
def  __init__ (self, batch_shape=torch.Size(), event_shape=torch.Size(), validate_args=None) 

def  expand (self, batch_shape, _instance=None) 

def  batch_shape (self) 

def  event_shape (self) 

def  arg_constraints (self) 

def  support (self) 

def  mean (self) 

def  variance (self) 

def  stddev (self) 

def  sample (self, sample_shape=torch.Size()) 

def  rsample (self, sample_shape=torch.Size()) 

def  sample_n (self, n) 

def  log_prob (self, value) 

def  cdf (self, value) 

def  icdf (self, value) 

def  enumerate_support (self, expand=True) 

def  entropy (self) 

def  perplexity (self) 

def  __repr__ (self) 


def  set_default_validate_args (value) 


 has_rsample 

 has_enumerate_support 

 support 

 arg_constraints 

Definition at line 7 of file distribution.py.
def torch.distributions.distribution.Distribution.arg_constraints 
( 

self  ) 

Returns a dictionary from argument names to
:class:`~torch.distributions.constraints.Constraint` objects that
should be satisfied by each argument of this distribution. Args that
are not tensors need not appear in this dict.
Definition at line 75 of file distribution.py.
def torch.distributions.distribution.Distribution.batch_shape 
( 

self  ) 

Returns the shape over which parameters are batched.
Definition at line 61 of file distribution.py.
def torch.distributions.distribution.Distribution.cdf 
( 

self, 



value 

) 
 
Returns the cumulative density/mass function evaluated at
`value`.
Args:
value (Tensor):
Definition at line 147 of file distribution.py.
def torch.distributions.distribution.Distribution.entropy 
( 

self  ) 

Returns entropy of distribution, batched over batch_shape.
Returns:
Tensor of shape batch_shape.
Definition at line 191 of file distribution.py.
def torch.distributions.distribution.Distribution.enumerate_support 
( 

self, 



expand = True 

) 
 
Returns tensor containing all values supported by a discrete
distribution. The result will enumerate over dimension 0, so the shape
of the result will be `(cardinality,) + batch_shape + event_shape`
(where `event_shape = ()` for univariate distributions).
Note that this enumerates over all batched tensors in lockstep
`[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens
along dim 0, but with the remaining batch dimensions being
singleton dimensions, `[[0], [1], ..`.
To iterate over the full Cartesian product use
`itertools.product(m.enumerate_support())`.
Args:
expand (bool): whether to expand the support over the
batch dims to match the distribution's `batch_shape`.
Returns:
Tensor iterating over dimension 0.
Definition at line 167 of file distribution.py.
def torch.distributions.distribution.Distribution.event_shape 
( 

self  ) 

Returns the shape of a single sample (without batching).
Definition at line 68 of file distribution.py.
def torch.distributions.distribution.Distribution.expand 
( 

self, 



batch_shape, 



_instance = None 

) 
 
Returns a new distribution instance (or populates an existing instance
provided by a derived class) with batch dimensions expanded to
`batch_shape`. This method calls :class:`~torch.Tensor.expand` on
the distribution's parameters. As such, this does not allocate new
memory for the expanded distribution instance. Additionally,
this does not repeat any args checking or parameter broadcasting in
`__init__.py`, when an instance is first created.
Args:
batch_shape (torch.Size): the desired expanded size.
_instance: new instance provided by subclasses that
need to override `.expand`.
Returns:
New distribution instance with batch dimensions expanded to
`batch_size`.
Definition at line 39 of file distribution.py.
def torch.distributions.distribution.Distribution.icdf 
( 

self, 



value 

) 
 
Returns the inverse cumulative density/mass function evaluated at
`value`.
Args:
value (Tensor):
Definition at line 157 of file distribution.py.
def torch.distributions.distribution.Distribution.log_prob 
( 

self, 



value 

) 
 
Returns the log of the probability density/mass function evaluated at
`value`.
Args:
value (Tensor):
Definition at line 137 of file distribution.py.
def torch.distributions.distribution.Distribution.mean 
( 

self  ) 

def torch.distributions.distribution.Distribution.perplexity 
( 

self  ) 

Returns perplexity of distribution, batched over batch_shape.
Returns:
Tensor of shape batch_shape.
Definition at line 200 of file distribution.py.
def torch.distributions.distribution.Distribution.rsample 
( 

self, 



sample_shape = torch.Size() 

) 
 
Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched.
Definition at line 121 of file distribution.py.
def torch.distributions.distribution.Distribution.sample 
( 

self, 



sample_shape = torch.Size() 

) 
 
Generates a sample_shape shaped sample or sample_shape shaped batch of
samples if the distribution parameters are batched.
Definition at line 113 of file distribution.py.
def torch.distributions.distribution.Distribution.sample_n 
( 

self, 



n 

) 
 
Generates n samples or n batches of samples if the distribution
parameters are batched.
Definition at line 129 of file distribution.py.
def torch.distributions.distribution.Distribution.stddev 
( 

self  ) 

Returns the standard deviation of the distribution.
Definition at line 107 of file distribution.py.
def torch.distributions.distribution.Distribution.support 
( 

self  ) 

Returns a :class:`~torch.distributions.constraints.Constraint` object
representing this distribution's support.
Definition at line 85 of file distribution.py.
def torch.distributions.distribution.Distribution.variance 
( 

self  ) 

The documentation for this class was generated from the following file: