def __init__(self,
bijector,
distribution,
seed=None,
name="transformed_distribution"):
"""
Constructor of transformed_distribution class.
"""
param = dict(locals())
validator.check_value_type('bijector', bijector,
[nn.probability.bijector.Bijector], type(self).__name__)
validator.check_value_type('distribution', distribution,
[Distribution], type(self).__name__)
super(TransformedDistribution, self).__init__(seed, distribution.dtype, name, param)
self._bijector = bijector
self._distribution = distribution
self._is_linear_transformation = bijector.is_constant_jacobian
self.default_parameters = distribution.default_parameters
self.parameter_names = distribution.parameter_names
self.exp = exp_generic
self.log = log_generic
self.isnan = P.IsNan()
self.equal_base = P.Equal()
self.select_base = P.Select()
self.fill = P.Fill()
# check if batch shape of the distribution and event shape is broadcastable
if hasattr(self.bijector, 'event_shape'):
event_shape_tensor = self.fill(self.dtype, self.bijector.event_shape, 0.0)
broadcast_shape_tensor = self.fill(self.dtype, self.broadcast_shape, 0.0)
self._batch_event = (event_shape_tensor + broadcast_shape_tensor).shape
else:
self._batch_event = self.broadcast_shape
def __init__(self,
probs=None,
seed=None,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
param['param_dict'] = {'probs': probs}
valid_dtype = mstype.uint_type + mstype.int_type + mstype.float_type
Validator.check_type_name("dtype", dtype, valid_dtype,
type(self).__name__)
super(Categorical, self).__init__(seed, dtype, name, param)
self._probs = self._add_parameter(probs, 'probs')
if self.probs is not None:
check_rank(self.probs)
check_prob(self.probs)
check_sum_equal_one(probs)
# update is_scalar_batch and broadcast_shape
# drop one dimension
if self.probs.shape[:-1] == ():
self._is_scalar_batch = True
self._broadcast_shape = self._broadcast_shape[:-1]
self.argmax = P.ArgMaxWithValue(axis=-1)
self.broadcast = broadcast_to
self.cast = P.Cast()
self.clip_by_value = C.clip_by_value
self.concat = P.Concat(-1)
self.cumsum = P.CumSum()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expand_dim = P.ExpandDims()
self.fill = P.Fill()
self.gather = P.GatherNd()
self.greater = P.Greater()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.log = log_generic
self.log_softmax = P.LogSoftmax()
self.logicor = P.LogicalOr()
self.logicand = P.LogicalAnd()
self.multinomial = P.Multinomial(seed=self.seed)
self.reshape = P.Reshape()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.select = P.Select()
self.shape = P.Shape()
self.softmax = P.Softmax()
self.squeeze = P.Squeeze()
self.squeeze_first_axis = P.Squeeze(0)
self.squeeze_last_axis = P.Squeeze(-1)
self.square = P.Square()
self.transpose = P.Transpose()
self.is_nan = P.IsNan()
self.index_type = mstype.int32
self.nan = np.nan
def __init__(self,
bijector,
distribution,
seed=None,
name="transformed_distribution"):
"""
Constructor of transformed_distribution class.
"""
param = dict(locals())
validator.check_value_type('bijector', bijector,
[nn.probability.bijector.Bijector],
type(self).__name__)
validator.check_value_type('distribution', distribution,
[Distribution],
type(self).__name__)
validator.check_type_name("dtype", distribution.dtype,
mstype.float_type,
type(self).__name__)
super(TransformedDistribution, self).__init__(seed, distribution.dtype,
name, param)
self._bijector = bijector
self._distribution = distribution
# set attributes
self._is_linear_transformation = self.bijector.is_constant_jacobian
self._dtype = self.distribution.dtype
self._is_scalar_batch = self.distribution.is_scalar_batch and self.bijector.is_scalar_batch
self._batch_shape = self.distribution.batch_shape
self.default_parameters = self.distribution.default_parameters
self.parameter_names = self.distribution.parameter_names
# by default, set the parameter_type to be the distribution's parameter_type
self.parameter_type = self.distribution.parameter_type
self.exp = exp_generic
self.log = log_generic
self.isnan = P.IsNan()
self.cast_base = P.Cast()
self.equal_base = P.Equal()
self.select_base = P.Select()
self.fill_base = P.Fill()
# broadcast bijector batch_shape and distribution batch_shape
self._broadcast_shape = self._broadcast_bijector_dist()
def __init__(self):
super(Netnan, self).__init__()
self.isnan = P.IsNan()
