Mercurial > cpython-lru_cache-weakref
annotate Lib/random.py @ 32527:0da660e05e69 legacy-trunk
"#if WITH_THREAD" is incorrect; must be #ifdef instead; WITH_THREAD
isn't always set to an integer value when it's defined.
| author | Tim Peters <tim.peters@gmail.com> |
|---|---|
| date | Wed, 07 Jul 2004 20:42:07 +0000 |
| parents | 854a2417d35e |
| children | 97fca735c8da |
| 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 pick random sample |
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11 generate random permutation |
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12 |
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13 distributions on the real line: |
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14 ------------------------------ |
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15 uniform |
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16 normal (Gaussian) |
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17 lognormal |
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18 negative exponential |
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19 gamma |
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20 beta |
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21 pareto |
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22 Weibull |
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24 distributions on the circle (angles 0 to 2pi) |
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25 --------------------------------------------- |
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26 circular uniform |
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27 von Mises |
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29 General notes on the underlying Mersenne Twister core generator: |
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30 |
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31 * The period is 2**19937-1. |
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32 * It is one of the most extensively tested generators in existence |
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33 * Without a direct way to compute N steps forward, the |
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34 semantics of jumpahead(n) are weakened to simply jump |
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35 to another distant state and rely on the large period |
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36 to avoid overlapping sequences. |
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37 * The random() method is implemented in C, executes in |
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38 a single Python step, and is, therefore, threadsafe. |
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39 |
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40 """ |
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41 |
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42 from warnings import warn as _warn |
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43 from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType |
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44 from math import log as _log, exp as _exp, pi as _pi, e as _e |
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45 from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin |
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46 from math import floor as _floor |
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47 |
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48 __all__ = ["Random","seed","random","uniform","randint","choice","sample", |
| 16613 | 49 "randrange","shuffle","normalvariate","lognormvariate", |
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50 "expovariate","vonmisesvariate","gammavariate", |
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51 "gauss","betavariate","paretovariate","weibullvariate", |
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52 "getstate","setstate","jumpahead", "WichmannHill", "getrandbits"] |
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54 NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0) |
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55 TWOPI = 2.0*_pi |
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56 LOG4 = _log(4.0) |
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57 SG_MAGICCONST = 1.0 + _log(4.5) |
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58 BPF = 53 # Number of bits in a float |
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59 |
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60 # Translated by Guido van Rossum from C source provided by |
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61 # Adrian Baddeley. Adapted by Raymond Hettinger for use with |
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62 # the Mersenne Twister core generator. |
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63 |
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64 import _random |
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65 |
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66 class Random(_random.Random): |
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67 """Random number generator base class used by bound module functions. |
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68 |
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69 Used to instantiate instances of Random to get generators that don't |
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70 share state. Especially useful for multi-threaded programs, creating |
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71 a different instance of Random for each thread, and using the jumpahead() |
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72 method to ensure that the generated sequences seen by each thread don't |
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73 overlap. |
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74 |
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75 Class Random can also be subclassed if you want to use a different basic |
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76 generator of your own devising: in that case, override the following |
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77 methods: random(), seed(), getstate(), setstate() and jumpahead(). |
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78 Optionally, implement a getrandombits() method so that randrange() |
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79 can cover arbitrarily large ranges. |
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80 |
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81 """ |
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82 |
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83 VERSION = 2 # used by getstate/setstate |
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84 |
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85 def __init__(self, x=None): |
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86 """Initialize an instance. |
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87 |
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88 Optional argument x controls seeding, as for Random.seed(). |
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89 """ |
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90 |
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91 self.seed(x) |
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92 self.gauss_next = None |
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93 |
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94 def seed(self, a=None): |
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95 """Initialize internal state from hashable object. |
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96 |
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97 None or no argument seeds from current time. |
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98 |
| 16386 | 99 If a is not None or an int or long, hash(a) is used instead. |
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100 """ |
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101 |
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102 if a is None: |
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103 import time |
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104 a = long(time.time() * 256) # use fractional seconds |
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105 super(Random, self).seed(a) |
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106 self.gauss_next = None |
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107 |
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108 def getstate(self): |
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109 """Return internal state; can be passed to setstate() later.""" |
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110 return self.VERSION, super(Random, self).getstate(), self.gauss_next |
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111 |
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112 def setstate(self, state): |
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113 """Restore internal state from object returned by getstate().""" |
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114 version = state[0] |
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115 if version == 2: |
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116 version, internalstate, self.gauss_next = state |
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117 super(Random, self).setstate(internalstate) |
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118 else: |
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119 raise ValueError("state with version %s passed to " |
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120 "Random.setstate() of version %s" % |
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121 (version, self.VERSION)) |
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122 |
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123 ## ---- Methods below this point do not need to be overridden when |
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124 ## ---- subclassing for the purpose of using a different core generator. |
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125 |
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126 ## -------------------- pickle support ------------------- |
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127 |
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128 def __getstate__(self): # for pickle |
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129 return self.getstate() |
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130 |
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131 def __setstate__(self, state): # for pickle |
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132 self.setstate(state) |
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133 |
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134 def __reduce__(self): |
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135 return self.__class__, (), self.getstate() |
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136 |
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137 ## -------------------- integer methods ------------------- |
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138 |
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139 def randrange(self, start, stop=None, step=1, int=int, default=None, |
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140 maxwidth=1L<<BPF): |
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141 """Choose a random item from range(start, stop[, step]). |
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142 |
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143 This fixes the problem with randint() which includes the |
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144 endpoint; in Python this is usually not what you want. |
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145 Do not supply the 'int', 'default', and 'maxwidth' arguments. |
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146 """ |
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147 |
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148 # This code is a bit messy to make it fast for the |
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149 # common case while still doing adequate error checking. |
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150 istart = int(start) |
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151 if istart != start: |
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152 raise ValueError, "non-integer arg 1 for randrange()" |
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153 if stop is default: |
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154 if istart > 0: |
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155 if istart >= maxwidth: |
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156 return self._randbelow(istart) |
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157 return int(self.random() * istart) |
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158 raise ValueError, "empty range for randrange()" |
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159 |
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160 # stop argument supplied. |
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161 istop = int(stop) |
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162 if istop != stop: |
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163 raise ValueError, "non-integer stop for randrange()" |
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164 width = istop - istart |
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165 if step == 1 and width > 0: |
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166 # Note that |
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167 # int(istart + self.random()*width) |
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168 # instead would be incorrect. For example, consider istart |
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169 # = -2 and istop = 0. Then the guts would be in |
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170 # -2.0 to 0.0 exclusive on both ends (ignoring that random() |
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171 # might return 0.0), and because int() truncates toward 0, the |
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172 # final result would be -1 or 0 (instead of -2 or -1). |
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173 # istart + int(self.random()*width) |
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174 # would also be incorrect, for a subtler reason: the RHS |
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175 # can return a long, and then randrange() would also return |
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176 # a long, but we're supposed to return an int (for backward |
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177 # compatibility). |
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178 |
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179 if width >= maxwidth: |
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180 return int(istart + self._randbelow(width)) |
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181 return int(istart + int(self.random()*width)) |
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182 if step == 1: |
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183 raise ValueError, "empty range for randrange() (%d,%d, %d)" % (istart, istop, width) |
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184 |
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185 # Non-unit step argument supplied. |
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186 istep = int(step) |
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187 if istep != step: |
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188 raise ValueError, "non-integer step for randrange()" |
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189 if istep > 0: |
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190 n = (width + istep - 1) / istep |
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191 elif istep < 0: |
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192 n = (width + istep + 1) / istep |
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193 else: |
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194 raise ValueError, "zero step for randrange()" |
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195 |
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196 if n <= 0: |
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197 raise ValueError, "empty range for randrange()" |
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198 |
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199 if n >= maxwidth: |
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200 return istart + self._randbelow(n) |
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201 return istart + istep*int(self.random() * n) |
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203 def randint(self, a, b): |
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204 """Return random integer in range [a, b], including both end points. |
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205 """ |
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206 |
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207 return self.randrange(a, b+1) |
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208 |
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209 def _randbelow(self, n, _log=_log, int=int, _maxwidth=1L<<BPF, |
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210 _Method=_MethodType, _BuiltinMethod=_BuiltinMethodType): |
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211 """Return a random int in the range [0,n) |
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212 |
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213 Handles the case where n has more bits than returned |
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214 by a single call to the underlying generator. |
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215 """ |
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216 |
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217 try: |
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218 getrandbits = self.getrandbits |
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219 except AttributeError: |
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220 pass |
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221 else: |
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222 # Only call self.getrandbits if the original random() builtin method |
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223 # has not been overridden or if a new getrandbits() was supplied. |
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224 # This assures that the two methods correspond. |
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225 if type(self.random) is _BuiltinMethod or type(getrandbits) is _Method: |
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226 k = int(1.00001 + _log(n-1, 2.0)) # 2**k > n-1 > 2**(k-2) |
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227 r = getrandbits(k) |
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228 while r >= n: |
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229 r = getrandbits(k) |
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230 return r |
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231 if n >= _maxwidth: |
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232 _warn("Underlying random() generator does not supply \n" |
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233 "enough bits to choose from a population range this large") |
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234 return int(self.random() * n) |
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235 |
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236 ## -------------------- sequence methods ------------------- |
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237 |
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238 def choice(self, seq): |
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239 """Choose a random element from a non-empty sequence.""" |
| 32287 | 240 return seq[int(self.random() * len(seq))] # raises IndexError if seq is empty |
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241 |
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242 def shuffle(self, x, random=None, int=int): |
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243 """x, random=random.random -> shuffle list x in place; return None. |
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244 |
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245 Optional arg random is a 0-argument function returning a random |
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246 float in [0.0, 1.0); by default, the standard random.random. |
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247 |
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248 Note that for even rather small len(x), the total number of |
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249 permutations of x is larger than the period of most random number |
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250 generators; this implies that "most" permutations of a long |
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251 sequence can never be generated. |
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252 """ |
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253 |
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254 if random is None: |
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255 random = self.random |
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256 for i in reversed(xrange(1, len(x))): |
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257 # pick an element in x[:i+1] with which to exchange x[i] |
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258 j = int(random() * (i+1)) |
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259 x[i], x[j] = x[j], x[i] |
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260 |
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261 def sample(self, population, k): |
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262 """Chooses k unique random elements from a population sequence. |
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263 |
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264 Returns a new list containing elements from the population while |
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265 leaving the original population unchanged. The resulting list is |
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266 in selection order so that all sub-slices will also be valid random |
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267 samples. This allows raffle winners (the sample) to be partitioned |
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268 into grand prize and second place winners (the subslices). |
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269 |
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270 Members of the population need not be hashable or unique. If the |
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271 population contains repeats, then each occurrence is a possible |
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272 selection in the sample. |
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273 |
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274 To choose a sample in a range of integers, use xrange as an argument. |
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275 This is especially fast and space efficient for sampling from a |
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276 large population: sample(xrange(10000000), 60) |
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277 """ |
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278 |
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279 # Sampling without replacement entails tracking either potential |
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280 # selections (the pool) in a list or previous selections in a |
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281 # dictionary. |
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282 |
| 31578 | 283 # When the number of selections is small compared to the |
| 284 # population, then tracking selections is efficient, requiring | |
| 285 # only a small dictionary and an occasional reselection. For | |
| 286 # a larger number of selections, the pool tracking method is | |
| 287 # preferred since the list takes less space than the | |
| 288 # dictionary and it doesn't suffer from frequent reselections. | |
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289 |
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290 n = len(population) |
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291 if not 0 <= k <= n: |
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292 raise ValueError, "sample larger than population" |
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293 random = self.random |
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294 _int = int |
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295 result = [None] * k |
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296 if n < 6 * k: # if n len list takes less space than a k len dict |
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297 pool = list(population) |
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298 for i in xrange(k): # invariant: non-selected at [0,n-i) |
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299 j = _int(random() * (n-i)) |
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300 result[i] = pool[j] |
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301 pool[j] = pool[n-i-1] # move non-selected item into vacancy |
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302 else: |
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303 try: |
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304 n > 0 and (population[0], population[n//2], population[n-1]) |
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305 except (TypeError, KeyError): # handle sets and dictionaries |
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306 population = tuple(population) |
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307 selected = {} |
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308 for i in xrange(k): |
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309 j = _int(random() * n) |
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310 while j in selected: |
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311 j = _int(random() * n) |
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312 result[i] = selected[j] = population[j] |
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313 return result |
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314 |
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315 ## -------------------- real-valued distributions ------------------- |
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316 |
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317 ## -------------------- uniform distribution ------------------- |
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318 |
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319 def uniform(self, a, b): |
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320 """Get a random number in the range [a, b).""" |
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321 return a + (b-a) * self.random() |
| 1575 | 322 |
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323 ## -------------------- normal distribution -------------------- |
| 1575 | 324 |
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325 def normalvariate(self, mu, sigma): |
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326 """Normal distribution. |
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327 |
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328 mu is the mean, and sigma is the standard deviation. |
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329 |
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330 """ |
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331 # mu = mean, sigma = standard deviation |
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332 |
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333 # Uses Kinderman and Monahan method. Reference: Kinderman, |
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334 # A.J. and Monahan, J.F., "Computer generation of random |
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335 # variables using the ratio of uniform deviates", ACM Trans |
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336 # Math Software, 3, (1977), pp257-260. |
| 1575 | 337 |
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338 random = self.random |
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339 while True: |
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340 u1 = random() |
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341 u2 = 1.0 - random() |
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342 z = NV_MAGICCONST*(u1-0.5)/u2 |
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343 zz = z*z/4.0 |
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344 if zz <= -_log(u2): |
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345 break |
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346 return mu + z*sigma |
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348 ## -------------------- lognormal distribution -------------------- |
| 1575 | 349 |
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350 def lognormvariate(self, mu, sigma): |
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351 """Log normal distribution. |
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352 |
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353 If you take the natural logarithm of this distribution, you'll get a |
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354 normal distribution with mean mu and standard deviation sigma. |
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355 mu can have any value, and sigma must be greater than zero. |
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356 |
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357 """ |
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358 return _exp(self.normalvariate(mu, sigma)) |
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360 ## -------------------- exponential distribution -------------------- |
| 1575 | 361 |
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362 def expovariate(self, lambd): |
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363 """Exponential distribution. |
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364 |
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365 lambd is 1.0 divided by the desired mean. (The parameter would be |
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366 called "lambda", but that is a reserved word in Python.) Returned |
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367 values range from 0 to positive infinity. |
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368 |
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369 """ |
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370 # lambd: rate lambd = 1/mean |
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371 # ('lambda' is a Python reserved word) |
| 1575 | 372 |
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373 random = self.random |
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374 u = random() |
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375 while u <= 1e-7: |
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376 u = random() |
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377 return -_log(u)/lambd |
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379 ## -------------------- von Mises distribution -------------------- |
| 1575 | 380 |
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381 def vonmisesvariate(self, mu, kappa): |
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382 """Circular data distribution. |
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383 |
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384 mu is the mean angle, expressed in radians between 0 and 2*pi, and |
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385 kappa is the concentration parameter, which must be greater than or |
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386 equal to zero. If kappa is equal to zero, this distribution reduces |
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387 to a uniform random angle over the range 0 to 2*pi. |
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388 |
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389 """ |
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390 # mu: mean angle (in radians between 0 and 2*pi) |
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391 # kappa: concentration parameter kappa (>= 0) |
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392 # if kappa = 0 generate uniform random angle |
| 1575 | 393 |
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394 # Based upon an algorithm published in: Fisher, N.I., |
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395 # "Statistical Analysis of Circular Data", Cambridge |
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396 # University Press, 1993. |
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397 |
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398 # Thanks to Magnus Kessler for a correction to the |
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399 # implementation of step 4. |
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400 |
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401 random = self.random |
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402 if kappa <= 1e-6: |
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403 return TWOPI * random() |
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|
404 |
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405 a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa) |
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406 b = (a - _sqrt(2.0 * a))/(2.0 * kappa) |
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407 r = (1.0 + b * b)/(2.0 * b) |
| 1575 | 408 |
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409 while True: |
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410 u1 = random() |
| 1575 | 411 |
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412 z = _cos(_pi * u1) |
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413 f = (1.0 + r * z)/(r + z) |
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414 c = kappa * (r - f) |
| 1575 | 415 |
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416 u2 = random() |
| 1575 | 417 |
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418 if not (u2 >= c * (2.0 - c) and u2 > c * _exp(1.0 - c)): |
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419 break |
| 1575 | 420 |
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421 u3 = random() |
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422 if u3 > 0.5: |
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423 theta = (mu % TWOPI) + _acos(f) |
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424 else: |
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425 theta = (mu % TWOPI) - _acos(f) |
| 1575 | 426 |
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427 return theta |
| 1575 | 428 |
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429 ## -------------------- gamma distribution -------------------- |
| 1575 | 430 |
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431 def gammavariate(self, alpha, beta): |
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432 """Gamma distribution. Not the gamma function! |
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433 |
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434 Conditions on the parameters are alpha > 0 and beta > 0. |
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435 |
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436 """ |
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437 |
|
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438 # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2 |
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439 |
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440 # Warning: a few older sources define the gamma distribution in terms |
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441 # of alpha > -1.0 |
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442 if alpha <= 0.0 or beta <= 0.0: |
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443 raise ValueError, 'gammavariate: alpha and beta must be > 0.0' |
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444 |
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445 random = self.random |
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446 if alpha > 1.0: |
| 1575 | 447 |
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448 # Uses R.C.H. Cheng, "The generation of Gamma |
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449 # variables with non-integral shape parameters", |
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450 # Applied Statistics, (1977), 26, No. 1, p71-74 |
| 1575 | 451 |
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452 ainv = _sqrt(2.0 * alpha - 1.0) |
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453 bbb = alpha - LOG4 |
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454 ccc = alpha + ainv |
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Whitespace normalization.
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|
455 |
|
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|
456 while True: |
|
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|
457 u1 = random() |
|
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458 if not 1e-7 < u1 < .9999999: |
|
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|
459 continue |
|
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|
460 u2 = 1.0 - random() |
|
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|
461 v = _log(u1/(1.0-u1))/ainv |
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|
462 x = alpha*_exp(v) |
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|
463 z = u1*u1*u2 |
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|
464 r = bbb+ccc*v-x |
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465 if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z): |
|
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|
466 return x * beta |
| 1575 | 467 |
|
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|
468 elif alpha == 1.0: |
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|
469 # expovariate(1) |
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|
470 u = random() |
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471 while u <= 1e-7: |
|
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|
472 u = random() |
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|
473 return -_log(u) * beta |
| 1575 | 474 |
|
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|
475 else: # alpha is between 0 and 1 (exclusive) |
| 1575 | 476 |
|
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|
477 # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle |
| 1575 | 478 |
|
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479 while True: |
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|
480 u = random() |
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|
481 b = (_e + alpha)/_e |
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482 p = b*u |
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483 if p <= 1.0: |
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484 x = pow(p, 1.0/alpha) |
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485 else: |
|
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486 # p > 1 |
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487 x = -_log((b-p)/alpha) |
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|
488 u1 = random() |
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489 if not (((p <= 1.0) and (u1 > _exp(-x))) or |
|
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490 ((p > 1) and (u1 > pow(x, alpha - 1.0)))): |
|
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|
491 break |
|
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|
492 return x * beta |
|
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|
493 |
|
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|
494 ## -------------------- Gauss (faster alternative) -------------------- |
|
1576
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|
495 |
|
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|
496 def gauss(self, mu, sigma): |
|
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|
497 """Gaussian distribution. |
|
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|
498 |
|
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|
499 mu is the mean, and sigma is the standard deviation. This is |
|
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|
500 slightly faster than the normalvariate() function. |
|
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|
501 |
|
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|
502 Not thread-safe without a lock around calls. |
|
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|
503 |
|
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|
504 """ |
|
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|
505 |
|
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|
506 # When x and y are two variables from [0, 1), uniformly |
|
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|
507 # distributed, then |
|
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|
508 # |
|
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509 # cos(2*pi*x)*sqrt(-2*log(1-y)) |
|
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|
510 # sin(2*pi*x)*sqrt(-2*log(1-y)) |
|
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|
511 # |
|
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512 # are two *independent* variables with normal distribution |
|
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|
513 # (mu = 0, sigma = 1). |
|
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|
514 # (Lambert Meertens) |
|
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|
515 # (corrected version; bug discovered by Mike Miller, fixed by LM) |
|
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|
516 |
|
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517 # Multithreading note: When two threads call this function |
|
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518 # simultaneously, it is possible that they will receive the |
|
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|
519 # same return value. The window is very small though. To |
|
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|
520 # avoid this, you have to use a lock around all calls. (I |
|
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|
521 # didn't want to slow this down in the serial case by using a |
|
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|
522 # lock here.) |
|
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|
523 |
|
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|
524 random = self.random |
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|
525 z = self.gauss_next |
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|
526 self.gauss_next = None |
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|
527 if z is None: |
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|
528 x2pi = random() * TWOPI |
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|
529 g2rad = _sqrt(-2.0 * _log(1.0 - random())) |
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|
530 z = _cos(x2pi) * g2rad |
|
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|
531 self.gauss_next = _sin(x2pi) * g2rad |
|
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|
532 |
|
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|
533 return mu + z*sigma |
|
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|
534 |
|
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|
535 ## -------------------- beta -------------------- |
|
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|
536 ## See |
|
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|
537 ## http://sourceforge.net/bugs/?func=detailbug&bug_id=130030&group_id=5470 |
|
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|
538 ## for Ivan Frohne's insightful analysis of why the original implementation: |
|
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|
539 ## |
|
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|
540 ## def betavariate(self, alpha, beta): |
|
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|
541 ## # Discrete Event Simulation in C, pp 87-88. |
|
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|
542 ## |
|
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|
543 ## y = self.expovariate(alpha) |
|
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|
544 ## z = self.expovariate(1.0/beta) |
|
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|
545 ## return z/(y+z) |
|
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|
546 ## |
|
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|
547 ## was dead wrong, and how it probably got that way. |
|
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|
548 |
|
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|
549 def betavariate(self, alpha, beta): |
|
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|
550 """Beta distribution. |
|
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|
551 |
|
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|
552 Conditions on the parameters are alpha > -1 and beta} > -1. |
|
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|
553 Returned values range between 0 and 1. |
|
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|
554 |
|
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|
555 """ |
|
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|
556 |
|
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|
557 # This version due to Janne Sinkkonen, and matches all the std |
|
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|
558 # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). |
|
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|
559 y = self.gammavariate(alpha, 1.) |
|
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|
560 if y == 0: |
|
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|
561 return 0.0 |
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|
562 else: |
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|
563 return y / (y + self.gammavariate(beta, 1.)) |
|
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|
564 |
|
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|
565 ## -------------------- Pareto -------------------- |
|
6698
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Added Pareto and Weibull distributions, courtesy Jeremy Hylton.
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diff
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|
566 |
|
16242
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parents:
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|
567 def paretovariate(self, alpha): |
|
23531
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Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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|
568 """Pareto distribution. alpha is the shape parameter.""" |
|
16242
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parents:
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|
569 # Jain, pg. 495 |
|
6698
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Added Pareto and Weibull distributions, courtesy Jeremy Hylton.
Guido van Rossum <guido@python.org>
parents:
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|
570 |
|
27085
90350b5d3397
Correct long standing bugs in the methods for random distributions.
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|
571 u = 1.0 - self.random() |
|
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parents:
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|
572 return 1.0 / pow(u, 1.0/alpha) |
|
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Guido van Rossum <guido@python.org>
parents:
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|
573 |
|
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d614b6e9fb4e
Cosmetic changes after some sleep; no change in semantics.
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|
574 ## -------------------- Weibull -------------------- |
|
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|
575 |
|
16242
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|
576 def weibullvariate(self, alpha, beta): |
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|
577 """Weibull distribution. |
|
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|
578 |
|
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|
579 alpha is the scale parameter and beta is the shape parameter. |
|
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Whitespace normalized.
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diff
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580 |
|
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Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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|
581 """ |
|
16242
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|
582 # Jain, pg. 499; bug fix courtesy Bill Arms |
|
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Add Tim Peters' shuffle() algorithm.
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parents:
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|
583 |
|
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Raymond Hettinger <python@rcn.com>
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|
584 u = 1.0 - self.random() |
|
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|
585 return alpha * pow(-_log(u), 1.0/beta) |
|
11200
bca2d14f3e36
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parents:
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diff
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|
586 |
|
26829
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587 ## -------------------- Wichmann-Hill ------------------- |
|
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|
588 |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
589 class WichmannHill(Random): |
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|
590 |
|
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591 VERSION = 1 # used by getstate/setstate |
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|
592 |
|
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|
593 def seed(self, a=None): |
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|
594 """Initialize internal state from hashable object. |
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|
595 |
|
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|
596 None or no argument seeds from current time. |
|
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changeset
|
597 |
|
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changeset
|
598 If a is not None or an int or long, hash(a) is used instead. |
|
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|
599 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
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changeset
|
600 If a is an int or long, a is used directly. Distinct values between |
|
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changeset
|
601 0 and 27814431486575L inclusive are guaranteed to yield distinct |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
602 internal states (this guarantee is specific to the default |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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changeset
|
603 Wichmann-Hill generator). |
|
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|
604 """ |
|
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changeset
|
605 |
|
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|
606 if a is None: |
|
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|
607 # Initialize from current time |
|
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|
608 import time |
|
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|
609 a = long(time.time() * 256) |
|
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|
610 |
|
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|
611 if not isinstance(a, (int, long)): |
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|
612 a = hash(a) |
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|
613 |
|
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|
614 a, x = divmod(a, 30268) |
|
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|
615 a, y = divmod(a, 30306) |
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|
616 a, z = divmod(a, 30322) |
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617 self._seed = int(x)+1, int(y)+1, int(z)+1 |
|
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|
618 |
|
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|
619 self.gauss_next = None |
|
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|
620 |
|
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|
621 def random(self): |
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|
622 """Get the next random number in the range [0.0, 1.0).""" |
|
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|
623 |
|
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|
624 # Wichman-Hill random number generator. |
|
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|
625 # |
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|
626 # Wichmann, B. A. & Hill, I. D. (1982) |
|
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|
627 # Algorithm AS 183: |
|
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|
628 # An efficient and portable pseudo-random number generator |
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629 # Applied Statistics 31 (1982) 188-190 |
|
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|
630 # |
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631 # see also: |
|
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|
632 # Correction to Algorithm AS 183 |
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633 # Applied Statistics 33 (1984) 123 |
|
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|
634 # |
|
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635 # McLeod, A. I. (1985) |
|
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|
636 # A remark on Algorithm AS 183 |
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|
637 # Applied Statistics 34 (1985),198-200 |
|
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|
638 |
|
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|
639 # This part is thread-unsafe: |
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|
640 # BEGIN CRITICAL SECTION |
|
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641 x, y, z = self._seed |
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|
642 x = (171 * x) % 30269 |
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|
643 y = (172 * y) % 30307 |
|
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|
644 z = (170 * z) % 30323 |
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|
645 self._seed = x, y, z |
|
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|
646 # END CRITICAL SECTION |
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|
647 |
|
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|
648 # Note: on a platform using IEEE-754 double arithmetic, this can |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
649 # never return 0.0 (asserted by Tim; proof too long for a comment). |
|
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|
650 return (x/30269.0 + y/30307.0 + z/30323.0) % 1.0 |
|
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|
651 |
|
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|
652 def getstate(self): |
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653 """Return internal state; can be passed to setstate() later.""" |
|
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|
654 return self.VERSION, self._seed, self.gauss_next |
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|
655 |
|
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|
656 def setstate(self, state): |
|
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|
657 """Restore internal state from object returned by getstate().""" |
|
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|
658 version = state[0] |
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|
659 if version == 1: |
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|
660 version, self._seed, self.gauss_next = state |
|
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|
661 else: |
|
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662 raise ValueError("state with version %s passed to " |
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|
663 "Random.setstate() of version %s" % |
|
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|
664 (version, self.VERSION)) |
|
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|
665 |
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|
666 def jumpahead(self, n): |
|
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|
667 """Act as if n calls to random() were made, but quickly. |
|
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|
668 |
|
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|
669 n is an int, greater than or equal to 0. |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
670 |
|
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|
671 Example use: If you have 2 threads and know that each will |
|
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diff
changeset
|
672 consume no more than a million random numbers, create two Random |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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diff
changeset
|
673 objects r1 and r2, then do |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
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|
674 r2.setstate(r1.getstate()) |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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changeset
|
675 r2.jumpahead(1000000) |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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|
676 Then r1 and r2 will use guaranteed-disjoint segments of the full |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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|
677 period. |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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diff
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|
678 """ |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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changeset
|
679 |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
680 if not n >= 0: |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
681 raise ValueError("n must be >= 0") |
|
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changeset
|
682 x, y, z = self._seed |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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|
683 x = int(x * pow(171, n, 30269)) % 30269 |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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|
684 y = int(y * pow(172, n, 30307)) % 30307 |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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changeset
|
685 z = int(z * pow(170, n, 30323)) % 30323 |
|
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|
686 self._seed = x, y, z |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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diff
changeset
|
687 |
|
f64c96922708
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parents:
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diff
changeset
|
688 def __whseed(self, x=0, y=0, z=0): |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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|
689 """Set the Wichmann-Hill seed from (x, y, z). |
|
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diff
changeset
|
690 |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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diff
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|
691 These must be integers in the range [0, 256). |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
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changeset
|
692 """ |
|
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Raymond Hettinger <python@rcn.com>
parents:
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changeset
|
693 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
694 if not type(x) == type(y) == type(z) == int: |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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changeset
|
695 raise TypeError('seeds must be integers') |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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changeset
|
696 if not (0 <= x < 256 and 0 <= y < 256 and 0 <= z < 256): |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
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changeset
|
697 raise ValueError('seeds must be in range(0, 256)') |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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changeset
|
698 if 0 == x == y == z: |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
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parents:
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changeset
|
699 # Initialize from current time |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
700 import time |
|
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SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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|
701 t = long(time.time() * 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
702 t = int((t&0xffffff) ^ (t>>24)) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
703 t, x = divmod(t, 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
704 t, y = divmod(t, 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
705 t, z = divmod(t, 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
706 # Zero is a poor seed, so substitute 1 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
707 self._seed = (x or 1, y or 1, z or 1) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
708 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
709 self.gauss_next = None |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
710 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
711 def whseed(self, a=None): |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
712 """Seed from hashable object's hash code. |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
713 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
714 None or no argument seeds from current time. It is not guaranteed |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
715 that objects with distinct hash codes lead to distinct internal |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
716 states. |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
717 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
718 This is obsolete, provided for compatibility with the seed routine |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
719 used prior to Python 2.1. Use the .seed() method instead. |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
720 """ |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
721 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
722 if a is None: |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
723 self.__whseed() |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
724 return |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
725 a = hash(a) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
726 a, x = divmod(a, 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
727 a, y = divmod(a, 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
728 a, z = divmod(a, 256) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
729 x = (x + a) % 256 or 1 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
730 y = (y + a) % 256 or 1 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
731 z = (z + a) % 256 or 1 |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
732 self.__whseed(x, y, z) |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
733 |
|
16265
d614b6e9fb4e
Cosmetic changes after some sleep; no change in semantics.
Tim Peters <tim.peters@gmail.com>
parents:
16243
diff
changeset
|
734 ## -------------------- test program -------------------- |
| 1575 | 735 |
|
30243
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
736 def _test_generator(n, func, args): |
|
15871
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
737 import time |
|
30243
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
738 print n, 'times', func.__name__ |
|
29250
34609fa35b11
'sum' is no longer a good variable name. Use 'total' instead.
Raymond Hettinger <python@rcn.com>
parents:
27279
diff
changeset
|
739 total = 0.0 |
|
15871
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
740 sqsum = 0.0 |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
741 smallest = 1e10 |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
742 largest = -1e10 |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
743 t0 = time.time() |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
744 for i in range(n): |
|
30243
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
745 x = func(*args) |
|
29250
34609fa35b11
'sum' is no longer a good variable name. Use 'total' instead.
Raymond Hettinger <python@rcn.com>
parents:
27279
diff
changeset
|
746 total += x |
|
15871
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
747 sqsum = sqsum + x*x |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
748 smallest = min(x, smallest) |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
749 largest = max(x, largest) |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
750 t1 = time.time() |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
751 print round(t1-t0, 3), 'sec,', |
|
29250
34609fa35b11
'sum' is no longer a good variable name. Use 'total' instead.
Raymond Hettinger <python@rcn.com>
parents:
27279
diff
changeset
|
752 avg = total/n |
|
16242
3c5f09422b69
Reworked random.py so that it no longer depends on, and offers all the
Tim Peters <tim.peters@gmail.com>
parents:
15871
diff
changeset
|
753 stddev = _sqrt(sqsum/n - avg*avg) |
|
15871
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
754 print 'avg %g, stddev %g, min %g, max %g' % \ |
|
62aeefa01fdd
Whitespace normalization.
Tim Peters <tim.peters@gmail.com>
parents:
14617
diff
changeset
|
755 (avg, stddev, smallest, largest) |
| 1575 | 756 |
|
26155
8ceb455f5541
SF patch 629637: Add sample(population, k) method to the random module.
Raymond Hettinger <python@rcn.com>
parents:
24861
diff
changeset
|
757 |
|
8ceb455f5541
SF patch 629637: Add sample(population, k) method to the random module.
Raymond Hettinger <python@rcn.com>
parents:
24861
diff
changeset
|
758 def _test(N=2000): |
|
30243
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
759 _test_generator(N, random, ()) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
760 _test_generator(N, normalvariate, (0.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
761 _test_generator(N, lognormvariate, (0.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
762 _test_generator(N, vonmisesvariate, (0.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
763 _test_generator(N, gammavariate, (0.01, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
764 _test_generator(N, gammavariate, (0.1, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
765 _test_generator(N, gammavariate, (0.1, 2.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
766 _test_generator(N, gammavariate, (0.5, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
767 _test_generator(N, gammavariate, (0.9, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
768 _test_generator(N, gammavariate, (1.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
769 _test_generator(N, gammavariate, (2.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
770 _test_generator(N, gammavariate, (20.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
771 _test_generator(N, gammavariate, (200.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
772 _test_generator(N, gauss, (0.0, 1.0)) |
|
53a322c6de52
Eliminate unnecessary eval() in tests
Raymond Hettinger <python@rcn.com>
parents:
30114
diff
changeset
|
773 _test_generator(N, betavariate, (3.0, 3.0)) |
|
16265
d614b6e9fb4e
Cosmetic changes after some sleep; no change in semantics.
Tim Peters <tim.peters@gmail.com>
parents:
16243
diff
changeset
|
774 |
|
16292
0501c2a10315
New comment block to Clarify a subtlety.
Tim Peters <tim.peters@gmail.com>
parents:
16272
diff
changeset
|
775 # Create one instance, seeded from current time, and export its methods |
|
26829
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
776 # as module-level functions. The functions share state across all uses |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
777 #(both in the user's code and in the Python libraries), but that's fine |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
778 # for most programs and is easier for the casual user than making them |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
parents:
26564
diff
changeset
|
779 # instantiate their own Random() instance. |
|
f64c96922708
SF patch 658251: Install a C implementation of the Mersenne Twister as the
Raymond Hettinger <python@rcn.com>
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780 |
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781 _inst = Random() |
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782 seed = _inst.seed |
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783 random = _inst.random |
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784 uniform = _inst.uniform |
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785 randint = _inst.randint |
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786 choice = _inst.choice |
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787 randrange = _inst.randrange |
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788 sample = _inst.sample |
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789 shuffle = _inst.shuffle |
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790 normalvariate = _inst.normalvariate |
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791 lognormvariate = _inst.lognormvariate |
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792 expovariate = _inst.expovariate |
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793 vonmisesvariate = _inst.vonmisesvariate |
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794 gammavariate = _inst.gammavariate |
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795 gauss = _inst.gauss |
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796 betavariate = _inst.betavariate |
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797 paretovariate = _inst.paretovariate |
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798 weibullvariate = _inst.weibullvariate |
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799 getstate = _inst.getstate |
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800 setstate = _inst.setstate |
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801 jumpahead = _inst.jumpahead |
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802 getrandbits = _inst.getrandbits |
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803 |
| 1575 | 804 if __name__ == '__main__': |
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805 _test() |
