Package cc.mallet.types

Examples of cc.mallet.types.LabelAlphabet

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    public Instance pipe (Instance carrier)
    {
      Object inputData = carrier.getData();
      Alphabet features = getDataAlphabet();
      LabelAlphabet labels;
      LabelSequence target = null;
      String [][] tokens;
      if (inputData instanceof String)
        tokens = parseSentence((String)inputData);
      else if (inputData instanceof String[][])
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     * Creates a new
     * <code>SimpleTaggerSentence2FeatureVectorSequence</code> instance.
     */
    public SimpleTaggerSentence2FeatureVectorSequence ()
    {
      super (new Alphabet(), new LabelAlphabet());
    }
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    public Instance pipe (Instance carrier)
    {
      Object inputData = carrier.getData();
      Alphabet features = getDataAlphabet();
      LabelAlphabet labels;
      LabelSequence target = null;
      String [][] tokens;
      if (inputData instanceof String)
        tokens = parseSentence((String)inputData);
      else if (inputData instanceof String[][])
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public class SvmLight2FeatureVectorAndLabel extends Pipe {

  private static final long serialVersionUID = 1L;
 
  public SvmLight2FeatureVectorAndLabel () {
    super (new Alphabet(), new LabelAlphabet());
  }
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     * Creates a new
     * <code>SimpleTaggerSentence2FeatureVectorSequence</code> instance.
     */
    public SimpleTaggerSentence2FeatureVectorSequence ()
    {
      super (new Alphabet(), new LabelAlphabet());
    }
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    public Instance pipe (Instance carrier)
    {
      Object inputData = carrier.getData();
      Alphabet features = getDataAlphabet();
      LabelAlphabet labels;
      LabelSequence target = null;
      String [][] tokens;
      if (inputData instanceof String)
        tokens = parseSentence((String)inputData);
      else if (inputData instanceof String[][])
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  public MaxEntOptimizableByLabelDistribution (InstanceList trainingSet, MaxEnt initialClassifier)
  {
    this.trainingList = trainingSet;
    Alphabet fd = trainingSet.getDataAlphabet();
    LabelAlphabet ld = (LabelAlphabet) trainingSet.getTargetAlphabet();
    // Don't fd.stopGrowth, because someone might want to do feature induction
    ld.stopGrowth();
    // Add one feature for the "default feature".
    this.numLabels = ld.size();
    this.numFeatures = fd.size() + 1;
    this.defaultFeatureIndex = numFeatures-1;
    this.parameters = new double [numLabels * numFeatures];
    this.constraints = new double [numLabels * numFeatures];
    this.cachedGradient = new double [numLabels * numFeatures];
 
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          maxidx = assn.get (var);
        } else {
          maxidx = 0;
        }

        LabelAlphabet dict = labelOfVar (var).getLabelAlphabet ();
        theseLabels[i] = dict.lookupLabel (maxidx);
      }

      lbls[t] = new Labels (theseLabels);
    }
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  }

  public void print (PrintStream out)
  {
    final Alphabet dict = getAlphabet();
    final LabelAlphabet labelDict = getLabelAlphabet();

    int numFeatures = dict.size() + 1;
    int numLabels = labelDict.size();

    // Include the feature weights according to each label
    for (int li = 0; li < numLabels; li++) {
      out.println ("FEATURES FOR CLASS "+labelDict.lookupObject (li));
      out.println (" <default> "+parameters [li*numFeatures + defaultFeatureIndex]);
      for (int i = 0; i < defaultFeatureIndex; i++) {
        Object name = dict.lookupObject (i);
        double weight = parameters [li*numFeatures + i];
        out.println (" "+name+" "+weight);
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  //printRank, added by Limin Yao
  public void printRank (PrintWriter out)
  {
    final Alphabet dict = getAlphabet();
    final LabelAlphabet labelDict = getLabelAlphabet();

    int numFeatures = dict.size() + 1;
    int numLabels = labelDict.size();
  // Include the feature weights according to each label
    RankedFeatureVector rfv;
    double[] weights = new double[numFeatures-1]; // do not deal with the default feature
    for (int li = 0; li < numLabels; li++) {
      out.print ("FEATURES FOR CLASS "+labelDict.lookupObject (li) + " ");
      for (int i = 0; i < defaultFeatureIndex; i++) {
        double weight = parameters [li*numFeatures + i];
        weights[i] = weight;
      }
      rfv = new RankedFeatureVector(dict,weights);
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Related Classes of cc.mallet.types.LabelAlphabet

  • cc.mallet.classify.evaluate.ConfusionMatrix
  • cc.mallet.classify.MaxEnt
  • cc.mallet.classify.MaxEntOptimizableByLabelDistribution
  • cc.mallet.classify.MaxEntOptimizableByLabelLikelihood
  • cc.mallet.classify.MCMaxEnt
  • cc.mallet.classify.MCMaxEntTrainer$MaximizableTrainer
  • cc.mallet.classify.RankMaxEnt
  • cc.mallet.classify.RankMaxEntTrainer$MaximizableTrainer
  • cc.mallet.cluster.examples.FirstOrderClusterExample$OverlappingFeaturePipe
  • cc.mallet.cluster.tui.Clusterings2Clusterer$ClusteringPipe
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