org.encog.ml.bayesian

Class BayesianNetwork

java.lang.Object

org.encog.ml.BasicML

org.encog.ml.bayesian.BayesianNetwork

All Implemented Interfaces:

java.io.Serializable, MLClassification, MLError, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLResettable

public class BayesianNetwork
extends BasicML
implements MLClassification, MLResettable, java.io.Serializable, MLError

The Bayesian Network is a machine learning method that is based on probability, and particularly Bayes' Rule. The Bayesian Network also forms the basis for the Hidden Markov Model and Naive Bayesian Network. The Bayesian Network is either constructed directly or inferred from training data using an algorithm such as K2. http://www.heatonresearch.com/wiki/Bayesian_Network

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static java.lang.String[]	CHOICES_TRUE_FALSE Default choices for a boolean event.

Constructor Summary

Constructors

Constructor and Description
BayesianNetwork()

Method Summary

Modifier and Type	Method and Description
double	calculateError(MLDataSet data) Calculate the error of the ML method, given a dataset.
int	calculateParameterCount()
int	classify(MLData input) Classify the input.
double	computeProbability(MLData input)
void	createDependency(BayesianEvent parentEvent, BayesianEvent... children) Create a dependency between a parent and multiple children.
void	createDependency(BayesianEvent parentEvent, BayesianEvent childEvent) Create a dependency between two events.
void	createDependency(java.lang.String parentEventLabel, java.lang.String childEventLabel) Create a dependency between two labels.
void	createEvent(BayesianEvent event) Create, or register, the specified event with this bayesian network.
BayesianEvent	createEvent(java.lang.String label, java.util.List<BayesianChoice> options) Create an event specified on the label and options provided.
BayesianEvent	createEvent(java.lang.String label, java.lang.String... options) Create the specified events based on a variable number of options, or choices.
void	defineClassificationStructure(java.lang.String line) Define a classification structure of the form P(A|B) = P(C)
void	defineProbability(java.lang.String line) Define a probability.
void	defineProbability(java.lang.String line, double probability) Define the probability for an event.
void	defineRelationship(java.lang.String line) Define a relationship.
int[]	determineClasses(MLData input) Determine the classes for the specified input.
boolean	eventExists(java.lang.String label) Return true if the specified event exists.
void	finalizeStructure() Finalize the structure of this Bayesian network.
java.lang.String	getClassificationStructure()
int	getClassificationTarget() Get the classification target.
BayesianEvent	getClassificationTargetEvent()
java.lang.String	getContents()
BayesianEvent	getEvent(java.lang.String label) Get an event based on the string label.
BayesianEvent	getEventError(java.lang.String label) Get an event based on label, throw an error if not found.
int	getEventIndex(BayesianEvent event)
java.util.Map<java.lang.String,BayesianEvent>	getEventMap()
java.util.List<BayesianEvent>	getEvents()
int	getInputCount()
int	getOutputCount()
BayesianQuery	getQuery()
boolean	hasValidClassificationTarget()
boolean	isCondIndependent(BayesianEvent a, BayesianEvent b, BayesianEvent... given)
boolean	isDescendant(BayesianEvent a, BayesianEvent b) Determine if one event is a descendant of another.
boolean	isInputPresent(int idx) Determine if the specified input is present.
double	performQuery(java.lang.String line) Perform a query.
void	removeAllRelations() Remove all relations between nodes.
BayesianEvent	requireEvent(java.lang.String label) Require the specified event, thrown an error if it does not exist.
void	reset() Reset the weights.
void	reset(int seed) Reset the weights with a seed.
void	setContents(java.lang.String line) Define the structure of the Bayesian network as a string.
void	setQuery(BayesianQuery query)
java.lang.String	toString()
void	updateProperties() Update any objeccts when a property changes.
void	validate() Validate the structure of this Bayesian network.

Methods inherited from class org.encog.ml.BasicML

getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

CHOICES_TRUE_FALSE

public static final java.lang.String[] CHOICES_TRUE_FALSE

Default choices for a boolean event.

Constructor Detail

BayesianNetwork

public BayesianNetwork()

Method Detail

getEventMap

public java.util.Map<java.lang.String,BayesianEvent> getEventMap()

Returns:

The mapping from string names to events.

getEvents

public java.util.List<BayesianEvent> getEvents()

Returns:

The events.

getEvent

public BayesianEvent getEvent(java.lang.String label)

Get an event based on the string label.

Parameters:

label - The label to locate.

Returns:

The event found.

getEventError

public BayesianEvent getEventError(java.lang.String label)

Get an event based on label, throw an error if not found.

Parameters:

label - THe event label to find.

Returns:

The event.

eventExists

public boolean eventExists(java.lang.String label)

Return true if the specified event exists.

Parameters:

label - The label we are searching for.

Returns:

True, if the event exists by label.

createEvent

public void createEvent(BayesianEvent event)

Create, or register, the specified event with this bayesian network.

Parameters:

event - The event to add.

createEvent

public BayesianEvent createEvent(java.lang.String label,
                                 java.util.List<BayesianChoice> options)

Create an event specified on the label and options provided.

Parameters:

label - The label to create this event as.

options - The options, or states, that this event can have.

Returns:

The newly created event.

createEvent

public BayesianEvent createEvent(java.lang.String label,
                                 java.lang.String... options)

Create the specified events based on a variable number of options, or choices.

Parameters:

label - The label of the event to create.

options - The states that the event can have.

Returns:

The newly created event.

createDependency

public void createDependency(BayesianEvent parentEvent,
                             BayesianEvent childEvent)

Create a dependency between two events.

Parameters:

parentEvent - The parent event.

childEvent - The child event.

createDependency

public void createDependency(BayesianEvent parentEvent,
                             BayesianEvent... children)

Create a dependency between a parent and multiple children.

Parameters:

parentEvent - The parent event.

children - The child events.

createDependency

public void createDependency(java.lang.String parentEventLabel,
                             java.lang.String childEventLabel)

Create a dependency between two labels.

Parameters:

parentEventLabel - The parent event.

childEventLabel - The child event.

getContents

public java.lang.String getContents()

Returns:

The contents as a string. Shows both events and dependences.

setContents

public void setContents(java.lang.String line)

Define the structure of the Bayesian network as a string.

Parameters:

line - The string to define events and relations.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

calculateParameterCount

public int calculateParameterCount()

Returns:

The number of parameters in this Bayesian network.

finalizeStructure

public void finalizeStructure()

Finalize the structure of this Bayesian network.

validate

public void validate()

Validate the structure of this Bayesian network.

isDescendant

public boolean isDescendant(BayesianEvent a,
                            BayesianEvent b)

Determine if one event is a descendant of another.

Parameters:

a - The event to check.

b - The event that has children.

Returns:

True if a is amoung b's children.

isCondIndependent

public boolean isCondIndependent(BayesianEvent a,
                                 BayesianEvent b,
                                 BayesianEvent... given)

getQuery

public BayesianQuery getQuery()

setQuery

public void setQuery(BayesianQuery query)

getInputCount

public int getInputCount()

Specified by:

getInputCount in interface MLInput

Returns:

The input.

getOutputCount

public int getOutputCount()

Specified by:

getOutputCount in interface MLOutput

Returns:

The output count.

computeProbability

public double computeProbability(MLData input)

defineProbability

public void defineProbability(java.lang.String line,
                              double probability)

Define the probability for an event.

Parameters:

line - The event.

probability - The probability.

defineProbability

public void defineProbability(java.lang.String line)

Define a probability.

Parameters:

line - The line to define the probability.

requireEvent

public BayesianEvent requireEvent(java.lang.String label)

Require the specified event, thrown an error if it does not exist.

Parameters:

label - The label.

Returns:

The event.

defineRelationship

public void defineRelationship(java.lang.String line)

Define a relationship.

Parameters:

line - The relationship to define.

performQuery

public double performQuery(java.lang.String line)

Perform a query.

Parameters:

line - The query.

Returns:

The probability.

updateProperties

public void updateProperties()

Update any objeccts when a property changes.

Specified by:

updateProperties in interface MLProperties

Specified by:

updateProperties in class BasicML

getEventIndex

public int getEventIndex(BayesianEvent event)

removeAllRelations

public void removeAllRelations()

Remove all relations between nodes.

reset

public void reset()

Reset the weights.

Specified by:

reset in interface MLResettable

reset

public void reset(int seed)

Reset the weights with a seed.

Specified by:

reset in interface MLResettable

Parameters:

seed - The seed value.

determineClasses

public int[] determineClasses(MLData input)

Determine the classes for the specified input.

Parameters:

input - The input.

Returns:

An array of class indexes.

classify

public int classify(MLData input)

Classify the input.

Specified by:

classify in interface MLClassification

Parameters:

input - The input to classify.

Returns:

The group that the data was classified into.

getClassificationTarget

public int getClassificationTarget()

Get the classification target.

Returns:

The index of the classification target.

isInputPresent

public boolean isInputPresent(int idx)

Determine if the specified input is present.

Parameters:

idx - The index of the input.

Returns:

True, if the input is present.

defineClassificationStructure

public void defineClassificationStructure(java.lang.String line)

Define a classification structure of the form P(A|B) = P(C)

Parameters:

line - The line.

getClassificationTargetEvent

public BayesianEvent getClassificationTargetEvent()

Returns:

The classification target.

calculateError

public double calculateError(MLDataSet data)

Calculate the error of the ML method, given a dataset.

Specified by:

calculateError in interface MLError

Parameters:

data - The dataset.

Returns:

The error.

getClassificationStructure

public java.lang.String getClassificationStructure()

Returns:

Returns a string representation of the classification structure. Of the form P(a|b,c,d)

hasValidClassificationTarget

public boolean hasValidClassificationTarget()

Returns:

True if this network has a valid classification target.