forked from mindspore-Ecosystem/mindspore
add logistic distribution
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9c79b9d712
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@ -25,6 +25,7 @@ from .uniform import Uniform
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from .geometric import Geometric
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from .categorical import Categorical
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from .log_normal import LogNormal
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from .logistic import Logistic
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__all__ = ['Distribution',
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'TransformedDistribution',
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@ -35,4 +36,5 @@ __all__ = ['Distribution',
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'Categorical',
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'Geometric',
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'LogNormal',
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'Logistic',
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]
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@ -0,0 +1,327 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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"""Logistic Distribution"""
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import numpy as np
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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from mindspore.common import dtype as mstype
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from .distribution import Distribution
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from ._utils.utils import check_greater_zero, check_type
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from ._utils.custom_ops import exp_generic, expm1_generic, log_generic, log1p_generic
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class Logistic(Distribution):
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"""
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Logistic distribution.
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Args:
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loc (int, float, list, numpy.ndarray, Tensor, Parameter): The location of the Logistic distribution.
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scale (int, float, list, numpy.ndarray, Tensor, Parameter): The scale of the Logistic distribution.
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seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
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dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
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name (str): The name of the distribution. Default: 'Logistic'.
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Note:
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`scale` must be greater than zero.
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`dist_spec_args` are `loc` and `scale`.
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`dtype` must be a float type because Logistic distributions are continuous.
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Examples:
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>>> # To initialize a Logistic distribution of loc 3.0 and scale 4.0.
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>>> import mindspore.nn.probability.distribution as msd
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>>> n = msd.Logistic(3.0, 4.0, dtype=mstype.float32)
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>>>
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>>> # The following creates two independent Logistic distributions.
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>>> n = msd.Logistic([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
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>>>
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>>> # A Logistic distribution can be initilize without arguments.
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>>> # In this case, `loc` and `scale` must be passed in through arguments.
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>>> n = msd.Logistic(dtype=mstype.float32)
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>>>
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>>> # To use a Normal distribution in a network.
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>>> class net(Cell):
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>>> def __init__(self):
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>>> super(net, self).__init__():
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>>> self.l1 = msd.Logistic(0.0, 1.0, dtype=mstype.float32)
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>>> self.l2 = msd.Logistic(dtype=mstype.float32)
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>>>
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>>> # The following calls are valid in construct.
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>>> def construct(self, value, loc_b, scale_b, loc_a, scale_a):
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>>>
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>>> # Private interfaces of probability functions corresponding to public interfaces, including
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>>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments as follows.
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>>> # Args:
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>>> # value (Tensor): the value to be evaluated.
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>>> # loc (Tensor): the location of the distribution. Default: self.loc.
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>>> # scale (Tensor): the scale of the distribution. Default: self.scale.
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>>>
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>>> # Examples of `prob`.
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>>> # Similar calls can be made to other probability functions
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>>> # by replacing 'prob' by the name of the function
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>>> ans = self.l1.prob(value)
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>>> # Evaluate with respect to distribution b.
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>>> ans = self.l1.prob(value, loc_b, scale_b)
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>>> # `loc` and `scale` must be passed in during function calls
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>>> ans = self.l2.prob(value, loc_a, scale_a)
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>>>
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>>> # Functions `mean`, `mode`, `sd`, `var`, and `entropy` have the same arguments.
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>>> # Args:
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>>> # loc (Tensor): the location of the distribution. Default: self.loc.
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>>> # scale (Tensor): the scale of the distribution. Default: self.scale.
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>>>
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>>> # Example of `mean`. `mode`, `sd`, `var`, and `entropy` are similar.
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>>> ans = self.l1.mean() # return 0.0
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>>> ans = self.l1.mean(loc_b, scale_b) # return loc_b
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>>> # `loc` and `scale` must be passed in during function calls.
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>>> ans = self.l2.mean(loc_a, scale_a)
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>>>
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>>> # Examples of `sample`.
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>>> # Args:
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>>> # shape (tuple): the shape of the sample. Default: ()
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>>> # loc (Tensor): the location of the distribution. Default: self.loc.
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>>> # scale (Tensor): the scale of the distribution. Default: self.scale.
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>>> ans = self.l1.sample()
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>>> ans = self.l1.sample((2,3))
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>>> ans = self.l1.sample((2,3), scale_b, scale_b)
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>>> ans = self.l2.sample((2,3), scale_a, scale_a)
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"""
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def __init__(self,
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loc=None,
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scale=None,
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seed=None,
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dtype=mstype.float32,
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name="Logistic"):
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"""
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Constructor of Logistic.
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"""
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param = dict(locals())
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param['param_dict'] = {'loc': loc, 'scale': scale}
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valid_dtype = mstype.float_type
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check_type(dtype, valid_dtype, type(self).__name__)
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super(Logistic, self).__init__(seed, dtype, name, param)
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self._loc = self._add_parameter(loc, 'loc')
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self._scale = self._add_parameter(scale, 'scale')
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if self._scale is not None:
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check_greater_zero(self._scale, "scale")
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# ops needed for the class
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self.cast = P.Cast()
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self.const = P.ScalarToArray()
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self.dtypeop = P.DType()
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self.exp = exp_generic
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self.expm1 = expm1_generic
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self.fill = P.Fill()
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self.less = P.Less()
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self.log = log_generic
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self.log1p = log1p_generic
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self.logicalor = P.LogicalOr()
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self.erf = P.Erf()
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self.greater = P.Greater()
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self.sigmoid = P.Sigmoid()
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self.squeeze = P.Squeeze(0)
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self.select = P.Select()
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self.shape = P.Shape()
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self.softplus = self._softplus
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self.sqrt = P.Sqrt()
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self.uniform = C.uniform
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self.threshold = np.log(np.finfo(np.float32).eps) + 1.
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self.tiny = np.finfo(np.float).tiny
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def _softplus(self, x):
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too_small = self.less(x, self.threshold)
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too_large = self.greater(x, -self.threshold)
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too_small_value = self.exp(x)
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too_large_value = x
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ones = self.fill(self.dtypeop(x), self.shape(x), 1.0)
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too_small_or_too_large = self.logicalor(too_small, too_large)
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x = self.select(too_small_or_too_large, ones, x)
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y = self.log(self.exp(x) + 1.0)
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return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y))
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def extend_repr(self):
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if self.is_scalar_batch:
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str_info = f'location = {self._loc}, scale = {self._scale}'
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else:
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str_info = f'batch_shape = {self._broadcast_shape}'
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return str_info
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@property
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def loc(self):
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"""
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Return the location of the distribution.
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"""
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return self._loc
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@property
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def scale(self):
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"""
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Return the scale of the distribution.
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"""
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return self._scale
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def _mean(self, loc=None, scale=None):
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"""
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The mean of the distribution.
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"""
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loc, scale = self._check_param_type(loc, scale)
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return loc
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def _mode(self, loc=None, scale=None):
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"""
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The mode of the distribution.
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"""
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loc, scale = self._check_param_type(loc, scale)
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return loc
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def _sd(self, loc=None, scale=None):
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"""
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The standard deviation of the distribution.
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"""
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loc, scale = self._check_param_type(loc, scale)
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return scale * self.const(np.pi) / self.sqrt(self.const(3.0))
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def _entropy(self, loc=None, scale=None):
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r"""
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Evaluate entropy.
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.. math::
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H(X) = \log(scale) + 2.
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"""
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loc, scale = self._check_param_type(loc, scale)
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return self.log(scale) + 2.
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def _log_prob(self, value, loc=None, scale=None):
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r"""
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Evaluate log probability.
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Args:
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value (Tensor): The value to be evaluated.
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loc (Tensor): The location of the distribution. Default: self.loc.
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scale (Tensor): The scale of the distribution. Default: self.scale.
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.. math::
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z = (x - \mu) / \sigma
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L(x) = -z * -2. * softplus(-z) - \log(\sigma)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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loc, scale = self._check_param_type(loc, scale)
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z = (value - loc) / scale
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return -z - 2. * self.softplus(-z) - self.log(scale)
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def _cdf(self, value, loc=None, scale=None):
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r"""
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Evaluate the cumulative distribution function on the given value.
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Args:
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value (Tensor): The value to be evaluated.
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loc (Tensor): The location of the distribution. Default: self.loc.
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scale (Tensor): The scale the distribution. Default: self.scale.
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.. math::
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cdf(x) = sigmoid((x - loc) / scale)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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loc, scale = self._check_param_type(loc, scale)
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z = (value - loc) / scale
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return self.sigmoid(z)
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def _log_cdf(self, value, loc=None, scale=None):
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r"""
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Evaluate the log cumulative distribution function on the given value.
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Args:
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value (Tensor): The value to be evaluated.
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loc (Tensor): The location of the distribution. Default: self.loc.
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scale (Tensor): The scale the distribution. Default: self.scale.
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.. math::
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log_cdf(x) = -softplus(-(x - loc) / scale)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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loc, scale = self._check_param_type(loc, scale)
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z = (value - loc) / scale
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return -self.softplus(-z)
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def _survival_function(self, value, loc=None, scale=None):
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r"""
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Evaluate the survival function on the given value.
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Args:
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value (Tensor): The value to be evaluated.
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loc (Tensor): The location of the distribution. Default: self.loc.
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scale (Tensor): The scale the distribution. Default: self.scale.
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.. math::
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survival(x) = sigmoid(-(x - loc) / scale)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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loc, scale = self._check_param_type(loc, scale)
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z = (value - loc) / scale
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return self.sigmoid(-z)
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def _log_survival(self, value, loc=None, scale=None):
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r"""
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Evaluate the log survival function on the given value.
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Args:
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value (Tensor): The value to be evaluated.
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loc (Tensor): The location of the distribution. Default: self.loc.
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scale (Tensor): The scale the distribution. Default: self.scale.
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.. math::
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survival(x) = -softplus((x - loc) / scale)
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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loc, scale = self._check_param_type(loc, scale)
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z = (value - loc) / scale
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return -self.softplus(z)
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def _sample(self, shape=(), loc=None, scale=None):
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"""
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Sampling.
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Args:
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shape (tuple): The shape of the sample. Default: ().
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loc (Tensor): The location of the samples. Default: self.loc.
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scale (Tensor): The scale of the samples. Default: self.scale.
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Returns:
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Tensor, with the shape being shape + batch_shape.
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"""
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shape = self.checktuple(shape, 'shape')
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loc, scale = self._check_param_type(loc, scale)
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batch_shape = self.shape(loc + scale)
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origin_shape = shape + batch_shape
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if origin_shape == ():
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sample_shape = (1,)
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else:
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sample_shape = origin_shape
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l_zero = self.const(self.tiny)
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h_one = self.const(1.0)
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sample_uniform = self.uniform(sample_shape, l_zero, h_one, self.seed)
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sample = self.log(sample_uniform) - self.log1p(sample_uniform)
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sample = sample * scale + loc
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value = self.cast(sample, self.dtype)
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if origin_shape == ():
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value = self.squeeze(value)
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return value
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@ -0,0 +1,227 @@
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# Copyright 2019 Huawei Technologies Co., Ltd
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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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"""test cases for Logistic distribution"""
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import numpy as np
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from scipy import stats
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import Tensor
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from mindspore import dtype
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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class Prob(nn.Cell):
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"""
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Test class: probability of Logistic distribution.
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"""
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def __init__(self):
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super(Prob, self).__init__()
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self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.l.prob(x_)
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def test_pdf():
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"""
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Test pdf.
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"""
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logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_pdf = logistic_benchmark.pdf([1.0, 2.0]).astype(np.float32)
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pdf = Prob()
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output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
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class LogProb(nn.Cell):
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"""
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Test class: log probability of Logistic distribution.
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"""
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def __init__(self):
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super(LogProb, self).__init__()
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self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
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def construct(self, x_):
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return self.l.log_prob(x_)
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def test_log_likelihood():
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"""
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Test log_pdf.
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"""
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logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
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expect_logpdf = logistic_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
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logprob = LogProb()
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output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
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tol = 1e-6
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assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
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class Basics(nn.Cell):
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"""
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Test class: mean/sd/mode of Logistic distribution.
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"""
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def __init__(self):
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super(Basics, self).__init__()
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self.l = msd.Logistic(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
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def construct(self):
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return self.l.mean(), self.l.sd(), self.l.mode()
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def test_basics():
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"""
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Test mean/standard deviation/mode.
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"""
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basics = Basics()
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mean, sd, mode = basics()
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expect_mean = [3.0, 3.0]
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expect_sd = np.pi * np.array([2.0, 4.0]) / np.sqrt(np.array([3.0]))
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tol = 1e-6
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assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
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assert (np.abs(mode.asnumpy() - expect_mean) < tol).all()
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assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
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class Sampling(nn.Cell):
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"""
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Test class: sample of Logistic distribution.
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"""
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def __init__(self, shape, seed=0):
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super(Sampling, self).__init__()
|
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self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
|
||||
self.shape = shape
|
||||
|
||||
def construct(self, mean=None, sd=None):
|
||||
return self.l.sample(self.shape, mean, sd)
|
||||
|
||||
def test_sample():
|
||||
"""
|
||||
Test sample.
|
||||
"""
|
||||
shape = (2, 3)
|
||||
seed = 10
|
||||
mean = Tensor([2.0], dtype=dtype.float32)
|
||||
sd = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
|
||||
sample = Sampling(shape, seed=seed)
|
||||
output = sample(mean, sd)
|
||||
assert output.shape == (2, 3, 3)
|
||||
|
||||
class CDF(nn.Cell):
|
||||
"""
|
||||
Test class: cdf of Logistic distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(CDF, self).__init__()
|
||||
self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.l.cdf(x_)
|
||||
|
||||
|
||||
def test_cdf():
|
||||
"""
|
||||
Test cdf.
|
||||
"""
|
||||
logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_cdf = logistic_benchmark.cdf([1.0, 2.0]).astype(np.float32)
|
||||
cdf = CDF()
|
||||
output = cdf(Tensor([1.0, 2.0], dtype=dtype.float32))
|
||||
tol = 2e-5
|
||||
assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
|
||||
|
||||
class LogCDF(nn.Cell):
|
||||
"""
|
||||
Test class: log_cdf of Logistic distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogCDF, self).__init__()
|
||||
self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.l.log_cdf(x_)
|
||||
|
||||
def test_log_cdf():
|
||||
"""
|
||||
Test log cdf.
|
||||
"""
|
||||
logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_logcdf = logistic_benchmark.logcdf([1.0, 2.0]).astype(np.float32)
|
||||
logcdf = LogCDF()
|
||||
output = logcdf(Tensor([1.0, 2.0], dtype=dtype.float32))
|
||||
tol = 5e-5
|
||||
assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
|
||||
|
||||
class SF(nn.Cell):
|
||||
"""
|
||||
Test class: survival function of Logistic distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(SF, self).__init__()
|
||||
self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.l.survival_function(x_)
|
||||
|
||||
def test_survival():
|
||||
"""
|
||||
Test log_survival.
|
||||
"""
|
||||
logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_survival = logistic_benchmark.sf([1.0, 2.0]).astype(np.float32)
|
||||
survival_function = SF()
|
||||
output = survival_function(Tensor([1.0, 2.0], dtype=dtype.float32))
|
||||
tol = 2e-5
|
||||
assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
|
||||
|
||||
class LogSF(nn.Cell):
|
||||
"""
|
||||
Test class: log survival function of Logistic distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogSF, self).__init__()
|
||||
self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.l.log_survival(x_)
|
||||
|
||||
def test_log_survival():
|
||||
"""
|
||||
Test log_survival.
|
||||
"""
|
||||
logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_log_survival = logistic_benchmark.logsf([1.0, 2.0]).astype(np.float32)
|
||||
log_survival = LogSF()
|
||||
output = log_survival(Tensor([1.0, 2.0], dtype=dtype.float32))
|
||||
tol = 2e-5
|
||||
assert (np.abs(output.asnumpy() - expect_log_survival) < tol).all()
|
||||
|
||||
class EntropyH(nn.Cell):
|
||||
"""
|
||||
Test class: entropy of Logistic distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(EntropyH, self).__init__()
|
||||
self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
return self.l.entropy()
|
||||
|
||||
def test_entropy():
|
||||
"""
|
||||
Test entropy.
|
||||
"""
|
||||
logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_entropy = logistic_benchmark.entropy().astype(np.float32)
|
||||
entropy = EntropyH()
|
||||
output = entropy()
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
|
||||
|
|
@ -0,0 +1,195 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Test nn.probability.distribution.logistic.
|
||||
"""
|
||||
import pytest
|
||||
|
||||
import mindspore.nn as nn
|
||||
import mindspore.nn.probability.distribution as msd
|
||||
from mindspore import dtype
|
||||
from mindspore import Tensor
|
||||
|
||||
def test_logistic_shape_errpr():
|
||||
"""
|
||||
Invalid shapes.
|
||||
"""
|
||||
with pytest.raises(ValueError):
|
||||
msd.Logistic([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
|
||||
|
||||
def test_type():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Logistic(0., 1., dtype=dtype.int32)
|
||||
|
||||
def test_name():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Logistic(0., 1., name=1.0)
|
||||
|
||||
def test_seed():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Logistic(0., 1., seed='seed')
|
||||
|
||||
def test_scale():
|
||||
with pytest.raises(ValueError):
|
||||
msd.Logistic(0., 0.)
|
||||
with pytest.raises(ValueError):
|
||||
msd.Logistic(0., -1.)
|
||||
|
||||
def test_arguments():
|
||||
"""
|
||||
args passing during initialization.
|
||||
"""
|
||||
l = msd.Logistic()
|
||||
assert isinstance(l, msd.Distribution)
|
||||
l = msd.Logistic([3.0], [4.0], dtype=dtype.float32)
|
||||
assert isinstance(l, msd.Distribution)
|
||||
|
||||
|
||||
class LogisticProb(nn.Cell):
|
||||
"""
|
||||
logistic distribution: initialize with loc/scale.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogisticProb, self).__init__()
|
||||
self.logistic = msd.Logistic(3.0, 4.0, dtype=dtype.float32)
|
||||
|
||||
def construct(self, value):
|
||||
prob = self.logistic.prob(value)
|
||||
log_prob = self.logistic.log_prob(value)
|
||||
cdf = self.logistic.cdf(value)
|
||||
log_cdf = self.logistic.log_cdf(value)
|
||||
sf = self.logistic.survival_function(value)
|
||||
log_sf = self.logistic.log_survival(value)
|
||||
return prob + log_prob + cdf + log_cdf + sf + log_sf
|
||||
|
||||
def test_logistic_prob():
|
||||
"""
|
||||
Test probability functions: passing value through construct.
|
||||
"""
|
||||
net = LogisticProb()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
ans = net(value)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
|
||||
class LogisticProb1(nn.Cell):
|
||||
"""
|
||||
logistic distribution: initialize without loc/scale.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogisticProb1, self).__init__()
|
||||
self.logistic = msd.Logistic()
|
||||
|
||||
def construct(self, value, mu, s):
|
||||
prob = self.logistic.prob(value, mu, s)
|
||||
log_prob = self.logistic.log_prob(value, mu, s)
|
||||
cdf = self.logistic.cdf(value, mu, s)
|
||||
log_cdf = self.logistic.log_cdf(value, mu, s)
|
||||
sf = self.logistic.survival_function(value, mu, s)
|
||||
log_sf = self.logistic.log_survival(value, mu, s)
|
||||
return prob + log_prob + cdf + log_cdf + sf + log_sf
|
||||
|
||||
def test_logistic_prob1():
|
||||
"""
|
||||
Test probability functions: passing loc/scale, value through construct.
|
||||
"""
|
||||
net = LogisticProb1()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
mu = Tensor([0.0], dtype=dtype.float32)
|
||||
s = Tensor([1.0], dtype=dtype.float32)
|
||||
ans = net(value, mu, s)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class KL(nn.Cell):
|
||||
"""
|
||||
Test kl_loss. Should raise NotImplementedError.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(KL, self).__init__()
|
||||
self.logistic = msd.Logistic(3.0, 4.0)
|
||||
|
||||
def construct(self, mu, s):
|
||||
kl = self.logistic.kl_loss('Logistic', mu, s)
|
||||
return kl
|
||||
|
||||
class Crossentropy(nn.Cell):
|
||||
"""
|
||||
Test cross entropy. Should raise NotImplementedError.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Crossentropy, self).__init__()
|
||||
self.logistic = msd.Logistic(3.0, 4.0)
|
||||
|
||||
def construct(self, mu, s):
|
||||
cross_entropy = self.logistic.cross_entropy('Logistic', mu, s)
|
||||
return cross_entropy
|
||||
|
||||
|
||||
class LogisticBasics(nn.Cell):
|
||||
"""
|
||||
Test class: basic loc/scale function.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogisticBasics, self).__init__()
|
||||
self.logistic = msd.Logistic(3.0, 4.0, dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
mean = self.logistic.mean()
|
||||
sd = self.logistic.sd()
|
||||
mode = self.logistic.mode()
|
||||
entropy = self.logistic.entropy()
|
||||
return mean + sd + mode + entropy
|
||||
|
||||
def test_bascis():
|
||||
"""
|
||||
Test mean/sd/mode/entropy functionality of logistic.
|
||||
"""
|
||||
net = LogisticBasics()
|
||||
ans = net()
|
||||
assert isinstance(ans, Tensor)
|
||||
mu = Tensor(1.0, dtype=dtype.float32)
|
||||
s = Tensor(1.0, dtype=dtype.float32)
|
||||
with pytest.raises(NotImplementedError):
|
||||
kl = KL()
|
||||
ans = kl(mu, s)
|
||||
with pytest.raises(NotImplementedError):
|
||||
crossentropy = Crossentropy()
|
||||
ans = crossentropy(mu, s)
|
||||
|
||||
class LogisticConstruct(nn.Cell):
|
||||
"""
|
||||
logistic distribution: going through construct.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogisticConstruct, self).__init__()
|
||||
self.logistic = msd.Logistic(3.0, 4.0)
|
||||
self.logistic1 = msd.Logistic()
|
||||
|
||||
def construct(self, value, mu, s):
|
||||
prob = self.logistic('prob', value)
|
||||
prob1 = self.logistic('prob', value, mu, s)
|
||||
prob2 = self.logistic1('prob', value, mu, s)
|
||||
return prob + prob1 + prob2
|
||||
|
||||
def test_logistic_construct():
|
||||
"""
|
||||
Test probability function going through construct.
|
||||
"""
|
||||
net = LogisticConstruct()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
mu = Tensor([0.0], dtype=dtype.float32)
|
||||
s = Tensor([1.0], dtype=dtype.float32)
|
||||
ans = net(value, mu, s)
|
||||
assert isinstance(ans, Tensor)
|
||||
Loading…
Reference in New Issue