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1# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""Random functions."""
16
17# pylint: disable=g-direct-tensorflow-import
18
19import numpy as onp
20
21from tensorflow.python.framework import random_seed
22from tensorflow.python.ops import array_ops
23from tensorflow.python.ops import random_ops
24from tensorflow.python.ops.numpy_ops import np_array_ops
25from tensorflow.python.ops.numpy_ops import np_dtypes
26from tensorflow.python.ops.numpy_ops import np_utils
27
28# TODO(agarwal): deprecate this.
29DEFAULT_RANDN_DTYPE = onp.float32
30
31
32@np_utils.np_doc('random.seed')
33def seed(s):
34  """Sets the seed for the random number generator.
35
36  Uses `tf.set_random_seed`.
37
38  Args:
39    s: an integer.
40  """
41  try:
42    s = int(s)
43  except TypeError:
44    # TODO(wangpeng): support this?
45    raise ValueError(
46        f'Argument `s` got an invalid value {s}. Only integers are supported.')
47  random_seed.set_seed(s)
48
49
50@np_utils.np_doc('random.randn')
51def randn(*args):
52  """Returns samples from a normal distribution.
53
54  Uses `tf.random_normal`.
55
56  Args:
57    *args: The shape of the output array.
58
59  Returns:
60    An ndarray with shape `args` and dtype `float64`.
61  """
62  return standard_normal(size=args)
63
64
65@np_utils.np_doc('random.standard_normal')
66def standard_normal(size=None):
67  # TODO(wangpeng): Use new stateful RNG
68  if size is None:
69    size = ()
70  elif np_utils.isscalar(size):
71    size = (size,)
72  dtype = np_dtypes.default_float_type()
73  return random_ops.random_normal(size, dtype=dtype)
74
75
76@np_utils.np_doc('random.uniform')
77def uniform(low=0.0, high=1.0, size=None):
78  dtype = np_dtypes.default_float_type()
79  low = np_array_ops.asarray(low, dtype=dtype)
80  high = np_array_ops.asarray(high, dtype=dtype)
81  if size is None:
82    size = array_ops.broadcast_dynamic_shape(low.shape, high.shape)
83  return random_ops.random_uniform(
84      shape=size, minval=low, maxval=high, dtype=dtype)
85
86
87@np_utils.np_doc('random.poisson')
88def poisson(lam=1.0, size=None):
89  if size is None:
90    size = ()
91  elif np_utils.isscalar(size):
92    size = (size,)
93  return random_ops.random_poisson(shape=size, lam=lam, dtype=np_dtypes.int_)
94
95
96@np_utils.np_doc('random.random')
97def random(size=None):
98  return uniform(0., 1., size)
99
100
101@np_utils.np_doc('random.rand')
102def rand(*size):
103  return uniform(0., 1., size)
104
105
106@np_utils.np_doc('random.randint')
107def randint(low, high=None, size=None, dtype=onp.int64):  # pylint: disable=missing-function-docstring
108  low = int(low)
109  if high is None:
110    high = low
111    low = 0
112  if size is None:
113    size = ()
114  elif isinstance(size, int):
115    size = (size,)
116  dtype_orig = dtype
117  dtype = np_utils.result_type(dtype)
118  accepted_dtypes = (onp.int32, onp.int64)
119  if dtype not in accepted_dtypes:
120    raise ValueError(
121        f'Argument `dtype` got an invalid value {dtype_orig}. Only those '
122        f'convertible to {accepted_dtypes} are supported.')
123  return random_ops.random_uniform(
124      shape=size, minval=low, maxval=high, dtype=dtype)
125