Package cc.mallet.fst.semi_supervised.pr.constraints

Class OneLabelL2IndPRConstraints

java.lang.Object

cc.mallet.fst.semi_supervised.pr.constraints.OneLabelL2IndPRConstraints

All Implemented Interfaces:

PRConstraint

public class OneLabelL2IndPRConstraints
extends java.lang.Object
implements PRConstraint

A set of constraints on individual input feature label pairs. This is to be used with PR, and penalizes L_2^2 difference from target expectations. Multiple constraints are grouped together here to make things more efficient.

Author:

Gregory Druck

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
protected class	OneLabelL2IndPRConstraints.OneLabelL2IndPRConstraint

Field Summary

Fields

Modifier and Type	Field	Description
protected com.carrotsearch.hppc.IntArrayList	cache
protected com.carrotsearch.hppc.IntObjectHashMap<OneLabelL2IndPRConstraints.OneLabelL2IndPRConstraint>	constraints
protected StateLabelMap	map
protected boolean	normalized
protected int	numDimensions

Constructor Summary

Constructors

Modifier	Constructor	Description
	OneLabelL2IndPRConstraints(boolean normalized)
protected	OneLabelL2IndPRConstraints(com.carrotsearch.hppc.IntObjectHashMap<OneLabelL2IndPRConstraints.OneLabelL2IndPRConstraint> constraints, StateLabelMap map, boolean normalized)

Method Summary

Modifier and Type	Method	Description
void	addConstraint(int fi, int li, double target, double weight)
void	addExpectations(double[] expectations)
PRConstraint	copy()	This is used in multi-threading.
double	getAuxiliaryValueContribution(double[] parameters)
double	getCompleteValueContribution(double[] parameters)
void	getExpectations(double[] expectations)
void	getGradient(double[] parameters, double[] gradient)
double	getScore(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double[] parameters)
void	incrementExpectations(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double prob)
int	numDimensions()
void	preProcess(FeatureVector fv)	Gives the constraint the option to do some caching using only the FeatureVector.
java.util.BitSet	preProcess(InstanceList data)
void	setStateLabelMap(StateLabelMap map)	Sets that map between the state indices and label indices.
void	zeroExpectations()	Zero expectation values.

Methods inherited from class java.lang.Object

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

Field Detail

normalized

protected boolean normalized

numDimensions

protected int numDimensions

constraints

protected com.carrotsearch.hppc.IntObjectHashMap<OneLabelL2IndPRConstraints.OneLabelL2IndPRConstraint> constraints

map

protected StateLabelMap map

cache

protected com.carrotsearch.hppc.IntArrayList cache

Constructor Detail

OneLabelL2IndPRConstraints

public OneLabelL2IndPRConstraints(boolean normalized)

OneLabelL2IndPRConstraints

protected OneLabelL2IndPRConstraints(com.carrotsearch.hppc.IntObjectHashMap<OneLabelL2IndPRConstraints.OneLabelL2IndPRConstraint> constraints,
                                     StateLabelMap map,
                                     boolean normalized)

Method Detail

copy

public PRConstraint copy()

Description copied from interface: PRConstraint

This is used in multi-threading.

Specified by:

copy in interface PRConstraint

Returns:

A copy of the GEConstraint.

addConstraint

public void addConstraint(int fi,
                          int li,
                          double target,
                          double weight)

numDimensions

public int numDimensions()

Specified by:

numDimensions in interface PRConstraint

setStateLabelMap

public void setStateLabelMap(StateLabelMap map)

Description copied from interface: PRConstraint

Sets that map between the state indices and label indices.

Specified by:

setStateLabelMap in interface PRConstraint

Parameters:

map - StateLabelMap

preProcess

public void preProcess(FeatureVector fv)

Description copied from interface: PRConstraint

Gives the constraint the option to do some caching using only the FeatureVector. For example, the constrained input features could be cached.

Specified by:

preProcess in interface PRConstraint

Parameters:

fv - FeatureVector input

preProcess

public java.util.BitSet preProcess(InstanceList data)

Specified by:

preProcess in interface PRConstraint

Parameters:

data - Unlabeled data

Returns:

Returns a bitset of the size of the data, with the bit set if a constraint feature fires in that instance.

getScore

public double getScore(FeatureVector input,
                       int inputPosition,
                       int srcIndex,
                       int destIndex,
                       double[] parameters)

Specified by:

getScore in interface PRConstraint

incrementExpectations

public void incrementExpectations(FeatureVector input,
                                  int inputPosition,
                                  int srcIndex,
                                  int destIndex,
                                  double prob)

Specified by:

incrementExpectations in interface PRConstraint

getExpectations

public void getExpectations(double[] expectations)

Specified by:

getExpectations in interface PRConstraint

addExpectations

public void addExpectations(double[] expectations)

Specified by:

addExpectations in interface PRConstraint

zeroExpectations

public void zeroExpectations()

Description copied from interface: PRConstraint

Zero expectation values. Called before re-computing gradient.

Specified by:

zeroExpectations in interface PRConstraint

getAuxiliaryValueContribution

public double getAuxiliaryValueContribution(double[] parameters)

Specified by:

getAuxiliaryValueContribution in interface PRConstraint

getCompleteValueContribution

public double getCompleteValueContribution(double[] parameters)

Specified by:

getCompleteValueContribution in interface PRConstraint

getGradient

public void getGradient(double[] parameters,
                        double[] gradient)

Specified by:

getGradient in interface PRConstraint