@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;
}
}
@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;
}
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);
}
}
