Package edu.cmu.tetrad.search.score

Class GicScores

java.lang.Object

edu.cmu.tetrad.search.score.GicScores

All Implemented Interfaces:

Score

public class GicScores
extends Object
implements Score

Implements scores motivated by the Generalized Information Criterion (GIC) approach as given in Kim et al. (2012).

Kim, Y., Kwon, S., & Choi, H. (2012). Consistent model selection criteria on high dimensions. The Journal of Machine Learning Research, 13(1), 1037-1057.

As for all scores in Tetrad, higher scores mean more dependence, and negative scores indicate independence.

Author:

josephramsey

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	GicScores.RuleType	Gives the options for the rules to use for calculating the scores.

Constructor Summary

Constructors

Constructor	Description
GicScores(DataSet dataSet)	Constructs the score using a covariance matrix.
GicScores(ICovarianceMatrix covariances)	Constructs the score using a covariance matrix.

Method Summary

Modifier and Type	Method	Description
boolean	determines(List<Node> z, Node y)
ICovarianceMatrix	getCovariances()	Specialized scoring method for a single parent.
DataSet	getDataSet()
int	getMaxDegree()
double	getPenaltyDiscount()
int	getSampleSize()	The sample size of the data.
List<Node>	getVariables()	The variables of the score.
boolean	isEffectEdge(double bump)
boolean	isVerbose()
double	localScore(int i, int... parents)	The score of a node given its parents.
double	localScoreDiff(int x, int y, int[] z)
void	setLambda(double lambda)
void	setPenaltyDiscount(double penaltyDiscount)
void	setRuleType(GicScores.RuleType ruleType)
void	setVariables(List<Node> variables)
void	setVerbose(boolean verbose)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface edu.cmu.tetrad.search.score.Score

append, getVariable, localScore, localScore, localScoreDiff, toString

Constructor Details

GicScores

public GicScores(ICovarianceMatrix covariances)

Constructs the score using a covariance matrix.

GicScores

public GicScores(DataSet dataSet)

Constructs the score using a covariance matrix.

Method Details

localScoreDiff

public double localScoreDiff(int x,
 int y,
 int[] z)

Specified by:

localScoreDiff in interface Score

localScore

public double localScore(int i,
 int... parents)

Description copied from interface: Score

The score of a node given its parents.

Specified by:

localScore in interface Score

Parameters:

i - The node.

parents - The parents.

Returns:

The score.

getCovariances

public ICovarianceMatrix getCovariances()

Specialized scoring method for a single parent. Used to speed up the effect edges search.

getSampleSize

public int getSampleSize()

Description copied from interface: Score

The sample size of the data.

Specified by:

getSampleSize in interface Score

Returns:

This size.

isEffectEdge

public boolean isEffectEdge(double bump)

Specified by:

isEffectEdge in interface Score

getDataSet

public DataSet getDataSet()

isVerbose

public boolean isVerbose()

setVerbose

public void setVerbose(boolean verbose)

getVariables

public List<Node> getVariables()

Description copied from interface: Score

The variables of the score.

Specified by:

getVariables in interface Score

Returns:

This list.

setVariables

public void setVariables(List<Node> variables)

getMaxDegree

public int getMaxDegree()

Specified by:

getMaxDegree in interface Score

determines

public boolean determines(List<Node> z,
 Node y)

Specified by:

determines in interface Score

setRuleType

public void setRuleType(GicScores.RuleType ruleType)

setLambda

public void setLambda(double lambda)

getPenaltyDiscount

public double getPenaltyDiscount()

setPenaltyDiscount

public void setPenaltyDiscount(double penaltyDiscount)