com.bayesia.api.learning.settings

Class AbstractLearningSettings

java.lang.Object

com.bayesia.api.learning.settings.AbstractLearningSettings

All Implemented Interfaces:

LearningSettings, java.lang.Cloneable

Direct Known Subclasses:

EQSettings, GreedySettings, MaximumSpanningTreeSettings, ParameterEstimationSettings, SupervisedLearningSettings, TabooOrderSettings, TabooSettings

public abstract class AbstractLearningSettings
extends java.lang.Object
implements LearningSettings

Method Summary

Modifier and Type	Method and Description
double	getSmoothedProbabilityEstimation() Returns the weight of the non informative prior, i.e. the virtual case(s) spread uniformly across the joint probability distribution.
double	getStructuralCoefficient() Returns the weight associated to the structural part used in the Minimum Description Length score.
boolean	isVerbose() Tests if the learning algorithm will output information on the standard output stream System.out during learning.
void	setSmoothedProbabilityEstimation(double weight) Sets the weight of the non informative prior, i.e. the virtual case(s) spread uniformly across the joint probability distribution.
void	setStructuralCoefficient(double weight) Sets the weight associated to the structural part used in the Minimum Description Length score.
void	setVerbose(boolean verbose) Tells the learning algorithm to print output information to the standard output stream System.out during learning.
java.lang.String	toString()

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.bayesia.api.learning.settings.LearningSettings

copy, getLearningAlgorithm, getTargetNodeName, isSupervised, isValid

Method Detail

getSmoothedProbabilityEstimation

public double getSmoothedProbabilityEstimation()

Description copied from interface: LearningSettings

Returns the weight of the non informative prior, i.e. the virtual case(s) spread uniformly across the joint probability distribution. This weight must be determined by taking into account the total weight of the database. It is not necessary a whole number. When 0 is returned, the learning process only uses the data from the database. The default value is 0.

Specified by:

getSmoothedProbabilityEstimation in interface LearningSettings

Returns:

the weight associated to each virtual case

setSmoothedProbabilityEstimation

public void setSmoothedProbabilityEstimation(double weight)

Sets the weight of the non informative prior, i.e. the virtual case(s) spread uniformly across the joint probability distribution. A weight set to 0, means only the data from the database is taken into account.

Parameters:

weight - the weight associated to each virtual case

getStructuralCoefficient

public double getStructuralCoefficient()

Description copied from interface: LearningSettings

Returns the weight associated to the structural part used in the Minimum Description Length score. The higher its value, the stronger the probabilistic relations would have to be to result in a corresponding arc in the network. Conversely, the lower its value, the weaker the probabilistic relation can be while still being represented with an arc. By default, its value is set to a conservative value of 1. The default value is 1.

Specified by:

getStructuralCoefficient in interface LearningSettings

Returns:

the weight associated to the structural part of the MDL score

setStructuralCoefficient

public void setStructuralCoefficient(double weight)

Sets the weight associated to the structural part used in the Minimum Description Length score. The higher its value, the stronger the probabilistic relations would have to be to result in a corresponding arc in the network. Conversely, the lower its value, the weaker the probabilistic relation can be while still being represented with an arc. By default, its value is set to a conservative value of 1. The default value is 1.

Parameters:

weight - the weight associated to the structural part of the MDL score

isVerbose

public boolean isVerbose()

Description copied from interface: LearningSettings

Tests if the learning algorithm will output information on the standard output stream System.out during learning.

Specified by:

isVerbose in interface LearningSettings

Returns:

true if the learning algorithm prints output information

setVerbose

public void setVerbose(boolean verbose)

Tells the learning algorithm to print output information to the standard output stream System.out during learning.

Parameters:

verbose - true if the information is printed

toString

public java.lang.String toString()

Specified by:

toString in interface LearningSettings

Overrides:

toString in class java.lang.Object