Mercurial > cpython-lru_cache-weakref
annotate Lib/random.py @ 51749:1408feec0084 3.0
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r69798 | benjamin.peterson | 2009-02-19 22:02:38 -0600 (Thu, 19 Feb 2009) | 13 lines
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r69769 | georg.brandl | 2009-02-19 02:30:06 -0600 (Thu, 19 Feb 2009) | 1 line
#5310, #3558: fix operator precedence table.
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r69776 | georg.brandl | 2009-02-19 10:34:51 -0600 (Thu, 19 Feb 2009) | 2 lines
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| author | Benjamin Peterson <benjamin@python.org> |
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| date | Fri, 20 Feb 2009 04:05:32 +0000 |
| parents | 13b2e11f3cf5 |
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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 binascii import hexlify as _hexlify |
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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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50 "gauss","betavariate","paretovariate","weibullvariate", |
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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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81 |
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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): |
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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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100 """ |
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101 |
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102 if a is None: |
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103 try: |
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104 a = int(_hexlify(_urandom(16)), 16) |
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105 except NotImplementedError: |
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106 import time |
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107 a = int(time.time() * 256) # use fractional seconds |
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108 |
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109 super().seed(a) |
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110 self.gauss_next = None |
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111 |
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112 def getstate(self): |
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113 """Return internal state; can be passed to setstate() later.""" |
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114 return self.VERSION, super().getstate(), self.gauss_next |
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115 |
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116 def setstate(self, state): |
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117 """Restore internal state from object returned by getstate().""" |
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118 version = state[0] |
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119 if version == 3: |
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120 version, internalstate, self.gauss_next = state |
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121 super().setstate(internalstate) |
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122 elif version == 2: |
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123 version, internalstate, self.gauss_next = state |
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124 # In version 2, the state was saved as signed ints, which causes |
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125 # inconsistencies between 32/64-bit systems. The state is |
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126 # really unsigned 32-bit ints, so we convert negative ints from |
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127 # version 2 to positive longs for version 3. |
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128 try: |
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129 internalstate = tuple( x % (2**32) for x in internalstate ) |
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130 except ValueError as e: |
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131 raise TypeError from e |
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132 super(Random, self).setstate(internalstate) |
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133 else: |
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134 raise ValueError("state with version %s passed to " |
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135 "Random.setstate() of version %s" % |
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136 (version, self.VERSION)) |
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137 |
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138 ## ---- Methods below this point do not need to be overridden when |
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139 ## ---- subclassing for the purpose of using a different core generator. |
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140 |
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141 ## -------------------- pickle support ------------------- |
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142 |
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143 def __getstate__(self): # for pickle |
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144 return self.getstate() |
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145 |
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146 def __setstate__(self, state): # for pickle |
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147 self.setstate(state) |
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148 |
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149 def __reduce__(self): |
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150 return self.__class__, (), self.getstate() |
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151 |
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152 ## -------------------- integer methods ------------------- |
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153 |
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154 def randrange(self, start, stop=None, step=1, int=int, default=None, |
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155 maxwidth=1<<BPF): |
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156 """Choose a random item from range(start, stop[, step]). |
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157 |
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158 This fixes the problem with randint() which includes the |
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159 endpoint; in Python this is usually not what you want. |
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160 Do not supply the 'int', 'default', and 'maxwidth' arguments. |
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161 """ |
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162 |
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163 # This code is a bit messy to make it fast for the |
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164 # common case while still doing adequate error checking. |
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165 istart = int(start) |
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166 if istart != start: |
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167 raise ValueError("non-integer arg 1 for randrange()") |
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168 if stop is default: |
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169 if istart > 0: |
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170 if istart >= maxwidth: |
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171 return self._randbelow(istart) |
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172 return int(self.random() * istart) |
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173 raise ValueError("empty range for randrange()") |
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174 |
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175 # stop argument supplied. |
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176 istop = int(stop) |
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177 if istop != stop: |
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178 raise ValueError("non-integer stop for randrange()") |
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179 width = istop - istart |
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180 if step == 1 and width > 0: |
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181 # Note that |
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182 # int(istart + self.random()*width) |
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183 # instead would be incorrect. For example, consider istart |
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184 # = -2 and istop = 0. Then the guts would be in |
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185 # -2.0 to 0.0 exclusive on both ends (ignoring that random() |
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186 # might return 0.0), and because int() truncates toward 0, the |
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187 # final result would be -1 or 0 (instead of -2 or -1). |
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188 # istart + int(self.random()*width) |
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189 # would also be incorrect, for a subtler reason: the RHS |
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190 # can return a long, and then randrange() would also return |
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191 # a long, but we're supposed to return an int (for backward |
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192 # compatibility). |
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193 |
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194 if width >= maxwidth: |
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195 return int(istart + self._randbelow(width)) |
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196 return int(istart + int(self.random()*width)) |
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197 if step == 1: |
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198 raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width)) |
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199 |
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200 # Non-unit step argument supplied. |
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201 istep = int(step) |
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202 if istep != step: |
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203 raise ValueError("non-integer step for randrange()") |
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204 if istep > 0: |
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205 n = (width + istep - 1) // istep |
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206 elif istep < 0: |
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207 n = (width + istep + 1) // istep |
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208 else: |
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209 raise ValueError("zero step for randrange()") |
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210 |
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211 if n <= 0: |
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212 raise ValueError("empty range for randrange()") |
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213 |
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214 if n >= maxwidth: |
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215 return istart + istep*self._randbelow(n) |
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216 return istart + istep*int(self.random() * n) |
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218 def randint(self, a, b): |
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219 """Return random integer in range [a, b], including both end points. |
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220 """ |
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221 |
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222 return self.randrange(a, b+1) |
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223 |
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224 def _randbelow(self, n, _log=_log, int=int, _maxwidth=1<<BPF, |
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225 _Method=_MethodType, _BuiltinMethod=_BuiltinMethodType): |
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226 """Return a random int in the range [0,n) |
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227 |
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228 Handles the case where n has more bits than returned |
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229 by a single call to the underlying generator. |
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230 """ |
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231 |
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232 try: |
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233 getrandbits = self.getrandbits |
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234 except AttributeError: |
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235 pass |
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236 else: |
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237 # Only call self.getrandbits if the original random() builtin method |
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238 # has not been overridden or if a new getrandbits() was supplied. |
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239 # This assures that the two methods correspond. |
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240 if type(self.random) is _BuiltinMethod or type(getrandbits) is _Method: |
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241 k = int(1.00001 + _log(n-1, 2.0)) # 2**k > n-1 > 2**(k-2) |
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242 r = getrandbits(k) |
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243 while r >= n: |
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244 r = getrandbits(k) |
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245 return r |
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246 if n >= _maxwidth: |
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247 _warn("Underlying random() generator does not supply \n" |
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248 "enough bits to choose from a population range this large") |
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249 return int(self.random() * n) |
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250 |
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251 ## -------------------- sequence methods ------------------- |
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252 |
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253 def choice(self, seq): |
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254 """Choose a random element from a non-empty sequence.""" |
| 32287 | 255 return seq[int(self.random() * len(seq))] # raises IndexError if seq is empty |
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256 |
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257 def shuffle(self, x, random=None, int=int): |
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258 """x, random=random.random -> shuffle list x in place; return None. |
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259 |
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260 Optional arg random is a 0-argument function returning a random |
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261 float in [0.0, 1.0); by default, the standard random.random. |
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262 """ |
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263 |
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264 if random is None: |
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265 random = self.random |
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266 for i in reversed(range(1, len(x))): |
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267 # pick an element in x[:i+1] with which to exchange x[i] |
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268 j = int(random() * (i+1)) |
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269 x[i], x[j] = x[j], x[i] |
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270 |
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271 def sample(self, population, k): |
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272 """Chooses k unique random elements from a population sequence or set. |
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273 |
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274 Returns a new list containing elements from the population while |
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275 leaving the original population unchanged. The resulting list is |
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276 in selection order so that all sub-slices will also be valid random |
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277 samples. This allows raffle winners (the sample) to be partitioned |
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278 into grand prize and second place winners (the subslices). |
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279 |
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280 Members of the population need not be hashable or unique. If the |
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281 population contains repeats, then each occurrence is a possible |
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282 selection in the sample. |
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283 |
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284 To choose a sample in a range of integers, use range as an argument. |
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285 This is especially fast and space efficient for sampling from a |
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286 large population: sample(range(10000000), 60) |
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287 """ |
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288 |
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289 # Sampling without replacement entails tracking either potential |
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290 # selections (the pool) in a list or previous selections in a set. |
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291 |
| 31578 | 292 # When the number of selections is small compared to the |
| 293 # population, then tracking selections is efficient, requiring | |
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294 # only a small set and an occasional reselection. For |
| 31578 | 295 # a larger number of selections, the pool tracking method is |
| 296 # preferred since the list takes less space than the | |
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297 # set and it doesn't suffer from frequent reselections. |
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298 |
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299 if isinstance(population, (set, frozenset)): |
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300 population = tuple(population) |
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301 if not hasattr(population, '__getitem__') or hasattr(population, 'keys'): |
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302 raise TypeError("Population must be a sequence or set. For dicts, use dict.keys().") |
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303 random = self.random |
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304 n = len(population) |
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305 if not 0 <= k <= n: |
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306 raise ValueError("Sample larger than population") |
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307 _int = int |
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308 result = [None] * k |
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309 setsize = 21 # size of a small set minus size of an empty list |
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310 if k > 5: |
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311 setsize += 4 ** _ceil(_log(k * 3, 4)) # table size for big sets |
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312 if n <= setsize: |
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313 # An n-length list is smaller than a k-length set |
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314 pool = list(population) |
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315 for i in range(k): # invariant: non-selected at [0,n-i) |
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316 j = _int(random() * (n-i)) |
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317 result[i] = pool[j] |
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318 pool[j] = pool[n-i-1] # move non-selected item into vacancy |
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319 else: |
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320 selected = set() |
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321 selected_add = selected.add |
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322 for i in range(k): |
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323 j = _int(random() * n) |
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324 while j in selected: |
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325 j = _int(random() * n) |
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326 selected_add(j) |
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327 result[i] = population[j] |
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328 return result |
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329 |
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330 ## -------------------- real-valued distributions ------------------- |
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331 |
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332 ## -------------------- uniform distribution ------------------- |
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333 |
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334 def uniform(self, a, b): |
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335 """Get a random number in the range [a, b).""" |
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336 return a + (b-a) * self.random() |
| 1575 | 337 |
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338 ## -------------------- triangular -------------------- |
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339 |
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340 def triangular(self, low=0.0, high=1.0, mode=None): |
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341 """Triangular distribution. |
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342 |
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343 Continuous distribution bounded by given lower and upper limits, |
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344 and having a given mode value in-between. |
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345 |
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346 http://en.wikipedia.org/wiki/Triangular_distribution |
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347 |
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348 """ |
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349 u = self.random() |
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350 c = 0.5 if mode is None else (mode - low) / (high - low) |
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351 if u > c: |
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352 u = 1.0 - u |
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353 c = 1.0 - c |
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354 low, high = high, low |
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355 return low + (high - low) * (u * c) ** 0.5 |
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356 |
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357 ## -------------------- normal distribution -------------------- |
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359 def normalvariate(self, mu, sigma): |
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360 """Normal distribution. |
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361 |
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362 mu is the mean, and sigma is the standard deviation. |
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363 |
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364 """ |
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365 # mu = mean, sigma = standard deviation |
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366 |
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367 # Uses Kinderman and Monahan method. Reference: Kinderman, |
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368 # A.J. and Monahan, J.F., "Computer generation of random |
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369 # variables using the ratio of uniform deviates", ACM Trans |
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370 # Math Software, 3, (1977), pp257-260. |
| 1575 | 371 |
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372 random = self.random |
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373 while 1: |
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374 u1 = random() |
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375 u2 = 1.0 - random() |
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376 z = NV_MAGICCONST*(u1-0.5)/u2 |
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377 zz = z*z/4.0 |
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378 if zz <= -_log(u2): |
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379 break |
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380 return mu + z*sigma |
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382 ## -------------------- lognormal distribution -------------------- |
| 1575 | 383 |
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384 def lognormvariate(self, mu, sigma): |
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385 """Log normal distribution. |
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386 |
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387 If you take the natural logarithm of this distribution, you'll get a |
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388 normal distribution with mean mu and standard deviation sigma. |
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389 mu can have any value, and sigma must be greater than zero. |
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390 |
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391 """ |
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392 return _exp(self.normalvariate(mu, sigma)) |
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394 ## -------------------- exponential distribution -------------------- |
| 1575 | 395 |
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396 def expovariate(self, lambd): |
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397 """Exponential distribution. |
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398 |
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399 lambd is 1.0 divided by the desired mean. (The parameter would be |
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400 called "lambda", but that is a reserved word in Python.) Returned |
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401 values range from 0 to positive infinity. |
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402 |
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403 """ |
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404 # lambd: rate lambd = 1/mean |
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405 # ('lambda' is a Python reserved word) |
| 1575 | 406 |
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407 random = self.random |
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408 u = random() |
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409 while u <= 1e-7: |
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410 u = random() |
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411 return -_log(u)/lambd |
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413 ## -------------------- von Mises distribution -------------------- |
| 1575 | 414 |
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415 def vonmisesvariate(self, mu, kappa): |
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416 """Circular data distribution. |
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417 |
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418 mu is the mean angle, expressed in radians between 0 and 2*pi, and |
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419 kappa is the concentration parameter, which must be greater than or |
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420 equal to zero. If kappa is equal to zero, this distribution reduces |
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421 to a uniform random angle over the range 0 to 2*pi. |
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422 |
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423 """ |
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424 # mu: mean angle (in radians between 0 and 2*pi) |
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425 # kappa: concentration parameter kappa (>= 0) |
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|
426 # if kappa = 0 generate uniform random angle |
| 1575 | 427 |
|
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|
428 # Based upon an algorithm published in: Fisher, N.I., |
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|
429 # "Statistical Analysis of Circular Data", Cambridge |
|
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|
430 # University Press, 1993. |
|
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|
431 |
|
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|
432 # Thanks to Magnus Kessler for a correction to the |
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433 # implementation of step 4. |
|
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|
434 |
|
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|
435 random = self.random |
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436 if kappa <= 1e-6: |
|
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|
437 return TWOPI * random() |
|
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|
438 |
|
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|
439 a = 1.0 + _sqrt(1.0 + 4.0 * kappa * kappa) |
|
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|
440 b = (a - _sqrt(2.0 * a))/(2.0 * kappa) |
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441 r = (1.0 + b * b)/(2.0 * b) |
| 1575 | 442 |
|
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443 while 1: |
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|
444 u1 = random() |
| 1575 | 445 |
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|
446 z = _cos(_pi * u1) |
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|
447 f = (1.0 + r * z)/(r + z) |
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|
448 c = kappa * (r - f) |
| 1575 | 449 |
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450 u2 = random() |
| 1575 | 451 |
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452 if u2 < c * (2.0 - c) or u2 <= c * _exp(1.0 - c): |
|
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453 break |
| 1575 | 454 |
|
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455 u3 = random() |
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456 if u3 > 0.5: |
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457 theta = (mu % TWOPI) + _acos(f) |
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458 else: |
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459 theta = (mu % TWOPI) - _acos(f) |
| 1575 | 460 |
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461 return theta |
| 1575 | 462 |
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463 ## -------------------- gamma distribution -------------------- |
| 1575 | 464 |
|
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|
465 def gammavariate(self, alpha, beta): |
|
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|
466 """Gamma distribution. Not the gamma function! |
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|
467 |
|
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468 Conditions on the parameters are alpha > 0 and beta > 0. |
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469 |
|
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470 """ |
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|
471 |
|
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472 # alpha > 0, beta > 0, mean is alpha*beta, variance is alpha*beta**2 |
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|
473 |
|
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474 # Warning: a few older sources define the gamma distribution in terms |
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475 # of alpha > -1.0 |
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|
476 if alpha <= 0.0 or beta <= 0.0: |
|
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477 raise ValueError('gammavariate: alpha and beta must be > 0.0') |
|
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|
478 |
|
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479 random = self.random |
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480 if alpha > 1.0: |
| 1575 | 481 |
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482 # Uses R.C.H. Cheng, "The generation of Gamma |
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483 # variables with non-integral shape parameters", |
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|
484 # Applied Statistics, (1977), 26, No. 1, p71-74 |
| 1575 | 485 |
|
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|
486 ainv = _sqrt(2.0 * alpha - 1.0) |
|
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487 bbb = alpha - LOG4 |
|
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|
488 ccc = alpha + ainv |
|
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|
489 |
|
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490 while 1: |
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|
491 u1 = random() |
|
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492 if not 1e-7 < u1 < .9999999: |
|
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|
493 continue |
|
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|
494 u2 = 1.0 - random() |
|
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|
495 v = _log(u1/(1.0-u1))/ainv |
|
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|
496 x = alpha*_exp(v) |
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|
497 z = u1*u1*u2 |
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|
498 r = bbb+ccc*v-x |
|
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|
499 if r + SG_MAGICCONST - 4.5*z >= 0.0 or r >= _log(z): |
|
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|
500 return x * beta |
| 1575 | 501 |
|
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|
502 elif alpha == 1.0: |
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|
503 # expovariate(1) |
|
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|
504 u = random() |
|
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|
505 while u <= 1e-7: |
|
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|
506 u = random() |
|
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|
507 return -_log(u) * beta |
| 1575 | 508 |
|
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|
509 else: # alpha is between 0 and 1 (exclusive) |
| 1575 | 510 |
|
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|
511 # Uses ALGORITHM GS of Statistical Computing - Kennedy & Gentle |
| 1575 | 512 |
|
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|
513 while 1: |
|
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|
514 u = random() |
|
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|
515 b = (_e + alpha)/_e |
|
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|
516 p = b*u |
|
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|
517 if p <= 1.0: |
|
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|
518 x = p ** (1.0/alpha) |
|
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|
519 else: |
|
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|
520 x = -_log((b-p)/alpha) |
|
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|
521 u1 = random() |
|
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522 if p > 1.0: |
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|
523 if u1 <= x ** (alpha - 1.0): |
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|
524 break |
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|
525 elif u1 <= _exp(-x): |
|
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|
526 break |
|
23447
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Closes patch 529408 deprecating random.stdgamma().
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diff
changeset
|
527 return x * beta |
|
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|
528 |
|
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|
529 ## -------------------- Gauss (faster alternative) -------------------- |
|
1576
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Added gauss() (same as normal but twice as fast) and betavariate();
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parents:
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|
530 |
|
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|
531 def gauss(self, mu, sigma): |
|
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Raymond Hettinger <python@rcn.com>
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|
532 """Gaussian distribution. |
|
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|
533 |
|
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|
534 mu is the mean, and sigma is the standard deviation. This is |
|
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|
535 slightly faster than the normalvariate() function. |
|
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|
536 |
|
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|
537 Not thread-safe without a lock around calls. |
|
23536
0a4c92559a0b
Whitespace normalized.
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|
538 |
|
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|
539 """ |
|
1577
444e349f962f
Use float constants directly; cosmetic changes; initialize largest
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parents:
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|
540 |
|
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parents:
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|
541 # When x and y are two variables from [0, 1), uniformly |
|
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|
542 # distributed, then |
|
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|
543 # |
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544 # cos(2*pi*x)*sqrt(-2*log(1-y)) |
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545 # sin(2*pi*x)*sqrt(-2*log(1-y)) |
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546 # |
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547 # are two *independent* variables with normal distribution |
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548 # (mu = 0, sigma = 1). |
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|
549 # (Lambert Meertens) |
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550 # (corrected version; bug discovered by Mike Miller, fixed by LM) |
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551 |
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552 # Multithreading note: When two threads call this function |
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553 # simultaneously, it is possible that they will receive the |
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554 # same return value. The window is very small though. To |
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555 # avoid this, you have to use a lock around all calls. (I |
|
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556 # didn't want to slow this down in the serial case by using a |
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557 # lock here.) |
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558 |
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559 random = self.random |
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560 z = self.gauss_next |
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561 self.gauss_next = None |
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562 if z is None: |
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563 x2pi = random() * TWOPI |
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564 g2rad = _sqrt(-2.0 * _log(1.0 - random())) |
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565 z = _cos(x2pi) * g2rad |
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566 self.gauss_next = _sin(x2pi) * g2rad |
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567 |
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568 return mu + z*sigma |
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569 |
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570 ## -------------------- beta -------------------- |
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571 ## See |
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572 ## http://sourceforge.net/bugs/?func=detailbug&bug_id=130030&group_id=5470 |
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573 ## for Ivan Frohne's insightful analysis of why the original implementation: |
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574 ## |
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575 ## def betavariate(self, alpha, beta): |
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576 ## # Discrete Event Simulation in C, pp 87-88. |
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577 ## |
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578 ## y = self.expovariate(alpha) |
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579 ## z = self.expovariate(1.0/beta) |
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580 ## return z/(y+z) |
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581 ## |
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582 ## was dead wrong, and how it probably got that way. |
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583 |
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584 def betavariate(self, alpha, beta): |
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585 """Beta distribution. |
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586 |
|
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587 Conditions on the parameters are alpha > 0 and beta > 0. |
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588 Returned values range between 0 and 1. |
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589 |
|
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590 """ |
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591 |
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592 # This version due to Janne Sinkkonen, and matches all the std |
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|
593 # texts (e.g., Knuth Vol 2 Ed 3 pg 134 "the beta distribution"). |
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|
594 y = self.gammavariate(alpha, 1.) |
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595 if y == 0: |
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596 return 0.0 |
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597 else: |
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|
598 return y / (y + self.gammavariate(beta, 1.)) |
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599 |
|
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600 ## -------------------- Pareto -------------------- |
|
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601 |
|
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602 def paretovariate(self, alpha): |
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603 """Pareto distribution. alpha is the shape parameter.""" |
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604 # Jain, pg. 495 |
|
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605 |
|
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606 u = 1.0 - self.random() |
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607 return 1.0 / pow(u, 1.0/alpha) |
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608 |
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609 ## -------------------- Weibull -------------------- |
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610 |
|
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611 def weibullvariate(self, alpha, beta): |
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612 """Weibull distribution. |
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613 |
|
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614 alpha is the scale parameter and beta is the shape parameter. |
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615 |
|
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616 """ |
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617 # Jain, pg. 499; bug fix courtesy Bill Arms |
|
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618 |
|
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619 u = 1.0 - self.random() |
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620 return alpha * pow(-_log(u), 1.0/beta) |
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621 |
|
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622 ## --------------- Operating System Random Source ------------------ |
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623 |
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624 class SystemRandom(Random): |
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625 """Alternate random number generator using sources provided |
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626 by the operating system (such as /dev/urandom on Unix or |
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627 CryptGenRandom on Windows). |
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628 |
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629 Not available on all systems (see os.urandom() for details). |
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630 """ |
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631 |
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632 def random(self): |
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633 """Get the next random number in the range [0.0, 1.0).""" |
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634 return (int(_hexlify(_urandom(7)), 16) >> 3) * RECIP_BPF |
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635 |
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636 def getrandbits(self, k): |
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637 """getrandbits(k) -> x. Generates a long int with k random bits.""" |
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638 if k <= 0: |
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639 raise ValueError('number of bits must be greater than zero') |
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640 if k != int(k): |
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641 raise TypeError('number of bits should be an integer') |
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642 bytes = (k + 7) // 8 # bits / 8 and rounded up |
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643 x = int(_hexlify(_urandom(bytes)), 16) |
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644 return x >> (bytes * 8 - k) # trim excess bits |
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645 |
|
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646 def seed(self, *args, **kwds): |
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647 "Stub method. Not used for a system random number generator." |
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648 return None |
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649 |
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650 def _notimplemented(self, *args, **kwds): |
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651 "Method should not be called for a system random number generator." |
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652 raise NotImplementedError('System entropy source does not have state.') |
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653 getstate = setstate = _notimplemented |
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654 |
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655 ## -------------------- test program -------------------- |
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657 def _test_generator(n, func, args): |
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658 import time |
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659 print(n, 'times', func.__name__) |
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660 total = 0.0 |
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661 sqsum = 0.0 |
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662 smallest = 1e10 |
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663 largest = -1e10 |
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664 t0 = time.time() |
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665 for i in range(n): |
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666 x = func(*args) |
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667 total += x |
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668 sqsum = sqsum + x*x |
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669 smallest = min(x, smallest) |
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670 largest = max(x, largest) |
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671 t1 = time.time() |
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672 print(round(t1-t0, 3), 'sec,', end=' ') |
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673 avg = total/n |
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674 stddev = _sqrt(sqsum/n - avg*avg) |
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675 print('avg %g, stddev %g, min %g, max %g' % \ |
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676 (avg, stddev, smallest, largest)) |
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678 |
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679 def _test(N=2000): |
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680 _test_generator(N, random, ()) |
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681 _test_generator(N, normalvariate, (0.0, 1.0)) |
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682 _test_generator(N, lognormvariate, (0.0, 1.0)) |
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683 _test_generator(N, vonmisesvariate, (0.0, 1.0)) |
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684 _test_generator(N, gammavariate, (0.01, 1.0)) |
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685 _test_generator(N, gammavariate, (0.1, 1.0)) |
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686 _test_generator(N, gammavariate, (0.1, 2.0)) |
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687 _test_generator(N, gammavariate, (0.5, 1.0)) |
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688 _test_generator(N, gammavariate, (0.9, 1.0)) |
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689 _test_generator(N, gammavariate, (1.0, 1.0)) |
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690 _test_generator(N, gammavariate, (2.0, 1.0)) |
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691 _test_generator(N, gammavariate, (20.0, 1.0)) |
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692 _test_generator(N, gammavariate, (200.0, 1.0)) |
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693 _test_generator(N, gauss, (0.0, 1.0)) |
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694 _test_generator(N, betavariate, (3.0, 3.0)) |
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695 _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0)) |
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696 |
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697 # Create one instance, seeded from current time, and export its methods |
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698 # as module-level functions. The functions share state across all uses |
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699 #(both in the user's code and in the Python libraries), but that's fine |
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700 # for most programs and is easier for the casual user than making them |
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701 # instantiate their own Random() instance. |
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702 |
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703 _inst = Random() |
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704 seed = _inst.seed |
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705 random = _inst.random |
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706 uniform = _inst.uniform |
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707 triangular = _inst.triangular |
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708 randint = _inst.randint |
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709 choice = _inst.choice |
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710 randrange = _inst.randrange |
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711 sample = _inst.sample |
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712 shuffle = _inst.shuffle |
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713 normalvariate = _inst.normalvariate |
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714 lognormvariate = _inst.lognormvariate |
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715 expovariate = _inst.expovariate |
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716 vonmisesvariate = _inst.vonmisesvariate |
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717 gammavariate = _inst.gammavariate |
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718 gauss = _inst.gauss |
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719 betavariate = _inst.betavariate |
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720 paretovariate = _inst.paretovariate |
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721 weibullvariate = _inst.weibullvariate |
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722 getstate = _inst.getstate |
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723 setstate = _inst.setstate |
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724 getrandbits = _inst.getrandbits |
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725 |
| 1575 | 726 if __name__ == '__main__': |
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727 _test() |
