Package org.fnlp.ml.classifier.linear.inf

Examples of org.fnlp.ml.classifier.linear.inf.Inferencer

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     *
     * 更新参数的准则
     */
    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);
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      InstanceSet instset = new InstanceSet(pipe,factory);
      instset.loadThruStagePipes(new SimpleFileReader(trainFile," ",true,Type.LabelData));
      Generator gen = new SFGenerator();
      ZeroOneLoss l = new ZeroOneLoss();
      Inferencer ms = new LinearMax(gen, factory.getLabelSize());
      Update update = new LinearMaxPAUpdate(l);
      OnlineTrainer trainer = new OnlineTrainer(ms, update,l, factory.getFeatureSize(), 50,0.005f);
      Linear pclassifier = trainer.train(instset,instset);
      pipe.removeTargetPipe();
      pclassifier.setPipe(pipe);
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    float c = 1.0f;
    int round = 20;
   
    BaseGenerator featureGen = new BaseGenerator();
    ZeroOneLoss loss = new ZeroOneLoss();
    Inferencer msolver = new MultiLinearMax(featureGen, al, null,2);

    PATrainer trainer = new PATrainer(msolver, featureGen, loss, round,c, null);
    Linear pclassifier = trainer.train(train, null);
   
    String modelFile = path+".m.gz";
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    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,
        features.size(), 50,0.1f);
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    Generator featureGen = new SFGenerator();
    ZeroOneLoss loss = new ZeroOneLoss();
    LinearMaxPAUpdate update = new LinearMaxPAUpdate(loss);
   
   
    Inferencer msolver = new LinearMax(featureGen, al.size() );
    OnlineTrainer trainer = new OnlineTrainer(msolver, update, loss, af.size(), round,
        c);

    Linear classify = trainer.train(train, test);
    String modelFile = path+".m.gz";
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    float c = 1.0f;
    int round = 10;
   
    BaseGenerator featureGen = new BaseGenerator();
    ZeroOneLoss loss = new ZeroOneLoss();
    Inferencer msolver = new MultiLinearMax(featureGen, lf, null,2);

    PATrainer trainer = new PATrainer(msolver, featureGen, loss, round,c, null);
    Linear pclassifier = trainer.train(trainset, null);
    String modelFile = "./tmp/m.gz";
    pclassifier.saveTo(modelFile);
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     *
     * 更新参数的准则
     */
    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);
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