normal.py

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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import math
import numpy as np
import paddle
from paddle.distribution import distribution
from paddle.fluid.data_feeder import (check_type, convert_dtype)
from paddle.fluid.framework import _non_static_mode
from paddle.fluid.layers import (elementwise_add, elementwise_div,
                                 elementwise_sub, nn, ops, tensor)
try:
    from collections.abc import Iterable
except:
    from collections import Iterable


class Normal(distribution.Distribution):
    r"""The Normal distribution with location `loc` and `scale` parameters.

    Mathematical details

    The probability density function (pdf) is

    .. math::

        pdf(x; \mu, \sigma) = \frac{1}{Z}e^{\frac {-0.5 (x - \mu)^2}  {\sigma^2} }

    .. math::

        Z = (2 \pi \sigma^2)^{0.5}

    In the above equation:

    * :math:`loc = \mu`: is the mean.
    * :math:`scale = \sigma`: is the std.
    * :math:`Z`: is the normalization constant.

    Args:
        loc(int|float|list|tuple|numpy.ndarray|Tensor): The mean of normal distribution.The data type is float32 and float64.
        scale(int|float|list|tuple|numpy.ndarray|Tensor): The std of normal distribution.The data type is float32 and float64.
        name(str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Examples:
        .. code-block:: python

            import paddle
            from paddle.distribution import Normal

            # Define a single scalar Normal distribution.
            dist = Normal(loc=0., scale=3.)
            # Define a batch of two scalar valued Normals.
            # The first has mean 1 and standard deviation 11, the second 2 and 22.
            dist = Normal(loc=[1., 2.], scale=[11., 22.])
            # Get 3 samples, returning a 3 x 2 tensor.
            dist.sample([3])

            # Define a batch of two scalar valued Normals.
            # Both have mean 1, but different standard deviations.
            dist = Normal(loc=1., scale=[11., 22.])

            # Complete example
            value_tensor = paddle.to_tensor([0.8], dtype="float32")

            normal_a = Normal([0.], [1.])
            normal_b = Normal([0.5], [2.])
            sample = normal_a.sample([2])
            # a random tensor created by normal distribution with shape: [2, 1]
            entropy = normal_a.entropy()
            # [1.4189385] with shape: [1]
            lp = normal_a.log_prob(value_tensor)
            # [-1.2389386] with shape: [1]
            p = normal_a.probs(value_tensor)
            # [0.28969154] with shape: [1]
            kl = normal_a.kl_divergence(normal_b)
            # [0.34939718] with shape: [1]
    """

    def __init__(self, loc, scale, name=None):
        if not _non_static_mode():
            check_type(loc, 'loc',
                       (int, float, np.ndarray, tensor.Variable, list, tuple),
                       'Normal')
            check_type(scale, 'scale',
                       (int, float, np.ndarray, tensor.Variable, list, tuple),
                       'Normal')

        self.batch_size_unknown = False
        self.all_arg_is_float = False
        self.name = name if name is not None else 'Normal'
        self.dtype = 'float32'

        if isinstance(loc, int):
            loc = float(loc)
        if isinstance(scale, int):
            scale = float(scale)

        if self._validate_args(loc, scale):
            self.batch_size_unknown = True
            self.loc = loc
            self.scale = scale
            self.dtype = convert_dtype(loc.dtype)
        else:
            if isinstance(loc, float) and isinstance(scale, float):
                self.all_arg_is_float = True
            if isinstance(loc, np.ndarray) and str(
                    loc.dtype) in ['float32', 'float64']:
                self.dtype = loc.dtype
            elif isinstance(scale, np.ndarray) and str(
                    scale.dtype) in ['float32', 'float64']:
                self.dtype = scale.dtype
            # pylint: disable=unbalanced-tuple-unpacking
            self.loc, self.scale = self._to_tensor(loc, scale)
            if self.dtype != convert_dtype(self.loc.dtype):
                self.loc = tensor.cast(self.loc, dtype=self.dtype)
                self.scale = tensor.cast(self.scale, dtype=self.dtype)
        super(Normal, self).__init__(self.loc.shape)

    @property
    def mean(self):
        """Mean of multinomial distribuion.

        Returns:
            Tensor: mean value.
        """
        return self.loc

    @property
    def variance(self):
        """Variance of lognormal distribution.

        Returns:
            Tensor: variance value.
        """
        return self.scale.pow(2)

    def sample(self, shape=(), seed=0):
        """Generate samples of the specified shape.

        Args:
            shape (Sequence[int], optional): 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 isinstance(shape, Iterable):
            raise TypeError('sample shape must be Iterable object.')

        if not _non_static_mode():
            check_type(seed, 'seed', (int), 'sample')

        shape = list(shape)
        batch_shape = list((self.loc + self.scale).shape)
        name = self.name + '_sample'

        if self.batch_size_unknown:
            output_shape = shape + batch_shape
            zero_tmp = tensor.fill_constant_batch_size_like(
                self.loc + self.scale, batch_shape + shape, self.dtype, 0.)
            zero_tmp_reshape = nn.reshape(zero_tmp, output_shape)
            zero_tmp_shape = nn.shape(zero_tmp_reshape)
            normal_random_tmp = nn.gaussian_random(zero_tmp_shape,
                                                   mean=0.,
                                                   std=1.,
                                                   seed=seed,
                                                   dtype=self.dtype)
            output = normal_random_tmp * (zero_tmp_reshape + self.scale)
            output = elementwise_add(output, self.loc, name=name)
            return output
        else:
            output_shape = shape + batch_shape
            output = nn.gaussian_random(
                output_shape, mean=0., std=1., seed=seed, dtype=self.dtype) * (
                    tensor.zeros(output_shape, dtype=self.dtype) + self.scale)
            output = elementwise_add(output, self.loc, name=name)
            if self.all_arg_is_float:
                return nn.reshape(output, shape, name=name)
            else:
                return output

    def rsample(self, shape=()):
        """Generate reparameterized samples of the specified shape.

        Args:
          shape (Sequence[int], optional): Shape of the generated samples.

        Returns:
          Tensor: A tensor with prepended dimensions shape.The data type is float32.

        """
        if not isinstance(shape, Iterable):
            raise TypeError('sample shape must be Iterable object.')

        shape = self._extend_shape(tuple(shape))
        eps = paddle.normal(shape=shape)
        return (self.loc + eps * self.scale)

    def entropy(self):
        r"""Shannon entropy in nats.

        The entropy is

        .. math::

            entropy(\sigma) = 0.5 \log (2 \pi e \sigma^2)

        In the above equation:

        * :math:`scale = \sigma`: is the std.

        Returns:
            Tensor, Shannon entropy of normal distribution.The data type is float32.

        """
        name = self.name + '_entropy'
        batch_shape = list((self.loc + self.scale).shape)
        zero_tmp = tensor.fill_constant_batch_size_like(self.loc + self.scale,
                                                        batch_shape, self.dtype,
                                                        0.)
        return elementwise_add(0.5 + zero_tmp,
                               0.5 * math.log(2 * math.pi) + nn.log(
                                   (self.scale + zero_tmp)),
                               name=name)

    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 :attr:`value` .

        """
        name = self.name + '_log_prob'
        value = self._check_values_dtype_in_probs(self.loc, value)

        var = self.scale * self.scale
        log_scale = nn.log(self.scale)
        return elementwise_sub(-1. * ((value - self.loc) * (value - self.loc)) /
                               (2. * var),
                               log_scale + math.log(math.sqrt(2. * math.pi)),
                               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 :attr:`value` .

        """
        name = self.name + '_probs'
        value = self._check_values_dtype_in_probs(self.loc, value)

        var = self.scale * self.scale
        return elementwise_div(ops.exp(-1. * ((value - self.loc) *
                                              (value - self.loc)) / (2. * var)),
                               (math.sqrt(2 * math.pi) * self.scale),
                               name=name)

    def kl_divergence(self, other):
        r"""The KL-divergence between two normal distributions.

        The probability density function (pdf) is

        .. math::

            KL\_divergence(\mu_0, \sigma_0; \mu_1, \sigma_1) = 0.5 (ratio^2 + (\frac{diff}{\sigma_1})^2 - 1 - 2 \ln {ratio})

        .. math::

            ratio = \frac{\sigma_0}{\sigma_1}

        .. math::

            diff = \mu_1 - \mu_0

        In the above equation:

        * :math:`loc = \mu_0`: is the mean of current Normal distribution.
        * :math:`scale = \sigma_0`: is the std of current Normal distribution.
        * :math:`loc = \mu_1`: is the mean of other Normal distribution.
        * :math:`scale = \sigma_1`: is the std of other Normal distribution.
        * :math:`ratio`: is the ratio of scales.
        * :math:`diff`: is the difference between means.

        Args:
            other (Normal): instance of Normal.

        Returns:
            Tensor, kl-divergence between two normal distributions.The data type is float32.

        """
        if not _non_static_mode():
            check_type(other, 'other', Normal, 'kl_divergence')

        name = self.name + '_kl_divergence'
        var_ratio = self.scale / other.scale
        var_ratio = (var_ratio * var_ratio)
        t1 = (self.loc - other.loc) / other.scale
        t1 = (t1 * t1)
        return elementwise_add(0.5 * var_ratio,
                               0.5 * (t1 - 1. - nn.log(var_ratio)),
                               name=name)