public void evaluate(
final EvolutionState state,
final Individual ind,
final int subpopulation,
final int threadnum) {
if (!ind.evaluated) // don't bother reevaluating
{
// trees[0].child is the root
double score = fitness(((GPIndividual) ind).trees[0].child, state);
SimpleFitness f = ((SimpleFitness) ind.fitness);
f.setFitness(state, score, false);
ind.evaluated = true;
}
}
public void evaluate(
final EvolutionState state,
final Individual ind,
final int subpopulation,
final int threadnum) {
float accuracy = (float) 0.0;
int i;
double total_correct = 0;
if (!ind.evaluated) // don't bother reevaluating
{
SVM_GP.setInd(ind);
SVM_GP.setInput(input);
SVM_GP.setProblem(this);
SVM_GP.setStack(stack);
SVM_GP.setState(state);
SVM_GP.setSubpopulation(subpopulation);
SVM_GP.setThreadnum(threadnum);
double[] target = new double[prob.l];
SVM_GP.svm_cross_validation(prob, param, nr_fold, target);
/*((GPIndividual)ind).trees[0].child.eval(
state,threadnum,input,stack,((GPIndividual)ind),this);*/
// result = Math.abs(expectedResult - input.x);
for (i = 0; i < prob.l; i++) if (target[i] == prob.y[i]) ++total_correct;
accuracy = (float) (total_correct / prob.l);
System.out.print("Cross Validation Accuracy = " + 100.0 * accuracy + "%\n");
}
SimpleFitness f = ((SimpleFitness) ind.fitness);
f.setFitness(state, accuracy, accuracy == 1.0);
// the fitness better be KozaFitness!
// KozaFitness f = ((KozaFitness)ind.fitness);
// f.setStandardizedFitness(state,(float)accuracy);
// f.hits = hits;
ind.evaluated = true;
}
