support get_dist_args
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@ -153,6 +153,16 @@ class Bernoulli(Distribution):
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"""
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return self._probs
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def _get_dist_type(self):
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return "Bernoulli"
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def _get_dist_args(self, probs1=None):
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if probs1 is not None:
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self.checktensor(probs1, 'probs')
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else:
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probs1 = self.probs
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return (probs1,)
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def _mean(self, probs1=None):
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r"""
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.. math::
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@ -169,6 +169,16 @@ class Categorical(Distribution):
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"""
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return self._probs
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def _get_dist_type(self):
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return "Categorical"
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def _get_dist_args(self, probs=None):
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if probs is not None:
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self.checktensor(probs, 'probs')
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else:
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probs = self.probs
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return (probs,)
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def _mean(self, probs=None):
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r"""
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.. math::
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@ -344,6 +344,33 @@ class Distribution(Cell):
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else:
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self._call_cross_entropy = self._raise_not_implemented_error('cross_entropy')
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def _get_dist_args(self, *args, **kwargs):
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return raise_not_implemented_util('get_dist_args', self.name, *args, **kwargs)
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def get_dist_args(self, *args, **kwargs):
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"""
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Check the availability and validity of default parameters and `dist_spec_args`.
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Args:
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*args (list): the list of positional arguments forwarded to subclasses.
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**kwargs (dictionary): the dictionary of keyword arguments forwarded to subclasses.
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Note:
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`dist_spec_args` must be passed in through list or dictionary. The order of `dist_spec_args`
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should follow the initialization order of default parameters through `_add_parameter`.
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If some `dist_spec_args` is None, the corresponding default parameter is returned.
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"""
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return self._get_dist_args(*args, **kwargs)
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def _get_dist_type(self, *args, **kwargs):
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return raise_not_implemented_util('get_dist_type', self.name, *args, **kwargs)
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def get_dist_type(self, *args, **kwargs):
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"""
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Return the type of the distribution.
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"""
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return self._get_dist_type(*args, **kwargs)
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def _raise_not_implemented_error(self, func_name):
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name = self.name
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def raise_error(*args, **kwargs):
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@ -721,4 +748,8 @@ class Distribution(Cell):
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return self._call_cross_entropy(*args, **kwargs)
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if name == 'sample':
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return self._sample(*args, **kwargs)
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if name == 'get_dist_args':
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return self._get_dist_args(*args, **kwargs)
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if name == 'get_dist_type':
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return self._get_dist_type(*args, **kwargs)
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return raise_not_implemented_util(name, self.name, *args, **kwargs)
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@ -157,6 +157,16 @@ class Exponential(Distribution):
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"""
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return self._rate
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def _get_dist_type(self):
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return "Exponential"
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def _get_dist_args(self, rate=None):
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if rate is not None:
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self.checktensor(rate, 'rate')
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else:
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rate = self.rate
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return (rate,)
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def _mean(self, rate=None):
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r"""
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.. math::
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@ -162,6 +162,16 @@ class Geometric(Distribution):
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"""
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return self._probs
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def _get_dist_type(self):
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return "Geometric"
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def _get_dist_args(self, probs1=None):
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if probs1 is not None:
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self.checktensor(probs1, 'probs')
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else:
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probs1 = self.probs
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return (probs1,)
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def _mean(self, probs1=None):
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r"""
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.. math::
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@ -109,7 +109,7 @@ class Gumbel(TransformedDistribution):
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bijector=msb.Invert(gumbel_cdf),
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seed=seed, name=name)
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self._parameter_type = gumbel_cdf.parameter_type
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self.parameter_type = gumbel_cdf.parameter_type
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self._broadcast_shape = gumbel_cdf.event_shape
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if self._broadcast_shape != ():
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self._is_scalar_batch = False
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@ -146,6 +146,20 @@ class Gumbel(TransformedDistribution):
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str_info = f'batch_shape = {self._broadcast_shape}'
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return str_info
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def _get_dist_type(self):
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return "Gumbel"
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def _get_dist_args(self, loc=None, scale=None):
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if loc is not None:
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self.checktensor(loc, 'loc')
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else:
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loc = self.loc
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if scale is not None:
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self.checktensor(scale, 'scale')
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else:
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scale = self.scale
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return loc, scale
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def _mean(self):
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r"""
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The mean of the distribution.
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@ -161,6 +161,20 @@ class LogNormal(msd.TransformedDistribution):
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"""Distribution parameter for the pre-transformed standard deviation."""
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return self.distribution("sd")
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def _get_dist_type(self):
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return "LogNormal"
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def _get_dist_args(self, loc=None, scale=None):
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if loc is not None:
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self.checktensor(loc, 'loc')
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else:
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loc = self.distribution("mean")
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if scale is not None:
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self.checktensor(scale, 'scale')
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else:
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scale = self.distribution("sd")
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return loc, scale
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def extend_repr(self):
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if self.is_scalar_batch:
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s = f'loc = {self._mean_value}, scale = {self._sd_value}'
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@ -175,6 +175,20 @@ class Logistic(Distribution):
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"""
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return self._scale
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def _get_dist_type(self):
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return "Logistic"
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def _get_dist_args(self, loc=None, scale=None):
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if loc is not None:
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self.checktensor(loc, 'loc')
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else:
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loc = self.loc
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if scale is not None:
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self.checktensor(scale, 'scale')
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else:
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scale = self.scale
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return loc, 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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@ -154,6 +154,20 @@ class Normal(Distribution):
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s = f'batch_shape = {self._broadcast_shape}'
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return s
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def _get_dist_type(self):
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return "Normal"
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def _get_dist_args(self, mean=None, sd=None):
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if mean is not None:
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self.checktensor(mean, 'mean')
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else:
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mean = self._mean_value
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if sd is not None:
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self.checktensor(sd, 'sd')
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else:
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sd = self._sd_value
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return mean, sd
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def _mean(self, mean=None, sd=None):
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"""
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The mean of the distribution.
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@ -173,6 +173,20 @@ class Uniform(Distribution):
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"""
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return self._high
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def _get_dist_type(self):
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return "Uniform"
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def _get_dist_args(self, low=None, high=None):
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if low is not None:
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self.checktensor(low, 'low')
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else:
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low = self.low
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if high is not None:
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self.checktensor(high, 'high')
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else:
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high = self.high
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return high, low
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def _range(self, low=None, high=None):
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r"""
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Return the range of the distribution.
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@ -0,0 +1,101 @@
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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 Normal distribution"""
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import numpy as np
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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 Net1(nn.Cell):
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"""
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Test class: Normal distribution. `dist_spec_args` are `mean`, `sd`.
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"""
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def __init__(self):
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super(Net1, self).__init__()
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self.normal = msd.Normal(dtype=dtype.float32)
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self.normal1 = msd.Normal(0.0, 1.0, dtype=dtype.float32)
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self.normal2 = msd.Normal(3.0, 4.0, dtype=dtype.float32)
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def construct(self, value, mean, sd, mean_a, sd_a):
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args_list = self.normal.get_dist_args(mean, sd)
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prob = self.normal1.prob(value, *args_list)
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args_list1 = self.normal.get_dist_args()
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prob1 = self.normal2.prob(value, *args_list1)
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args_list2 = self.normal1.get_dist_args()
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dist_type = self.normal1.get_dist_type()
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kl_loss = self.normal2.kl_loss(dist_type, *args_list2)
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args_list3 = self.normal.get_dist_args(mean_a, sd_a)
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dist_type = self.normal1.get_dist_type()
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kl_loss1 = self.normal2.kl_loss(dist_type, *args_list3)
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return prob, prob1, kl_loss, kl_loss1
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def test1():
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"""
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Test Normal with two `dist_spec_args`.
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"""
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net = Net1()
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mean = Tensor(3.0, dtype=dtype.float32)
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sd = Tensor(4.0, dtype=dtype.float32)
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mean_a = Tensor(0.0, dtype=dtype.float32)
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sd_a = Tensor(1.0, dtype=dtype.float32)
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value = Tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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ans, expected, ans1, expected1 = net(value, mean, sd, mean_a, sd_a)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected.asnumpy()) < tol).all()
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assert (np.abs(ans1.asnumpy() - expected1.asnumpy()) < tol).all()
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class Net2(nn.Cell):
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"""
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Test class: Exponential distribution. `dist_spec_args` is `rate`.
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"""
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def __init__(self):
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super(Net2, self).__init__()
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self.expon = msd.Exponential(dtype=dtype.float32)
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self.expon1 = msd.Exponential(1.0, dtype=dtype.float32)
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self.expon2 = msd.Exponential(2.0, dtype=dtype.float32)
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def construct(self, value, rate, rate1):
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args_list = self.expon.get_dist_args(rate)
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prob = self.expon1.prob(value, *args_list)
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args_list1 = self.expon.get_dist_args()
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prob1 = self.expon2.prob(value, *args_list1)
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args_list2 = self.expon1.get_dist_args()
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dist_type = self.expon1.get_dist_type()
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kl_loss = self.expon2.kl_loss(dist_type, *args_list2)
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args_list3 = self.expon.get_dist_args(rate1)
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dist_type = self.expon.get_dist_type()
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kl_loss1 = self.expon2.kl_loss(dist_type, *args_list3)
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return prob, prob1, kl_loss, kl_loss1
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def test2():
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"""
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Test Expomential with single `dist_spec_args`.
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"""
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net = Net2()
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rate = Tensor(2.0, dtype=dtype.float32)
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rate1 = Tensor(1.0, dtype=dtype.float32)
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value = Tensor([-2.0, -1.0, 0.0, 1.0, 2.0])
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ans, expected, ans1, expected1 = net(value, rate, rate1)
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected.asnumpy()) < tol).all()
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assert (np.abs(ans1.asnumpy() - expected1.asnumpy()) < tol).all()
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@ -98,6 +98,8 @@ def test_kl_cross_entropy():
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"""
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Test kl_loss and cross_entropy.
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"""
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from mindspore import context
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context.set_context(device_target="Ascend")
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net = KL()
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loc_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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scale_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
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