List of addFullyConnectedStatesForLabels() Examples

Examples of addFullyConnectedStatesForLabels()

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

     training.addThruPipe (new ArrayIterator (data0));
     InstanceList testing = new InstanceList (pipe);
     testing.addThruPipe (new ArrayIterator (data1));


     CRF crf = new CRF (pipe, null);
     crf.addFullyConnectedStatesForLabels ();
     CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf);
     crft.trainIncremental (training);


     CRFExtractor extor = TestLatticeViewer.hackCrfExtor (crf);
     Extraction extraction = extor.extract (new ArrayIterator (data1));

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

    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new Random(1), new double[] {
        .5, .5 });
    CRF crf = new CRF(p, p2);
    crf.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
    if (testValueAndGradient) {
      Optimizable.ByGradientValue optable = crft
          .getOptimizableCRF(lists[0]);
      // TestOptimizable.testValueAndGradient(minable);

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

    File f = new File("TestObject.obj");
    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[] { .5, .5 });
    CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
    crf.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
    crft.setUseSparseWeights(useSparseWeights);
    if (testValueAndGradient) {
      Optimizable.ByGradientValue minable = crft
          .getOptimizableCRF(lists[0]);

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


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));


    CRF crf1 = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
    crf1.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood(
        crf1);
    crft1.trainIncremental(instances);


    CRF crf2 = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());

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

    CRFTrainerByLabelLikelihood crft1 = new CRFTrainerByLabelLikelihood(
        crf1);
    crft1.trainIncremental(instances);


    CRF crf2 = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
    crf2.addFullyConnectedStatesForLabels();
    // Freeze some weights, before training
    for (int i = 0; i < crf2.getWeights().length; i += 2)
      crf2.freezeWeights(i);
    CRFTrainerByLabelLikelihood crft2 = new CRFTrainerByLabelLikelihood(
        crf2);

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


    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[] { .5, .5 });
    CRF crf = new CRF(p, p2);
    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]));

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

    // first do normal training for getting weights
    InstanceList instances = new InstanceList(p);
    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new double[] { .5, .5 });
    CRF crf = new CRF(p, p2);
    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]));

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

    instances.addThruPipe(new ArrayIterator(data));
    InstanceList[] lists = instances.split(new Random(777), new double[] {
        .5, .5 });


    CRF crf = new CRF(p.getDataAlphabet(), p.getTargetAlphabet());
    crf.addFullyConnectedStatesForLabels();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood(crf);
    crft.setUseSparseWeights(true);


    crft.trainIncremental(lists[0]);

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

    training.addThruPipe (new ArrayIterator (data0));
    InstanceList testing = new InstanceList (pipe);
    testing.addThruPipe (new ArrayIterator (data1));


    CRF crf = new CRF (pipe, null);
    crf.addFullyConnectedStatesForLabels ();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf);
    crft.trainIncremental (training);


    CRFExtractor extor = hackCrfExtor (crf);
    Extraction extration = extor.extract (new ArrayIterator (data1));

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

    training.addThruPipe (new ArrayIterator (data0));
    InstanceList testing = new InstanceList (pipe);
    testing.addThruPipe (new ArrayIterator (data1));


    CRF crf = new CRF (pipe, null);
    crf.addFullyConnectedStatesForLabels ();
    CRFTrainerByLabelLikelihood crft = new CRFTrainerByLabelLikelihood (crf);
    TokenAccuracyEvaluator eval = new TokenAccuracyEvaluator (new InstanceList[] {training, testing}, new String[] {"Training", "Testing"});
    for (int i = 0; i < 5; i++) {
      crft.train (training, 1);
      eval.evaluate(crft);

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