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package jcgp.backend.modules.problem;
import jcgp.backend.function.DoubleArithmetic;
import jcgp.backend.population.Chromosome;
import jcgp.backend.population.Population;
import jcgp.backend.resources.Resources;
import jcgp.backend.resources.parameters.BooleanParameter;
import jcgp.backend.resources.parameters.DoubleParameter;
import jcgp.backend.resources.parameters.Parameter;
import jcgp.backend.resources.parameters.ParameterStatus;
public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
private DoubleParameter errorThreshold, perfectionThreshold;
private BooleanParameter hitsBasedFitness;
public SymbolicRegressionProblem(Resources resources) {
super(resources);
functionSet = new DoubleArithmetic();
errorThreshold = new DoubleParameter(0.01, "Error threshold") {
@Override
public void validate(Number newValue) {
if (newValue.doubleValue() < 0) {
status = ParameterStatus.INVALID;
status.setDetails("Error threshold must be a positive value.");
} else if (newValue.doubleValue() == 0) {
status = ParameterStatus.WARNING;
status.setDetails("An error threshold of 0 is very rigorous and difficult to achieve.");
} else {
status = ParameterStatus.VALID;
}
}
};
perfectionThreshold = new DoubleParameter(0.000001, "Perfection threshold") {
@Override
public void validate(Number newValue) {
if (newValue.doubleValue() < 0) {
status = ParameterStatus.INVALID;
status.setDetails("Perfection threshold must be a positive value.");
} else if (newValue.doubleValue() == 0) {
status = ParameterStatus.WARNING;
status.setDetails("A perfection threshold of 0 is very rigorous and difficult to achieve.");
} else {
status = ParameterStatus.VALID;
}
}
};
hitsBasedFitness = new BooleanParameter(true, "Hits-based fitness") {
@Override
public void validate(Boolean newValue) {
// blank
}
};
}
@Override
public void evaluate(Population population, Resources resources) {
// set fittest to 0, change it whenever a fitter one is found
population.setFittest(0);
// for every chromosome in the population
for (int i = 0; i < resources.populationSize(); i++) {
// assume an initial fitness of 0
double fitness = 0;
// for each test case
for (int t = 0; t < testCases.size(); t++) {
population.getChromosome(i).setInputs((Object[]) testCases.get(t).getInputs());
// check each output
for (int o = 0; o < resources.outputs(); o++) {
Double cgpValue = (Double) population.getChromosome(i).getOutput(o).calculate();
Double dataValue = testCases.get(t).getOutput(o);
if (hitsBasedFitness.get()) {
if (Math.abs(cgpValue - dataValue) <= errorThreshold.get()) {
fitness++;
}
} else {
fitness += 1 - Math.abs(cgpValue - dataValue);
}
}
}
// assign the resulting fitness to the respective individual
population.getChromosome(i).setFitness(fitness);
if (fitness >= population.getFittest().getFitness()) {
population.setFittest(i);
}
}
}
@Override
public String toString() {
return "Symbolic regression";
}
@Override
public void addTestCase(String[] inputs, String[] outputs) {
Double[] inputCases = new Double[inputs.length];
Double[] outputCases = new Double[outputs.length];
for (int i = 0; i < inputCases.length; i++) {
inputCases[i] = Double.parseDouble(inputs[i]);
}
for (int o = 0; o < outputCases.length; o++) {
outputCases[o] = Double.parseDouble(outputs[o]);
}
addTestCase(new TestCase<Double>(inputCases, outputCases));
}
@Override
public boolean isPerfectSolution(Chromosome fittest) {
return fittest.getFitness() >= maxFitness.get() - perfectionThreshold.get();
}
@Override
public Parameter<?>[] getLocalParameters() {
return new Parameter[]{maxFitness, errorThreshold, perfectionThreshold, hitsBasedFitness};
}
@Override
public String getFileExtension() {
return ".dat";
}
}
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