead(100)
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49 state3 = self.gen.getstate() # s/b distinct from state2
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50 self.assertNotEqual(state2, state3)
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51
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52 self.assertRaises(TypeError, self.gen.jumpahead) # needs an arg
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53 self.assertRaises(TypeError, self.gen.jumpahead, "ick") # wrong type
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54 self.assertRaises(TypeError, self.gen.jumpahead, 2.3) # wrong type
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55 self.assertRaises(TypeError, self.gen.jumpahead, 2, 3) # too many
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56
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57 def test_sample(self):
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58 # For the entire allowable range of 0 <= k <= N, validate that
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59 # the sample is of the correct length and contains only unique items
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60 N = 100
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61 population = xrange(N)
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62 for k in xrange(N+1):
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63 s = self.gen.sample(population, k)
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64 self.assertEqual(len(s), k)
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65 uniq = Set(s)
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66 self.assertEqual(len(uniq), k)
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67 self.failUnless(uniq <= Set(population))
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68 self.assertEqual(self.gen.sample([], 0), []) # test edge case N==k==0
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69
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70 def test_sample_distribution(self):
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71 # For the entire allowable range of 0 <= k <= N, validate that
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72 # sample generates all possible permutations
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73 n = 5
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74 pop = range(n)
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75 trials = 10000 # large num prevents false negatives without slowing normal case
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76 def factorial(n):
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77 return reduce(int.__mul__, xrange(1, n), 1)
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78 for k in xrange(n):
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79 expected = factorial(n) / factorial(n-k)
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80 perms = {}
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81 for i in xrange(trials):
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82 perms[tuple(self.gen.sample(pop, k))] = None
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83 if len(perms) == expected:
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84 break
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85 else:
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86 self.fail()
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87
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88 def test_gauss(self):
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89 # Ensure that the seed() method initializes all the hidden state. In
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90 # particular, through 2.2.1 it failed to reset a piece of state used
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91 # by (and only by) the .gauss() method.
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92
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93 for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
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94 self.gen.seed(seed)
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95 x1 = self.gen.random()
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96 y1 = self.gen.gauss(0, 1)
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97
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98 self.gen.seed(seed)
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99 x2 = self.gen.random()
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100 y2 = self.gen.gauss(0, 1)
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101
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102 self.assertEqual(x1, x2)
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103 self.assertEqual(y1, y2)
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104
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105
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106 class WichmannHill_TestBasicOps(TestBasicOps):
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107 gen = random.WichmannHill()
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108
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109 def test_strong_jumpahead(self):
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110 # tests that jumpahead(n) semantics correspond to n calls to random()
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111 N = 1000
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112 s = self.gen.getstate()
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113 self.gen.jumpahead(N)
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114 r1 = self.gen.random()
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115 # now do it the slow way
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116 self.gen.setstate(s)
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117 for i in xrange(N):
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118 self.gen.random()
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119 r2 = self.gen.random()
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120 self.assertEqual(r1, r2)
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121
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122 def test_gauss_with_whseed(self):
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123 # Ensure that the seed() method initializes all the hidden state. In
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124 # particular, through 2.2.1 it failed to reset a piece of state used
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125 # by (and only by) the .gauss() method.
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126
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127 for seed in 1, 12, 123, 1234, 12345, 123456, 654321:
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128 self.gen.whseed(seed)
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129 x1 = self.gen.random()
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130 y1 = self.gen.gauss(0, 1)
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131
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132 self.gen.whseed(seed)
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133 x2 = self.gen.random()
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134 y2 = self.gen.gauss(0, 1)
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135
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136 self.assertEqual(x1, x2)
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137 self.assertEqual(y1, y2)
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138
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139 class MersenneTwister_TestBasicOps(TestBasicOps):
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140 gen = random.Random()
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141
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142 def test_referenceImplementation(self):
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143 # Compare the python implementation with results from the original
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144 # code. Create 2000 53-bit precision random floats. Compare only
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145 # the last ten entries to show that the independent implementations
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146 # are tracking. Here is the main() function needed to create the
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147 # list of expected random numbers:
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148 # void main(void){
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149 # int i;
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150 # unsigned long init[4]={61731, 24903, 614, 42143}, length=4;
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151 # init_by_array(init, length);
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152 # for (i=0; i<2000; i++) {
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153 # printf("%.15f ", genrand_res53());
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154 # if (i%5==4) printf("\n");
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155 # }
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156 # }
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157 expected = [0.45839803073713259,
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158 0.86057815201978782,
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159 0.92848331726782152,
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160 0.35932681119782461,
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161 0.081823493762449573,
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162 0.14332226470169329,
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163 0.084297823823520024,
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164 0.53814864671831453,
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165 0.089215024911993401,
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166 0.78486196105372907]
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167
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168 self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
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169 actual = self.randomlist(2000)[-10:]
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170 for a, e in zip(actual, expected):
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171 self.assertAlmostEqual(a,e,places=14)
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172
26829
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173 def test_strong_reference_implementation(self):
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174 # Like test_referenceImplementation, but checks for exact bit-level
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175 # equality. This should pass on any box where C double contains
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176 # at least 53 bits of precision (the underlying algorithm suffers
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177 # no rounding errors -- all results are exact).
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178 from math import ldexp
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179
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180 expected = [0x0eab3258d2231fL,
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181 0x1b89db315277a5L,
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182 0x1db622a5518016L,
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183 0x0b7f9af0d575bfL,
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184 0x029e4c4db82240L,
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185 0x04961892f5d673L,
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186 0x02b291598e4589L,
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187 0x11388382c15694L,
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188 0x02dad977c9e1feL,
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189 0x191d96d4d334c6L]
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190
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191 self.gen.seed(61731L + (24903L<<32) + (614L<<64) + (42143L<<96))
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192 actual = self.randomlist(2000)[-10:]
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193 for a, e in zip(actual, expected):
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194 self.assertEqual(long(ldexp(a, 53)), e)
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195
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196 def test_long_seed(self):
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197 # This is most interesting to run in debug mode, just to make sure
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198 # nothing blows up. Under the covers, a dynamically resized array
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199 # is allocated, consuming space proportional to the number of bits
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200 # in the seed. Unfortunately, that's a quadratic-time algorithm,
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201 # so don't make this horribly big.
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202 seed = (1L << (10000 * 8)) - 1 # about 10K bytes
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203 self.gen.seed(seed)
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204
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205 _gammacoeff = (0.9999999999995183, 676.5203681218835, -1259.139216722289,
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206 771.3234287757674, -176.6150291498386, 12.50734324009056,
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207 -0.1385710331296526, 0.9934937113930748e-05, 0.1659470187408462e-06)
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208
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209 def gamma(z, cof=_gammacoeff, g=7):
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210 z -= 1.0
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211 sum = cof[0]
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212 for i in xrange(1,len(cof)):
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213 sum += cof[i] / (z+i)
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214 z += 0.5
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215 return (z+g)**z / exp(z+g) * sqrt(2*pi) * sum
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216
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217 class TestDistributions(unittest.TestCase):
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218 def test_zeroinputs(self):
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219 # Verify that distributions can handle a series of zero inputs'
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220 g = random.Random()
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221 x = [g.random() for i in xrange(50)] + [0.0]*5
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222 g.random = x[:].pop; g.uniform(1,10)
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223 g.random = x[:].pop; g.paretovariate(1.0)
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224 g.random = x[:].pop; g.expovariate(1.0)
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225 g.random = x[:].pop; g.weibullvariate(1.0, 1.0)
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226 g.random = x[:].pop; g.normalvariate(0.0, 1.0)
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227 g.random = x[:].pop; g.gauss(0.0, 1.0)
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228 g.random = x[:].pop; g.lognormvariate(0.0, 1.0)
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229 g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0)
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230 g.random = x[:].pop; g.gammavariate(0.01, 1.0)
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231 g.random = x[:].pop; g.gammavariate(1.0, 1.0)
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232 g.random = x[:].pop; g.gammavariate(200.0, 1.0)
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233 g.random = x[:].pop; g.betavariate(3.0, 3.0)
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234
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235 def test_avg_std(self):
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236 # Use integration to test distribution average and standard deviation.
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237 # Only works for distributions which do not consume variates in pairs
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238 g = random.Random()
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239 N = 5000
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240 x = [i/float(N) for i in xrange(1,N)]
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241 for variate, args, mu, sigmasqrd in [
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242 (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
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243 (g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
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244 (g.paretovariate, (5.0,), 5.0/(5.0-1),
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245 5.0/((5.0-1)**2*(5.0-2))),
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246 (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0),
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247 gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]:
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248 g.random = x[:].pop
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249 y = []
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250 for i in xrange(len(x)):
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251 try:
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252 y.append(variate(*args))
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253 except IndexError:
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254 pass
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255 s1 = s2 = 0
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256 for e in y:
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257 s1 += e
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258 s2 += (e - mu) ** 2
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259 N = len(y)
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260 self.assertAlmostEqual(s1/N, mu, 2)
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261 self.assertAlmostEqual(s2/(N-1), sigmasqrd, 2)
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262
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263 class TestModule(unittest.TestCase):
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264 def testMagicConstants(self):
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265 self.assertAlmostEqual(random.NV_MAGICCONST, 1.71552776992141)
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266 self.assertAlmostEqual(random.TWOPI, 6.28318530718)
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267 self.assertAlmostEqual(random.LOG4, 1.38629436111989)
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268 self.assertAlmostEqual(random.SG_MAGICCONST, 2.50407739677627)
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269
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270 def test__all__(self):
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271 # tests validity but not completeness of the __all__ list
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272 self.failUnless(Set(random.__all__) <= Set(dir(random)))
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273
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274 def test_main(verbose=None):
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275 suite = unittest.TestSuite()
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276 for testclass in (WichmannHill_TestBasicOps,
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277 MersenneTwister_TestBasicOps,
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278 TestDistributions,
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279 TestModule):
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280 suite.addTest(unittest.makeSuite(testclass))
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281 test_support.run_suite(suite)
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282
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283 # verify reference counting
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284 import sys
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285 if verbose and hasattr(sys, "gettotalrefcount"):
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286 counts = []
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287 for i in xrange(5):
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288 test_support.run_suite(suite)
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289 counts.append(sys.gettotalrefcount()-i)
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290 print counts
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291
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292 if __name__ == "__main__":
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293 test_main(verbose=True)