/* Copyright (C) 2006 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.grmm.examples; import cc.mallet.grmm.learning.ACRF; import cc.mallet.grmm.types.Variable; import cc.mallet.grmm.util.LabelsAssignment; import cc.mallet.types.FeatureVector; import cc.mallet.types.FeatureVectorSequence; /** * $Id: CrossTemplate1.java,v 1.1 2007/10/22 21:38:02 mccallum Exp $ */ public class CrossTemplate1 extends ACRF.SequenceTemplate { private int lvl1 = 0; private int lvl2 = 1; public CrossTemplate1 (int lvl1, int lvl2) { this.lvl1 = lvl1; this.lvl2 = lvl2; } protected void addInstantiatedCliques (ACRF.UnrolledGraph graph, FeatureVectorSequence fvs, LabelsAssignment lblseq) { for (int t = 0; t < lblseq.size() - 1; t++) { Variable var1 = lblseq.varOfIndex (t, lvl1); Variable var2 = lblseq.varOfIndex (t + 1, lvl2); assert var1 != null : "Couldn't get label factor "+lvl1+" time "+t; assert var2 != null : "Couldn't get label factor "+lvl2+" time "+(t+1); Variable[] vars = new Variable[] { var1, var2 }; FeatureVector fv = fvs.getFeatureVector (t); ACRF.UnrolledVarSet vs = new ACRF.UnrolledVarSet (graph, this, vars, fv); graph.addClique (vs); } } }