Examples of batchTrain()

Examples of classification.AdaBoost.batchTrain()

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  public static LinearClassifier trainAdaBoost(int numIters,
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    AdaBoost b = new AdaBoost(numIters, xA, yA,
        new CompleteFeatureFunction(xA, yA));
    LinearClassifier h = b.batchTrain(train);
    return h;
  }

}
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Examples of classification.MaxEntropy.batchTrain()

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  public static LinearClassifier trainMaxEnt(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    MaxEntropy maxent = new MaxEntropy(10.0, xA, yA,
        new CompleteFeatureFunction(xA, yA));
    LinearClassifier h = maxent.batchTrain(train);
    return h;
  }

  public static LinearClassifier trainNaivBayes(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
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Examples of classification.MaxEntropy.batchTrain()

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  public static LinearClassifier trainMaxEnt(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    MaxEntropy maxent = new MaxEntropy(10.0, xA, yA,
        new CompleteFeatureFunction(xA, yA));
    LinearClassifier h = maxent.batchTrain(train);
    return h;
  }

  public static LinearClassifier trainNaivBayes(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
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Examples of classification.NaiveBayes.batchTrain()

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  }

  public static LinearClassifier trainNaivBayes(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    NaiveBayes nb = new NaiveBayes(0.1, 0.1, xA, yA);
    LinearClassifier h = nb.batchTrain(train);
    return h;
  }

  public static LinearClassifier trainPerceptron(boolean doAveraging,
      int numIters, ArrayList<ClassificationInstance> train, Alphabet xA,
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Examples of classification.NaiveBayes.batchTrain()

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  }

  public static LinearClassifier trainNaivBayes(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    NaiveBayes nb = new NaiveBayes(0.1, 0.1, xA, yA);
    LinearClassifier h = nb.batchTrain(train);
    return h;
  }

  public static LinearClassifier trainPerceptron(boolean doAveraging,
      int numIters, ArrayList<ClassificationInstance> train, Alphabet xA,
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Examples of classification.Perceptron.batchTrain()

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  public static LinearClassifier trainPerceptron(boolean doAveraging,
      int numIters, ArrayList<ClassificationInstance> train, Alphabet xA,
      Alphabet yA) {
    Perceptron p = new Perceptron(doAveraging, numIters, xA, yA,
        new CompleteFeatureFunction(xA, yA));
    LinearClassifier h = p.batchTrain(train);
    return h;
  }

  public static LinearClassifier trainAdaBoost(int numIters,
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
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Examples of classification.Perceptron.batchTrain()

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  public static LinearClassifier trainPerceptron(boolean doAveraging,
      int numIters, ArrayList<ClassificationInstance> train, Alphabet xA,
      Alphabet yA) {
    Perceptron p = new Perceptron(doAveraging, numIters, xA, yA,
        new CompleteFeatureFunction(xA, yA));
    LinearClassifier h = p.batchTrain(train);
    return h;
  }

}
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Examples of sequence.CRF.batchTrain()

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  }

  public static LinearTagger trainCRF(ArrayList<SequenceInstance> train,
      Alphabet xA, Alphabet yA) {
    CRF crf = new CRF(10, xA, yA, new OneYwithXFeatureFunction(xA, yA));
    LinearTagger h = crf.batchTrain(train);
    return h;
  }

}
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Examples of sequence.CRF.batchTrain()

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  private LinearTagger trainCRF(ArrayList<SequenceInstance> data,
      Alphabet xA, Alphabet yA) {

    CRF crf = new CRF(10, xA, yA, new OneYwithXFeatureFunction(xA, yA));
    return crf.batchTrain(data);
  }

  @SuppressWarnings("unused")
  private LinearTagger trainMira(ArrayList<SequenceInstance> data,
      Alphabet xA, Alphabet yA, int numIters, boolean doAveraging) {
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Examples of sequence.Mira.batchTrain()

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  public static LinearTagger trainMira(boolean doAveraging, int numIters,
      ArrayList<SequenceInstance> train, Alphabet xA, Alphabet yA) {
    Mira p = new Mira(doAveraging, numIters, xA, yA,
        new TwoYwithXFeatureFunction(xA, yA), new HammingLoss());
    LinearTagger h = p.batchTrain(train);
    return h;
  }

  public static LinearTagger trainCRF(ArrayList<SequenceInstance> train,
      Alphabet xA, Alphabet yA) {
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