Mercurial > repos > guerler > springsuite

annotate planemo/lib/python3.7/site-packages/networkx/utils/random_sequence.py @ 1:56ad4e20f292 draft

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"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author guerler
date Fri, 31 Jul 2020 00:32:28 -0400
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1 # Copyright (C) 2004-2019 by
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2 # Aric Hagberg <hagberg@lanl.gov>
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3 # Dan Schult <dschult@colgate.edu>
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4 # Pieter Swart <swart@lanl.gov>
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5 # All rights reserved.
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6 # BSD license.
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7 #
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8 # Authors: Aric Hagberg (hagberg@lanl.gov)
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9 # Dan Schult (dschult@colgate.edu)
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10 # Ben Edwards (bedwards@cs.unm.edu)
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11 """
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12 Utilities for generating random numbers, random sequences, and
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13 random selections.
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14 """
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15
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16 import random
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17 import sys
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18 import networkx as nx
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19 from networkx.utils import py_random_state
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20
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21
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22 # The same helpers for choosing random sequences from distributions
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23 # uses Python's random module
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24 # https://docs.python.org/2/library/random.html
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25
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26 @py_random_state(2)
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27 def powerlaw_sequence(n, exponent=2.0, seed=None):
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28 """
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29 Return sample sequence of length n from a power law distribution.
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30 """
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31 return [seed.paretovariate(exponent - 1) for i in range(n)]
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32
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33
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34 @py_random_state(2)
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35 def zipf_rv(alpha, xmin=1, seed=None):
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36 r"""Returns a random value chosen from the Zipf distribution.
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37
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38 The return value is an integer drawn from the probability distribution
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39
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40 .. math::
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41
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42 p(x)=\frac{x^{-\alpha}}{\zeta(\alpha, x_{\min})},
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43
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44 where $\zeta(\alpha, x_{\min})$ is the Hurwitz zeta function.
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45
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46 Parameters
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47 ----------
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48 alpha : float
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49 Exponent value of the distribution
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50 xmin : int
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51 Minimum value
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52 seed : integer, random_state, or None (default)
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53 Indicator of random number generation state.
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54 See :ref:`Randomness<randomness>`.
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55
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56 Returns
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57 -------
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58 x : int
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59 Random value from Zipf distribution
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60
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61 Raises
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62 ------
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63 ValueError:
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64 If xmin < 1 or
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65 If alpha <= 1
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66
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67 Notes
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68 -----
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69 The rejection algorithm generates random values for a the power-law
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70 distribution in uniformly bounded expected time dependent on
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71 parameters. See [1]_ for details on its operation.
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72
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73 Examples
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74 --------
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75 >>> nx.zipf_rv(alpha=2, xmin=3, seed=42) # doctest: +SKIP
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76
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77 References
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78 ----------
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79 .. [1] Luc Devroye, Non-Uniform Random Variate Generation,
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80 Springer-Verlag, New York, 1986.
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81 """
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82 if xmin < 1:
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83 raise ValueError("xmin < 1")
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84 if alpha <= 1:
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85 raise ValueError("a <= 1.0")
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86 a1 = alpha - 1.0
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87 b = 2**a1
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88 while True:
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89 u = 1.0 - seed.random() # u in (0,1]
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90 v = seed.random() # v in [0,1)
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91 x = int(xmin * u**-(1.0 / a1))
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92 t = (1.0 + (1.0 / x))**a1
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93 if v * x * (t - 1.0) / (b - 1.0) <= t / b:
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94 break
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95 return x
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96
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97
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98 def cumulative_distribution(distribution):
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99 """Returns normalized cumulative distribution from discrete distribution."""
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100
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101 cdf = [0.0]
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102 psum = float(sum(distribution))
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103 for i in range(0, len(distribution)):
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104 cdf.append(cdf[i] + distribution[i] / psum)
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105 return cdf
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106
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107
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108 @py_random_state(3)
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109 def discrete_sequence(n, distribution=None, cdistribution=None, seed=None):
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110 """
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111 Return sample sequence of length n from a given discrete distribution
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112 or discrete cumulative distribution.
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113
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114 One of the following must be specified.
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115
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116 distribution = histogram of values, will be normalized
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117
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118 cdistribution = normalized discrete cumulative distribution
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119
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120 """
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121 import bisect
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122
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123 if cdistribution is not None:
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124 cdf = cdistribution
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125 elif distribution is not None:
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126 cdf = cumulative_distribution(distribution)
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127 else:
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128 raise nx.NetworkXError(
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129 "discrete_sequence: distribution or cdistribution missing")
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130
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131 # get a uniform random number
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132 inputseq = [seed.random() for i in range(n)]
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133
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134 # choose from CDF
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135 seq = [bisect.bisect_left(cdf, s) - 1 for s in inputseq]
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136 return seq
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137
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138
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139 @py_random_state(2)
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140 def random_weighted_sample(mapping, k, seed=None):
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141 """Returns k items without replacement from a weighted sample.
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142
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143 The input is a dictionary of items with weights as values.
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144 """
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145 if k > len(mapping):
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146 raise ValueError("sample larger than population")
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147 sample = set()
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148 while len(sample) < k:
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149 sample.add(weighted_choice(mapping, seed))
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150 return list(sample)
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151
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152
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153 @py_random_state(1)
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154 def weighted_choice(mapping, seed=None):
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155 """Returns a single element from a weighted sample.
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156
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157 The input is a dictionary of items with weights as values.
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158 """
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159 # use roulette method
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160 rnd = seed.random() * sum(mapping.values())
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161 for k, w in mapping.items():
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162 rnd -= w
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163 if rnd < 0:
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164 return k