Package cc.mallet.fst

Examples of cc.mallet.fst.CRF.averageTokenAccuracy()

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    crf.addFullyConnectedStatesForLabels();
    crf.setWeightsDimensionAsIn(lists[0], false);
    CRFTrainerByStochasticGradient crft = new CRFTrainerByStochasticGradient(
        crf, 0.0001);
    System.out.println("Training Accuracy before training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy before training = "
        + crf.averageTokenAccuracy(lists[1]));
    System.out.println("Training...");
    // either fixed learning rate or selected on a sample
    crft.setLearningRateByLikelihood(lists[0]);
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    CRFTrainerByStochasticGradient crft = new CRFTrainerByStochasticGradient(
        crf, 0.0001);
    System.out.println("Training Accuracy before training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy before training = "
        + crf.averageTokenAccuracy(lists[1]));
    System.out.println("Training...");
    // either fixed learning rate or selected on a sample
    crft.setLearningRateByLikelihood(lists[0]);
    // crft.setLearningRate(0.01);
    crft.train(lists[0], 100);
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    crft.setLearningRateByLikelihood(lists[0]);
    // crft.setLearningRate(0.01);
    crft.train(lists[0], 100);
    crf.print();
    System.out.println("Training Accuracy after training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy after training = "
        + crf.averageTokenAccuracy(lists[1]));
  }

  public void testSumLatticeImplementations() {
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    crft.train(lists[0], 100);
    crf.print();
    System.out.println("Training Accuracy after training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy after training = "
        + crf.averageTokenAccuracy(lists[1]));
  }

  public void testSumLatticeImplementations() {
    Pipe p = makeSpacePredictionPipe();
    Pipe p2 = new TestCRF2String();
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    crf.addFullyConnectedStatesForLabels();
    crf.setWeightsDimensionAsIn(lists[0], false);
    CRFTrainerByStochasticGradient crft = new CRFTrainerByStochasticGradient(
        crf, 0.0001);
    System.out.println("Training Accuracy before training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy before training = "
        + crf.averageTokenAccuracy(lists[1]));
    System.out.println("Training...");
    // either fixed learning rate or selected on a sample
    crft.setLearningRateByLikelihood(lists[0]);
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    CRFTrainerByStochasticGradient crft = new CRFTrainerByStochasticGradient(
        crf, 0.0001);
    System.out.println("Training Accuracy before training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy before training = "
        + crf.averageTokenAccuracy(lists[1]));
    System.out.println("Training...");
    // either fixed learning rate or selected on a sample
    crft.setLearningRateByLikelihood(lists[0]);
    // crft.setLearningRate(0.01);
    crft.train(lists[0], 100);
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    crft.setLearningRateByLikelihood(lists[0]);
    // crft.setLearningRate(0.01);
    crft.train(lists[0], 100);
    crf.print();
    System.out.println("Training Accuracy after training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy after training = "
        + crf.averageTokenAccuracy(lists[1]));

    // now check the speeds of SumLatticeDefault vs SumLatticeScaling
    long totalTimeDefault = 0, totalTimeScaling = 0;
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    crft.train(lists[0], 100);
    crf.print();
    System.out.println("Training Accuracy after training = "
        + crf.averageTokenAccuracy(lists[0]));
    System.out.println("Testing  Accuracy after training = "
        + crf.averageTokenAccuracy(lists[1]));

    // now check the speeds of SumLatticeDefault vs SumLatticeScaling
    long totalTimeDefault = 0, totalTimeScaling = 0;
    for (int iter = 0; iter < 10000; iter++) {
      for (int ii = 0; ii < lists[1].size(); ii++) {
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