Examples of HammingLoss

This class implements Hamming Loss function @author Kuzman Ganchev and Georgi Georgiev georgi.georgiev@ontotext.com

Examples of org.fnlp.ml.loss.struct.HammingLoss

     */
    Update update;
    // viterbi解码
    Inferencer inference;


    HammingLoss loss = new HammingLoss();
    if (standard) {
      inference = new LinearViterbi(templets, labels.size());
      update = new LinearViterbiPAUpdate((LinearViterbi) inference, loss);
    } else {
      inference = new HigherOrderViterbi(templets, labels.size());

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Examples of org.fnlp.ml.loss.struct.HammingLoss




    float error = 0;
    int senError = 0;
    int len = 0;
    Loss loss = new HammingLoss();


    String[][] predictSet = new String[testSet.size()][];
    String[][] goldSet = new String[testSet.size()][];
    LabelAlphabet la = cl.getAlphabetFactory().DefaultLabelAlphabet();
    for (int i = 0; i < testSet.size(); i++) {
      Instance carrier = testSet.get(i);
      int[] pred = (int[]) cl.classify(carrier).getLabel(0);
      if (hasLabel) {
        len += pred.length;
        float e = loss.calc(carrier.getTarget(), pred);
        error += e;
        if(e != 0)
          senError++;


      }

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Examples of org.fnlp.ml.loss.struct.HammingLoss

    features.setStopIncrement(true);
    labels.setStopIncrement(true);




    // viterbi解码
    HammingLoss loss = new HammingLoss();
    Inferencer inference = new LinearViterbi(templets, labels.size());
    Update update = new LinearViterbiPAUpdate((LinearViterbi) inference, loss);




    OnlineTrainer trainer = new OnlineTrainer(inference, update, loss,

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Examples of org.fnlp.ml.loss.struct.HammingLoss

     */
    Update update;
    // viterbi解码
    Inferencer inference;
    boolean standard = true;
    HammingLoss loss = new HammingLoss();
    if (standard) {
      inference = new LinearViterbi(templets, labels.size());
      update = new LinearViterbiPAUpdate((LinearViterbi) inference, loss);
    } else {
      inference = new HigherOrderViterbi(templets, labels.size());

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Examples of org.fnlp.ml.loss.struct.HammingLoss

    double error = 0;
    int senError = 0;
    int len = 0;
    boolean hasENG = false;
    int ENG_all = 0, ENG_right = 0;
    Loss loss = new HammingLoss();


    String[][] labelsSet = new String[testSet.size()][];
    String[][] targetSet = new String[testSet.size()][];
    LabelAlphabet labels = cl.getAlphabetFactory().buildLabelAlphabet(
        "labels");
    for (int i = 0; i < testSet.size(); i++) {
      Instance carrier = testSet.get(i);
      int[] pred = (int[]) cl.classify(carrier).getLabel(0);
      if (acc) {
        len += pred.length;
        double e = loss.calc(carrier.getTarget(), pred);
        error += e;
        if(e != 0)
          senError++;
        //测试中英混杂语料
        if(hasENG) {

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Examples of org.fnlp.ml.loss.struct.HammingLoss

    float error = 0;
    int senError = 0;
    int len = 0;
    boolean hasENG = false;
    int ENG_all = 0, ENG_right = 0;
    Loss loss = new HammingLoss();


    String[][] labelsSet = new String[testSet.size()][];
    String[][] targetSet = new String[testSet.size()][];
    LabelAlphabet labels = cl.getAlphabetFactory().buildLabelAlphabet(
        "labels");
    for (int i = 0; i < testSet.size(); i++) {
      Instance carrier = testSet.get(i);
      int[] pred = (int[]) cl.classify(carrier).getLabel(0);
      if (acc) {
        len += pred.length;
        double e = loss.calc(carrier.getTarget(), pred);
        error += e;
        if(e != 0)
          senError++;
        //测试中英混杂语料
        if(hasENG) {

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Examples of org.fnlp.ml.loss.struct.HammingLoss


    Instance inst = new Instance(line);
    seg.doProcess(inst);




    Loss loss = new HammingLoss();




    LabelAlphabet la = cl.getAlphabetFactory().DefaultLabelAlphabet();


    Predict xx = cl.classify(inst,5);

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Examples of sequence.HammingLoss

  }


  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;
  }

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Examples of sequence.HammingLoss

  @SuppressWarnings("unused")
  private LinearTagger trainMira(ArrayList<SequenceInstance> data,
      Alphabet xA, Alphabet yA, int numIters, boolean doAveraging) {


    Mira mira = new Mira(doAveraging, numIters, xA, yA,
        new TwoYwithXFeatureFunction(xA, yA), new HammingLoss());
    return mira.batchTrain(data);
  }

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