Mercurial > hg > vamp-build-and-test
comparison DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/random/__init__.py @ 87:2a2c65a20a8b
Add Python libs and headers
author | Chris Cannam |
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date | Wed, 25 Feb 2015 14:05:22 +0000 |
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86:413a9d26189e | 87:2a2c65a20a8b |
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1 """ | |
2 ======================== | |
3 Random Number Generation | |
4 ======================== | |
5 | |
6 ==================== ========================================================= | |
7 Utility functions | |
8 ============================================================================== | |
9 random Uniformly distributed values of a given shape. | |
10 bytes Uniformly distributed random bytes. | |
11 random_integers Uniformly distributed integers in a given range. | |
12 random_sample Uniformly distributed floats in a given range. | |
13 random Alias for random_sample | |
14 ranf Alias for random_sample | |
15 sample Alias for random_sample | |
16 choice Generate a weighted random sample from a given array-like | |
17 permutation Randomly permute a sequence / generate a random sequence. | |
18 shuffle Randomly permute a sequence in place. | |
19 seed Seed the random number generator. | |
20 ==================== ========================================================= | |
21 | |
22 ==================== ========================================================= | |
23 Compatibility functions | |
24 ============================================================================== | |
25 rand Uniformly distributed values. | |
26 randn Normally distributed values. | |
27 ranf Uniformly distributed floating point numbers. | |
28 randint Uniformly distributed integers in a given range. | |
29 ==================== ========================================================= | |
30 | |
31 ==================== ========================================================= | |
32 Univariate distributions | |
33 ============================================================================== | |
34 beta Beta distribution over ``[0, 1]``. | |
35 binomial Binomial distribution. | |
36 chisquare :math:`\\chi^2` distribution. | |
37 exponential Exponential distribution. | |
38 f F (Fisher-Snedecor) distribution. | |
39 gamma Gamma distribution. | |
40 geometric Geometric distribution. | |
41 gumbel Gumbel distribution. | |
42 hypergeometric Hypergeometric distribution. | |
43 laplace Laplace distribution. | |
44 logistic Logistic distribution. | |
45 lognormal Log-normal distribution. | |
46 logseries Logarithmic series distribution. | |
47 negative_binomial Negative binomial distribution. | |
48 noncentral_chisquare Non-central chi-square distribution. | |
49 noncentral_f Non-central F distribution. | |
50 normal Normal / Gaussian distribution. | |
51 pareto Pareto distribution. | |
52 poisson Poisson distribution. | |
53 power Power distribution. | |
54 rayleigh Rayleigh distribution. | |
55 triangular Triangular distribution. | |
56 uniform Uniform distribution. | |
57 vonmises Von Mises circular distribution. | |
58 wald Wald (inverse Gaussian) distribution. | |
59 weibull Weibull distribution. | |
60 zipf Zipf's distribution over ranked data. | |
61 ==================== ========================================================= | |
62 | |
63 ==================== ========================================================= | |
64 Multivariate distributions | |
65 ============================================================================== | |
66 dirichlet Multivariate generalization of Beta distribution. | |
67 multinomial Multivariate generalization of the binomial distribution. | |
68 multivariate_normal Multivariate generalization of the normal distribution. | |
69 ==================== ========================================================= | |
70 | |
71 ==================== ========================================================= | |
72 Standard distributions | |
73 ============================================================================== | |
74 standard_cauchy Standard Cauchy-Lorentz distribution. | |
75 standard_exponential Standard exponential distribution. | |
76 standard_gamma Standard Gamma distribution. | |
77 standard_normal Standard normal distribution. | |
78 standard_t Standard Student's t-distribution. | |
79 ==================== ========================================================= | |
80 | |
81 ==================== ========================================================= | |
82 Internal functions | |
83 ============================================================================== | |
84 get_state Get tuple representing internal state of generator. | |
85 set_state Set state of generator. | |
86 ==================== ========================================================= | |
87 | |
88 """ | |
89 from __future__ import division, absolute_import, print_function | |
90 | |
91 import warnings | |
92 | |
93 # To get sub-modules | |
94 from .info import __doc__, __all__ | |
95 | |
96 | |
97 with warnings.catch_warnings(): | |
98 warnings.filterwarnings("ignore", message="numpy.ndarray size changed") | |
99 from .mtrand import * | |
100 | |
101 # Some aliases: | |
102 ranf = random = sample = random_sample | |
103 __all__.extend(['ranf', 'random', 'sample']) | |
104 | |
105 def __RandomState_ctor(): | |
106 """Return a RandomState instance. | |
107 | |
108 This function exists solely to assist (un)pickling. | |
109 | |
110 Note that the state of the RandomState returned here is irrelevant, as this function's | |
111 entire purpose is to return a newly allocated RandomState whose state pickle can set. | |
112 Consequently the RandomState returned by this function is a freshly allocated copy | |
113 with a seed=0. | |
114 | |
115 See https://github.com/numpy/numpy/issues/4763 for a detailed discussion | |
116 | |
117 """ | |
118 return RandomState(seed=0) | |
119 | |
120 from numpy.testing import Tester | |
121 test = Tester().test | |
122 bench = Tester().bench |