Mercurial > cpython-lru_cache-weakref
annotate Doc/library/random.rst @ 54294:dba767bc3155 legacy-trunk
remove mention of old ctypes version
| author | Benjamin Peterson <benjamin@python.org> |
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| date | Sat, 23 May 2009 20:59:09 +0000 |
| parents | 2a27f9ada8e3 |
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1 |
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2 :mod:`random` --- Generate pseudo-random numbers |
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3 ================================================ |
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4 |
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5 .. module:: random |
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6 :synopsis: Generate pseudo-random numbers with various common distributions. |
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7 |
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8 |
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9 This module implements pseudo-random number generators for various |
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10 distributions. |
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11 |
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12 For integers, uniform selection from a range. For sequences, uniform selection |
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13 of a random element, a function to generate a random permutation of a list |
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14 in-place, and a function for random sampling without replacement. |
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15 |
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16 On the real line, there are functions to compute uniform, normal (Gaussian), |
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17 lognormal, negative exponential, gamma, and beta distributions. For generating |
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18 distributions of angles, the von Mises distribution is available. |
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19 |
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20 Almost all module functions depend on the basic function :func:`random`, which |
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21 generates a random float uniformly in the semi-open range [0.0, 1.0). Python |
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22 uses the Mersenne Twister as the core generator. It produces 53-bit precision |
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23 floats and has a period of 2\*\*19937-1. The underlying implementation in C is |
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24 both fast and threadsafe. The Mersenne Twister is one of the most extensively |
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25 tested random number generators in existence. However, being completely |
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26 deterministic, it is not suitable for all purposes, and is completely unsuitable |
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27 for cryptographic purposes. |
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28 |
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29 The functions supplied by this module are actually bound methods of a hidden |
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30 instance of the :class:`random.Random` class. You can instantiate your own |
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31 instances of :class:`Random` to get generators that don't share state. This is |
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32 especially useful for multi-threaded programs, creating a different instance of |
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33 :class:`Random` for each thread, and using the :meth:`jumpahead` method to make |
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34 it likely that the generated sequences seen by each thread don't overlap. |
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35 |
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36 Class :class:`Random` can also be subclassed if you want to use a different |
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37 basic generator of your own devising: in that case, override the :meth:`random`, |
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38 :meth:`seed`, :meth:`getstate`, :meth:`setstate` and :meth:`jumpahead` methods. |
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39 Optionally, a new generator can supply a :meth:`getrandbits` method --- this |
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40 allows :meth:`randrange` to produce selections over an arbitrarily large range. |
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41 |
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42 .. versionadded:: 2.4 |
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43 the :meth:`getrandbits` method. |
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44 |
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45 As an example of subclassing, the :mod:`random` module provides the |
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46 :class:`WichmannHill` class that implements an alternative generator in pure |
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47 Python. The class provides a backward compatible way to reproduce results from |
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48 earlier versions of Python, which used the Wichmann-Hill algorithm as the core |
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49 generator. Note that this Wichmann-Hill generator can no longer be recommended: |
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50 its period is too short by contemporary standards, and the sequence generated is |
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51 known to fail some stringent randomness tests. See the references below for a |
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52 recent variant that repairs these flaws. |
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53 |
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54 .. versionchanged:: 2.3 |
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55 Substituted MersenneTwister for Wichmann-Hill. |
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56 |
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57 Bookkeeping functions: |
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58 |
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59 |
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60 .. function:: seed([x]) |
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61 |
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62 Initialize the basic random number generator. Optional argument *x* can be any |
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63 :term:`hashable` object. If *x* is omitted or ``None``, current system time is used; |
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64 current system time is also used to initialize the generator when the module is |
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65 first imported. If randomness sources are provided by the operating system, |
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66 they are used instead of the system time (see the :func:`os.urandom` function |
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67 for details on availability). |
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68 |
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69 .. versionchanged:: 2.4 |
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70 formerly, operating system resources were not used. |
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71 |
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72 If *x* is not ``None`` or an int or long, ``hash(x)`` is used instead. If *x* is |
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73 an int or long, *x* is used directly. |
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74 |
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75 |
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76 .. function:: getstate() |
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77 |
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78 Return an object capturing the current internal state of the generator. This |
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79 object can be passed to :func:`setstate` to restore the state. |
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80 |
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81 .. versionadded:: 2.1 |
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82 |
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83 .. versionchanged:: 2.6 |
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84 State values produced in Python 2.6 cannot be loaded into earlier versions. |
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85 |
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86 |
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87 .. function:: setstate(state) |
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88 |
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89 *state* should have been obtained from a previous call to :func:`getstate`, and |
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90 :func:`setstate` restores the internal state of the generator to what it was at |
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91 the time :func:`setstate` was called. |
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92 |
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93 .. versionadded:: 2.1 |
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94 |
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95 |
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96 .. function:: jumpahead(n) |
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97 |
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98 Change the internal state to one different from and likely far away from the |
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99 current state. *n* is a non-negative integer which is used to scramble the |
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100 current state vector. This is most useful in multi-threaded programs, in |
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101 conjunction with multiple instances of the :class:`Random` class: |
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102 :meth:`setstate` or :meth:`seed` can be used to force all instances into the |
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103 same internal state, and then :meth:`jumpahead` can be used to force the |
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104 instances' states far apart. |
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105 |
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106 .. versionadded:: 2.1 |
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107 |
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108 .. versionchanged:: 2.3 |
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109 Instead of jumping to a specific state, *n* steps ahead, ``jumpahead(n)`` |
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110 jumps to another state likely to be separated by many steps. |
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111 |
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112 |
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113 .. function:: getrandbits(k) |
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114 |
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115 Returns a python :class:`long` int with *k* random bits. This method is supplied |
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116 with the MersenneTwister generator and some other generators may also provide it |
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117 as an optional part of the API. When available, :meth:`getrandbits` enables |
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118 :meth:`randrange` to handle arbitrarily large ranges. |
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119 |
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120 .. versionadded:: 2.4 |
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121 |
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122 Functions for integers: |
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123 |
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124 |
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125 .. function:: randrange([start,] stop[, step]) |
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126 |
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127 Return a randomly selected element from ``range(start, stop, step)``. This is |
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128 equivalent to ``choice(range(start, stop, step))``, but doesn't actually build a |
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129 range object. |
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130 |
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131 .. versionadded:: 1.5.2 |
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132 |
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133 |
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134 .. function:: randint(a, b) |
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135 |
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136 Return a random integer *N* such that ``a <= N <= b``. |
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137 |
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138 Functions for sequences: |
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139 |
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140 |
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141 .. function:: choice(seq) |
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142 |
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143 Return a random element from the non-empty sequence *seq*. If *seq* is empty, |
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144 raises :exc:`IndexError`. |
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145 |
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146 |
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147 .. function:: shuffle(x[, random]) |
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148 |
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149 Shuffle the sequence *x* in place. The optional argument *random* is a |
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150 0-argument function returning a random float in [0.0, 1.0); by default, this is |
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151 the function :func:`random`. |
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152 |
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153 Note that for even rather small ``len(x)``, the total number of permutations of |
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154 *x* is larger than the period of most random number generators; this implies |
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155 that most permutations of a long sequence can never be generated. |
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156 |
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157 |
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158 .. function:: sample(population, k) |
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159 |
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160 Return a *k* length list of unique elements chosen from the population sequence. |
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161 Used for random sampling without replacement. |
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162 |
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163 .. versionadded:: 2.3 |
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164 |
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165 Returns a new list containing elements from the population while leaving the |
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166 original population unchanged. The resulting list is in selection order so that |
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167 all sub-slices will also be valid random samples. This allows raffle winners |
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168 (the sample) to be partitioned into grand prize and second place winners (the |
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169 subslices). |
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170 |
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171 Members of the population need not be :term:`hashable` or unique. If the population |
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172 contains repeats, then each occurrence is a possible selection in the sample. |
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173 |
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174 To choose a sample from a range of integers, use an :func:`xrange` object as an |
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175 argument. This is especially fast and space efficient for sampling from a large |
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176 population: ``sample(xrange(10000000), 60)``. |
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177 |
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178 The following functions generate specific real-valued distributions. Function |
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179 parameters are named after the corresponding variables in the distribution's |
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180 equation, as used in common mathematical practice; most of these equations can |
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181 be found in any statistics text. |
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182 |
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183 |
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184 .. function:: random() |
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185 |
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186 Return the next random floating point number in the range [0.0, 1.0). |
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187 |
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188 |
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189 .. function:: uniform(a, b) |
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190 |
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191 Return a random floating point number *N* such that ``a <= N <= b`` for |
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192 ``a <= b`` and ``b <= N <= a`` for ``b < a``. |
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193 |
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194 |
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195 .. function:: triangular(low, high, mode) |
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196 |
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197 Return a random floating point number *N* such that ``low <= N <= high`` and |
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198 with the specified *mode* between those bounds. The *low* and *high* bounds |
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199 default to zero and one. The *mode* argument defaults to the midpoint |
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200 between the bounds, giving a symmetric distribution. |
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201 |
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202 .. versionadded:: 2.6 |
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203 |
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204 |
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205 .. function:: betavariate(alpha, beta) |
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206 |
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207 Beta distribution. Conditions on the parameters are ``alpha > 0`` and |
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208 ``beta > 0``. Returned values range between 0 and 1. |
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209 |
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210 |
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211 .. function:: expovariate(lambd) |
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212 |
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213 Exponential distribution. *lambd* is 1.0 divided by the desired |
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214 mean. It should be nonzero. (The parameter would be called |
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215 "lambda", but that is a reserved word in Python.) Returned values |
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216 range from 0 to positive infinity if *lambd* is positive, and from |
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217 negative infinity to 0 if *lambd* is negative. |
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218 |
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219 |
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220 .. function:: gammavariate(alpha, beta) |
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221 |
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222 Gamma distribution. (*Not* the gamma function!) Conditions on the |
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223 parameters are ``alpha > 0`` and ``beta > 0``. |
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224 |
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225 |
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226 .. function:: gauss(mu, sigma) |
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227 |
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228 Gaussian distribution. *mu* is the mean, and *sigma* is the standard |
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229 deviation. This is slightly faster than the :func:`normalvariate` function |
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230 defined below. |
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231 |
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232 |
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233 .. function:: lognormvariate(mu, sigma) |
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234 |
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235 Log normal distribution. If you take the natural logarithm of this |
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236 distribution, you'll get a normal distribution with mean *mu* and standard |
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237 deviation *sigma*. *mu* can have any value, and *sigma* must be greater than |
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238 zero. |
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239 |
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240 |
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241 .. function:: normalvariate(mu, sigma) |
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242 |
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243 Normal distribution. *mu* is the mean, and *sigma* is the standard deviation. |
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244 |
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245 |
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246 .. function:: vonmisesvariate(mu, kappa) |
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247 |
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248 *mu* is the mean angle, expressed in radians between 0 and 2\*\ *pi*, and *kappa* |
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249 is the concentration parameter, which must be greater than or equal to zero. If |
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250 *kappa* is equal to zero, this distribution reduces to a uniform random angle |
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251 over the range 0 to 2\*\ *pi*. |
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252 |
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253 |
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254 .. function:: paretovariate(alpha) |
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255 |
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256 Pareto distribution. *alpha* is the shape parameter. |
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257 |
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258 |
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259 .. function:: weibullvariate(alpha, beta) |
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260 |
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261 Weibull distribution. *alpha* is the scale parameter and *beta* is the shape |
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262 parameter. |
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263 |
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264 |
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265 Alternative Generators: |
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266 |
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267 .. class:: WichmannHill([seed]) |
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268 |
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269 Class that implements the Wichmann-Hill algorithm as the core generator. Has all |
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270 of the same methods as :class:`Random` plus the :meth:`whseed` method described |
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271 below. Because this class is implemented in pure Python, it is not threadsafe |
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272 and may require locks between calls. The period of the generator is |
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273 6,953,607,871,644 which is small enough to require care that two independent |
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274 random sequences do not overlap. |
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275 |
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276 |
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277 .. function:: whseed([x]) |
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278 |
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279 This is obsolete, supplied for bit-level compatibility with versions of Python |
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280 prior to 2.1. See :func:`seed` for details. :func:`whseed` does not guarantee |
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281 that distinct integer arguments yield distinct internal states, and can yield no |
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282 more than about 2\*\*24 distinct internal states in all. |
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283 |
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284 |
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285 .. class:: SystemRandom([seed]) |
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286 |
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287 Class that uses the :func:`os.urandom` function for generating random numbers |
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288 from sources provided by the operating system. Not available on all systems. |
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289 Does not rely on software state and sequences are not reproducible. Accordingly, |
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290 the :meth:`seed` and :meth:`jumpahead` methods have no effect and are ignored. |
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291 The :meth:`getstate` and :meth:`setstate` methods raise |
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292 :exc:`NotImplementedError` if called. |
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293 |
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294 .. versionadded:: 2.4 |
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295 |
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296 Examples of basic usage:: |
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297 |
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298 >>> random.random() # Random float x, 0.0 <= x < 1.0 |
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299 0.37444887175646646 |
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300 >>> random.uniform(1, 10) # Random float x, 1.0 <= x < 10.0 |
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301 1.1800146073117523 |
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302 >>> random.randint(1, 10) # Integer from 1 to 10, endpoints included |
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303 7 |
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304 >>> random.randrange(0, 101, 2) # Even integer from 0 to 100 |
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305 26 |
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306 >>> random.choice('abcdefghij') # Choose a random element |
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307 'c' |
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308 |
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309 >>> items = [1, 2, 3, 4, 5, 6, 7] |
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310 >>> random.shuffle(items) |
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311 >>> items |
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312 [7, 3, 2, 5, 6, 4, 1] |
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313 |
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314 >>> random.sample([1, 2, 3, 4, 5], 3) # Choose 3 elements |
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315 [4, 1, 5] |
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316 |
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317 |
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318 |
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319 .. seealso:: |
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320 |
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321 M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally |
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322 equidistributed uniform pseudorandom number generator", ACM Transactions on |
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323 Modeling and Computer Simulation Vol. 8, No. 1, January pp.3-30 1998. |
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324 |
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325 Wichmann, B. A. & Hill, I. D., "Algorithm AS 183: An efficient and portable |
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326 pseudo-random number generator", Applied Statistics 31 (1982) 188-190. |
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327 |
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328 `Complementary-Multiply-with-Carry recipe |
| 52762 | 329 <http://code.activestate.com/recipes/576707/>`_ for a compatible alternative |
| 330 random number generator with a long period and comparatively simple update | |
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331 operations. |
