public double[] normalizedInstance(Instance inst) {
// Normalize Instance
double[] normalizedInstance = new double[inst.numAttributes()];
for (int j = 0; j < inst.numAttributes() - 1; j++) {
int instAttIndex = modelAttIndexToInstanceAttIndex(j, inst);
double mean = perceptronattributeStatistics.getValue(j) / perceptronYSeen;
double sd =
computeSD(
squaredperceptronattributeStatistics.getValue(j),
perceptronattributeStatistics.getValue(j),
perceptronYSeen);
if (sd > SD_THRESHOLD) normalizedInstance[j] = (inst.value(instAttIndex) - mean) / sd;
else normalizedInstance[j] = inst.value(instAttIndex) - mean;
}
return normalizedInstance;
}
/** Update the model using the provided instance */
public void trainOnInstanceImpl(Instance inst) {
accumulatedError =
Math.abs(this.prediction(inst) - inst.classValue()) + fadingFactor * accumulatedError;
nError = 1 + fadingFactor * nError;
// Initialise Perceptron if necessary
if (this.initialisePerceptron == true) {
this.fadingFactor = this.fadingFactorOption.getValue();
this.classifierRandom.setSeed(randomSeedOption.getValue());
this.initialisePerceptron = false; // not in resetLearningImpl() because it needs Instance!
this.weightAttribute = new double[inst.numAttributes()];
for (int j = 0; j < inst.numAttributes(); j++) {
weightAttribute[j] = 2 * this.classifierRandom.nextDouble() - 1;
}
// Update Learning Rate
learningRatio = learningRatioOption.getValue();
this.learningRateDecay = learningRateDecayOption.getValue();
}
// Update attribute statistics
this.perceptronInstancesSeen++;
this.perceptronYSeen++;
for (int j = 0; j < inst.numAttributes() - 1; j++) {
perceptronattributeStatistics.addToValue(j, inst.value(j));
squaredperceptronattributeStatistics.addToValue(j, inst.value(j) * inst.value(j));
}
this.perceptronsumY += inst.classValue();
this.squaredperceptronsumY += inst.classValue() * inst.classValue();
if (constantLearningRatioDecayOption.isSet() == false) {
learningRatio =
learningRatioOption.getValue() / (1 + perceptronInstancesSeen * learningRateDecay);
}
// double prediction = this.updateWeights(inst,learningRatio);
// accumulatedError= Math.abs(prediction-inst.classValue()) + fadingFactor*accumulatedError;
this.updateWeights(inst, learningRatio);
}