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MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.

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/* 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);
    }
  }

}