Examples of BayesNet

aima.core.probability.bayes.impl.BayesNet
Default implementation of the BayesianNetwork interface. @author Ciaran O'Reilly @author Ravi Mohan

weka.classifiers.bayes.BayesNet

eforge.net/projects/weka/files/documentation/WekaManual-3-6-1.pdf/download

Valid options are:

 -D Do not use ADTree data structure

 -B <BIF file> BIF file to compare with

 -Q weka.classifiers.bayes.net.search.SearchAlgorithm Search algorithm

 -E weka.classifiers.bayes.net.estimate.SimpleEstimator Estimator algorithm

@author Remco Bouckaert (rrb@xm.co.nz) @version $Revision: 5725 $

Examples of aima.core.probability.bayes.impl.BayesNet

            // cloudy
            0.29,
            // snow
            0.01 });


    return new BayesNet(cavity, weather);
  }

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Examples of aima.core.probability.bayes.impl.BayesNet

            0.009986199723994478,
            // M=false, S=false
            0.9900138002760055


        }, meningitis);
    return new BayesNet(meningitis);
  }

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Examples of aima.core.probability.bayes.impl.BayesNet

            // A=false, M=true
            0.01,
            // A=false, M=false
            0.99 }, alarm);


    return new BayesNet(burglary, earthquake);
  }

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Examples of aima.core.probability.bayes.impl.BayesNet

            // Sprinkler=false, Rain=false, WetGrass=true
            0.0,
            // Sprinkler=false, Rain=false, WetGrass=false
            1.0 }, sprinkler, rain);


    return new BayesNet(cloudy);
  }

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Examples of weka.classifiers.bayes.BayesNet

   *
   * @param data  
   * @return the bayes net
   */
  public BayesNet trainBayesNet(Instances data) {
      BayesNet cls = new BayesNet(); 
      try {
        String[] str = cls.getOptions();
     //   str[2] = "weka.classifiers.bayes.net.search.local.TAN";
     //   str[4] = "";
      //  str[5] = ""; 
        cls.setOptions(str);
      cls.buildClassifier(data);
    } catch (Exception e) {
      e.printStackTrace();
    }
    
    return cls;

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Examples of weka.classifiers.bayes.BayesNet

    Instances data1 = wekaInter.getWekaDataFromUMatrix("Type", pars);
    
    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
    // training 
    wekaInter.saveWekaFile("world.arff", data1);
    BayesNet BN = wekaInter.trainBayesNet(data1);
    wekaInter.saveBIFFromFile("world.xml", BN);
  }

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Examples of weka.classifiers.bayes.BayesNet

            }
        }
        // ok, now all data is binary, except the class attribute
        // now learn the empty and tree network


        BayesNet EmptyNet = new BayesNet();
        K2 oSearchAlgorithm = new K2();
        oSearchAlgorithm.setInitAsNaiveBayes(false);
        oSearchAlgorithm.setMaxNrOfParents(0);
        EmptyNet.setSearchAlgorithm(oSearchAlgorithm);
        EmptyNet.buildClassifier(instances);


        BayesNet NBNet = new BayesNet();
        oSearchAlgorithm.setInitAsNaiveBayes(true);
        oSearchAlgorithm.setMaxNrOfParents(1);
        NBNet.setSearchAlgorithm(oSearchAlgorithm);
        NBNet.buildClassifier(instances);


        // estimate CPTs
        for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
            if (iAttribute != instances.classIndex()) {
                  double w1 = 0.0, w2 = 0.0;

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Examples of weka.classifiers.bayes.BayesNet

            if (bayesNet.getParentSet(iAttribute).getNrOfParents() > 1) {
                throw new Exception("Cannot handle networks with nodes with more than 1 parent (yet).");
            }
        }


        BayesNet EmptyNet = new BayesNet();
        K2 oSearchAlgorithm = new K2();
        oSearchAlgorithm.setInitAsNaiveBayes(false);
        oSearchAlgorithm.setMaxNrOfParents(0);
        EmptyNet.setSearchAlgorithm(oSearchAlgorithm);
        EmptyNet.buildClassifier(instances);


        BayesNet NBNet = new BayesNet();
        oSearchAlgorithm.setInitAsNaiveBayes(true);
        oSearchAlgorithm.setMaxNrOfParents(1);
        NBNet.setSearchAlgorithm(oSearchAlgorithm);
        NBNet.buildClassifier(instances);


        // estimate CPTs
        for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
            if (iAttribute != instances.classIndex()) {
                  double w1 = 0.0, w2 = 0.0;

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Examples of weka.classifiers.bayes.BayesNet

    }
    
    m_random = new Random(m_nSeed);


    // keeps track of best structure found so far 
    BayesNet bestBayesNet;
    // keeps track of score pf best structure found so far 
    double fBestScore = calcScore(bayesNet);  


    // initialize bestBayesNet
    bestBayesNet = new BayesNet();
    bestBayesNet.m_Instances = instances;
    bestBayesNet.initStructure();
    copyParentSets(bestBayesNet, bayesNet);
    
                
        // initialize population        
    BayesNetRepresentation  [] population = new BayesNetRepresentation [getPopulationSize()];

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Examples of weka.classifiers.bayes.BayesNet

    // keeps track of score pf best structure found so far 
    double fBestScore;  
    double fCurrentScore = calcScore(bayesNet);


    // keeps track of best structure found so far 
    BayesNet bestBayesNet;


    // initialize bestBayesNet
    fBestScore = fCurrentScore;
    bestBayesNet = new BayesNet();
    bestBayesNet.m_Instances = instances;
    bestBayesNet.initStructure();
    copyParentSets(bestBayesNet, bayesNet);
    
                
        // go do the search        
        for (int iRun = 0; iRun < m_nRuns; iRun++) {

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