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uniform.py
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uniform.py
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle
from paddle import _C_ops
from paddle.base.data_feeder import check_type, convert_dtype
from paddle.base.framework import Variable
from paddle.distribution import distribution
from paddle.framework import in_dynamic_mode
from paddle.tensor import random
class Uniform(distribution.Distribution):
r"""Uniform distribution with `low` and `high` parameters.
Mathematical Details
The probability density function (pdf) is
.. math::
pdf(x; a, b) = \frac{1}{Z}, \ a <=x <b
.. math::
Z = b - a
In the above equation:
* :math:`low = a`,
* :math:`high = b`,
* :math:`Z`: is the normalizing constant.
The parameters `low` and `high` must be shaped in a way that supports
`Boardcasting` (e.g., `high - low` is a valid operation).
Note:
If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .
.. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
Args:
low(int|float|list|tuple|numpy.ndarray|Tensor): The lower boundary of
uniform distribution.The data type is float32 and float64.
high(int|float|list|tuple|numpy.ndarray|Tensor): The higher boundary
of uniform distribution.The data type is float32 and float64.
name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
Examples:
.. code-block:: python
>>> import paddle
>>> from paddle.distribution import Uniform
>>> paddle.seed(2023)
>>> # Without broadcasting, a single uniform distribution [3, 4]:
>>> u1 = Uniform(low=3.0, high=4.0)
>>> # 2 distributions [1, 3], [2, 4]
>>> u2 = Uniform(low=[1.0, 2.0], high=[3.0, 4.0])
>>> # 4 distributions
>>> u3 = Uniform(low=[[1.0, 2.0], [3.0, 4.0]],
... high=[[1.5, 2.5], [3.5, 4.5]])
...
>>> # With broadcasting:
>>> u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])
>>> # Complete example
>>> value_tensor = paddle.to_tensor([0.8], dtype="float32")
>>> uniform = Uniform([0.], [2.])
>>> sample = uniform.sample([2])
>>> # a random tensor created by uniform distribution with shape: [2, 1]
>>> entropy = uniform.entropy()
>>> print(entropy)
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.69314718])
>>> lp = uniform.log_prob(value_tensor)
>>> print(lp)
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[-0.69314718])
>>> p = uniform.probs(value_tensor)
>>> print(p)
Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
[0.50000000])
"""
def __init__(self, low, high, name=None):
if not in_dynamic_mode():
check_type(
low,
'low',
(int, float, np.ndarray, Variable, list, tuple),
'Uniform',
)
check_type(
high,
'high',
(int, float, np.ndarray, Variable, list, tuple),
'Uniform',
)
self.all_arg_is_float = False
self.batch_size_unknown = False
self.name = name if name is not None else 'Uniform'
self.dtype = 'float32'
if isinstance(low, int):
low = float(low)
if isinstance(high, int):
high = float(high)
if self._validate_args(low, high):
self.low = low
self.high = high
self.dtype = convert_dtype(low.dtype)
else:
if isinstance(low, float) and isinstance(high, float):
self.all_arg_is_float = True
if isinstance(low, np.ndarray) and str(low.dtype) in [
'float32',
'float64',
]:
self.dtype = low.dtype
elif isinstance(high, np.ndarray) and str(high.dtype) in [
'float32',
'float64',
]:
self.dtype = high.dtype
self.low, self.high = self._to_tensor(low, high)
if self.dtype != convert_dtype(self.low.dtype):
self.low = paddle.cast(self.low, dtype=self.dtype)
self.high = paddle.cast(self.high, dtype=self.dtype)
super().__init__(self.low.shape)
def sample(self, shape, seed=0):
"""Generate samples of the specified shape.
Args:
shape (list): 1D `int32`. Shape of the generated samples.
seed (int): Python integer number.
Returns:
Tensor, A tensor with prepended dimensions shape. The data type is float32.
"""
if not in_dynamic_mode():
check_type(shape, 'shape', (list), 'sample')
check_type(seed, 'seed', (int), 'sample')
name = self.name + '_sample'
batch_shape = list((self.low + self.high).shape)
if -1 in batch_shape:
output_shape = shape + batch_shape
fill_shape = list(batch_shape + shape)
fill_shape[0] = paddle.shape(self.low + self.high)[0].item()
zero_tmp = paddle.full(fill_shape, 0.0, self.dtype)
uniform_random_tmp = random.uniform_random_batch_size_like(
zero_tmp,
zero_tmp.shape,
dtype=self.dtype,
min=0.0,
max=1.0,
seed=seed,
)
zero_tmp_reshape = paddle.reshape(zero_tmp, output_shape)
uniform_random_tmp_reshape = paddle.reshape(
uniform_random_tmp, output_shape
)
output = uniform_random_tmp_reshape * (
zero_tmp_reshape + self.high - self.low
)
output = paddle.add(output, self.low, name=name)
return output
else:
output_shape = shape + batch_shape
output = paddle.uniform(
output_shape, dtype=self.dtype, min=0.0, max=1.0, seed=seed
) * (
paddle.zeros(output_shape, dtype=self.dtype)
+ (self.high - self.low)
)
output = paddle.add(output, self.low, name=name)
if self.all_arg_is_float:
return paddle.reshape(output, shape, name=name)
else:
return output
def log_prob(self, value):
"""Log probability density/mass function.
Args:
value (Tensor): The input tensor.
Returns:
Tensor, log probability.The data type is same with value.
"""
value = self._check_values_dtype_in_probs(self.low, value)
if in_dynamic_mode():
# ensure value in [low, high]
lb_bool = self.low < value
ub_bool = value < self.high
lb = _C_ops.cast(lb_bool, value.dtype)
ub = _C_ops.cast(ub_bool, value.dtype)
return paddle.log(lb * ub) - paddle.log(self.high - self.low)
else:
name = self.name + '_log_prob'
lb_bool = self.low < value
ub_bool = value < self.high
lb = paddle.cast(lb_bool, dtype=value.dtype)
ub = paddle.cast(ub_bool, dtype=value.dtype)
return paddle.subtract(
paddle.log(lb * ub), paddle.log(self.high - self.low), name=name
)
def probs(self, value):
"""Probability density/mass function.
Args:
value (Tensor): The input tensor.
Returns:
Tensor, probability. The data type is same with value.
"""
value = self._check_values_dtype_in_probs(self.low, value)
if in_dynamic_mode():
lb_bool = self.low < value
ub_bool = value < self.high
lb = _C_ops.cast(lb_bool, value.dtype)
ub = _C_ops.cast(ub_bool, value.dtype)
return (lb * ub) / (self.high - self.low)
else:
name = self.name + '_probs'
lb_bool = self.low < value
ub_bool = value < self.high
lb = paddle.cast(lb_bool, dtype=value.dtype)
ub = paddle.cast(ub_bool, dtype=value.dtype)
return paddle.divide((lb * ub), (self.high - self.low), name=name)
def entropy(self):
r"""Shannon entropy in nats.
The entropy is
.. math::
entropy(low, high) = \\log (high - low)
Returns:
Tensor, Shannon entropy of uniform distribution.The data type is float32.
"""
name = self.name + '_entropy'
return paddle.log(self.high - self.low, name=name)