Examples of org.encog.ml.bayesian.BayesianEvent

org.encog.ml.bayesian.BayesianEvent
Events make up a Bayesian network. Each evidence or outcome event usually corresponds to one number in the training data. A event is always discrete. However, continues values can be range-mapped to discrete values.

  public boolean iteration() {
    
    if( index==-1 ) {
      orderNodes();  
    } else {
      BayesianEvent event = this.nodeOrdering.get(index);
      double oldP = this.calculateG(network, event, event.getParents());


      while(  event.getParents().size()<this.train.getMaximumParents() ) {
        BayesianEvent z = findZ(event,index,oldP);
        if(z!=null) {
          this.network.createDependency(z, event);
          oldP = this.lastCalculatedP;
        } else {
          break;

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  /**
   * {@inheritDoc}
   */
  @Override
  public boolean iteration() {
    BayesianEvent event = this.network.getEvents().get(this.index);
    for(TableLine line : event.getTable().getLines() ) {
      line.setProbability(calculateProbability(event,line.getResult(),line.getArguments()));
    }
    index++;
    
    return index<this.network.getEvents().size();

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  private void initNaiveBayes() {
    // clear out anything from before
    this.network.removeAllRelations();


    // locate the classification target event
    BayesianEvent classificationTarget = this.network
        .getClassificationTargetEvent();


    // now link everything to this event
    for (BayesianEvent event : this.network.getEvents()) {
      if (event != classificationTarget) {

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org.encog.ml.bayesian.bif.BIFHandler

org.encog.ml.bayesian.parse.ParsedProbability

org.encog.ml.bayesian.query.enumerate.EnumerationQuery

org.encog.ml.bayesian.table.BayesianTable

org.encog.ml.bayesian.training.estimator.SimpleEstimator

org.encog.ml.bayesian.training.search.k2.SearchK2

org.encog.ml.bayesian.training.TrainBayesian

org.encog.persist.TestPersistBayes

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