Create distributions.py

Author: NikitinaMaria

src/irt/distributions.py

@@ -0,0 +1,96 @@
+class torch.distributions.Distribution:
+	'''
+	The abstract base class for probability distributions, which we inherit from. These methods are implied
+	to be implemented for each subclass.
+	'''
+	def __init__(batch_shape=torch.Size([]), event_shape=torch.Size([])):
+		'''
+		Basic constructer of distribution.
+		'''
+	
+	@property
+	def arg_constraints():
+		'''
+		Returns a dictionary from argument names to Constraint objects that should
+		be satisfied by each argument of this distribution. Args that are not tensors need not appear
+		in this dict.
+		'''
+	
+	def cdf(value):
+		'''
+		Returns the cumulative density/mass function evaluated at value.
+		'''
+		
+	def entropy():
+		'''
+		Returns entropy of distribution, batched over batch_shape.
+		'''
+
+	def enumerate_support(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).
+		'''
+	
+	@property
+	def mean(expand=True):
+		'''
+		Returns mean of the distributio.
+		'''
+
+	@property
+	def mode(expand=True):
+		'''
+		Returns mean of the distributio.
+		'''
+	def perplexity():
+		'''
+		Returns perplexity of distribution, batched over batch_shape.
+		'''
+	
+	def rsample(sample_shape=torch.Size([])):
+		'''
+		Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution
+		parameters are batched.
+		'''
+
+	def sample(sample_shape=torch.Size([])):
+		'''
+		Generates a sample_shape shaped sample or sample_shape shaped batch of reparameterized samples
+		if the distribution parameters are batched.
+		'''
+
+class torch.distributions.implicit.Normal(Distribution):
+	'''
+	A Gaussian distribution class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__(mean_matrix, covariance_matrix=None):
+		pass
+
+class torch.distributions.implicit.Dirichlet(Distribution):
+	'''
+	A Dirichlet distribution class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__(concentration, validate_args=None):
+		pass
+		
+class torch.distributions.implicit.Mixture(Distribution):
+	'''
+	A Mixture of distributions class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__(distributions : List[Distribution]):
+		pass
+
+class torch.distributions.implicit.Student(Distribution):
+	'''
+	A Student's distribution class with backpropagation capability for the rsample function through IRT.
+	'''
+	def __init__():
+		pass
+
+class torch.distributions.implicit.Factorized(Distribution):
+	'''
+	A class for an arbitrary factorized distribution with backpropagation capability for the rsample
+	function through IRT.
+	'''