Examples of trust.jfcm.learning.LearningConcept

trust.jfcm.learning.LearningConcept
This abstract class represents a generic learning method used for nodes within a fcm with a training mechanism @author t3 @version $Revision: 1.6 $

      
      if(c==null)
        System.err.println("Error: Concept "+e1.getConcept().getName()+" not found");
      
      
        LearningConcept lc = null;
        if(c instanceof LearningConcept)
          lc = (LearningConcept) c;
        
        if(lc != null && lc.isOutput()){
          OutputLearningConcept oc = (OutputLearningConcept) lc;
          oc.setDesirableOutput(e1.getValue());
          sum += oc.getDesirableOutput() - oc.getOutput().doubleValue();
        }      
    }
    
    double mean_error = sum / outputs.size();
    //logger.debug("The sum error is: "+mean_error);
    /*
     * Put mean error on the input nodes of the whole map
     */
    int num=0;
    Iterator<Concept> it = map.getConceptsIterator();
    while(it.hasNext()){
      Concept c = it.next();
      
        LearningConcept lc = null;
        if(c instanceof LearningConcept)
          lc = (LearningConcept) c;
        
        if(lc != null && lc.isInput()){
          InputLearningConcept ic = (InputLearningConcept) lc;
          ic.setError(mean_error);
          num++;
          //logger.debug("Error on concept "+ic.getName()+": "+ic.getError());
        }

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      Iterator<Concept> it = map.getConceptsIterator();
      while(it.hasNext()){
        Concept c = it.next();
        
        if(c instanceof LearningConcept){
          LearningConcept lc = (LearningConcept) c;
          if(!lc.isErrorCalculated()){
            
            //error to be calculated
            //logger.debug(" error to be calculated for " + lc.getName());
            if(calculateErrorOnConcept(lc)){
              //calculation successful

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      LearningWeightedConnection wconn = null;
      if(conn instanceof LearningWeightedConnection)
        wconn = (LearningWeightedConnection) conn;
      
      Concept cin = wconn.getFrom();
      LearningConcept lin = null;
      if(cin instanceof LearningConcept)
        lin = (LearningConcept) cin;
      
      if(!lin.isErrorCalculated()){
        //logger.debug("  Error for "+c+" cannot be yet calculated because "+lin+" is not updated");
        /*
         * Error cannot be computed yet
         */
        return false;
      }
      
      val += wconn.getWeight() * lin.getError();
      //val +=  wconn.getWeight()* wconn.getWeightUncertainty() * lin.getError();
      
    }
    
    /*

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  private void setTrainingInputs(List<EntryStructure> inputs){
    for(int i=0; i<inputs.size(); i++){
      EntryStructure e1 = inputs.get(i);
      //logger.debug("EntryStructure: "+e1);
      
      LearningConcept lc = (LearningConcept) e1.getConcept();
      if(lc != null && lc.isInput()){
        InputLearningConcept ilc = (InputLearningConcept) lc;
        ilc.setInput(e1.getValue());
      }


    }

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    Iterator<Concept> it = map.getConcepts().values().iterator();
    while(it.hasNext()){
      Concept c = it.next();
        
      if(c instanceof LearningConcept){
        LearningConcept lc = (LearningConcept) c;
        Set<FcmConnection> out = lc.getOutConnections();
        Iterator<FcmConnection> it1 = out.iterator();
        
        while(it1.hasNext()){
          FcmConnection conn = it1.next();
          
          LearningWeightedConnection wconn = null;
          if(conn instanceof LearningWeightedConnection){
            wconn = (LearningWeightedConnection) conn;
            /*
             * Update learning connection
             */
            lc.getTrainingFunction().update(wconn);
          }
          
        }
          
      }

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Related Classes of trust.jfcm.learning.LearningConcept

trust.jfcm.learning.supervised.LinearBP

trust.jfcm.learning.training.ForwardPropagation

trust.jfcm.learning.training.LinearRegression

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