inference.EnumerationEngine.EnumerationEngine Class Reference

Inheritance diagram for inference.EnumerationEngine.EnumerationEngine:

Collaboration diagram for inference.EnumerationEngine.EnumerationEngine:

Public Member Functions
def	__init__ (self, bnet, do_print=False, is_quantum=False)
def	get_unipot_list (self, node_list)
def	story_generator (self)
def	get_story_potential_val (self, annotated_story)
Public Member Functions inherited from inference.InferenceEngine.InferenceEngine
def	__init__ (self, bnet, do_print=False, is_quantum=False)

Public Attributes
	bnet_ord_nodes
Public Attributes inherited from inference.InferenceEngine.InferenceEngine
	bnet
	do_print
	is_quantum

Additional Inherited Members
Static Public Member Functions inherited from inference.InferenceEngine.InferenceEngine
def	print_annotated_story (annotated_story)

Detailed Description

EnumerationEngine is an InferenceEngine that enumerates all (brute force
method) possible instantiations (aka stories, histories, Feynman paths)
consistent with the active states of each node.

Attributes
----------
bnet_ord_nodes : list[BayesNode]

bnet : BayesNet
do_print : bool
is_quantum : bool

Constructor & Destructor Documentation

def inference.EnumerationEngine.EnumerationEngine.__init__	(		self,
			bnet,
			do_print = False,
			is_quantum = False
	)

Constructor

Parameters
----------
bnet : BayesNet
do_print : bool
is_quantum : bool

Returns
-------

Member Function Documentation

def inference.EnumerationEngine.EnumerationEngine.get_story_potential_val	(		self,
			annotated_story
	)

Given an annotated story (i.e., a dictionary that maps all nodes to
their current state), it returns a float for CBnets and a complex
for QBnet. The returned value is the pot value for that particular
annotated story.

Parameters
----------
annotated_story : dict[Node, int]

Returns
-------
complex

def inference.EnumerationEngine.EnumerationEngine.get_unipot_list	(		self,
			node_list
	)

For each node in node_list, this method returns a uni-potential that
gives the probabilities for the states of that node. Obviously,
such a PD has the active states of the node as support.

Parameters
----------
node_list : list[BayesNode]

Returns
-------
list[DiscreteUniPot]

def inference.EnumerationEngine.EnumerationEngine.story_generator

(

self

)

Generate the next possible story constrained by the evidence (i.e.,
by the active states of each node).

Returns
-------
itertools.product

---

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

• inference/EnumerationEngine.py