Examples of ISparseVector

Examples of org.fnlp.ml.types.sv.ISparseVector

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  }

  @Override
  public ISparseVector getVector(Instance inst, Object label) {
    int[] data = (int[]) inst.getData();
    ISparseVector fv = new BinarySparseVector(data.length);
    for(int i = 0; i < data.length; i++)  {
      int idx = data[i]+(Integer)label;
      fv.put(idx);
    }
    return fv;
  }
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Examples of org.fnlp.ml.types.sv.ISparseVector

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    //构建训练集
    trainset = new InstanceSet(factory);
   
    for(int i=0;i<data.length;i++){
      ISparseVector sv = new HashSparseVector(data[i],true);
      int l = lf.lookupIndex(target[i]);
      Instance inst = new Instance(sv,l);
      trainset.add(inst);
    }
    lf.setStopIncrement(true);
   
    //构建测试集

    System.out.println("Train Number: " + trainset.size());
    System.out.println("Class Number: " + lf.size());

    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);
    pclassifier = null;
   
    System.out.println("分类器测试");
    pclassifier = Linear.loadFrom(modelFile);
    float[] tdata = new float[]{1,0,1};
    ISparseVector sv = new HashSparseVector(tdata,true);
    Instance inst = new Instance(sv);
    String lab = pclassifier.getStringLabel(inst);
    System.out.println("分类结果：\t"+lab);
   
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Examples of org.fnlp.ml.types.sv.ISparseVector

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    if (target != null) {
      oracle = new Predict<Integer>(n);
    }

    for (int i = 0; i < ysize; i++) {
      ISparseVector fv = generator.getVector(inst, i);
      float score = fv.dotProduct(weights);
      if (target != null && target == i)
        oracle.add(i,score);
      else
        pred.add(i,score);
    }
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Examples of org.fnlp.ml.types.sv.ISparseVector

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      means[i]=new HashSparseVector();
    }

    for (int ii = 0; ii < trainingList.size(); ii++){
      Instance inst = trainingList.getInstance(ii);
      ISparseVector fv = (ISparseVector) inst.getData ();
      int target = (Integer) inst.getTarget();
      if(tree!=null){
        int[] anc = tree.getPath(target);
        for(int j=0;j<anc.length;j++){
          means[anc[j]].plus(fv);
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Examples of org.fnlp.ml.types.sv.ISparseVector

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  public Predict getBest(Instance inst, int n) {
    Integer target =null;
    if(isUseTarget)
      target = (Integer) inst.getTarget();

    ISparseVector fv = featureGen.getVector(inst);

    //每个类对应的内积
    float[] sw = new float[alphabet.size()];
    Callable<Float>[] c= new Multiplesolve[numClass];
    Future<Float>[] f = new Future[numClass];
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