Mercurial > cpython-lru_cache-weakref
annotate Lib/random.py @ 87690:66c1c9f32567 default tip
Add use_weakref option to functools.lru_cache
The use_weakref option makes lru_cache create weak references to the function
arguments when creating the cache key, which prevents them from staying in the
memory forever even after all other references to them are gone.
This is a proof of concept for http://bugs.python.org/issue19859
| author | Radomir Dopieralski <devel@sheep.art.pl> |
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| date | Tue, 03 Dec 2013 16:02:48 +0100 |
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1 """Random variable generators. |
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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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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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37 """ |
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38 |
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39 from warnings import warn as _warn |
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40 from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType |
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41 from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil |
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42 from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin |
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43 from os import urandom as _urandom |
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44 from _collections_abc import Set as _Set, Sequence as _Sequence |
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45 from hashlib import sha512 as _sha512 |
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46 |
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47 __all__ = ["Random","seed","random","uniform","randint","choice","sample", |
| 16613 | 48 "randrange","shuffle","normalvariate","lognormvariate", |
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49 "expovariate","vonmisesvariate","gammavariate","triangular", |
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51 "getstate","setstate", "getrandbits", |
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52 "SystemRandom"] |
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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 RECIP_BPF = 2**-BPF |
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60 |
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61 |
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62 # Translated by Guido van Rossum from C source provided by |
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63 # Adrian Baddeley. Adapted by Raymond Hettinger for use with |
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64 # the Mersenne Twister and os.urandom() core generators. |
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65 |
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66 import _random |
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67 |
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68 class Random(_random.Random): |
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69 """Random number generator base class used by bound module functions. |
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70 |
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71 Used to instantiate instances of Random to get generators that don't |
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72 share state. |
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73 |
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74 Class Random can also be subclassed if you want to use a different basic |
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75 generator of your own devising: in that case, override the following |
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76 methods: random(), seed(), getstate(), and setstate(). |
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77 Optionally, implement a getrandbits() method so that randrange() |
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78 can cover arbitrarily large ranges. |
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79 |
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80 """ |
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82 VERSION = 3 # used by getstate/setstate |
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83 |
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84 def __init__(self, x=None): |
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85 """Initialize an instance. |
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86 |
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87 Optional argument x controls seeding, as for Random.seed(). |
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88 """ |
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89 |
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90 self.seed(x) |
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91 self.gauss_next = None |
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92 |
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93 def seed(self, a=None, version=2): |
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94 """Initialize internal state from hashable object. |
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95 |
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96 None or no argument seeds from current time or from an operating |
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97 system specific randomness source if available. |
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98 |
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99 For version 2 (the default), all of the bits are used if *a* is a str, |
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100 bytes, or bytearray. For version 1, the hash() of *a* is used instead. |
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101 |
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102 If *a* is an int, all bits are used. |
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103 |
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104 """ |
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105 |
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106 if a is None: |
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107 try: |
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108 a = int.from_bytes(_urandom(32), 'big') |
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109 except NotImplementedError: |
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110 import time |
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111 a = int(time.time() * 256) # use fractional seconds |
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113 if version == 2: |
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114 if isinstance(a, (str, bytes, bytearray)): |
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115 if isinstance(a, str): |
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116 a = a.encode() |
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117 a += _sha512(a).digest() |
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118 a = int.from_bytes(a, 'big') |
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119 |
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120 super().seed(a) |
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121 self.gauss_next = None |
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123 def getstate(self): |
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124 """Return internal state; can be passed to setstate() later.""" |
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125 return self.VERSION, super().getstate(), self.gauss_next |
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126 |
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127 def setstate(self, state): |
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128 """Restore internal state from object returned by getstate().""" |
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129 version = state[0] |
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130 if version == 3: |
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131 version, internalstate, self.gauss_next = state |
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132 super().setstate(internalstate) |
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133 elif version == 2: |
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134 version, internalstate, self.gauss_next = state |
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135 # In version 2, the state was saved as signed ints, which causes |
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136 # inconsistencies between 32/64-bit systems. The state is |
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137 # really unsigned 32-bit ints, so we convert negative ints from |
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138 # version 2 to positive longs for version 3. |
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139 try: |
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140 internalstate = tuple(x % (2**32) for x in internalstate) |
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141 except ValueError as e: |
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142 raise TypeError from e |
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143 super().setstate(internalstate) |
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144 else: |
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145 raise ValueError("state with version %s passed to " |
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146 "Random.setstate() of version %s" % |
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147 (version, self.VERSION)) |
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148 |
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149 ## ---- Methods below this point do not need to be overridden when |
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150 ## ---- subclassing for the purpose of using a different core generator. |
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151 |
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152 ## -------------------- pickle support ------------------- |
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153 |
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154 # Issue 17489: Since __reduce__ was defined to fix #759889 this is no |
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155 # longer called; we leave it here because it has been here since random was |
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156 # rewritten back in 2001 and why risk breaking something. |
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157 def __getstate__(self): # for pickle |
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158 return self.getstate() |
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159 |
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160 def __setstate__(self, state): # for pickle |
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161 self.setstate(state) |
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162 |
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163 def __reduce__(self): |
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164 return self.__class__, (), self.getstate() |
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165 |
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166 ## -------------------- integer methods ------------------- |
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167 |
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168 def randrange(self, start, stop=None, step=1, _int=int): |
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169 """Choose a random item from range(start, stop[, step]). |
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170 |
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171 This fixes the problem with randint() which includes the |
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172 endpoint; in Python this is usually not what you want. |
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174 """ |
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175 |
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176 # This code is a bit messy to make it fast for the |
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177 # common case while still doing adequate error checking. |
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178 istart = _int(start) |
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179 if istart != start: |
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180 raise ValueError("non-integer arg 1 for randrange()") |
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182 if istart > 0: |
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183 return self._randbelow(istart) |
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184 raise ValueError("empty range for randrange()") |
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185 |
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186 # stop argument supplied. |
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187 istop = _int(stop) |
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188 if istop != stop: |
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189 raise ValueError("non-integer stop for randrange()") |
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190 width = istop - istart |
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191 if step == 1 and width > 0: |
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193 if step == 1: |
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194 raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width)) |
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195 |
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196 # Non-unit step argument supplied. |
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197 istep = _int(step) |
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198 if istep != step: |
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199 raise ValueError("non-integer step for randrange()") |
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200 if istep > 0: |
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201 n = (width + istep - 1) // istep |
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202 elif istep < 0: |
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203 n = (width + istep + 1) // istep |
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204 else: |
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205 raise ValueError("zero step for randrange()") |
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206 |
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207 if n <= 0: |
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208 raise ValueError("empty range for randrange()") |
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209 |
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210 return istart + istep*self._randbelow(n) |
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212 def randint(self, a, b): |
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213 """Return random integer in range [a, b], including both end points. |
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214 """ |
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215 |
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216 return self.randrange(a, b+1) |
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217 |
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218 def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type, |
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219 Method=_MethodType, BuiltinMethod=_BuiltinMethodType): |
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220 "Return a random int in the range [0,n). Raises ValueError if n==0." |
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221 |
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222 random = self.random |
| 64516 | 223 getrandbits = self.getrandbits |
| 224 # Only call self.getrandbits if the original random() builtin method | |
| 225 # has not been overridden or if a new getrandbits() was supplied. | |
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226 if type(random) is BuiltinMethod or type(getrandbits) is Method: |
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227 k = n.bit_length() # don't use (n-1) here because n can be 1 |
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228 r = getrandbits(k) # 0 <= r < 2**k |
| 64516 | 229 while r >= n: |
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230 r = getrandbits(k) |
| 64516 | 231 return r |
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232 # There's an overriden random() method but no new getrandbits() method, |
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233 # so we can only use random() from here. |
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234 if n >= maxsize: |
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235 _warn("Underlying random() generator does not supply \n" |
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236 "enough bits to choose from a population range this large.\n" |
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237 "To remove the range limitation, add a getrandbits() method.") |
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238 return int(random() * n) |
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239 rem = maxsize % n |
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240 limit = (maxsize - rem) / maxsize # int(limit * maxsize) % n == 0 |
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241 r = random() |
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242 while r >= limit: |
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243 r = random() |
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244 return int(r*maxsize) % n |
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245 |
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246 ## -------------------- sequence methods ------------------- |
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247 |
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248 def choice(self, seq): |
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249 """Choose a random element from a non-empty sequence.""" |
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250 try: |
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251 i = self._randbelow(len(seq)) |
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252 except ValueError: |
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253 raise IndexError('Cannot choose from an empty sequence') |
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254 return seq[i] |
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255 |
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256 def shuffle(self, x, random=None): |
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257 """Shuffle list x in place, and return None. |
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258 |
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259 Optional argument random is a 0-argument function returning a |
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260 random float in [0.0, 1.0); if it is the default None, the |
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261 standard random.random will be used. |
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262 |
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263 """ |
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264 |
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265 if random is None: |
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266 randbelow = self._randbelow |
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267 for i in reversed(range(1, len(x))): |
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268 # pick an element in x[:i+1] with which to exchange x[i] |
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269 j = randbelow(i+1) |
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270 x[i], x[j] = x[j], x[i] |
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271 else: |
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272 _int = int |
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273 for i in reversed(range(1, len(x))): |
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274 # pick an element in x[:i+1] with which to exchange x[i] |
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275 j = _int(random() * (i+1)) |
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276 x[i], x[j] = x[j], x[i] |
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277 |
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278 def sample(self, population, k): |
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279 """Chooses k unique random elements from a population sequence or set. |
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280 |
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281 Returns a new list containing elements from the population while |
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282 leaving the original population unchanged. The resulting list is |
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283 in selection order so that all sub-slices will also be valid random |
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284 samples. This allows raffle winners (the sample) to be partitioned |
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285 into grand prize and second place winners (the subslices). |
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286 |
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287 Members of the population need not be hashable or unique. If the |
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288 population contains repeats, then each occurrence is a possible |
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289 selection in the sample. |
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290 |
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291 To choose a sample in a range of integers, use range as an argument. |
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292 This is especially fast and space efficient for sampling from a |
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293 large population: sample(range(10000000), 60) |
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294 """ |
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295 |
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296 # Sampling without replacement entails tracking either potential |
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297 # selections (the pool) in a list or previous selections in a set. |
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298 |
| 31578 | 299 # When the number of selections is small compared to the |
| 300 # population, then tracking selections is efficient, requiring | |
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301 # only a small set and an occasional reselection. For |
| 31578 | 302 # a larger number of selections, the pool tracking method is |
| 303 # preferred since the list takes less space than the | |
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304 # set and it doesn't suffer from frequent reselections. |
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305 |
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306 if isinstance(population, _Set): |
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307 population = tuple(population) |
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308 if not isinstance(population, _Sequence): |
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309 raise TypeError("Population must be a sequence or set. For dicts, use list(d).") |
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310 randbelow = self._randbelow |
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311 n = len(population) |
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312 if not 0 <= k <= n: |
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313 raise ValueError("Sample larger than population") |
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314 result = [None] * k |
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315 setsize = 21 # size of a small set minus size of an empty list |
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316 if k > 5: |
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317 setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets |
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318 if n <= setsize: |
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319 # An n-length list is smaller than a k-length set |
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320 pool = list(population) |
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321 for i in range(k): # invariant: non-selected at [0,n-i) |
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322 j = randbelow(n-i) |
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323 result[i] = pool[j] |
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324 pool[j] = pool[n-i-1] # move non-selected item into vacancy |
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325 else: |
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326 selected = set() |
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327 selected_add = selected.add |
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328 for i in range(k): |
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329 j = randbelow(n) |
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330 while j in selected: |
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331 j = randbelow(n) |
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332 selected_add(j) |
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333 result[i] = population[j] |
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334 return result |
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335 |
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336 ## -------------------- real-valued distributions ------------------- |
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337 |
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338 ## -------------------- uniform distribution ------------------- |
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339 |
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340 def uniform(self, a, b): |
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341 "Get a random number in the range [a, b) or [a, b] depending on rounding." |
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342 return a + (b-a) * self.random() |
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344 ## -------------------- triangular -------------------- |
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345 |
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346 def triangular(self, low=0.0, high=1.0, mode=None): |
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347 """Triangular distribution. |
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348 |
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349 Continuous distribution bounded by given lower and upper limits, |
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350 and having a given mode value in-between. |
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351 |
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352 http://en.wikipedia.org/wiki/Triangular_distribution |
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353 |
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354 """ |
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355 u = self.random() |
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356 c = 0.5 if mode is None else (mode - low) / (high - low) |
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357 if u > c: |
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358 u = 1.0 - u |
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359 c = 1.0 - c |
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360 low, high = high, low |
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361 return low + (high - low) * (u * c) ** 0.5 |
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362 |
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363 ## -------------------- normal distribution -------------------- |
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365 def normalvariate(self, mu, sigma): |
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366 """Normal distribution. |
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367 |
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368 mu is the mean, and sigma is the standard deviation. |
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369 |
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370 """ |
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371 # mu = mean, sigma = standard deviation |
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372 |
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373 # Uses Kinderman and Monahan method. Reference: Kinderman, |
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374 # A.J. and Monahan, J.F., "Computer generation of random |
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375 # variables using the ratio of uniform deviates", ACM Trans |
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376 # Math Software, 3, (1977), pp257-260. |
| 1575 | 377 |
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378 random = self.random |
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379 while 1: |
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380 u1 = random() |
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381 u2 = 1.0 - random() |
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382 z = NV_MAGICCONST*(u1-0.5)/u2 |
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383 zz = z*z/4.0 |
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384 if zz <= -_log(u2): |
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385 break |
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386 return mu + z*sigma |
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388 ## -------------------- lognormal distribution -------------------- |
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390 def lognormvariate(self, mu, sigma): |
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391 """Log normal distribution. |
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392 |
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393 If you take the natural logarithm of this distribution, you'll get a |
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394 normal distribution with mean mu and standard deviation sigma. |
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395 mu can have any value, and sigma must be greater than zero. |
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396 |
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397 """ |
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398 return _exp(self.normalvariate(mu, sigma)) |
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400 ## -------------------- exponential distribution -------------------- |
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402 def expovariate(self, lambd): |
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403 """Exponential distribution. |
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404 |
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405 lambd is 1.0 divided by the desired mean. It should be |
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406 nonzero. (The parameter would be called "lambda", but that is |
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407 a reserved word in Python.) Returned values range from 0 to |
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408 positive infinity if lambd is positive, and from negative |
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409 infinity to 0 if lambd is negative. |
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410 |
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411 """ |
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412 # lambd: rate lambd = 1/mean |
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413 # ('lambda' is a Python reserved word) |
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415 # we use 1-random() instead of random() to preclude the |
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416 # possibility of taking the log of zero. |
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417 return -_log(1.0 - self.random())/lambd |
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419 ## -------------------- von Mises distribution -------------------- |
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421 def vonmisesvariate(self, mu, kappa): |
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422 """Circular data distribution. |
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423 |
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424 mu is the mean angle, expressed in radians between 0 and 2*pi, and |
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425 kappa is the concentration parameter, which must be greater than or |
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426 equal to zero. If kappa is equal to zero, this distribution reduces |
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427 to a uniform random angle over the range 0 to 2*pi. |
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428 |
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429 """ |
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430 # mu: mean angle (in radians between 0 and 2*pi) |
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431 # kappa: concentration parameter kappa (>= 0) |
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432 # if kappa = 0 generate uniform random angle |
| 1575 | 433 |
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434 # Based upon an algorithm published in: Fisher, N.I., |
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435 # "Statistical Analysis of Circular Data", Cambridge |
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436 # University Press, 1993. |
|
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437 |
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438 # Thanks to Magnus Kessler for a correction to the |
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439 # implementation of step 4. |
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440 |
|
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441 random = self.random |
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442 if kappa <= 1e-6: |
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443 return TWOPI * random() |
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444 |
|
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445 s = 0.5 / kappa |
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446 r = s + _sqrt(1.0 + s * s) |
| 1575 | 447 |
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448 while 1: |
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449 u1 = random() |
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450 z = _cos(_pi * u1) |
| 1575 | 451 |
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452 d = z / (r + z) |
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453 u2 = random() |
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454 if u2 < 1.0 - d * d or u2 <= (1.0 - d) * _exp(d): |
|
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455 break |
| 1575 | 456 |
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457 q = 1.0 / r |
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458 f = (q + z) / (1.0 + q * z) |
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459 u3 = random() |
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460 if u3 > 0.5: |
|
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461 theta = (mu + _acos(f)) % TWOPI |
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462 else: |
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463 theta = (mu - _acos(f)) % TWOPI |
| 1575 | 464 |
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465 return theta |
| 1575 | 466 |
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467 ## -------------------- gamma distribution -------------------- |
| 1575 | 468 |
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469 def gammavariate(self, alpha, beta): |
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470 """Gamma distribution. Not the gamma function! |
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471 |
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472 Conditions on the parameters are alpha > 0 and beta > 0. |
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473 |
|
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474 The probability distribution function is: |
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475 |
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476 x ** (alpha - 1) * math.exp(-x / beta) |
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477 pdf(x) = -------------------------------------- |
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478 math.gamma(alpha) * beta ** alpha |
|
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479 |
|
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480 """ |
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481 |
|
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482 # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2 |
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483 |
|
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484 # Warning: a few older sources define the gamma distribution in terms |
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485 # of alpha > -1.0 |
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486 if alpha <= 0.0 or beta <= 0.0: |
|
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487 raise ValueError('gammavariate: alpha and beta must be > 0.0') |
|
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488 |
|
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489 random = self.random |
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490 if alpha > 1.0: |
| 1575 | 491 |
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492 # Uses R.C.H. Cheng, "The generation of Gamma |
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493 # variables with non-integral shape parameters", |
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494 # Applied Statistics, (1977), 26, No. 1, p71-74 |
| 1575 | 495 |
|
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496 ainv = _sqrt(2.0 * alpha - 1.0) |
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497 bbb = alpha - LOG4 |
|
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498 ccc = alpha + ainv |
|
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499 |
|
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500 while 1: |
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|
501 u1 = random() |
|
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502 if not 1e-7 < u1 < .9999999: |
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|
503 continue |
|
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|
504 u2 = 1.0 - random() |
|
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|
505 v = _log(u1/(1.0-u1))/ainv |
|
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|
506 x = alpha*_exp(v) |
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|
507 z = u1*u1*u2 |
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|
508 r = bbb+ccc*v-x |
|
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|
509 if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z): |
|
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|
510 return x * beta |
| 1575 | 511 |
|
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512 elif alpha == 1.0: |
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|
513 # expovariate(1) |
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514 u = random() |
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515 while u <= 1e-7: |
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|
516 u = random() |
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|
517 return -_log(u) * beta |
| 1575 | 518 |
|
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519 else: # alpha is between 0 and 1 (exclusive) |
| 1575 | 520 |
|
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|
521 # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle |
| 1575 | 522 |
|
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523 while 1: |
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|
524 u = random() |
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|
525 b = (_e + alpha)/_e |
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|
526 p = b*u |
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527 if p <= 1.0: |
|
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528 x = p ** (1.0/alpha) |
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|
529 else: |
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|
530 x = -_log((b-p)/alpha) |
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|
531 u1 = random() |
|
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532 if p > 1.0: |
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533 if u1 <= x ** (alpha - 1.0): |
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|
534 break |
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535 elif u1 <= _exp(-x): |
|
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|
536 break |
|
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|
537 return x * beta |
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|
538 |
|
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|
539 ## -------------------- Gauss (faster alternative) -------------------- |
|
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Added gauss() (same as normal but twice as fast) and betavariate();
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|
540 |
|
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|
541 def gauss(self, mu, sigma): |
|
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|
542 """Gaussian distribution. |
|
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|
543 |
|
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544 mu is the mean, and sigma is the standard deviation. This is |
|
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|
545 slightly faster than the normalvariate() function. |
|
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|
546 |
|
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547 Not thread-safe without a lock around calls. |
|
23536
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548 |
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549 """ |
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550 |
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551 # When x and y are two variables from [0, 1), uniformly |
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552 # distributed, then |
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553 # |
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554 # cos(2*pi*x)*sqrt(-2*log(1-y)) |
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555 # sin(2*pi*x)*sqrt(-2*log(1-y)) |
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556 # |
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557 # are two *independent* variables with normal distribution |
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558 # (mu = 0, sigma = 1). |
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559 # (Lambert Meertens) |
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560 # (corrected version; bug discovered by Mike Miller, fixed by LM) |
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561 |
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562 # Multithreading note: When two threads call this function |
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563 # simultaneously, it is possible that they will receive the |
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564 # same return value. The window is very small though. To |
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565 # avoid this, you have to use a lock around all calls. (I |
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566 # didn't want to slow this down in the serial case by using a |
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567 # lock here.) |
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568 |
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569 random = self.random |
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570 z = self.gauss_next |
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571 self.gauss_next = None |
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572 if z is None: |
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573 x2pi = random() * TWOPI |
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574 g2rad = _sqrt(-2.0 * _log(1.0 - random())) |
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575 z = _cos(x2pi) * g2rad |
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576 self.gauss_next = _sin(x2pi) * g2rad |
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577 |
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578 return mu + z*sigma |
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579 |
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580 ## -------------------- beta -------------------- |
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581 ## See |
|
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582 ## http://mail.python.org/pipermail/python-bugs-list/2001-January/003752.html |
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583 ## for Ivan Frohne's insightful analysis of why the original implementation: |
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|
584 ## |
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585 ## def betavariate(self, alpha, beta): |
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586 ## # Discrete Event Simulation in C, pp 87-88. |
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587 ## |
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588 ## y = self.expovariate(alpha) |
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589 ## z = self.expovariate(1.0/beta) |
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590 ## return z/(y+z) |
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591 ## |
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592 ## was dead wrong, and how it probably got that way. |
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593 |
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594 def betavariate(self, alpha, beta): |
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595 """Beta distribution. |
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596 |
|
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597 Conditions on the parameters are alpha > 0 and beta > 0. |
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598 Returned values range between 0 and 1. |
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599 |
|
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600 """ |
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601 |
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602 # This version due to Janne Sinkkonen, and matches all the std |
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603 # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). |
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604 y = self.gammavariate(alpha, 1.) |
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605 if y == 0: |
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606 return 0.0 |
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607 else: |
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608 return y / (y + self.gammavariate(beta, 1.)) |
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609 |
|
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610 ## -------------------- Pareto -------------------- |
|
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611 |
|
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612 def paretovariate(self, alpha): |
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613 """Pareto distribution. alpha is the shape parameter.""" |
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614 # Jain, pg. 495 |
|
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615 |
|
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616 u = 1.0 - self.random() |
|
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617 return 1.0 / u ** (1.0/alpha) |
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618 |
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619 ## -------------------- Weibull -------------------- |
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620 |
|
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621 def weibullvariate(self, alpha, beta): |
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622 """Weibull distribution. |
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623 |
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624 alpha is the scale parameter and beta is the shape parameter. |
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625 |
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626 """ |
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627 # Jain, pg. 499; bug fix courtesy Bill Arms |
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628 |
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629 u = 1.0 - self.random() |
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630 return alpha * (-_log(u)) ** (1.0/beta) |
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631 |
|
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632 ## --------------- Operating System Random Source ------------------ |
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633 |
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634 class SystemRandom(Random): |
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635 """Alternate random number generator using sources provided |
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636 by the operating system (such as /dev/urandom on Unix or |
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|
637 CryptGenRandom on Windows). |
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638 |
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639 Not available on all systems (see os.urandom() for details). |
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640 """ |
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641 |
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642 def random(self): |
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643 """Get the next random number in the range [0.0, 1.0).""" |
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644 return (int.from_bytes(_urandom(7), 'big') >> 3) * RECIP_BPF |
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645 |
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646 def getrandbits(self, k): |
|
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647 """getrandbits(k) -> x. Generates an int with k random bits.""" |
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648 if k <= 0: |
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649 raise ValueError('number of bits must be greater than zero') |
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650 if k != int(k): |
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651 raise TypeError('number of bits should be an integer') |
|
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652 numbytes = (k + 7) // 8 # bits / 8 and rounded up |
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653 x = int.from_bytes(_urandom(numbytes), 'big') |
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654 return x >> (numbytes * 8 - k) # trim excess bits |
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655 |
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656 def seed(self, *args, **kwds): |
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657 "Stub method. Not used for a system random number generator." |
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658 return None |
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659 |
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660 def _notimplemented(self, *args, **kwds): |
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661 "Method should not be called for a system random number generator." |
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662 raise NotImplementedError('System entropy source does not have state.') |
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663 getstate = setstate = _notimplemented |
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664 |
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665 ## -------------------- test program -------------------- |
| 1575 | 666 |
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667 def _test_generator(n, func, args): |
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668 import time |
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669 print(n, 'times', func.__name__) |
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670 total = 0.0 |
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671 sqsum = 0.0 |
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672 smallest = 1e10 |
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673 largest = -1e10 |
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674 t0 = time.time() |
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675 for i in range(n): |
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676 x = func(*args) |
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677 total += x |
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678 sqsum = sqsum + x*x |
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679 smallest = min(x, smallest) |
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680 largest = max(x, largest) |
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681 t1 = time.time() |
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682 print(round(t1-t0, 3), 'sec,', end=' ') |
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683 avg = total/n |
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684 stddev = _sqrt(sqsum/n - avg*avg) |
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685 print('avg %g, stddev %g, min %g, max %g' % \ |
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686 (avg, stddev, smallest, largest)) |
| 1575 | 687 |
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688 |
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689 def _test(N=2000): |
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690 _test_generator(N, random, ()) |
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691 _test_generator(N, normalvariate, (0.0, 1.0)) |
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692 _test_generator(N, lognormvariate, (0.0, 1.0)) |
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693 _test_generator(N, vonmisesvariate, (0.0, 1.0)) |
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694 _test_generator(N, gammavariate, (0.01, 1.0)) |
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695 _test_generator(N, gammavariate, (0.1, 1.0)) |
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696 _test_generator(N, gammavariate, (0.1, 2.0)) |
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697 _test_generator(N, gammavariate, (0.5, 1.0)) |
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698 _test_generator(N, gammavariate, (0.9, 1.0)) |
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699 _test_generator(N, gammavariate, (1.0, 1.0)) |
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700 _test_generator(N, gammavariate, (2.0, 1.0)) |
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701 _test_generator(N, gammavariate, (20.0, 1.0)) |
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702 _test_generator(N, gammavariate, (200.0, 1.0)) |
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703 _test_generator(N, gauss, (0.0, 1.0)) |
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704 _test_generator(N, betavariate, (3.0, 3.0)) |
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705 _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0)) |
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706 |
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707 # Create one instance, seeded from current time, and export its methods |
|
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708 # as module-level functions. The functions share state across all uses |
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709 #(both in the user's code and in the Python libraries), but that's fine |
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710 # for most programs and is easier for the casual user than making them |
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711 # instantiate their own Random() instance. |
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712 |
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713 _inst = Random() |
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714 seed = _inst.seed |
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715 random = _inst.random |
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716 uniform = _inst.uniform |
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717 triangular = _inst.triangular |
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718 randint = _inst.randint |
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719 choice = _inst.choice |
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720 randrange = _inst.randrange |
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721 sample = _inst.sample |
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722 shuffle = _inst.shuffle |
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723 normalvariate = _inst.normalvariate |
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724 lognormvariate = _inst.lognormvariate |
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725 expovariate = _inst.expovariate |
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726 vonmisesvariate = _inst.vonmisesvariate |
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727 gammavariate = _inst.gammavariate |
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728 gauss = _inst.gauss |
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729 betavariate = _inst.betavariate |
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730 paretovariate = _inst.paretovariate |
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731 weibullvariate = _inst.weibullvariate |
|
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732 getstate = _inst.getstate |
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733 setstate = _inst.setstate |
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734 getrandbits = _inst.getrandbits |
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735 |
| 1575 | 736 if __name__ == '__main__': |
|
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737 _test() |
