/* 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); }