1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
|
"""
========================
Random Number Generation
========================
Use ``default_rng()`` to create a `Generator` and call its methods.
=============== =========================================================
Generator
--------------- ---------------------------------------------------------
Generator Class implementing all of the random number distributions
default_rng Default constructor for ``Generator``
=============== =========================================================
============================================= ===
BitGenerator Streams that work with Generator
--------------------------------------------- ---
MT19937
PCG64
Philox
SFC64
============================================= ===
============================================= ===
Getting entropy to initialize a BitGenerator
--------------------------------------------- ---
SeedSequence
============================================= ===
Legacy
------
For backwards compatibility with previous versions of numpy before 1.17, the
various aliases to the global `RandomState` methods are left alone and do not
use the new `Generator` API.
==================== =========================================================
Utility functions
-------------------- ---------------------------------------------------------
random Uniformly distributed floats over ``[0, 1)``
bytes Uniformly distributed random bytes.
permutation Randomly permute a sequence / generate a random sequence.
shuffle Randomly permute a sequence in place.
choice Random sample from 1-D array.
==================== =========================================================
==================== =========================================================
Compatibility
functions - removed
in the new API
-------------------- ---------------------------------------------------------
rand Uniformly distributed values.
randn Normally distributed values.
ranf Uniformly distributed floating point numbers.
random_integers Uniformly distributed integers in a given range.
(deprecated, use ``integers(..., closed=True)`` instead)
random_sample Alias for `random_sample`
randint Uniformly distributed integers in a given range
seed Seed the legacy random number generator.
==================== =========================================================
==================== =========================================================
Univariate
distributions
-------------------- ---------------------------------------------------------
beta Beta distribution over ``[0, 1]``.
binomial Binomial distribution.
chisquare :math:`\\chi^2` distribution.
exponential Exponential distribution.
f F (Fisher-Snedecor) distribution.
gamma Gamma distribution.
geometric Geometric distribution.
gumbel Gumbel distribution.
hypergeometric Hypergeometric distribution.
laplace Laplace distribution.
logistic Logistic distribution.
lognormal Log-normal distribution.
logseries Logarithmic series distribution.
negative_binomial Negative binomial distribution.
noncentral_chisquare Non-central chi-square distribution.
noncentral_f Non-central F distribution.
normal Normal / Gaussian distribution.
pareto Pareto distribution.
poisson Poisson distribution.
power Power distribution.
rayleigh Rayleigh distribution.
triangular Triangular distribution.
uniform Uniform distribution.
vonmises Von Mises circular distribution.
wald Wald (inverse Gaussian) distribution.
weibull Weibull distribution.
zipf Zipf's distribution over ranked data.
==================== =========================================================
==================== ==========================================================
Multivariate
distributions
-------------------- ----------------------------------------------------------
dirichlet Multivariate generalization of Beta distribution.
multinomial Multivariate generalization of the binomial distribution.
multivariate_normal Multivariate generalization of the normal distribution.
==================== ==========================================================
==================== =========================================================
Standard
distributions
-------------------- ---------------------------------------------------------
standard_cauchy Standard Cauchy-Lorentz distribution.
standard_exponential Standard exponential distribution.
standard_gamma Standard Gamma distribution.
standard_normal Standard normal distribution.
standard_t Standard Student's t-distribution.
==================== =========================================================
==================== =========================================================
Internal functions
-------------------- ---------------------------------------------------------
get_state Get tuple representing internal state of generator.
set_state Set state of generator.
==================== =========================================================
"""
from __future__ import division, absolute_import, print_function
__all__ = [
'beta',
'binomial',
'bytes',
'chisquare',
'choice',
'dirichlet',
'exponential',
'f',
'gamma',
'geometric',
'get_state',
'gumbel',
'hypergeometric',
'laplace',
'logistic',
'lognormal',
'logseries',
'multinomial',
'multivariate_normal',
'negative_binomial',
'noncentral_chisquare',
'noncentral_f',
'normal',
'pareto',
'permutation',
'poisson',
'power',
'rand',
'randint',
'randn',
'random',
'random_integers',
'random_sample',
'ranf',
'rayleigh',
'sample',
'seed',
'set_state',
'shuffle',
'standard_cauchy',
'standard_exponential',
'standard_gamma',
'standard_normal',
'standard_t',
'triangular',
'uniform',
'vonmises',
'wald',
'weibull',
'zipf',
]
# add these for module-freeze analysis (like PyInstaller)
from . import _pickle
from . import _common
from . import _bounded_integers
from ._generator import Generator, default_rng
from ._bit_generator import SeedSequence, BitGenerator
from ._mt19937 import MT19937
from ._pcg64 import PCG64
from ._philox import Philox
from ._sfc64 import SFC64
from .mtrand import *
__all__ += ['Generator', 'RandomState', 'SeedSequence', 'MT19937',
'Philox', 'PCG64', 'SFC64', 'default_rng', 'BitGenerator']
def __RandomState_ctor():
"""Return a RandomState instance.
This function exists solely to assist (un)pickling.
Note that the state of the RandomState returned here is irrelevant, as this
function's entire purpose is to return a newly allocated RandomState whose
state pickle can set. Consequently the RandomState returned by this function
is a freshly allocated copy with a seed=0.
See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
"""
return RandomState(seed=0)
from numpy._pytesttester import PytestTester
test = PytestTester(__name__)
del PytestTester
|
