@Override
 public void resetWithMemory() {
   for (int i = 0; i < this.ensemble.length; i++) {
     Perceptron p = new Perceptron((Perceptron) this.ensemble[i]);
     p.setLearningRatio(
         ((Perceptron) getPreparedClassOption(baseLearnerOption)).learningRatioOption.getValue());
     this.ensemble[i] = p;
   }
 }
예제 #2
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 @Override
 public double getCurrentError() {
   double error;
   if (this.perceptron != null) {
     if (targetMean == null) error = perceptron.getCurrentError();
     else {
       double errorP = perceptron.getCurrentError();
       double errorTM = targetMean.getCurrentError();
       error = (errorP < errorTM) ? errorP : errorTM;
     }
   } else error = Double.MAX_VALUE;
   return error;
 }
예제 #3
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  protected void debuganomaly(Instance instance, double uni, double multi, double probability) {
    double atribSum = 0.0;
    double atribSquredSum = 0.0;

    for (int x = 0; x < instance.numAttributes() - 1; x++) {
      int instAttIndex = AMRulesRegressorOld.modelAttIndexToInstanceAttIndex(x, instance);
      atribSum = perceptron.perceptronattributeStatistics.getValue(x);
      atribSquredSum = perceptron.squaredperceptronattributeStatistics.getValue(x);
      double mean = atribSum / perceptron.getInstancesSeen();
      double sd = computeSD(atribSquredSum, atribSum, perceptron.getInstancesSeen());
      debug("Attribute : " + x, 5);
      debug("Value : " + instance.value(instAttIndex), 5);
      debug("Mean : " + mean, 5);
      debug("SD : " + sd, 5);
      debug("Probability : " + probability, 5);
      debug("Univariate : " + uni, 5);
      debug("Multivariate : " + multi, 5);
      debug("Anomaly in rule :" + this.owner.ruleNumberID, 5);
    }
  }