pgm.representation package

Subpackages

• pgm.representation.algorithms package
  • Module contents

Submodules

pgm.representation.BN module

class pgm.representation.BN.Distribution(variables={})

Bases: object

class pgm.representation.BN.Graph(type='list')

Bases: object

Adjancency list and Adjancency matrix representation of the graph based on the input

add_edge(nodeA, nodeB, nodeA_distribution=None, nodeB_distribution=None)

adds a directed edge from nodeA to nodeB in the graph also helps in assigning the weightage to each edge

nodeA -> nodeB

nodeA: can be [‘int’, ‘str’] nodeB: can be [‘int’, ‘str’]

add_node(node, distribution=None)

adds node in the graph

node: can be [‘int’, ‘str’]

delete_edge(nodeA, nodeB)

deletes the directed edge from nodeA to nodeB function will remoce edge: nodeA -> nodeB

nodeA: can be [‘int’, ‘str’] nodeB: can be [‘int’, ‘str’]

delete_node(node)

deletes the node from the graph also removes all the edges connecting to that specific node

Example

nodeA -> nodeB <- nodeC

deleteing nodeB will remove

edges:

nodeA -> nodeB nodeC -> nodeB

node:

nodeB

node: can be [‘int’, ‘str’]

pgm.representation.LinkedListBN module

class pgm.representation.LinkedListBN.Graph(root)

Bases: object

Adjancency list and Adjancency matrix representation of the graph based on the input

add_edge(node1, node2)

adds edge netween node1 and node2

node1 -> node2

node1: can be [‘int’, ‘str’] or Node object node2: can be [‘int’, ‘str’] or Node object

add_node(node, parentNodes)

adds node in the graph

node: can be [‘int’, ‘str’] or Node object parentNodes: can be [‘int’, ‘str’] or Node object

calculate_conditional(nodes, values)

calculates conditional distribution fixing the values of given nodes

nodes: can be list of [‘int’, ‘str’ or Node object] values: [node_value in same order]

calculate_marginals(nodes)

removes the node and restimates marginals

nodes: can be list of [‘int’, ‘str’ or Node object]

delete_edge(node1, node2)

removes edge between node1 and node2

deletes directed edge from node1->node2

node1: can be [‘int’, ‘str’] or Node object node2: can be [‘int’, ‘str’] or Node object

delete_node(node)

removes all the edges and node from the graph

node: can be [‘int’, ‘str’] or Node object

get_leafnodes(return_obj=True)

finds all leaf nodes in the graph

return_obj: bool if true returns list of node object

else returns list of string (node names)

get_node(node)

search and returns the node

node: can be [‘int’, ‘str’]

print(node)

pgm.representation.LinkedListMN module

class pgm.representation.LinkedListMN.Graph(root)

Bases: object

Adjancency list and Adjancency matrix representation of the graph based on the input

add_edge(node1, node2)

adds edge netween node1 and node2 node1 -> node2 node1: can be [‘int’, ‘str’] node2: can be [‘int’, ‘str’]

add_node(node, nbrNodes)

adds node in the graph node: can be [‘int’, ‘str’] nbrNodes: can be list([‘int’ or ‘str’])

calculate_conditional(nodes, values)

calculates conditional distribution fixing the values of given nodes

nodes: can be list of [‘int’, ‘str’ or Node object] values: [node_value in same order]

calculate_marginals(nodes)

removes the node and restimates marginals

nodes: can be list of [‘int’, ‘str’ or Node object]

delete_edge(node1, node2)

removes edge between node1 and node2 deletes node1->node2 edge

node1: can be [‘int’, ‘str’] node2: can be [‘int’, ‘str’]

delete_node(node)

removes all the edges and node from the graph

node: can be [‘int’, ‘str’]

get_node(node)

search and returns the node

node: can be [‘int’, ‘str’]

print(node)

pgm.representation.MN module

class pgm.representation.MN.Graph(type='list')

Bases: object

Adjancency list and Adjancency matrix representation of the graph based on the input

add_edge(nodeA, nodeB, edge_weightage=1, nodeA_weightage=1, nodeB_weightage=1)

adds a directed edge from nodeA to nodeB in the graph also helps in assigning the weightage to each edge

nodeA -> nodeB

nodeA: can be [‘int’, ‘str’] nodeB: can be [‘int’, ‘str’] nodeA_weightage: [‘int’ or ‘float’] nodeA_weightage: [‘int’ or ‘float’] edge_weightage: [‘int’ or ‘float’]

add_node(node, weightage=1)

adds node in the graph also helps in assigning the weightage to each node

node: can be [‘int’, ‘str’] weightage: [‘int’ or ‘float’]

delete_edge(nodeA, nodeB)

deletes the directed edge from nodeA to nodeB

function will remoce edge: nodeA -> nodeB

nodeA: can be [‘int’, ‘str’] nodeB: can be [‘int’, ‘str’]

delete_node(node)

deletes the node from the graph also removes all the edges connecting to that specific node

Example

nodeA -> nodeB <- nodeC

deleteing nodeB will remove

edges:

nodeA -> nodeB nodeC -> nodeB

node:

nodeB

node: can be [‘int’, ‘str’]

Module contents