コード例 #1
0
ファイル: DecisionTable.java プロジェクト: alishakiba/jDenetX
  /**
   * Inserts an instance into the hash table
   *
   * @param inst instance to be inserted
   * @param instA to create the hash key from
   * @throws Exception if the instance can't be inserted
   */
  private void insertIntoTable(Instance inst, double[] instA) throws Exception {

    double[] tempClassDist2;
    double[] newDist;
    DecisionTableHashKey thekey;

    if (instA != null) {
      thekey = new DecisionTableHashKey(instA);
    } else {
      thekey = new DecisionTableHashKey(inst, inst.numAttributes(), false);
    }

    // see if this one is already in the table
    tempClassDist2 = (double[]) m_entries.get(thekey);
    if (tempClassDist2 == null) {
      if (m_classIsNominal) {
        newDist = new double[m_theInstances.classAttribute().numValues()];

        // Leplace estimation
        for (int i = 0; i < m_theInstances.classAttribute().numValues(); i++) {
          newDist[i] = 1.0;
        }

        newDist[(int) inst.classValue()] = inst.weight();

        // add to the table
        m_entries.put(thekey, newDist);
      } else {
        newDist = new double[2];
        newDist[0] = inst.classValue() * inst.weight();
        newDist[1] = inst.weight();

        // add to the table
        m_entries.put(thekey, newDist);
      }
    } else {

      // update the distribution for this instance
      if (m_classIsNominal) {
        tempClassDist2[(int) inst.classValue()] += inst.weight();

        // update the table
        m_entries.put(thekey, tempClassDist2);
      } else {
        tempClassDist2[0] += (inst.classValue() * inst.weight());
        tempClassDist2[1] += inst.weight();

        // update the table
        m_entries.put(thekey, tempClassDist2);
      }
    }
  }
コード例 #2
0
ファイル: FarthestFirst.java プロジェクト: bigbigbug/wekax
  /**
   * Updates the minimum and maximum values for all the attributes based on a new instance.
   *
   * @param instance the new instance
   */
  private void updateMinMax(Instance instance) {

    for (int j = 0; j < instance.numAttributes(); j++) {
      if (Double.isNaN(m_Min[j])) {
        m_Min[j] = instance.value(j);
        m_Max[j] = instance.value(j);
      } else {
        if (instance.value(j) < m_Min[j]) {
          m_Min[j] = instance.value(j);
        } else {
          if (instance.value(j) > m_Max[j]) {
            m_Max[j] = instance.value(j);
          }
        }
      }
    }
  }
コード例 #3
0
ファイル: DecisionTable.java プロジェクト: alishakiba/jDenetX
  /**
   * Calculates the class membership probabilities for the given test instance.
   *
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @throws Exception if distribution can't be computed
   */
  public double[] distributionForInstance(Instance instance) throws Exception {

    DecisionTableHashKey thekey;
    double[] tempDist;
    double[] normDist;

    m_disTransform.input(instance);
    m_disTransform.batchFinished();
    instance = m_disTransform.output();

    m_delTransform.input(instance);
    m_delTransform.batchFinished();
    instance = m_delTransform.output();

    thekey = new DecisionTableHashKey(instance, instance.numAttributes(), false);

    // if this one is not in the table
    if ((tempDist = (double[]) m_entries.get(thekey)) == null) {
      if (m_useIBk) {
        tempDist = m_ibk.distributionForInstance(instance);
      } else {
        if (!m_classIsNominal) {
          tempDist = new double[1];
          tempDist[0] = m_majority;
        } else {
          tempDist = m_classPriors.clone();
          /*tempDist = new double [m_theInstances.classAttribute().numValues()];
          tempDist[(int)m_majority] = 1.0; */
        }
      }
    } else {
      if (!m_classIsNominal) {
        normDist = new double[1];
        normDist[0] = (tempDist[0] / tempDist[1]);
        tempDist = normDist;
      } else {

        // normalise distribution
        normDist = new double[tempDist.length];
        System.arraycopy(tempDist, 0, normDist, 0, tempDist.length);
        Utils.normalize(normDist);
        tempDist = normDist;
      }
    }
    return tempDist;
  }