alex.ml.ep package

Submodules

alex.ml.ep.node module

class alex.ml.ep.node.ConstChangeGoal(name, desc, card, parameters, parents=None)

Bases: alex.ml.ep.node.GroupingGoal

ConstChangeGoal implements all functionality as is include in GroupingGoal; however, it that there are only two transition probabilites for transitions between the same values and the different values.

update()

This function update belief for the goal.

class alex.ml.ep.node.Goal(name, desc, card, parameters, parents=None)

Bases: alex.ml.ep.node.Node

Goal can contain only the same values as the observations.

As a consequence, it can contain values of its previous node.

probTable(value, parents)

This function defines how the coditional probability is computed.

pRemebering - probability that the previous value is correct pObserving - probability that the observed value is correct

setParents(parents)

setValues()

The function copy values from its previous node and from observation nodes.

update()

This function update belief for the goal.

class alex.ml.ep.node.GroupingGoal(name, desc, card, parameters, parents=None)

Bases: alex.ml.ep.node.GroupingNode, alex.ml.ep.node.Goal

GroupingGoal implements all functionality as is include in Goal; however, it only update the values for which was observed some evidence.

setValues()

The function copy values from its previous node and from observation nodes.

update()

This function update belief for the goal.

class alex.ml.ep.node.GroupingNode(name, desc, card)

Bases: alex.ml.ep.node.Node

addOthers(value, probability)

explain(full=None)

This function explains the values for this node.

In additon to the Node’s function, it prints the cardinality of the others set.

splitOff(value)

This function split off the value from the others set and place it into the values dict.

class alex.ml.ep.node.Node(name, desc, card)

Bases: object

A base class for all nodes in a belief state.

explain(full=None)

This function prints the values and their probailities for this node.

getMostProbableValue()

The function returns the most probable value and its probability in a tuple.

getTwoMostProbableValues()

This function returns two most probable values and their probabilities.

The function returns a tuple consisting of two tuples (value, probability).

normalise()

This function normlize the sum of all probabilities to 1.0

alex.ml.ep.test module

alex.ml.ep.test.random() → x in the interval [0, 1).

alex.ml.ep.turn module

class alex.ml.ep.turn.Turn

Module contents