Examples of weka.core.Instance.index()

weka.core.Instance.index()
Returns the index of the attribute stored at the given position. Just returns the given value. @param position the position @return the index of the attribute stored at the given position

          firstI = inst.index(p1); 
        }
        if (p2 >= cmp.numValues()) {
          secondI = m_trainInstances.numAttributes();
        } else {
          secondI = cmp.index(p2);
        }
        if (firstI == m_trainInstances.classIndex()) {
          p1++; continue;
        } 
        if (secondI == m_trainInstances.classIndex()) {

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          firstI = inst.index(p1); 
        }
        if (p2 >= cmp.numValues()) {
          secondI = m_trainInstances.numAttributes();
        } else {
          secondI = cmp.index(p2);
        }
        if (firstI == m_trainInstances.classIndex()) {
          p1++; continue;
        } 
        if (secondI == m_trainInstances.classIndex()) {

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                firstI = inst.index(p1); 
              }
              if (p2 >= cmp.numValues()) {
                secondI = m_trainInstances.numAttributes();
              } else {
                secondI = cmp.index(p2);
              }
              if (firstI == m_trainInstances.classIndex()) {
                p1++; continue;
              } 
              if (secondI == m_trainInstances.classIndex()) {

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      return 0;
    }
    Instance inst = m_data.instance(instIdx);
    double kl1 = 0.0, kl2 = 0.0, tmp = 0.0;    
    for (int i = 0; i < inst.numValues(); i++) {
      tmp = input.Py_x.get(inst.index(i), instIdx);      
      if(tmp != 0) {
  kl1 += tmp * Math.log(tmp / (tmp * pi1 + pi2 * T.Py_t.get(inst.index(i), t)));
      }
    }
    for (int i = 0; i < m_numAttributes; i++) {

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    Instance inst = m_data.instance(instIdx);
    double kl1 = 0.0, kl2 = 0.0, tmp = 0.0;    
    for (int i = 0; i < inst.numValues(); i++) {
      tmp = input.Py_x.get(inst.index(i), instIdx);      
      if(tmp != 0) {
  kl1 += tmp * Math.log(tmp / (tmp * pi1 + pi2 * T.Py_t.get(inst.index(i), t)));
      }
    }
    for (int i = 0; i < m_numAttributes; i++) {
      if ((tmp = T.Py_t.get(i, t)) != 0) {
  kl2 += tmp * Math.log(tmp / (input.Py_x.get(i, instIdx) * pi1 + pi2 * tmp));

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  private Matrix getTransposedMatrix(Instances data) {
    double[][] temp = new double[data.numAttributes()][data.numInstances()];
    for (int i = 0; i < data.numInstances(); i++) {
      Instance inst = data.instance(i);
      for (int v = 0; v < inst.numValues(); v++) {
  temp[inst.index(v)][i] = inst.valueSparse(v);
      }
    }
    Matrix My_x = new Matrix(temp);
    return My_x;
  }

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    double entropy = input.Hx + input.Hy;    
    for (int i=0; i < m_numInstances; i++) {
      Instance inst = m_data.instance(i);
      for (int v = 0; v < inst.numValues(); v++) {
  double tmp = m.get(inst.index(v), i);
  if(tmp <= 0) continue;
  entropy += tmp * Math.log(tmp);
      }
    }
    input.Ixy = entropy;

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  m_AvgDocLength = 0;
  for(int i=0; i<fv.size(); i++) {
    Instance inst = (Instance) fv.elementAt(i);
    double docLength = 0;
    for(int j=0; j<inst.numValues(); j++) {
      if(inst.index(j)>=firstCopy) {
        docLength += inst.valueSparse(j) * inst.valueSparse(j);
      }
    }        
    m_AvgDocLength += Math.sqrt(docLength);
  }

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   */
  protected void checkMissing(Instances instances) throws Exception {
    for (int i = 0; i < instances.numInstances(); i++) {
      Instance ins = instances.instance(i);
      for (int j = 0; j < ins.numValues(); j++) {
        if (ins.index(j) != ins.classIndex())
          if (ins.isMissingSparse(j)) {
            throw new Exception("ERROR: KDTree can not deal with missing "
                + "values. Please run ReplaceMissingValues filter "
                + "on the dataset before passing it on to the KDTree.");
          }

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   */
  protected void checkMissing(Instances instances) throws Exception {
    for (int i = 0; i < instances.numInstances(); i++) {
      Instance ins = instances.instance(i);
      for (int j = 0; j < ins.numValues(); j++) {
  if (ins.index(j) != ins.classIndex())
    if (ins.isMissingSparse(j)) {
      throw new Exception("ERROR: KDTree can not deal with missing "
    + "values. Please run ReplaceMissingValues filter "
    + "on the dataset before passing it on to the KDTree.");
    }

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