Package cc.mallet.types

Examples of cc.mallet.types.Alphabet

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  }


  private Alphabet makeVertexMap(ArrayList vars)
  {
    Alphabet map = new Alphabet (vars.size (), Variable.class);
    map.lookupIndices(vars.toArray(), true);
    return map;
  }
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                new String[] {"z"},
                new double[] {11},
                new String[] {"2"});

    seq = new ArrayListSequence ();
    Alphabet dict = transducer.getInputAlphabet ();
    seq.add ("a");
    seq.add ("a");
    seq.add ("b");
    seq.add ("c");
    seq.add ("d");
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  }
 
 
  public void testEstimating ()
  {
    Alphabet dict = new Alphabet ();
    Multinomial.Estimator e = new Multinomial.LaplaceEstimator (dict);
    FeatureSequence fs = new FeatureSequence (dict);
    fs.add (dict.lookupIndex ("a"));
    fs.add (dict.lookupIndex ("n"));
    fs.add (dict.lookupIndex ("d"));
    fs.add (dict.lookupIndex ("r"));
    fs.add (dict.lookupIndex ("e"));
    fs.add (dict.lookupIndex ("w"));
    fs.add (dict.lookupIndex ("m"));
    fs.add (dict.lookupIndex ("c"));
    fs.add (dict.lookupIndex ("c"));
    fs.add (dict.lookupIndex ("a"));
    fs.add (dict.lookupIndex ("l"));
    fs.add (dict.lookupIndex ("l"));
    fs.add (dict.lookupIndex ("u"));
    fs.add (dict.lookupIndex ("m"));
    //System.out.println (fs.toString());
    e.increment (fs);
    assertTrue (e.size() == 10);

    Multinomial m = e.estimate ();
    assertTrue (m.size() == 10);
    assertTrue (m.probability (dict.lookupIndex ("a")) == (2.0+1)/(14.0+10));
    assertTrue (m.probability ("w") == (1.0+1)/(14.0+10));
    Multinomial.Logged ml = new Multinomial.Logged (m);
    assertTrue (m.logProbability ("w") == ml.logProbability ("w"));
  }
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public class Csv2FeatureVector extends Pipe {

    private static Logger logger = MalletLogger.getLogger(Csv2FeatureVector.class.getName());

    public Csv2FeatureVector(int capacity) {
        this.dataAlphabet = new Alphabet(capacity);
    }
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  public FeatureValueString2FeatureVector (Alphabet dataDict) {
    super (dataDict, null);
  }

  public FeatureValueString2FeatureVector () {
    super(new Alphabet(), null);
  }
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  }


  private static Alphabet dictOfSize (int size)
  {
    Alphabet ret = new Alphabet ();
    for (int i = 0; i < size; i++)
      ret.lookupIndex ("feature"+i);
    return ret;
  }
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  private double testRandomTrainedOn (InstanceList training)
  {
    ClassifierTrainer trainer = new MaxEntTrainer ();

    Alphabet fd = dictOfSize (3);
    String[] classNames = new String[] {"class0", "class1", "class2"};

    Randoms r = new Randoms (1);
    Iterator<Instance> iter = new RandomTokenSequenceIterator (r,  new Dirichlet(fd, 2.0),
          30, 0, 10, 200, classNames);
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    return new SerialPipes(pipes.subList(start, end));
  }
 
  private void resolveAlphabets ()
  {
    Alphabet da = null, ta = null;
    for (Pipe p : pipes) {
      p.preceedingPipeDataAlphabetNotification(da);
      da = p.getDataAlphabet();
      p.preceedingPipeTargetAlphabetNotification(ta);
      ta = p.getTargetAlphabet();
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    t.setFeatureValue ("length", 3);
    t.setFeatureValue ("containsVowel", 1);
    t.setFeatureValue ("in /usr/dict/words", 0);

    Alphabet dict = new Alphabet();
    FeatureVector fv = t.toFeatureVector (dict, false);
    assertTrue (fv.numLocations() == 2);
    assertTrue (fv.value (dict.lookupIndex("length")) == 3);
  }
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  public static class TestCRFTokenSequenceRemoveSpaces extends Pipe implements
      Serializable {

    public TestCRFTokenSequenceRemoveSpaces() {
      super(null, new Alphabet());
    }
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Related Classes of cc.mallet.types.Alphabet

  • cc.mallet.classify.BalancedWinnowTrainer
  • cc.mallet.classify.FeatureConstraintUtil
  • 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.classify.WinnowTrainer
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