127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Random functions."""
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# pylint: disable=g-direct-tensorflow-import
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as onp
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from tensorflow.python.framework import random_seed
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops.numpy_ops import np_array_ops
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from tensorflow.python.ops.numpy_ops import np_dtypes
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from tensorflow.python.ops.numpy_ops import np_utils
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# TODO(agarwal): deprecate this.
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DEFAULT_RANDN_DTYPE = onp.float32
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@np_utils.np_doc('random.seed')
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def seed(s):
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"""Sets the seed for the random number generator.
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Uses `tf.set_random_seed`.
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Args:
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s: an integer.
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"""
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try:
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s = int(s)
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except TypeError:
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# TODO(wangpeng): support this?
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raise ValueError('np.seed currently only support integer arguments.')
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random_seed.set_seed(s)
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@np_utils.np_doc('random.randn')
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def randn(*args):
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"""Returns samples from a normal distribution.
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Uses `tf.random_normal`.
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Args:
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*args: The shape of the output array.
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Returns:
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An ndarray with shape `args` and dtype `float64`.
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"""
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return standard_normal(size=args)
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@np_utils.np_doc('random.standard_normal')
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def standard_normal(size=None):
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# TODO(wangpeng): Use new stateful RNG
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if size is None:
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size = ()
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elif np_utils.isscalar(size):
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size = (size,)
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dtype = np_dtypes.default_float_type()
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return np_utils.tensor_to_ndarray(random_ops.random_normal(size, dtype=dtype))
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@np_utils.np_doc('random.uniform')
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def uniform(low=0.0, high=1.0, size=None):
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dtype = np_dtypes.default_float_type()
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low = np_array_ops.asarray(low, dtype=dtype)
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high = np_array_ops.asarray(high, dtype=dtype)
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if size is None:
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size = array_ops.broadcast_dynamic_shape(low.shape, high.shape)
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return np_utils.tensor_to_ndarray(
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random_ops.random_uniform(
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shape=size, minval=low, maxval=high, dtype=dtype))
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@np_utils.np_doc('random.poisson')
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def poisson(lam=1.0, size=None):
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if size is None:
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size = ()
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elif np_utils.isscalar(size):
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size = (size,)
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return np_utils.tensor_to_ndarray(
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random_ops.random_poisson(shape=size, lam=lam, dtype=np_dtypes.int_))
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@np_utils.np_doc('random.random')
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def random(size=None):
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return uniform(0., 1., size)
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@np_utils.np_doc('random.rand')
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def rand(*size):
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return uniform(0., 1., size)
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@np_utils.np_doc('random.randint')
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def randint(low, high=None, size=None, dtype=onp.int): # pylint: disable=missing-function-docstring
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low = int(low)
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if high is None:
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high = low
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low = 0
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if size is None:
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size = ()
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elif isinstance(size, int):
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size = (size,)
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dtype = np_utils.result_type(dtype)
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if dtype not in (onp.int32, onp.int64):
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raise ValueError('Only np.int32 or np.int64 types are supported')
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return np_utils.tensor_to_ndarray(
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random_ops.random_uniform(
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shape=size, minval=low, maxval=high, dtype=dtype))
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