Did more commenting, implemented reflection and statistics

1 files changed, 54 insertions, 18 deletions

diff --git a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
index 5468157..04e9fe8 100644
--- a/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
+++ b/src/jcgp/backend/modules/problem/SymbolicRegressionProblem.java
@@ -1,24 +1,54 @@
 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.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;
 
+/**
+ * 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);
-		functionSet = new SymbolicRegressionFunctions();
-		setProblemName("Symbolic regression");
+		setFunctionSet(new SymbolicRegressionFunctions());
+		setName("Symbolic regression");
 		setFileExtension(".dat");
+		
 		errorThreshold = new DoubleParameter(0.01, "Error threshold") {
 			@Override
 			public void validate(Number newValue) {
@@ -33,6 +63,7 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 				}
 			}
 		};
+		
 		perfectionThreshold = new DoubleParameter(0.000001, "Perfection threshold") {
 			@Override
 			public void validate(Number newValue) {
@@ -47,12 +78,10 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 				}
 			}
 		};
-		hitsBasedFitness = new BooleanParameter(true, "HITS-based fitness") {
-			@Override
-			public void validate(Boolean newValue) {
-				// blank
-			}
-		};
+		
+		hitsBasedFitness = new BooleanParameter(true, "HITS-based fitness");
+		
+		registerParameters(errorThreshold, perfectionThreshold, hitsBasedFitness);
 	}
 	
 	@Override
@@ -75,7 +104,6 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 					} else {
 						fitness += 1 - Math.abs(cgpValue - dataValue);
 					}
-					
 				}
 			}
 			// assign the resulting fitness to the respective individual
@@ -87,7 +115,8 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 	}
 	
 	@Override
-	public void addTestCase(String[] inputs, String[] outputs) {
+	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++) {
@@ -97,16 +126,23 @@ public class SymbolicRegressionProblem extends TestCaseProblem<Double> {
 			outputCases[o] = Double.parseDouble(outputs[o]);
 		}
 		
-		addTestCase(new TestCase<Double>(inputCases, outputCases));
+		return new TestCase<Double>(inputCases, outputCases);
 	}
 
 	@Override
 	public boolean isPerfectSolution(Chromosome fittest) {
+		// higher fitness is better
 		return fittest.getFitness() >= maxFitness.get() - perfectionThreshold.get();
 	}
 	
 	@Override
-	public Parameter<?>[] getLocalParameters() {
-		return new Parameter[]{maxFitness, errorThreshold, perfectionThreshold, hitsBasedFitness};
+	public boolean isImprovement(Chromosome fittest) {
+		// higher fitness is better
+		if (fittest.getFitness() > bestFitness.get()) {
+			bestFitness.set(fittest.getFitness());
+			return true;
+		} else {
+			return false;
+		}
 	}
 }