Examples of weka.core.Instances.instance()

weka.core.Instances.instance()
Returns the instance at the given position. @param index the instance's index (index starts with 0) @return the instance at the given position

      ? inst.meanOrMode(j)
      : center.value(j);
    m_modelNormal[i][j][0] = mean;
    double stdv = (stdD.instance(i).isMissing(j))
      ? ((m_maxValues[j] - m_minValues[j]) / (2 * m_num_clusters))
      : stdD.instance(i).value(j);
    if (stdv < minStdD) {
      stdv = inst.attributeStats(j).numericStats.stdDev;
            if (Double.isInfinite(stdv)) {
              stdv = minStdD;
            }

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      for (int i = 0; i < nR; i++) {      
        // initialize m_data[][][]    
        m_Data[h][i] = new double[nI];
        double avg=0, std=0, num=0;
        for (int k=0; k<nI; k++){
          if(!currInsts.instance(k).isMissing(i)){
            m_Data[h][i][k] = currInsts.instance(k).value(i);
            avg += m_Data[h][i][k];
            std += m_Data[h][i][k]*m_Data[h][i][k];
            num++;
          }

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        // initialize m_data[][][]    
        m_Data[h][i] = new double[nI];
        double avg=0, std=0, num=0;
        for (int k=0; k<nI; k++){
          if(!currInsts.instance(k).isMissing(i)){
            m_Data[h][i][k] = currInsts.instance(k).value(i);
            avg += m_Data[h][i][k];
            std += m_Data[h][i][k]*m_Data[h][i][k];
            num++;
          }
          else

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    double[][] dat = new double [nI][nA+1];
    for(int j=0; j<nI; j++){
      dat[j][0]=1.0;
      int idx=1;
      for(int k=0; k<nA; k++){ 
        if(!ins.instance(j).isMissing(k))
          dat[j][idx] = ins.instance(j).value(k);
        else
          dat[j][idx] = xMean[idx-1];
        idx++;
      }

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    for(int j=0; j<nI; j++){
      dat[j][0]=1.0;
      int idx=1;
      for(int k=0; k<nA; k++){ 
        if(!ins.instance(j).isMissing(k))
          dat[j][idx] = ins.instance(j).value(k);
        else
          dat[j][idx] = xMean[idx-1];
        idx++;
      }
    }

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  all += orderedClasses[i];
      double expFPRate = orderedClasses[y] / all;      
    
      double classYWeights = 0, totalWeights = 0;
      for(int j=0; j < data.numInstances(); j++){
    Instance datum = data.instance(j);
    totalWeights += datum.weight();
    if((int)datum.classValue() == y){
        classYWeights += datum.weight();
    }            
      }

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    else{
      RipperRule oldRule = (RipperRule)ruleset.elementAt(position);
      boolean covers = false;
      // Test coverage of the next old rule
      for(int i=0; i<newData.numInstances(); i++)
        if(oldRule.covers(newData.instance(i))){
    covers = true;
    break;
        }
      
      if(!covers){// Null coverage, no variants can be generated

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    // convert the single-instance format to multi-instance format
    convertToMI.setInputFormat(insts);
    insts=Filter.useFilter( insts, convertToMI);


    inst = insts.instance(0);  


    if (!m_fitLogisticModels) {
      double[] result = new double[inst.numClasses()];
      for (int i = 0; i < inst.numClasses(); i++) {
        for (int j = i + 1; j < inst.numClasses(); j++) {

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      // The dataset may be large, so to make things easier we'll
      // output an instance at a time (rather than having to convert
      // the entire dataset to one large string)
      System.out.println(new Instances(aha, 0));
      for (int i = 0; i < aha.numInstances(); i++) {
  System.out.println(aha.instance(i));
      }
    } catch(Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }

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      sumProbs += weights[l];
      while ((k < data.numInstances()) &&
       (probabilities[k] <= sumProbs)) { 
  newData.add(data.instance(l));
  sampled[l] = true;
  newData.instance(k).setWeight(1);
  k++;
      }
      l++;
    }
    return newData;

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