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package jcgp.backend.modules.problem;
import jcgp.backend.function.SymbolicRegressionFunctions;
import jcgp.backend.parameters.BooleanParameter;
import jcgp.backend.parameters.DoubleParameter;
import jcgp.backend.parameters.ParameterStatus;
import jcgp.backend.population.Population;
import jcgp.backend.resources.Resources;
/**
* Symbolic regression functions
* <br><br>
* Using this problem type, regression problems can be solved.
* {@code parseData()} must be used to load the desired function
* data in the standard CGP .dat format.
* <br><br>
* This problem uses quite a few parameters:
* <ul>
* <li>Error threshold: the maximum difference allowed between an
* evolved output and the equivalent output from the problem data.
* Outputs within the error threshold will be considered correct.
* This is only used if hits is enabled.</li>
* <li>Perfection threshold: if the fitness is calculated without
* using the hits method, it is a decimal value. A solution is
* considered perfect when the difference between its fitness and
* the maximum possible fitness is within the perfection threshold.</li>
* <li>Hits-based fitness: increment the fitness by 1 whenever the
* chromosome output is within the error threshold.</li></ul>
*
*
* @see SymbolicRegressionFunctions
* @author Eduardo Pedroni
*
*/
public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
private DoubleParameter errorThreshold, perfectionThreshold;
private BooleanParameter hitsBasedFitness;
/**
* Creates a new instance of SymbolicRegressionProblem.
*
* @param resources a reference to the experiment's resources.
*/
public SymbolicRegressionProblem(Resources resources) {
super(resources);
setFunctionSet(new SymbolicRegressionFunctions());
setName("Symbolic regression");
setFileExtension(".dat");
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(false, "Hits-based fitness");
registerParameters(errorThreshold, perfectionThreshold, hitsBasedFitness);
}
@Override
public void evaluate(Population population) {
// for every chromosome in the population
for (int i = 0; i < getResources().populationSize(); i++) {
// assume an initial fitness of 0
double fitness = 0;
// for each test case
for (int t = 0; t < testCases.size(); t++) {
population.get(i).setInputs((Object[]) testCases.get(t).getInputs());
// check each output
for (int o = 0; o < getResources().outputs(); o++) {
Double cgpValue = (Double) population.get(i).getOutput(o).calculate();
Double dataValue = testCases.get(t).getOutputs()[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.get(i).setFitness(fitness);
}
}
@Override
public TestCase<Double> parseTestCase(String[] inputs, String[] outputs) {
// cast the test case values to UnsignedInteger
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]);
}
return new TestCase<Double>(inputCases, outputCases);
}
@Override
public int hasPerfectSolution(Population population) {
// higher fitness is better
for (int i = 0; i < getResources().populationSize(); i++) {
if (population.get(i).getFitness() >= maxFitness.get() - perfectionThreshold.get()) {
return i;
}
}
return -1;
}
}
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