Examples of weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes.addValue()

Package weka.classifiers.bayes.net.estimate

Class weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes

Examples of weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes.addValue()

weka.classifiers.bayes.net.estimate.DiscreteEstimatorBayes.addValue()
Add a new data value to the current estimator. @param data the new data value @param weight the weight assigned to the data value


      for (int i = 0; i < nCardinality; i++) {
        DiscreteEstimatorBayes d = (DiscreteEstimatorBayes) distributions[nBase + iNode][i];
        for (int iValue = 0; iValue < nValues; iValue++) {
          String sWeight = st.nextToken();
          d.addValue(iValue, new Double(sWeight).doubleValue());
        }
      }
      if (mode == EXECUTE) {
        m_nEvidence.insertElementAt(-1, nBase + iNode);
        m_fMarginP.insertElementAt(new double[getCardinality(nBase + iNode)], nBase + iNode);

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    }
    Estimator[] distributions = m_Distributions[nTargetNode];
    for (int iParent = 0; iParent < distributions.length; iParent++) {
      DiscreteEstimatorBayes distribution = new DiscreteEstimatorBayes(P[0].length, 0);
      for (int iValue = 0; iValue < distribution.getNumSymbols(); iValue++) {
        distribution.addValue(iValue, P[iParent][iValue]);
      }
      distributions[iParent] = distribution;
    }
    // m_Distributions[nTargetNode] = distributions;
  } // setDistribution

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    Estimator[] distributions = m_Distributions[nTargetNode];
    int nNewCard = values.size();
    for (int iParent = 0; iParent < distributions.length; iParent++) {
      DiscreteEstimatorBayes distribution = new DiscreteEstimatorBayes(nNewCard, 0);
      for (int iValue = 0; iValue < nNewCard - 1; iValue++) {
        distribution.addValue(iValue, distributions[iParent].getProbability(iValue));
      }
      distributions[iParent] = distribution;
    }


    // update distributions of all children

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          iTargetNode++;
        }
        for (int iPos = 0; iPos < nParentCard; iPos++) {
          DiscreteEstimatorBayes distribution = new DiscreteEstimatorBayes(nCard, 0);
          for (int iValue = 0; iValue < nCard; iValue++) {
            distribution.addValue(iValue, distributions[iOldPos].getProbability(iValue));
          }
          newDistributions[iPos] = distribution;
          // update values
          int i = 0;
          values2[i]++;

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      for (int iValue = 0; iValue < nCard; iValue++) {
        sum += distributions[iParent].getProbability(iValue);
      }
      if (sum > 0) {
        for (int iValue = 0; iValue < nCard; iValue++) {
          distribution.addValue(iValue, distributions[iParent].getProbability(iValue) / sum);
        }
      } else {
        for (int iValue = 0; iValue < nCard; iValue++) {
          distribution.addValue(iValue, 1.0 / nCard);
        }

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        for (int iValue = 0; iValue < nCard; iValue++) {
          distribution.addValue(iValue, distributions[iParent].getProbability(iValue) / sum);
        }
      } else {
        for (int iValue = 0; iValue < nCard; iValue++) {
          distribution.addValue(iValue, 1.0 / nCard);
        }
      }
      distributions[iParent] = distribution;
    }

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      for (int i = 0; i < nCardinality; i++) {
        DiscreteEstimatorBayes d = (DiscreteEstimatorBayes) distributions[nBase + iNode][i];
        for (int iValue = 0; iValue < nValues; iValue++) {
          String sWeight = st.nextToken();
          d.addValue(iValue, new Double(sWeight).doubleValue());
        }
      }
      if (mode == EXECUTE) {
        m_nEvidence.insertElementAt(-1, nBase + iNode);
        m_fMarginP.insertElementAt(new double[getCardinality(nBase + iNode)], nBase + iNode);

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    }
    Estimator[] distributions = m_Distributions[nTargetNode];
    for (int iParent = 0; iParent < distributions.length; iParent++) {
      DiscreteEstimatorBayes distribution = new DiscreteEstimatorBayes(P[0].length, 0);
      for (int iValue = 0; iValue < distribution.getNumSymbols(); iValue++) {
        distribution.addValue(iValue, P[iParent][iValue]);
      }
      distributions[iParent] = distribution;
    }
    // m_Distributions[nTargetNode] = distributions;
  } // setDistribution

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    Estimator[] distributions = m_Distributions[nTargetNode];
    int nNewCard = values.size();
    for (int iParent = 0; iParent < distributions.length; iParent++) {
      DiscreteEstimatorBayes distribution = new DiscreteEstimatorBayes(nNewCard, 0);
      for (int iValue = 0; iValue < nNewCard - 1; iValue++) {
        distribution.addValue(iValue, distributions[iParent].getProbability(iValue));
      }
      distributions[iParent] = distribution;
    }


    // update distributions of all children

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          iTargetNode++;
        }
        for (int iPos = 0; iPos < nParentCard; iPos++) {
          DiscreteEstimatorBayes distribution = new DiscreteEstimatorBayes(nCard, 0);
          for (int iValue = 0; iValue < nCard; iValue++) {
            distribution.addValue(iValue, distributions[iOldPos].getProbability(iValue));
          }
          newDistributions[iPos] = distribution;
          // update values
          int i = 0;
          values2[i]++;

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