Package cc.mallet.types

Examples of cc.mallet.types.Alphabet


  public boolean isTrainable() {
    return true;
  }

  private Alphabet getTransitionAlphabet() {
    Alphabet transitionAlphabet = new Alphabet();
    for (int i = 0; i < numStates(); i++)
      transitionAlphabet.lookupIndex(getState(i).getName(), true);
    return transitionAlphabet;
  }
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  public void reset() {
    emissionEstimator = new Multinomial.LaplaceEstimator[numStates()];
    transitionEstimator = new Multinomial.LaplaceEstimator[numStates()];
    emissionMultinomial = new Multinomial[numStates()];
    transitionMultinomial = new Multinomial[numStates()];
    Alphabet transitionAlphabet = getTransitionAlphabet();
    for (int i = 0; i < numStates(); i++) {
      emissionEstimator[i] = new Multinomial.LaplaceEstimator(
          inputAlphabet);
      transitionEstimator[i] = new Multinomial.LaplaceEstimator(
          transitionAlphabet);
      emissionMultinomial[i] = new Multinomial(
          getUniformArray(inputAlphabet.size()), inputAlphabet);
      transitionMultinomial[i] = new Multinomial(
          getUniformArray(transitionAlphabet.size()),
          transitionAlphabet);
    }
    initialMultinomial = new Multinomial(getUniformArray(transitionAlphabet
        .size()), transitionAlphabet);
    initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
  }
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   *            Random object (if null use uniform distribution)
   * @param noise
   *            Noise exponent to use. If zero, then uniform distribution.
   */
  public void initTransitions(Random random, double noise) {
    Alphabet transitionAlphabet = getTransitionAlphabet();
    initialMultinomial = new Multinomial(getRandomArray(transitionAlphabet
        .size(), random, noise), transitionAlphabet);
    initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
    transitionMultinomial = new Multinomial[numStates()];
    transitionEstimator = new Multinomial.LaplaceEstimator[numStates()];
    for (int i = 0; i < numStates(); i++) {
      transitionMultinomial[i] = new Multinomial(getRandomArray(
          transitionAlphabet.size(), random, noise),
          transitionAlphabet);
      transitionEstimator[i] = new Multinomial.LaplaceEstimator(
          transitionAlphabet);
      // set state's initial weight
      State s = (State) getState(i);
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          inputAlphabet);
    }
  }

  public void estimate() {
    Alphabet transitionAlphabet = getTransitionAlphabet();
    initialMultinomial = initialEstimator.estimate();
    initialEstimator = new Multinomial.LaplaceEstimator(transitionAlphabet);
    for (int i = 0; i < numStates(); i++) {
      State s = (State) getState(i);
      emissionMultinomial[i] = emissionEstimator[i].estimate();
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    if (emissionEstimator == null) {
      emissionEstimator = new Multinomial.LaplaceEstimator[numStates()];
      transitionEstimator = new Multinomial.LaplaceEstimator[numStates()];
      emissionMultinomial = new Multinomial[numStates()];
      transitionMultinomial = new Multinomial[numStates()];
      Alphabet transitionAlphabet = new Alphabet();
      for (int i = 0; i < numStates(); i++)
        transitionAlphabet.lookupIndex(((State) states.get(i))
            .getName(), true);
      for (int i = 0; i < numStates(); i++) {
        emissionEstimator[i] = new Multinomial.LaplaceEstimator(
            inputAlphabet);
        transitionEstimator[i] = new Multinomial.LaplaceEstimator(
            transitionAlphabet);
        emissionMultinomial[i] = new Multinomial(
            getUniformArray(inputAlphabet.size()), inputAlphabet);
        transitionMultinomial[i] = new Multinomial(
            getUniformArray(transitionAlphabet.size()),
            transitionAlphabet);
      }
      initialEstimator = new Multinomial.LaplaceEstimator(
          transitionAlphabet);
    }
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  public StringList2FeatureSequence (Alphabet dataDict) {
    super (dataDict, null);
  }

  public StringList2FeatureSequence () {
    super(new Alphabet(), null);
  }
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  FeatureSequence fs;
  FeatureVector fv;
 
  protected void setUp ()
  {
    dict = new Alphabet ();
    fs = new FeatureSequence (dict, 2);
    fs.add (dict.lookupIndex ("a"));
    fs.add (dict.lookupIndex ("n"));
    fs.add (dict.lookupIndex ("d"));
    fs.add (dict.lookupIndex ("r"));
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    m_predRanks2add = predRanks2add;
    m_binary = binary;
    m_tokenClassifiers = tokenClassifiers;
    m_inProduction = false;
    m_dataAlphabet = (Alphabet) tokenClassifiers.getAlphabet().clone();
    Alphabet labelAlphabet = tokenClassifiers.getLabelAlphabet();
   
    // add the token prediction features to the alphabet
    for (int i = 0; i < m_predRanks2add.length; i++) {
      for (int j = 0; j < labelAlphabet.size(); j++) {
        String featName = "TOK_PRED=" + labelAlphabet.lookupObject(j).toString() + "_@_RANK_" + m_predRanks2add[i];
        m_dataAlphabet.lookupIndex(featName, true);
      }
    }
   
    // evaluate token classifier 
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      "The GNU Project was launched in 1984 to develop a complete Unix-like operating system which is free software: the GNU system." };

  public void testGetSetParameters() {
    int inputVocabSize = 100;
    int numStates = 5;
    Alphabet inputAlphabet = new Alphabet();
    for (int i = 0; i < inputVocabSize; i++)
      inputAlphabet.lookupIndex("feature" + i);
    Alphabet outputAlphabet = new Alphabet();
    CRF crf = new CRF(inputAlphabet, outputAlphabet);
    String[] stateNames = new String[numStates];
    for (int i = 0; i < numStates; i++)
      stateNames[i] = "state" + i;
    crf.addFullyConnectedStates(stateNames);
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  }

  public void testSumLattice() {
    int inputVocabSize = 1;
    int numStates = 2;
    Alphabet inputAlphabet = new Alphabet();
    for (int i = 0; i < inputVocabSize; i++)
      inputAlphabet.lookupIndex("feature" + i);
    Alphabet outputAlphabet = new Alphabet();

    CRF crf = new CRF(inputAlphabet, outputAlphabet);

    String[] stateNames = new String[numStates];
    for (int i = 0; i < numStates; i++)
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