Examples of weka.core.Instance.classValue()

weka.core.Instance.classValue()
Returns an instance's class value in internal format. (ie. as a floating-point number) @return the corresponding value as a double (If the corresponding attribute is nominal (or a string) then it returns the value's index as a double). @throws UnassignedClassException if the class is not set or the instance doesn'thave access to a dataset

    m_intervalBounds[j][((int)inst.classValue()*2+1)]) {
        m_intervalBounds[j][((int)inst.classValue()*2+1)] = 
    inst.value(j);
      }
      if (inst.value(j) > 
    m_intervalBounds[j][((int)inst.classValue()*2+2)]) {
        m_intervalBounds[j][((int)inst.classValue()*2+2)] = 
    inst.value(j);
      }
    }
  }

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        m_intervalBounds[j][((int)inst.classValue()*2+1)] = 
    inst.value(j);
      }
      if (inst.value(j) > 
    m_intervalBounds[j][((int)inst.classValue()*2+2)]) {
        m_intervalBounds[j][((int)inst.classValue()*2+2)] = 
    inst.value(j);
      }
    }
  }
      }

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      double val = inst.value(j);
     
      int k;
      for (k = m_intervalBounds[j].length-1; k >= 0; k--) {
        if (val > m_intervalBounds[j][k]) {
    m_counts[j][k][(int)inst.classValue()] += inst.weight();
    break;
        } else if (val == m_intervalBounds[j][k]) {
    m_counts[j][k][(int)inst.classValue()] += 
      (inst.weight() / 2.0);
    m_counts[j][k-1][(int)inst.classValue()] +=

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      for (k = m_intervalBounds[j].length-1; k >= 0; k--) {
        if (val > m_intervalBounds[j][k]) {
    m_counts[j][k][(int)inst.classValue()] += inst.weight();
    break;
        } else if (val == m_intervalBounds[j][k]) {
    m_counts[j][k][(int)inst.classValue()] += 
      (inst.weight() / 2.0);
    m_counts[j][k-1][(int)inst.classValue()] += 
      (inst.weight() / 2.0);;
    break;
        }

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    m_counts[j][k][(int)inst.classValue()] += inst.weight();
    break;
        } else if (val == m_intervalBounds[j][k]) {
    m_counts[j][k][(int)inst.classValue()] += 
      (inst.weight() / 2.0);
    m_counts[j][k-1][(int)inst.classValue()] += 
      (inst.weight() / 2.0);;
    break;
        }
      }

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        }
      }
     
    } else {
      // nominal attribute
      m_counts[j][(int)inst.value(j)][(int)inst.classValue()] += 
        inst.weight();;
    }
  }
      }
    }

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      m_ErrorEstimator = null;


      for (int i = 0; i < train.numInstances(); i++) {
  Instance currentInst = train.instance(i);
  if (!currentInst.classIsMissing()) {
    addNumericTrainClass(currentInst.classValue(), 
        currentInst.weight());
  }
      }


    } else {

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                [numClasses] += inst.weight();
              counts[inst.index(i)][0][numClasses] -= inst.weight();
            }
          } else {
            counts[inst.index(i)][(int)inst.valueSparse(i)]
              [(int)inst.classValue()] += inst.weight();
            counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight();
          }
        }
      }
    }

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              counts[inst.index(i)][0][numClasses] -= inst.weight();
            }
          } else {
            counts[inst.index(i)][(int)inst.valueSparse(i)]
              [(int)inst.classValue()] += inst.weight();
            counts[inst.index(i)][0][(int)inst.classValue()] -= inst.weight();
          }
        }
      }
    }

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      System.out.println("Extracting data...");
    }


    for(int h=0; h<m_Data.length; h++){
      Instance current = train.instance(h);
      m_Classes[h] = (int)current.classValue();  // Class value starts from 0
      Instances currInsts = current.relationalValue(1);
      int nI = currInsts.numInstances();
      totIns += (double)nI;


      for (int i = 0; i < nR; i++) {

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