/**
* This method allows you to learn from several examples
*
* @param examples A population of examples.
* @param learnRate The learning rate.
*/
public void learnFrom(Population examples, double learnRate) {
InterfaceGAIndividual tmpIndy;
BitSet tmpBitSet;
for (int i = 0; i < examples.size(); i++) {
tmpIndy = (InterfaceGAIndividual) (examples.getEAIndividual(i)).clone();
tmpBitSet = tmpIndy.getBGenotype();
for (int j = 0; j < this.m_ProbabilityVector.length; j++) {
this.m_ProbabilityVector[j] = this.m_ProbabilityVector[j] * (1.0 - learnRate);
if (tmpBitSet.get(j)) this.m_ProbabilityVector[j] += learnRate;
}
}
}
/** This method creates a new population based on the bit probability vector */
public void initPBIL() {
InterfaceGAIndividual tmpIndy,
template = (InterfaceGAIndividual) ((AbstractEAIndividual) this.get(0)).clone();
BitSet tmpBitSet;
this.clear();
for (int i = 0; i < this.getTargetSize(); i++) {
tmpIndy = (InterfaceGAIndividual) ((AbstractEAIndividual) template).clone();
tmpBitSet = tmpIndy.getBGenotype();
for (int j = 0; j < this.m_ProbabilityVector.length; j++) {
if (RNG.flipCoin(this.m_ProbabilityVector[j])) tmpBitSet.set(j);
else tmpBitSet.clear(j);
}
tmpIndy.SetBGenotype(tmpBitSet);
super.add(tmpIndy);
}
}