# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
"""GumbelCDF Bijector"""
from mindspore.ops import operations as P
from ..distribution._utils.utils import check_greater_zero
from ..distribution._utils.custom_ops import exp_generic, log_generic
from .bijector import Bijector
[文档]class GumbelCDF(Bijector):
r"""
GumbelCDF Bijector.
This Bijector performs the operation:
.. math::
Y = \exp(-\exp(\frac{-(X - loc)}{scale}))
Args:
loc (float, list, numpy.ndarray, Tensor): The location. Default: 0.0.
scale (float, list, numpy.ndarray, Tensor): The scale. Default: 1.0.
name (str): The name of the Bijector. Default: 'GumbelCDF'.
Inputs and Outputs of APIs:
The accessible APIs of the Gumbel_cdf bijector are defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **inverse_log_jacobian**
It should be notice that the inputs of APIs of the Gumbel_cdf bijector should be always a tensor,
with a shape that can be broadcasted to that of `loc` and `scale`.
For more details of all APIs, including the inputs and outputs of APIs of the Gumbel_cdf bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``
Note:
`scale` must be greater than zero.
For `inverse` and `inverse_log_jacobian`, input should be in range of (0, 1).
The dtype of `loc` and `scale` must be float.
If `loc`, `scale` are passed in as numpy.ndarray or tensor, they have to have
the same dtype otherwise an error will be raised.
Raises:
TypeError: When the dtype of `loc` or `scale` is not float,
or when the dtype of `loc` and `scale` is not same.
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>>
>>> # To initialize a GumbelCDF bijector of loc 1.0, and scale 2.0.
>>> gumbel_cdf = msb.GumbelCDF(1.0, 2.0)
>>> # To use a GumbelCDF bijector in a network.
>>> x = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> y = Tensor([0.1, 0.2, 0.3], dtype=mindspore.float32)
>>> ans1 = gumbel_cdf.forward(x)
>>> print(ans1.shape)
(3,)
>>> ans2 = gumbel_cdf.inverse(y)
>>> print(ans2.shape)
(3,)
>>> ans3 = gumbel_cdf.forward_log_jacobian(x)
>>> print(ans3.shape)
(3,)
>>> ans4 = gumbel_cdf.inverse_log_jacobian(y)
>>> print(ans4.shape)
(3,)
"""
def __init__(self,
loc=0.0,
scale=1.0,
name='GumbelCDF'):
"""
Constructor of GumbelCDF Bijector.
"""
param = dict(locals())
param['param_dict'] = {'loc': loc, 'scale': scale}
super(GumbelCDF, self).__init__(name=name, param=param)
self._loc = self._add_parameter(loc, 'loc')
self._scale = self._add_parameter(scale, 'scale')
check_greater_zero(self._scale, "scale")
self.cast = P.Cast()
self.exp = exp_generic
self.log = log_generic
@property
def loc(self):
"""
Return the loc parameter of the bijector.
Output:
Tensor, the loc parameter of the bijector.
"""
return self._loc
@property
def scale(self):
"""
Return the scale parameter of the bijector.
Output:
Tensor, the scale parameter of the bijector.
"""
return self._scale
def extend_repr(self):
"""Display instance object as string."""
if self.is_scalar_batch:
str_info = 'loc = {}, scale = {}'.format(self.loc, self.scale)
else:
str_info = 'batch_shape = {}'.format(self.batch_shape)
return str_info
def _forward(self, x):
x = self._check_value_dtype(x)
loc_local = self.cast_param_by_value(x, self.loc)
scale_local = self.cast_param_by_value(x, self.scale)
z = (x - loc_local) / scale_local
return self.exp((-1) * self.exp(-z))
def _inverse(self, y):
y = self._check_value_dtype(y)
loc_local = self.cast_param_by_value(y, self.loc)
scale_local = self.cast_param_by_value(y, self.scale)
return loc_local - scale_local * self.log((-1) * self.log(y))
def _forward_log_jacobian(self, x):
x = self._check_value_dtype(x)
loc_local = self.cast_param_by_value(x, self.loc)
scale_local = self.cast_param_by_value(x, self.scale)
z = (x - loc_local) / scale_local
return -z - self.exp(-z) - self.log(scale_local)
def _inverse_log_jacobian(self, y):
y = self._check_value_dtype(y)
scale_local = self.cast_param_by_value(y, self.scale)
return self.log(scale_local / (-1. * y * self.log(y)))