Examples of cc.mallet.types.LabelAlphabet

cc.mallet.types.LabelAlphabet
A mapping from arbitrary objects (usually String's) to integers (and corresponding Label objects) and back. @author Andrew McCallum mccallum@cs.umass.edu

  /** Tests how serializing labels separately can lead to big losses.
   *   This currently fails.  I'm not sure what to do about this. -cas
   */
  public void testReadResolve () throws IOException, ClassNotFoundException
  {
    LabelAlphabet dict = new LabelAlphabet ();
    dict.lookupIndex ("TEST1");
    dict.lookupIndex ("TEST2");
    dict.lookupIndex ("TEST3");




    Label t1 = dict.lookupLabel ("TEST1");
    Labelee l = new Labelee (dict, t1);
    Labelee l2 = (Labelee) TestSerializable.cloneViaSerialization (l);


    assertTrue (l.dict == l2.dict);
    assertTrue (dict.lookupLabel("TEST1") == l.theLabel);
    assertTrue (dict.lookupLabel("TEST1") == l2.theLabel);
    assertTrue (l.theLabel == l2.theLabel);
  }

View Full Code Here


    double approxMatchThreshold;


    public ClusteringPipe(int[] exactMatchFields, int[] approxMatchFields,
        int[] substringMatchFields) {
      super(new Alphabet(), new LabelAlphabet());
      this.exactMatchFields = exactMatchFields;
      this.approxMatchFields = approxMatchFields;
      this.substringMatchFields = substringMatchFields;
    }

View Full Code Here

      features = addSubstringMatch(records, fieldAlph, valueAlph, features);
      carrier
          .setData(new FeatureVector(getDataAlphabet(), features,
              true));


      LabelAlphabet ldict = (LabelAlphabet) getTargetAlphabet();
      String label = (original.getLabel(cluster1[0]) == original
          .getLabel(cluster2[0])) ? "YES" : "NO";
      carrier.setTarget(ldict.lookupLabel(label));      
      return carrier;
    }

View Full Code Here

  }


  @Override
  public void print(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
    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);

View Full Code Here


  //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);

View Full Code Here

  }


  public void printExtremeFeatures (PrintWriter out,int num)
  {
    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++) {
        Object name = dict.lookupObject (i);
        double weight = parameters [li*numFeatures + i];
        weights[i] = weight;
      }

View Full Code Here

  }


  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);

View Full Code Here


  //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);

View Full Code Here

  }


  public void printExtremeFeatures (PrintWriter out,int num)
  {
    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++) {
        Object name = dict.lookupObject (i);
        double weight = parameters [li*numFeatures + i];
        weights[i] = weight;
      }

View Full Code Here

     * Creates a new
     * <code>SimpleTaggerSentence2FeatureVectorSequence</code> instance.
     */
    public SimpleTaggerSentence2FeatureVectorSequence ()
    {
      super (new Alphabet(), new LabelAlphabet());
    }

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