Package org.fnlp.ml.loss.struct

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

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     */
    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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    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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    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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     */
    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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    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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    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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    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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Related Classes of org.fnlp.ml.loss.struct.HammingLoss

  • org.fnlp.demo.ml.SequenceLabeling
  • org.fnlp.nlp.cn.rl.Seg2
  • org.fnlp.nlp.tag.AbstractTagger
  • org.fnlp.train.tag.addedTagger
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