Mercurial > cpython-lru_cache-weakref
annotate Lib/random.py @ 70626:aa07c1237f4e
(Merge 3.2) Issue #12016: Reindent decoders of HK and JP codecs
| author | Victor Stinner <victor.stinner@haypocalc.com> |
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| date | Fri, 03 Jun 2011 23:34:32 +0200 |
| parents | 1cfa5dab709d |
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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 triangular |
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17 normal (Gaussian) |
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18 lognormal |
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19 negative exponential |
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20 gamma |
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21 beta |
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22 pareto |
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23 Weibull |
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25 distributions on the circle (angles 0 to 2pi) |
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26 --------------------------------------------- |
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27 circular uniform |
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28 von Mises |
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30 General notes on the underlying Mersenne Twister core generator: |
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31 |
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32 * The period is 2**19937-1. |
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33 * It is one of the most extensively tested generators in existence. |
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34 * The random() method is implemented in C, executes in a single Python step, |
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35 and is, therefore, threadsafe. |
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36 |
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37 """ |
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38 |
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39 from __future__ import division |
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40 from warnings import warn as _warn |
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41 from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType |
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42 from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil |
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43 from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin |
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44 from os import urandom as _urandom |
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45 from collections.abc import Set as _Set, Sequence as _Sequence |
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46 from hashlib import sha512 as _sha512 |
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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","triangular", |
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51 "gauss","betavariate","paretovariate","weibullvariate", |
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52 "getstate","setstate", "getrandbits", |
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53 "SystemRandom"] |
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55 NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0) |
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56 TWOPI = 2.0*_pi |
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57 LOG4 = _log(4.0) |
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58 SG_MAGICCONST = 1.0 + _log(4.5) |
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59 BPF = 53 # Number of bits in a float |
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60 RECIP_BPF = 2**-BPF |
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61 |
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62 |
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63 # Translated by Guido van Rossum from C source provided by |
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64 # Adrian Baddeley. Adapted by Raymond Hettinger for use with |
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65 # the Mersenne Twister and os.urandom() core generators. |
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66 |
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67 import _random |
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68 |
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69 class Random(_random.Random): |
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70 """Random number generator base class used by bound module functions. |
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71 |
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72 Used to instantiate instances of Random to get generators that don't |
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73 share state. |
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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(), and setstate(). |
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78 Optionally, implement a getrandbits() 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 = 3 # 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, version=2): |
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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 or from an operating |
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98 system specific randomness source if available. |
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99 |
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100 For version 2 (the default), all of the bits are used if *a *is a str, |
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101 bytes, or bytearray. For version 1, the hash() of *a* is used instead. |
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102 |
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103 If *a* is an int, all bits are used. |
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104 |
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105 """ |
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106 |
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107 if a is None: |
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108 try: |
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109 a = int.from_bytes(_urandom(32), 'big') |
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110 except NotImplementedError: |
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111 import time |
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112 a = int(time.time() * 256) # use fractional seconds |
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113 |
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114 if version == 2: |
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115 if isinstance(a, (str, bytes, bytearray)): |
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116 if isinstance(a, str): |
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117 a = a.encode() |
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118 a += _sha512(a).digest() |
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119 a = int.from_bytes(a, 'big') |
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120 |
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121 super().seed(a) |
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122 self.gauss_next = None |
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123 |
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124 def getstate(self): |
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125 """Return internal state; can be passed to setstate() later.""" |
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126 return self.VERSION, super().getstate(), self.gauss_next |
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127 |
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128 def setstate(self, state): |
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129 """Restore internal state from object returned by getstate().""" |
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130 version = state[0] |
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131 if version == 3: |
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132 version, internalstate, self.gauss_next = state |
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133 super().setstate(internalstate) |
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134 elif version == 2: |
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135 version, internalstate, self.gauss_next = state |
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136 # In version 2, the state was saved as signed ints, which causes |
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137 # inconsistencies between 32/64-bit systems. The state is |
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138 # really unsigned 32-bit ints, so we convert negative ints from |
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139 # version 2 to positive longs for version 3. |
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140 try: |
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141 internalstate = tuple(x % (2**32) for x in internalstate) |
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142 except ValueError as e: |
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143 raise TypeError from e |
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144 super().setstate(internalstate) |
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145 else: |
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146 raise ValueError("state with version %s passed to " |
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147 "Random.setstate() of version %s" % |
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148 (version, self.VERSION)) |
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149 |
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150 ## ---- Methods below this point do not need to be overridden when |
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151 ## ---- subclassing for the purpose of using a different core generator. |
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152 |
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153 ## -------------------- pickle support ------------------- |
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154 |
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155 def __getstate__(self): # for pickle |
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156 return self.getstate() |
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157 |
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158 def __setstate__(self, state): # for pickle |
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159 self.setstate(state) |
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160 |
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161 def __reduce__(self): |
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162 return self.__class__, (), self.getstate() |
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163 |
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164 ## -------------------- integer methods ------------------- |
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165 |
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166 def randrange(self, start, stop=None, step=1, int=int): |
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167 """Choose a random item from range(start, stop[, step]). |
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168 |
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169 This fixes the problem with randint() which includes the |
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170 endpoint; in Python this is usually not what you want. |
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173 """ |
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174 |
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175 # This code is a bit messy to make it fast for the |
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176 # common case while still doing adequate error checking. |
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177 istart = int(start) |
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178 if istart != start: |
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179 raise ValueError("non-integer arg 1 for randrange()") |
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181 if istart > 0: |
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182 return self._randbelow(istart) |
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183 raise ValueError("empty range for randrange()") |
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184 |
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185 # stop argument supplied. |
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186 istop = int(stop) |
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187 if istop != stop: |
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188 raise ValueError("non-integer stop for randrange()") |
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189 width = istop - istart |
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190 if step == 1 and width > 0: |
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192 if step == 1: |
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193 raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width)) |
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194 |
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195 # Non-unit step argument supplied. |
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196 istep = int(step) |
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197 if istep != step: |
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198 raise ValueError("non-integer step for randrange()") |
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199 if istep > 0: |
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200 n = (width + istep - 1) // istep |
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201 elif istep < 0: |
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202 n = (width + istep + 1) // istep |
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203 else: |
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204 raise ValueError("zero step for randrange()") |
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205 |
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206 if n <= 0: |
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207 raise ValueError("empty range for randrange()") |
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208 |
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209 return istart + istep*self._randbelow(n) |
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211 def randint(self, a, b): |
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212 """Return random integer in range [a, b], including both end points. |
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213 """ |
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214 |
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215 return self.randrange(a, b+1) |
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216 |
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217 def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type, |
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218 Method=_MethodType, BuiltinMethod=_BuiltinMethodType): |
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219 "Return a random int in the range [0,n). Raises ValueError if n==0." |
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220 |
| 64516 | 221 getrandbits = self.getrandbits |
| 222 # Only call self.getrandbits if the original random() builtin method | |
| 223 # has not been overridden or if a new getrandbits() was supplied. | |
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224 if type(self.random) is BuiltinMethod or type(getrandbits) is Method: |
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225 k = n.bit_length() # don't use (n-1) here because n can be 1 |
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226 r = getrandbits(k) # 0 <= r < 2**k |
| 64516 | 227 while r >= n: |
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228 r = getrandbits(k) |
| 64516 | 229 return r |
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230 # There's an overriden random() method but no new getrandbits() method, |
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231 # so we can only use random() from here. |
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232 random = self.random |
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233 if n >= maxsize: |
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234 _warn("Underlying random() generator does not supply \n" |
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235 "enough bits to choose from a population range this large.\n" |
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236 "To remove the range limitation, add a getrandbits() method.") |
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237 return int(random() * n) |
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238 rem = maxsize % n |
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239 limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0 |
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240 r = random() |
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241 while r >= limit: |
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242 r = random() |
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243 return int(r*maxsize) % n |
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244 |
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245 ## -------------------- sequence methods ------------------- |
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246 |
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247 def choice(self, seq): |
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248 """Choose a random element from a non-empty sequence.""" |
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249 try: |
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250 i = self._randbelow(len(seq)) |
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251 except ValueError: |
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252 raise IndexError('Cannot choose from an empty sequence') |
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253 return seq[i] |
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254 |
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255 def shuffle(self, x, random=None, int=int): |
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256 """x, random=random.random -> shuffle list x in place; return None. |
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257 |
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258 Optional arg random is a 0-argument function returning a random |
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259 float in [0.0, 1.0); by default, the standard random.random. |
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260 """ |
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261 |
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262 randbelow = self._randbelow |
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263 for i in reversed(range(1, len(x))): |
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264 # pick an element in x[:i+1] with which to exchange x[i] |
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265 j = randbelow(i+1) if random is None else int(random() * (i+1)) |
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266 x[i], x[j] = x[j], x[i] |
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267 |
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268 def sample(self, population, k): |
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269 """Chooses k unique random elements from a population sequence or set. |
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270 |
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271 Returns a new list containing elements from the population while |
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272 leaving the original population unchanged. The resulting list is |
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273 in selection order so that all sub-slices will also be valid random |
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274 samples. This allows raffle winners (the sample) to be partitioned |
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275 into grand prize and second place winners (the subslices). |
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276 |
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277 Members of the population need not be hashable or unique. If the |
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278 population contains repeats, then each occurrence is a possible |
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279 selection in the sample. |
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280 |
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281 To choose a sample in a range of integers, use range as an argument. |
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282 This is especially fast and space efficient for sampling from a |
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283 large population: sample(range(10000000), 60) |
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284 """ |
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285 |
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286 # Sampling without replacement entails tracking either potential |
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287 # selections (the pool) in a list or previous selections in a set. |
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288 |
| 31578 | 289 # When the number of selections is small compared to the |
| 290 # population, then tracking selections is efficient, requiring | |
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291 # only a small set and an occasional reselection. For |
| 31578 | 292 # a larger number of selections, the pool tracking method is |
| 293 # preferred since the list takes less space than the | |
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294 # set and it doesn't suffer from frequent reselections. |
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295 |
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296 if isinstance(population, _Set): |
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297 population = tuple(population) |
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298 if not isinstance(population, _Sequence): |
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299 raise TypeError("Population must be a sequence or set. For dicts, use list(d).") |
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300 randbelow = self._randbelow |
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301 n = len(population) |
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302 if not 0 <= k <= n: |
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303 raise ValueError("Sample larger than population") |
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304 result = [None] * k |
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305 setsize = 21 # size of a small set minus size of an empty list |
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306 if k > 5: |
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307 setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets |
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308 if n <= setsize: |
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309 # An n-length list is smaller than a k-length set |
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310 pool = list(population) |
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311 for i in range(k): # invariant: non-selected at [0,n-i) |
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312 j = randbelow(n-i) |
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313 result[i] = pool[j] |
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314 pool[j] = pool[n-i-1] # move non-selected item into vacancy |
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315 else: |
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316 selected = set() |
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317 selected_add = selected.add |
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318 for i in range(k): |
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319 j = randbelow(n) |
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320 while j in selected: |
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321 j = randbelow(n) |
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322 selected_add(j) |
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323 result[i] = population[j] |
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324 return result |
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325 |
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326 ## -------------------- real-valued distributions ------------------- |
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327 |
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328 ## -------------------- uniform distribution ------------------- |
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329 |
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330 def uniform(self, a, b): |
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331 "Get a random number in the range [a, b) or [a, b] depending on rounding." |
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332 return a + (b-a) * self.random() |
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334 ## -------------------- triangular -------------------- |
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335 |
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336 def triangular(self, low=0.0, high=1.0, mode=None): |
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337 """Triangular distribution. |
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338 |
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339 Continuous distribution bounded by given lower and upper limits, |
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340 and having a given mode value in-between. |
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341 |
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342 http://en.wikipedia.org/wiki/Triangular_distribution |
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343 |
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344 """ |
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345 u = self.random() |
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346 c = 0.5 if mode is None else (mode - low) / (high - low) |
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347 if u > c: |
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348 u = 1.0 - u |
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349 c = 1.0 - c |
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350 low, high = high, low |
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351 return low + (high - low) * (u * c) ** 0.5 |
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352 |
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353 ## -------------------- normal distribution -------------------- |
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355 def normalvariate(self, mu, sigma): |
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356 """Normal distribution. |
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357 |
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358 mu is the mean, and sigma is the standard deviation. |
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359 |
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360 """ |
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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 = 1.0 - 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 |
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378 ## -------------------- lognormal distribution -------------------- |
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380 def lognormvariate(self, mu, sigma): |
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381 """Log normal distribution. |
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382 |
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383 If you take the natural logarithm of this distribution, you'll get a |
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384 normal distribution with mean mu and standard deviation sigma. |
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385 mu can have any value, and sigma must be greater than zero. |
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386 |
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387 """ |
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388 return _exp(self.normalvariate(mu, sigma)) |
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390 ## -------------------- exponential distribution -------------------- |
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392 def expovariate(self, lambd): |
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393 """Exponential distribution. |
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394 |
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395 lambd is 1.0 divided by the desired mean. It should be |
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396 nonzero. (The parameter would be called "lambda", but that is |
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397 a reserved word in Python.) Returned values range from 0 to |
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398 positive infinity if lambd is positive, and from negative |
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399 infinity to 0 if lambd is negative. |
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400 |
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401 """ |
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402 # lambd: rate lambd = 1/mean |
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403 # ('lambda' is a Python reserved word) |
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405 random = self.random |
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406 u = random() |
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407 while u <= 1e-7: |
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408 u = random() |
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409 return -_log(u)/lambd |
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411 ## -------------------- von Mises distribution -------------------- |
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413 def vonmisesvariate(self, mu, kappa): |
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414 """Circular data distribution. |
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415 |
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416 mu is the mean angle, expressed in radians between 0 and 2*pi, and |
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417 kappa is the concentration parameter, which must be greater than or |
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418 equal to zero. If kappa is equal to zero, this distribution reduces |
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419 to a uniform random angle over the range 0 to 2*pi. |
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420 |
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421 """ |
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422 # mu: mean angle (in radians between 0 and 2*pi) |
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423 # kappa: concentration parameter kappa (>= 0) |
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424 # if kappa = 0 generate uniform random angle |
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426 # Based upon an algorithm published in: Fisher, N.I., |
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427 # "Statistical Analysis of Circular Data", Cambridge |
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428 # University Press, 1993. |
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429 |
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430 # Thanks to Magnus Kessler for a correction to the |
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431 # implementation of step 4. |
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|
432 |
|
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433 random = self.random |
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|
434 if kappa <= 1e-6: |
|
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|
435 return TWOPI * random() |
|
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|
436 |
|
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|
437 a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa) |
|
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|
438 b = (a - _sqrt(2.0 * a))/(2.0 * kappa) |
|
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|
439 r = (1.0 + b * b)/(2.0 * b) |
| 1575 | 440 |
|
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441 while 1: |
|
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|
442 u1 = random() |
| 1575 | 443 |
|
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|
444 z = _cos(_pi * u1) |
|
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|
445 f = (1.0 + r * z)/(r + z) |
|
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|
446 c = kappa * (r - f) |
| 1575 | 447 |
|
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|
448 u2 = random() |
| 1575 | 449 |
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450 if u2 < c * (2.0 - c) or u2 <= c * _exp(1.0 - c): |
|
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|
451 break |
| 1575 | 452 |
|
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|
453 u3 = random() |
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|
454 if u3 > 0.5: |
|
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|
455 theta = (mu % TWOPI) + _acos(f) |
|
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|
456 else: |
|
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|
457 theta = (mu % TWOPI) - _acos(f) |
| 1575 | 458 |
|
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|
459 return theta |
| 1575 | 460 |
|
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|
461 ## -------------------- gamma distribution -------------------- |
| 1575 | 462 |
|
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|
463 def gammavariate(self, alpha, beta): |
|
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|
464 """Gamma distribution. Not the gamma function! |
|
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465 |
|
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466 Conditions on the parameters are alpha > 0 and beta > 0. |
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467 |
|
68846
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Issue 10787: Document the probability density function for random.gammavariate.
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|
468 The probability distribution function is: |
|
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|
469 |
|
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|
470 x ** (alpha - 1) * math.exp(-x / beta) |
|
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|
471 pdf(x) = -------------------------------------- |
|
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|
472 math.gamma(alpha) * beta ** alpha |
|
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|
473 |
|
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474 """ |
|
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|
475 |
|
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|
476 # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2 |
|
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|
477 |
|
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Use spaces, not tabs for indents.
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|
478 # Warning: a few older sources define the gamma distribution in terms |
|
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|
479 # of alpha > -1.0 |
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|
480 if alpha <= 0.0 or beta <= 0.0: |
|
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Raise statement normalization in Lib/.
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|
481 raise ValueError('gammavariate: alpha and beta must be > 0.0') |
|
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|
482 |
|
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483 random = self.random |
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484 if alpha > 1.0: |
| 1575 | 485 |
|
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|
486 # Uses R.C.H. Cheng, "The generation of Gamma |
|
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|
487 # variables with non-integral shape parameters", |
|
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|
488 # Applied Statistics, (1977), 26, No. 1, p71-74 |
| 1575 | 489 |
|
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|
490 ainv = _sqrt(2.0 * alpha - 1.0) |
|
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|
491 bbb = alpha - LOG4 |
|
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|
492 ccc = alpha + ainv |
|
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|
493 |
|
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|
494 while 1: |
|
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changeset
|
495 u1 = random() |
|
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|
496 if not 1e-7 < u1 < .9999999: |
|
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|
497 continue |
|
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|
498 u2 = 1.0 - random() |
|
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|
499 v = _log(u1/(1.0-u1))/ainv |
|
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|
500 x = alpha*_exp(v) |
|
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|
501 z = u1*u1*u2 |
|
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|
502 r = bbb+ccc*v-x |
|
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changeset
|
503 if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z): |
|
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Closes patch 529408 deprecating random.stdgamma().
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|
504 return x * beta |
| 1575 | 505 |
|
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|
506 elif alpha == 1.0: |
|
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|
507 # expovariate(1) |
|
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|
508 u = random() |
|
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|
509 while u <= 1e-7: |
|
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changeset
|
510 u = random() |
|
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Closes patch 529408 deprecating random.stdgamma().
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changeset
|
511 return -_log(u) * beta |
| 1575 | 512 |
|
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changeset
|
513 else: # alpha is between 0 and 1 (exclusive) |
| 1575 | 514 |
|
16242
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parents:
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changeset
|
515 # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle |
| 1575 | 516 |
|
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|
517 while 1: |
|
16242
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parents:
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changeset
|
518 u = random() |
|
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parents:
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changeset
|
519 b = (_e + alpha)/_e |
|
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parents:
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changeset
|
520 p = b*u |
|
3c5f09422b69
Reworked random.py so that it no longer depends on, and offers all the
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parents:
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changeset
|
521 if p <= 1.0: |
|
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|
522 x = p ** (1.0/alpha) |
|
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|
523 else: |
|
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changeset
|
524 x = -_log((b-p)/alpha) |
|
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parents:
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changeset
|
525 u1 = random() |
|
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|
526 if p > 1.0: |
|
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SF patch #1191489: Simplify logic in random.py
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changeset
|
527 if u1 <= x ** (alpha - 1.0): |
|
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parents:
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changeset
|
528 break |
|
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parents:
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|
529 elif u1 <= _exp(-x): |
|
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parents:
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changeset
|
530 break |
|
23447
08ccb0f3e2d4
Closes patch 529408 deprecating random.stdgamma().
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parents:
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diff
changeset
|
531 return x * beta |
|
08ccb0f3e2d4
Closes patch 529408 deprecating random.stdgamma().
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parents:
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diff
changeset
|
532 |
|
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Cosmetic changes after some sleep; no change in semantics.
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parents:
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|
533 ## -------------------- Gauss (faster alternative) -------------------- |
|
1576
695fb494a3a2
Added gauss() (same as normal but twice as fast) and betavariate();
Guido van Rossum <guido@python.org>
parents:
1575
diff
changeset
|
534 |
|
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parents:
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changeset
|
535 def gauss(self, mu, sigma): |
|
23531
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Raymond Hettinger <python@rcn.com>
parents:
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changeset
|
536 """Gaussian distribution. |
|
a98e93eafef7
Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
537 |
|
a98e93eafef7
Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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parents:
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changeset
|
538 mu is the mean, and sigma is the standard deviation. This is |
|
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Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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parents:
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diff
changeset
|
539 slightly faster than the normalvariate() function. |
|
a98e93eafef7
Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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parents:
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changeset
|
540 |
|
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Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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parents:
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diff
changeset
|
541 Not thread-safe without a lock around calls. |
|
23536
0a4c92559a0b
Whitespace normalized.
Raymond Hettinger <python@rcn.com>
parents:
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diff
changeset
|
542 |
|
23531
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Deprecated Random.cunifvariate clearing bug 506647. Also, added docstrings.
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parents:
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|
543 """ |
|
1577
444e349f962f
Use float constants directly; cosmetic changes; initialize largest
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parents:
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diff
changeset
|
544 |
|
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parents:
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changeset
|
545 # When x and y are two variables from [0, 1), uniformly |
|
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parents:
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changeset
|
546 # distributed, then |
|
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parents:
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changeset
|
547 # |
|
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parents:
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changeset
|
548 # cos(2*pi*x)*sqrt(-2*log(1-y)) |
|
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Tim Peters <tim.peters@gmail.com>
parents:
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changeset
|
549 # sin(2*pi*x)*sqrt(-2*log(1-y)) |
|
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Reworked random.py so that it no longer depends on, and offers all the
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parents:
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550 # |
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551 # are two *independent* variables with normal distribution |
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552 # (mu = 0, sigma = 1). |
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|
553 # (Lambert Meertens) |
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554 # (corrected version; bug discovered by Mike Miller, fixed by LM) |
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555 |
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556 # Multithreading note: When two threads call this function |
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557 # simultaneously, it is possible that they will receive the |
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558 # same return value. The window is very small though. To |
|
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559 # avoid this, you have to use a lock around all calls. (I |
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|
560 # didn't want to slow this down in the serial case by using a |
|
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561 # lock here.) |
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562 |
|
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563 random = self.random |
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564 z = self.gauss_next |
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|
565 self.gauss_next = None |
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|
566 if z is None: |
|
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|
567 x2pi = random() * TWOPI |
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|
568 g2rad = _sqrt(-2.0 * _log(1.0 - random())) |
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|
569 z = _cos(x2pi) * g2rad |
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|
570 self.gauss_next = _sin(x2pi) * g2rad |
|
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571 |
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572 return mu + z*sigma |
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573 |
|
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574 ## -------------------- beta -------------------- |
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575 ## See |
|
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|
576 ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html |
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577 ## for Ivan Frohne's insightful analysis of why the original implementation: |
|
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578 ## |
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|
579 ## def betavariate(self, alpha, beta): |
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580 ## # Discrete Event Simulation in C, pp 87-88. |
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581 ## |
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|
582 ## y = self.expovariate(alpha) |
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|
583 ## z = self.expovariate(1.0/beta) |
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584 ## return z/(y+z) |
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585 ## |
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|
586 ## was dead wrong, and how it probably got that way. |
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587 |
|
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588 def betavariate(self, alpha, beta): |
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589 """Beta distribution. |
|
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590 |
|
40737
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Merged revisions 53451-53537 via svnmerge from
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591 Conditions on the parameters are alpha > 0 and beta > 0. |
|
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592 Returned values range between 0 and 1. |
|
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593 |
|
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594 """ |
|
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|
595 |
|
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|
596 # This version due to Janne Sinkkonen, and matches all the std |
|
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|
597 # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). |
|
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|
598 y = self.gammavariate(alpha, 1.) |
|
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599 if y == 0: |
|
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|
600 return 0.0 |
|
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|
601 else: |
|
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|
602 return y / (y + self.gammavariate(beta, 1.)) |
|
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|
603 |
|
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604 ## -------------------- Pareto -------------------- |
|
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605 |
|
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606 def paretovariate(self, alpha): |
|
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607 """Pareto distribution. alpha is the shape parameter.""" |
|
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|
608 # Jain, pg. 495 |
|
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609 |
|
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|
610 u = 1.0 - self.random() |
|
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611 return 1.0 / u ** (1.0/alpha) |
|
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612 |
|
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613 ## -------------------- Weibull -------------------- |
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614 |
|
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|
615 def weibullvariate(self, alpha, beta): |
|
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616 """Weibull distribution. |
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617 |
|
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618 alpha is the scale parameter and beta is the shape parameter. |
|
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619 |
|
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620 """ |
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|
621 # Jain, pg. 499; bug fix courtesy Bill Arms |
|
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Add Tim Peters' shuffle() algorithm.
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622 |
|
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623 u = 1.0 - self.random() |
|
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624 return alpha * (-_log(u)) ** (1.0/beta) |
|
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|
625 |
|
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|
626 ## --------------- Operating System Random Source ------------------ |
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627 |
|
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628 class SystemRandom(Random): |
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629 """Alternate random number generator using sources provided |
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630 by the operating system (such as /dev/urandom on Unix or |
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|
631 CryptGenRandom on Windows). |
|
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632 |
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633 Not available on all systems (see os.urandom() for details). |
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634 """ |
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635 |
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636 def random(self): |
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637 """Get the next random number in the range [0.0, 1.0).""" |
|
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638 return (int.from_bytes(_urandom(7), 'big') >> 3) * RECIP_BPF |
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639 |
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640 def getrandbits(self, k): |
|
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641 """getrandbits(k) -> x. Generates a long int with k random bits.""" |
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642 if k <= 0: |
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643 raise ValueError('number of bits must be greater than zero') |
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644 if k != int(k): |
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645 raise TypeError('number of bits should be an integer') |
|
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Improve variable name (don't shadow a builtin).
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646 numbytes = (k + 7) // 8 # bits / 8 and rounded up |
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647 x = int.from_bytes(_urandom(numbytes), 'big') |
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|
648 return x >> (numbytes * 8 - k) # trim excess bits |
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|
649 |
|
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Remove defunct parts of the random module
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|
650 def seed(self, *args, **kwds): |
|
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651 "Stub method. Not used for a system random number generator." |
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652 return None |
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|
653 |
|
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654 def _notimplemented(self, *args, **kwds): |
|
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655 "Method should not be called for a system random number generator." |
|
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656 raise NotImplementedError('System entropy source does not have state.') |
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657 getstate = setstate = _notimplemented |
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658 |
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659 ## -------------------- test program -------------------- |
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661 def _test_generator(n, func, args): |
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662 import time |
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663 print(n, 'times', func.__name__) |
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664 total = 0.0 |
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665 sqsum = 0.0 |
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666 smallest = 1e10 |
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667 largest = -1e10 |
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668 t0 = time.time() |
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669 for i in range(n): |
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670 x = func(*args) |
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671 total += x |
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672 sqsum = sqsum + x*x |
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673 smallest = min(x, smallest) |
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674 largest = max(x, largest) |
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675 t1 = time.time() |
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676 print(round(t1-t0, 3), 'sec,', end=' ') |
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677 avg = total/n |
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678 stddev = _sqrt(sqsum/n - avg*avg) |
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679 print('avg %g, stddev %g, min %g, max %g' % \ |
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680 (avg, stddev, smallest, largest)) |
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682 |
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683 def _test(N=2000): |
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684 _test_generator(N, random, ()) |
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685 _test_generator(N, normalvariate, (0.0, 1.0)) |
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686 _test_generator(N, lognormvariate, (0.0, 1.0)) |
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687 _test_generator(N, vonmisesvariate, (0.0, 1.0)) |
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688 _test_generator(N, gammavariate, (0.01, 1.0)) |
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689 _test_generator(N, gammavariate, (0.1, 1.0)) |
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690 _test_generator(N, gammavariate, (0.1, 2.0)) |
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691 _test_generator(N, gammavariate, (0.5, 1.0)) |
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692 _test_generator(N, gammavariate, (0.9, 1.0)) |
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693 _test_generator(N, gammavariate, (1.0, 1.0)) |
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694 _test_generator(N, gammavariate, (2.0, 1.0)) |
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695 _test_generator(N, gammavariate, (20.0, 1.0)) |
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696 _test_generator(N, gammavariate, (200.0, 1.0)) |
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697 _test_generator(N, gauss, (0.0, 1.0)) |
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698 _test_generator(N, betavariate, (3.0, 3.0)) |
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699 _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0)) |
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700 |
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701 # Create one instance, seeded from current time, and export its methods |
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702 # as module-level functions. The functions share state across all uses |
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703 #(both in the user's code and in the Python libraries), but that's fine |
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704 # for most programs and is easier for the casual user than making them |
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705 # instantiate their own Random() instance. |
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706 |
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707 _inst = Random() |
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708 seed = _inst.seed |
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709 random = _inst.random |
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710 uniform = _inst.uniform |
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711 triangular = _inst.triangular |
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712 randint = _inst.randint |
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713 choice = _inst.choice |
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714 randrange = _inst.randrange |
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715 sample = _inst.sample |
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716 shuffle = _inst.shuffle |
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717 normalvariate = _inst.normalvariate |
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718 lognormvariate = _inst.lognormvariate |
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719 expovariate = _inst.expovariate |
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720 vonmisesvariate = _inst.vonmisesvariate |
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721 gammavariate = _inst.gammavariate |
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722 gauss = _inst.gauss |
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723 betavariate = _inst.betavariate |
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724 paretovariate = _inst.paretovariate |
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725 weibullvariate = _inst.weibullvariate |
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726 getstate = _inst.getstate |
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727 setstate = _inst.setstate |
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728 getrandbits = _inst.getrandbits |
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729 |
| 1575 | 730 if __name__ == '__main__': |
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731 _test() |
