Tooltips are looking strange, checking on a different machine

1 files changed, 124 insertions, 0 deletions

diff --git a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
new file mode 100644
index 0000000..52df0f2
--- /dev/null
+++ b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
@@ -0,0 +1,124 @@
+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";
+	}
+}