Mercurial > cpython-lru_cache-weakref
annotate Lib/random.py @ 20737:00a581bdedeb legacy-trunk
When overriding __str__ or __repr__, set the tp_print slot to NULL.
| author | Guido van Rossum <guido@python.org> |
|---|---|
| date | Mon, 29 Oct 2001 14:33:44 +0000 |
| parents | e2a63584566f |
| children | fed5071da957 |
| rev | line source |
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1 """Random variable generators. |
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2 |
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3 integers |
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4 -------- |
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5 uniform within range |
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6 |
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7 sequences |
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8 --------- |
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9 pick random element |
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10 generate random permutation |
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11 |
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12 distributions on the real line: |
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13 ------------------------------ |
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14 uniform |
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15 normal (Gaussian) |
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16 lognormal |
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17 negative exponential |
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18 gamma |
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19 beta |
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21 distributions on the circle (angles 0 to 2pi) |
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22 --------------------------------------------- |
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23 circular uniform |
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24 von Mises |
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26 Translated from anonymously contributed C/C++ source. |
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27 |
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28 Multi-threading note: the random number generator used here is not thread- |
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29 safe; it is possible that two calls return the same random value. However, |
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30 you can instantiate a different instance of Random() in each thread to get |
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31 generators that don't share state, then use .setstate() and .jumpahead() to |
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32 move the generators to disjoint segments of the full period. For example, |
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33 |
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34 def create_generators(num, delta, firstseed=None): |
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35 ""\"Return list of num distinct generators. |
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36 Each generator has its own unique segment of delta elements from |
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37 Random.random()'s full period. |
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38 Seed the first generator with optional arg firstseed (default is |
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39 None, to seed from current time). |
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40 ""\" |
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41 |
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42 from random import Random |
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43 g = Random(firstseed) |
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44 result = [g] |
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45 for i in range(num - 1): |
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46 laststate = g.getstate() |
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47 g = Random() |
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48 g.setstate(laststate) |
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49 g.jumpahead(delta) |
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50 result.append(g) |
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51 return result |
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52 |
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53 gens = create_generators(10, 1000000) |
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54 |
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55 That creates 10 distinct generators, which can be passed out to 10 distinct |
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56 threads. The generators don't share state so can be called safely in |
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57 parallel. So long as no thread calls its g.random() more than a million |
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58 times (the second argument to create_generators), the sequences seen by |
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59 each thread will not overlap. |
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60 |
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61 The period of the underlying Wichmann-Hill generator is 6,953,607,871,644, |
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62 and that limits how far this technique can be pushed. |
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63 |
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64 Just for fun, note that since we know the period, .jumpahead() can also be |
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65 used to "move backward in time": |
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66 |
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67 >>> g = Random(42) # arbitrary |
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68 >>> g.random() |
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69 0.25420336316883324 |
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70 >>> g.jumpahead(6953607871644L - 1) # move *back* one |
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71 >>> g.random() |
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72 0.25420336316883324 |
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73 """ |
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74 # XXX The docstring sucks. |
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75 |
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76 from math import log as _log, exp as _exp, pi as _pi, e as _e |
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77 from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin |
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78 |
| 16613 | 79 __all__ = ["Random","seed","random","uniform","randint","choice", |
| 80 "randrange","shuffle","normalvariate","lognormvariate", | |
| 81 "cunifvariate","expovariate","vonmisesvariate","gammavariate", | |
| 82 "stdgamma","gauss","betavariate","paretovariate","weibullvariate", | |
| 83 "getstate","setstate","jumpahead","whseed"] | |
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84 |
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85 def _verify(name, expected): |
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86 computed = eval(name) |
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87 if abs(computed - expected) > 1e-7: |
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88 raise ValueError( |
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89 "computed value for %s deviates too much " |
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90 "(computed %g, expected %g)" % (name, computed, expected)) |
| 1575 | 91 |
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92 NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0) |
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93 _verify('NV_MAGICCONST', 1.71552776992141) |
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94 |
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95 TWOPI = 2.0*_pi |
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96 _verify('TWOPI', 6.28318530718) |
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97 |
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98 LOG4 = _log(4.0) |
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99 _verify('LOG4', 1.38629436111989) |
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100 |
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101 SG_MAGICCONST = 1.0 + _log(4.5) |
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102 _verify('SG_MAGICCONST', 2.50407739677627) |
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103 |
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104 del _verify |
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105 |
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106 # Translated by Guido van Rossum from C source provided by |
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107 # Adrian Baddeley. |
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108 |
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109 class Random: |
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110 |
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111 VERSION = 1 # used by getstate/setstate |
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112 |
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113 def __init__(self, x=None): |
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114 """Initialize an instance. |
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115 |
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116 Optional argument x controls seeding, as for Random.seed(). |
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117 """ |
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118 |
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119 self.seed(x) |
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120 self.gauss_next = None |
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121 |
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122 ## -------------------- core generator ------------------- |
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123 |
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124 # Specific to Wichmann-Hill generator. Subclasses wishing to use a |
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125 # different core generator should override the seed(), random(), |
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126 # getstate(), setstate() and jumpahead() methods. |
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127 |
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128 def seed(self, a=None): |
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129 """Initialize internal state from hashable object. |
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130 |
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131 None or no argument seeds from current time. |
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132 |
| 16386 | 133 If a is not None or an int or long, hash(a) is used instead. |
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134 |
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135 If a is an int or long, a is used directly. Distinct values between |
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136 0 and 27814431486575L inclusive are guaranteed to yield distinct |
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137 internal states (this guarantee is specific to the default |
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138 Wichmann-Hill generator). |
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139 """ |
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140 |
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141 if a is None: |
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142 # Initialize from current time |
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143 import time |
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144 a = long(time.time() * 256) |
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145 |
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146 if type(a) not in (type(3), type(3L)): |
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147 a = hash(a) |
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148 |
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149 a, x = divmod(a, 30268) |
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150 a, y = divmod(a, 30306) |
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151 a, z = divmod(a, 30322) |
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152 self._seed = int(x)+1, int(y)+1, int(z)+1 |
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153 |
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154 def random(self): |
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155 """Get the next random number in the range [0.0, 1.0).""" |
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156 |
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157 # Wichman-Hill random number generator. |
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158 # |
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159 # Wichmann, B. A. & Hill, I. D. (1982) |
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160 # Algorithm AS 183: |
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161 # An efficient and portable pseudo-random number generator |
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162 # Applied Statistics 31 (1982) 188-190 |
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163 # |
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164 # see also: |
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165 # Correction to Algorithm AS 183 |
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166 # Applied Statistics 33 (1984) 123 |
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167 # |
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168 # McLeod, A. I. (1985) |
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169 # A remark on Algorithm AS 183 |
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170 # Applied Statistics 34 (1985),198-200 |
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171 |
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172 # This part is thread-unsafe: |
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173 # BEGIN CRITICAL SECTION |
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174 x, y, z = self._seed |
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175 x = (171 * x) % 30269 |
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176 y = (172 * y) % 30307 |
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177 z = (170 * z) % 30323 |
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178 self._seed = x, y, z |
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179 # END CRITICAL SECTION |
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180 |
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181 # Note: on a platform using IEEE-754 double arithmetic, this can |
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182 # never return 0.0 (asserted by Tim; proof too long for a comment). |
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183 return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0 |
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184 |
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185 def getstate(self): |
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186 """Return internal state; can be passed to setstate() later.""" |
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187 return self.VERSION, self._seed, self.gauss_next |
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188 |
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189 def setstate(self, state): |
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190 """Restore internal state from object returned by getstate().""" |
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191 version = state[0] |
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192 if version == 1: |
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193 version, self._seed, self.gauss_next = state |
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194 else: |
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195 raise ValueError("state with version %s passed to " |
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196 "Random.setstate() of version %s" % |
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197 (version, self.VERSION)) |
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198 |
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199 def jumpahead(self, n): |
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200 """Act as if n calls to random() were made, but quickly. |
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201 |
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202 n is an int, greater than or equal to 0. |
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203 |
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204 Example use: If you have 2 threads and know that each will |
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205 consume no more than a million random numbers, create two Random |
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206 objects r1 and r2, then do |
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207 r2.setstate(r1.getstate()) |
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208 r2.jumpahead(1000000) |
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209 Then r1 and r2 will use guaranteed-disjoint segments of the full |
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210 period. |
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211 """ |
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212 |
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213 if not n >= 0: |
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214 raise ValueError("n must be >= 0") |
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215 x, y, z = self._seed |
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216 x = int(x * pow(171, n, 30269)) % 30269 |
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217 y = int(y * pow(172, n, 30307)) % 30307 |
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218 z = int(z * pow(170, n, 30323)) % 30323 |
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219 self._seed = x, y, z |
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220 |
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221 def __whseed(self, x=0, y=0, z=0): |
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222 """Set the Wichmann-Hill seed from (x, y, z). |
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223 |
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224 These must be integers in the range [0, 256). |
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225 """ |
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226 |
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227 if not type(x) == type(y) == type(z) == type(0): |
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228 raise TypeError('seeds must be integers') |
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229 if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256): |
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230 raise ValueError('seeds must be in range(0, 256)') |
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231 if 0 == x == y == z: |
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232 # Initialize from current time |
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233 import time |
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234 t = long(time.time() * 256) |
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235 t = int((t&0xffffff) ^ (t>>24)) |
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236 t, x = divmod(t, 256) |
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237 t, y = divmod(t, 256) |
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238 t, z = divmod(t, 256) |
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239 # Zero is a poor seed, so substitute 1 |
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240 self._seed = (x or 1, y or 1, z or 1) |
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241 |
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242 def whseed(self, a=None): |
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243 """Seed from hashable object's hash code. |
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244 |
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245 None or no argument seeds from current time. It is not guaranteed |
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246 that objects with distinct hash codes lead to distinct internal |
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247 states. |
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248 |
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249 This is obsolete, provided for compatibility with the seed routine |
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250 used prior to Python 2.1. Use the .seed() method instead. |
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251 """ |
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252 |
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253 if a is None: |
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254 self.__whseed() |
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255 return |
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256 a = hash(a) |
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257 a, x = divmod(a, 256) |
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258 a, y = divmod(a, 256) |
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259 a, z = divmod(a, 256) |
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260 x = (x + a) % 256 or 1 |
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261 y = (y + a) % 256 or 1 |
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262 z = (z + a) % 256 or 1 |
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263 self.__whseed(x, y, z) |
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264 |
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265 ## ---- Methods below this point do not need to be overridden when |
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266 ## ---- subclassing for the purpose of using a different core generator. |
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267 |
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268 ## -------------------- pickle support ------------------- |
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269 |
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270 def __getstate__(self): # for pickle |
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271 return self.getstate() |
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272 |
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273 def __setstate__(self, state): # for pickle |
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274 self.setstate(state) |
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275 |
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276 ## -------------------- integer methods ------------------- |
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277 |
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278 def randrange(self, start, stop=None, step=1, int=int, default=None): |
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279 """Choose a random item from range(start, stop[, step]). |
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280 |
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281 This fixes the problem with randint() which includes the |
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282 endpoint; in Python this is usually not what you want. |
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283 Do not supply the 'int' and 'default' arguments. |
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284 """ |
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285 |
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286 # This code is a bit messy to make it fast for the |
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287 # common case while still doing adequate error checking |
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288 istart = int(start) |
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289 if istart != start: |
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290 raise ValueError, "non-integer arg 1 for randrange()" |
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291 if stop is default: |
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292 if istart > 0: |
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293 return int(self.random() * istart) |
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294 raise ValueError, "empty range for randrange()" |
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295 istop = int(stop) |
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296 if istop != stop: |
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297 raise ValueError, "non-integer stop for randrange()" |
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298 if step == 1: |
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299 if istart < istop: |
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300 return istart + int(self.random() * |
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301 (istop - istart)) |
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302 raise ValueError, "empty range for randrange()" |
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303 istep = int(step) |
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304 if istep != step: |
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305 raise ValueError, "non-integer step for randrange()" |
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306 if istep > 0: |
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307 n = (istop - istart + istep - 1) / istep |
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308 elif istep < 0: |
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309 n = (istop - istart + istep + 1) / istep |
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310 else: |
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311 raise ValueError, "zero step for randrange()" |
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312 |
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313 if n <= 0: |
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314 raise ValueError, "empty range for randrange()" |
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315 return istart + istep*int(self.random() * n) |
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317 def randint(self, a, b): |
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318 """Return random integer in range [a, b], including both end points. |
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319 |
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320 (Deprecated; use randrange(a, b+1).) |
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321 """ |
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322 |
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323 return self.randrange(a, b+1) |
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324 |
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325 ## -------------------- sequence methods ------------------- |
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326 |
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327 def choice(self, seq): |
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328 """Choose a random element from a non-empty sequence.""" |
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329 return seq[int(self.random() * len(seq))] |
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330 |
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331 def shuffle(self, x, random=None, int=int): |
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332 """x, random=random.random -> shuffle list x in place; return None. |
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333 |
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334 Optional arg random is a 0-argument function returning a random |
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335 float in [0.0, 1.0); by default, the standard random.random. |
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336 |
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337 Note that for even rather small len(x), the total number of |
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338 permutations of x is larger than the period of most random number |
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339 generators; this implies that "most" permutations of a long |
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340 sequence can never be generated. |
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341 """ |
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342 |
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343 if random is None: |
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344 random = self.random |
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345 for i in xrange(len(x)-1, 0, -1): |
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346 # pick an element in x[:i+1] with which to exchange x[i] |
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347 j = int(random() * (i+1)) |
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348 x[i], x[j] = x[j], x[i] |
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349 |
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350 ## -------------------- real-valued distributions ------------------- |
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351 |
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352 ## -------------------- uniform distribution ------------------- |
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353 |
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354 def uniform(self, a, b): |
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355 """Get a random number in the range [a, b).""" |
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356 return a + (b-a) * self.random() |
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358 ## -------------------- normal distribution -------------------- |
| 1575 | 359 |
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360 def normalvariate(self, mu, sigma): |
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361 # mu = mean, sigma = standard deviation |
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362 |
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363 # Uses Kinderman and Monahan method. Reference: Kinderman, |
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364 # A.J. and Monahan, J.F., "Computer generation of random |
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365 # variables using the ratio of uniform deviates", ACM Trans |
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366 # Math Software, 3, (1977), pp257-260. |
| 1575 | 367 |
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368 random = self.random |
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369 while 1: |
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370 u1 = random() |
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371 u2 = random() |
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372 z = NV_MAGICCONST*(u1-0.5)/u2 |
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373 zz = z*z/4.0 |
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374 if zz <= -_log(u2): |
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375 break |
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376 return mu + z*sigma |
| 1575 | 377 |
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378 ## -------------------- lognormal distribution -------------------- |
| 1575 | 379 |
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380 def lognormvariate(self, mu, sigma): |
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381 return _exp(self.normalvariate(mu, sigma)) |
| 1575 | 382 |
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383 ## -------------------- circular uniform -------------------- |
| 1575 | 384 |
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385 def cunifvariate(self, mean, arc): |
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386 # mean: mean angle (in radians between 0 and pi) |
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387 # arc: range of distribution (in radians between 0 and pi) |
| 1575 | 388 |
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389 return (mean + arc * (self.random() - 0.5)) % _pi |
| 1575 | 390 |
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391 ## -------------------- exponential distribution -------------------- |
| 1575 | 392 |
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393 def expovariate(self, lambd): |
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394 # lambd: rate lambd = 1/mean |
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395 # ('lambda' is a Python reserved word) |
| 1575 | 396 |
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397 random = self.random |
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398 u = random() |
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399 while u <= 1e-7: |
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400 u = random() |
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401 return -_log(u)/lambd |
| 1575 | 402 |
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403 ## -------------------- von Mises distribution -------------------- |
| 1575 | 404 |
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405 def vonmisesvariate(self, mu, kappa): |
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406 # mu: mean angle (in radians between 0 and 2*pi) |
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407 # kappa: concentration parameter kappa (>= 0) |
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408 # if kappa = 0 generate uniform random angle |
| 1575 | 409 |
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410 # Based upon an algorithm published in: Fisher, N.I., |
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411 # "Statistical Analysis of Circular Data", Cambridge |
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412 # University Press, 1993. |
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413 |
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414 # Thanks to Magnus Kessler for a correction to the |
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415 # implementation of step 4. |
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416 |
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417 random = self.random |
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418 if kappa <= 1e-6: |
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419 return TWOPI * random() |
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420 |
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421 a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa) |
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422 b = (a - _sqrt(2.0 * a))/(2.0 * kappa) |
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423 r = (1.0 + b * b)/(2.0 * b) |
| 1575 | 424 |
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425 while 1: |
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426 u1 = random() |
| 1575 | 427 |
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428 z = _cos(_pi * u1) |
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429 f = (1.0 + r * z)/(r + z) |
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430 c = kappa * (r - f) |
| 1575 | 431 |
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432 u2 = random() |
| 1575 | 433 |
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434 if not (u2 >= c * (2.0 - c) and u2 > c * _exp(1.0 - c)): |
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435 break |
| 1575 | 436 |
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437 u3 = random() |
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438 if u3 > 0.5: |
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439 theta = (mu % TWOPI) + _acos(f) |
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440 else: |
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441 theta = (mu % TWOPI) - _acos(f) |
| 1575 | 442 |
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443 return theta |
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445 ## -------------------- gamma distribution -------------------- |
| 1575 | 446 |
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447 def gammavariate(self, alpha, beta): |
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448 # beta times standard gamma |
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449 ainv = _sqrt(2.0 * alpha - 1.0) |
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450 return beta * self.stdgamma(alpha, ainv, alpha - LOG4, alpha + ainv) |
| 1575 | 451 |
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452 def stdgamma(self, alpha, ainv, bbb, ccc): |
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453 # ainv = sqrt(2 * alpha - 1) |
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454 # bbb = alpha - log(4) |
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455 # ccc = alpha + ainv |
| 1575 | 456 |
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457 random = self.random |
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458 if alpha <= 0.0: |
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459 raise ValueError, 'stdgamma: alpha must be > 0.0' |
| 1575 | 460 |
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461 if alpha > 1.0: |
| 1575 | 462 |
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463 # Uses R.C.H. Cheng, "The generation of Gamma |
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464 # variables with non-integral shape parameters", |
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465 # Applied Statistics, (1977), 26, No. 1, p71-74 |
| 1575 | 466 |
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467 while 1: |
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468 u1 = random() |
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469 u2 = random() |
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470 v = _log(u1/(1.0-u1))/ainv |
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471 x = alpha*_exp(v) |
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472 z = u1*u1*u2 |
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473 r = bbb+ccc*v-x |
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474 if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z): |
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475 return x |
| 1575 | 476 |
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477 elif alpha == 1.0: |
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478 # expovariate(1) |
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479 u = random() |
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480 while u <= 1e-7: |
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481 u = random() |
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482 return -_log(u) |
| 1575 | 483 |
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484 else: # alpha is between 0 and 1 (exclusive) |
| 1575 | 485 |
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486 # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle |
| 1575 | 487 |
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488 while 1: |
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489 u = random() |
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490 b = (_e + alpha)/_e |
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491 p = b*u |
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492 if p <= 1.0: |
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493 x = pow(p, 1.0/alpha) |
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494 else: |
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495 # p > 1 |
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496 x = -_log((b-p)/alpha) |
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497 u1 = random() |
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498 if not (((p <= 1.0) and (u1 > _exp(-x))) or |
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499 ((p > 1) and (u1 > pow(x, alpha - 1.0)))): |
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500 break |
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501 return x |
| 1575 | 502 |
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503 |
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504 ## -------------------- Gauss (faster alternative) -------------------- |
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505 |
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506 def gauss(self, mu, sigma): |
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507 |
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508 # When x and y are two variables from [0, 1), uniformly |
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509 # distributed, then |
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510 # |
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511 # cos(2*pi*x)*sqrt(-2*log(1-y)) |
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512 # sin(2*pi*x)*sqrt(-2*log(1-y)) |
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513 # |
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514 # are two *independent* variables with normal distribution |
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515 # (mu = 0, sigma = 1). |
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516 # (Lambert Meertens) |
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517 # (corrected version; bug discovered by Mike Miller, fixed by LM) |
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518 |
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519 # Multithreading note: When two threads call this function |
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520 # simultaneously, it is possible that they will receive the |
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521 # same return value. The window is very small though. To |
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522 # avoid this, you have to use a lock around all calls. (I |
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523 # didn't want to slow this down in the serial case by using a |
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524 # lock here.) |
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525 |
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526 random = self.random |
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527 z = self.gauss_next |
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528 self.gauss_next = None |
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529 if z is None: |
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530 x2pi = random() * TWOPI |
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531 g2rad = _sqrt(-2.0 * _log(1.0 - random())) |
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532 z = _cos(x2pi) * g2rad |
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533 self.gauss_next = _sin(x2pi) * g2rad |
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534 |
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535 return mu + z*sigma |
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536 |
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537 ## -------------------- beta -------------------- |
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538 ## See |
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539 ## http://sourceforge.net/bugs/?func=detailbug&bug_id=130030&group_id=5470 |
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540 ## for Ivan Frohne's insightful analysis of why the original implementation: |
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541 ## |
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542 ## def betavariate(self, alpha, beta): |
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543 ## # Discrete Event Simulation in C, pp 87-88. |
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544 ## |
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545 ## y = self.expovariate(alpha) |
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546 ## z = self.expovariate(1.0/beta) |
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547 ## return z/(y+z) |
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548 ## |
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|
549 ## was dead wrong, and how it probably got that way. |
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550 |
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551 def betavariate(self, alpha, beta): |
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552 # This version due to Janne Sinkkonen, and matches all the std |
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553 # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). |
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554 y = self.gammavariate(alpha, 1.) |
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555 if y == 0: |
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556 return 0.0 |
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557 else: |
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558 return y / (y + self.gammavariate(beta, 1.)) |
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559 |
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560 ## -------------------- Pareto -------------------- |
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561 |
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562 def paretovariate(self, alpha): |
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563 # Jain, pg. 495 |
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564 |
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565 u = self.random() |
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|
566 return 1.0 / pow(u, 1.0/alpha) |
|
6698
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|
567 |
|
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|
568 ## -------------------- Weibull -------------------- |
|
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|
569 |
|
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570 def weibullvariate(self, alpha, beta): |
|
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Reworked random.py so that it no longer depends on, and offers all the
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|
571 # Jain, pg. 499; bug fix courtesy Bill Arms |
|
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Add Tim Peters' shuffle() algorithm.
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572 |
|
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573 u = self.random() |
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574 return alpha * pow(-_log(u), 1.0/beta) |
|
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575 |
|
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576 ## -------------------- test program -------------------- |
| 1575 | 577 |
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578 def _test_generator(n, funccall): |
|
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579 import time |
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580 print n, 'times', funccall |
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581 code = compile(funccall, funccall, 'eval') |
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582 sum = 0.0 |
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583 sqsum = 0.0 |
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584 smallest = 1e10 |
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585 largest = -1e10 |
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586 t0 = time.time() |
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587 for i in range(n): |
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588 x = eval(code) |
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589 sum = sum + x |
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590 sqsum = sqsum + x*x |
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591 smallest = min(x, smallest) |
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592 largest = max(x, largest) |
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593 t1 = time.time() |
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594 print round(t1-t0, 3), 'sec,', |
|
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595 avg = sum/n |
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596 stddev = _sqrt(sqsum/n - avg*avg) |
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597 print 'avg %g, stddev %g, min %g, max %g' % \ |
|
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598 (avg, stddev, smallest, largest) |
| 1575 | 599 |
|
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600 def _test(N=200): |
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601 print 'TWOPI =', TWOPI |
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602 print 'LOG4 =', LOG4 |
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603 print 'NV_MAGICCONST =', NV_MAGICCONST |
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604 print 'SG_MAGICCONST =', SG_MAGICCONST |
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|
605 _test_generator(N, 'random()') |
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606 _test_generator(N, 'normalvariate(0.0, 1.0)') |
|
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607 _test_generator(N, 'lognormvariate(0.0, 1.0)') |
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608 _test_generator(N, 'cunifvariate(0.0, 1.0)') |
|
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609 _test_generator(N, 'expovariate(1.0)') |
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610 _test_generator(N, 'vonmisesvariate(0.0, 1.0)') |
|
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611 _test_generator(N, 'gammavariate(0.5, 1.0)') |
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612 _test_generator(N, 'gammavariate(0.9, 1.0)') |
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613 _test_generator(N, 'gammavariate(1.0, 1.0)') |
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614 _test_generator(N, 'gammavariate(2.0, 1.0)') |
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615 _test_generator(N, 'gammavariate(20.0, 1.0)') |
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616 _test_generator(N, 'gammavariate(200.0, 1.0)') |
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617 _test_generator(N, 'gauss(0.0, 1.0)') |
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618 _test_generator(N, 'betavariate(3.0, 3.0)') |
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619 _test_generator(N, 'paretovariate(1.0)') |
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620 _test_generator(N, 'weibullvariate(1.0, 1.0)') |
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621 |
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622 # Test jumpahead. |
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623 s = getstate() |
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624 jumpahead(N) |
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625 r1 = random() |
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626 # now do it the slow way |
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627 setstate(s) |
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628 for i in range(N): |
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629 random() |
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630 r2 = random() |
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|
631 if r1 != r2: |
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632 raise ValueError("jumpahead test failed " + `(N, r1, r2)`) |
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633 |
|
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634 # Create one instance, seeded from current time, and export its methods |
|
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635 # as module-level functions. The functions are not threadsafe, and state |
|
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636 # is shared across all uses (both in the user's code and in the Python |
|
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637 # libraries), but that's fine for most programs and is easier for the |
|
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638 # casual user than making them instantiate their own Random() instance. |
|
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639 _inst = Random() |
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|
640 seed = _inst.seed |
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|
641 random = _inst.random |
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642 uniform = _inst.uniform |
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|
643 randint = _inst.randint |
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644 choice = _inst.choice |
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645 randrange = _inst.randrange |
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646 shuffle = _inst.shuffle |
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647 normalvariate = _inst.normalvariate |
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648 lognormvariate = _inst.lognormvariate |
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649 cunifvariate = _inst.cunifvariate |
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650 expovariate = _inst.expovariate |
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651 vonmisesvariate = _inst.vonmisesvariate |
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652 gammavariate = _inst.gammavariate |
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653 stdgamma = _inst.stdgamma |
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654 gauss = _inst.gauss |
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655 betavariate = _inst.betavariate |
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656 paretovariate = _inst.paretovariate |
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|
657 weibullvariate = _inst.weibullvariate |
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658 getstate = _inst.getstate |
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|
659 setstate = _inst.setstate |
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|
660 jumpahead = _inst.jumpahead |
|
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661 whseed = _inst.whseed |
|
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|
662 |
| 1575 | 663 if __name__ == '__main__': |
|
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|
664 _test() |
