/* Copyright (C) 2011 Univ. of Massachusetts Amherst, Computer Science Dept.
This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
http://www.cs.umass.edu/~mccallum/mallet
This software is provided under the terms of the Common Public License,
version 1.0, as published by http://www.opensource.org. For further
information, see the file `LICENSE' included with this distribution. */
package cc.mallet.fst.semi_supervised.pr.constraints;
import cc.mallet.fst.semi_supervised.StateLabelMap;
import cc.mallet.types.FeatureVector;
import cc.mallet.types.InstanceList;
import java.util.BitSet;
/**
* Interface for PR constraint that considers
* either one or two states.
*
* @author Gregory Druck
*/
public interface PRConstraint {
int numDimensions();
double getScore(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double[] parameters);
void incrementExpectations(FeatureVector input, int inputPosition, int srcIndex, int destIndex, double prob);
double getAuxiliaryValueContribution(double[] parameters);
double getCompleteValueContribution(double[] parameters);
void getGradient(double[] parameters, double[] gradient);
/**
* Sets that map between the state indices and label indices.
*
* @param map StateLabelMap
*/
void setStateLabelMap(StateLabelMap map);
/**
* Zero expectation values. Called before re-computing gradient.
*/
void zeroExpectations();
/**
* @param data Unlabeled data
* @return Returns a bitset of the size of the data, with the bit set if a constraint feature fires in that instance.
*/
BitSet preProcess(InstanceList data);
/**
* Gives the constraint the option to do some caching
* using only the FeatureVector. For example, the
* constrained input features could be cached.
*
* @param input FeatureVector input
*/
void preProcess(FeatureVector input);
/**
* This is used in multi-threading.
*
* @return A copy of the GEConstraint.
*/
PRConstraint copy();
// multi-threading
void getExpectations(double[] expectations);
void addExpectations(double[] expectations);
}