diff --git a/java-wowa-training.iml b/java-wowa-training.iml
index db1671ee29173eb1bf8d5522e34901c8f4301baf..b26eb4a4490b4c5a2aef23f810f3db29ae49fefc 100644
--- a/java-wowa-training.iml
+++ b/java-wowa-training.iml
@@ -22,5 +22,14 @@
<SOURCES />
</library>
</orderEntry>
+ <orderEntry type="module-library">
+ <library>
+ <CLASSES>
+ <root url="jar://$MAVEN_REPOSITORY$/org/jetbrains/annotations/16.0.2/annotations-16.0.2.jar!/" />
+ </CLASSES>
+ <JAVADOC />
+ <SOURCES />
+ </library>
+ </orderEntry>
</component>
</module>
\ No newline at end of file
diff --git a/src/main/java/be/cylab/java/wowa/training/SolutionDistance.java b/src/main/java/be/cylab/java/wowa/training/SolutionDistance.java
index dd10c6de13e8008cdd1a1c0c9634592863b2a037..a6006d750fb10d730488564ff704afdf089ded5b 100644
--- a/src/main/java/be/cylab/java/wowa/training/SolutionDistance.java
+++ b/src/main/java/be/cylab/java/wowa/training/SolutionDistance.java
@@ -18,6 +18,10 @@ public class SolutionDistance implements Comparable<SolutionDistance> {
this.weights_p = new double[weights_number];
}
+ /**
+ *
+ * @return
+ */
@Override
public String toString() {
return "SolutionDistance{" +
@@ -27,13 +31,21 @@ public class SolutionDistance implements Comparable<SolutionDistance> {
+ '}';
}
+ /**
+ *
+ * @param data
+ * @param expected
+ */
public void computeScoreTo(List<double[]> data, double[] expected) {
this.distance = Math.random();
}
-
+ /**
+ *
+ * @param probability
+ */
public void randomlyMutateWithProbability(final double probability) {
double tos = Math.random();
if (tos > probability) {
@@ -55,18 +67,28 @@ public class SolutionDistance implements Comparable<SolutionDistance> {
}
+ /**
+ *
+ * @param solution
+ * @return
+ */
@Override
public int compareTo(final SolutionDistance solution) {
return this.getDistance() > solution.getDistance() ? -1 : this.getDistance() < solution.getDistance() ? 1 : 0;
}
-
+ /**
+ *
+ */
public void normalize() {
this.weights_w = Utils.normalizeWeights(this.weights_w);
this.weights_p = Utils.normalizeWeights(this.weights_p);
}
-
+ /**
+ *
+ * @return
+ */
public double getDistance() {
return distance;
}
diff --git a/src/main/java/be/cylab/java/wowa/training/Trainer.java b/src/main/java/be/cylab/java/wowa/training/Trainer.java
index bbcd0b5b17a93554f2aec05adf30e0b688f851fc..a0324be2bf20088a9c0876c8c5f5c0cfd2f0ed65 100644
--- a/src/main/java/be/cylab/java/wowa/training/Trainer.java
+++ b/src/main/java/be/cylab/java/wowa/training/Trainer.java
@@ -1,5 +1,7 @@
package be.cylab.java.wowa.training;
+import org.jetbrains.annotations.Contract;
+
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
@@ -12,6 +14,12 @@ public class Trainer {
this.parameters = parameters;
}
+ /**
+ *
+ * @param data
+ * @param expected
+ * @return
+ */
public SolutionDistance run(final List<double[]> data, final double[] expected) {
List<SolutionDistance> current_population = this.generateInitialPopulationAndComputeDistances(this.getParameters().getPopulationSize(), data, expected);
@@ -37,6 +45,11 @@ public class Trainer {
return best_solution;
}
+ /**
+ *
+ * @param solutions
+ * @return
+ */
public SolutionDistance findBestSolution(final List<SolutionDistance> solutions) {
SolutionDistance best_solution = new SolutionDistance(solutions.get(0).weights_p.length);
@@ -48,6 +61,12 @@ public class Trainer {
return best_solution;
}
+ /**
+ *
+ * @param numberOfWeights
+ * @param populationSize
+ * @return
+ */
public List<SolutionDistance> generateInitialPopulation(int numberOfWeights, int populationSize) {
List<SolutionDistance> population = new ArrayList<SolutionDistance>();
for (int i = 0; i < populationSize; i++) {
@@ -62,7 +81,13 @@ public class Trainer {
return population;
}
-
+ /**
+ *
+ * @param solutions
+ * @param data
+ * @param expected
+ * @return
+ */
public List<SolutionDistance> computeDistances(List<SolutionDistance> solutions, List<double[]> data, double[] expected) {
for (SolutionDistance solution : solutions) {
solution.computeScoreTo(data, expected);
@@ -71,6 +96,14 @@ public class Trainer {
}
+ /**
+ *
+ * @param solutions
+ * @param selected_elements
+ * @param count
+ * @return
+ */
+ @Contract("_, _, _ -> param2")
private List<SolutionDistance> rouletteWheelSelection(List<SolutionDistance> solutions, List<SolutionDistance> selected_elements, int count) {
double max = Utils.findMaxDistance(solutions);
double min = Utils.findMinDistance(solutions);
@@ -95,6 +128,14 @@ public class Trainer {
return selected_elements;
}
+ /**
+ *
+ * @param solutions
+ * @param selected_elements
+ * @param count
+ * @return
+ */
+ @Contract("_, _, _ -> param2")
private List<SolutionDistance> tournamentSelection(List<SolutionDistance> solutions, List<SolutionDistance> selected_elements, int count) {
while (selected_elements.size() < count) {
@@ -121,6 +162,13 @@ public class Trainer {
return selected_elements;
}
+ /**
+ *
+ * @param solutions
+ * @param count
+ * @param selectionMethod
+ * @return
+ */
public List<SolutionDistance> selectParents(List<SolutionDistance> solutions, int count, int selectionMethod) {
List<SolutionDistance> selected_parents = new ArrayList<>();
//Select the two best current solutions
@@ -144,6 +192,11 @@ public class Trainer {
}
+ /**
+ *
+ * @param solutions
+ * @return
+ */
public List<SolutionDistance> doReproduction(List<SolutionDistance> solutions) {
int nbr_weights = solutions.get(0).weights_p.length;
@@ -167,6 +220,15 @@ public class Trainer {
return solutions;
}
+ /**
+ *
+ * @param dad
+ * @param mom
+ * @param solutions
+ * @param cutPosition
+ * @param beta
+ * @return
+ */
public List<SolutionDistance> reproduce(SolutionDistance dad, SolutionDistance mom, List<SolutionDistance> solutions, int cutPosition, double beta) {
double pnew1W = dad.weights_w[cutPosition] - beta * (dad.weights_w[cutPosition] - mom.weights_w[cutPosition]);
double pnew2W = mom.weights_w[cutPosition] + beta * (dad.weights_w[cutPosition] - mom.weights_w[cutPosition]);
@@ -207,6 +269,11 @@ public class Trainer {
return solutions;
}
+ /**
+ *
+ * @param solutions
+ * @return
+ */
public List<SolutionDistance> randomlyMutateGenes(List<SolutionDistance> solutions) {
double probability = this.getParameters().getMutationRate() / 100;
@@ -219,16 +286,34 @@ public class Trainer {
return solutions;
}
+ /**
+ *
+ * @return
+ */
public TrainerParameters getParameters() {
return this.parameters;
}
+ /**
+ *
+ * @param populationSize
+ * @param data
+ * @param expected
+ * @return
+ */
public List<SolutionDistance> generateInitialPopulationAndComputeDistances(int populationSize, List<double[]> data, double[] expected) {
int number_of_weights = data.get(0).length;
List<SolutionDistance> initial_population = this.generateInitialPopulation(number_of_weights, populationSize);
return this.computeDistances(initial_population, data, expected);
}
+ /**
+ *
+ * @param population
+ * @param data
+ * @param expected
+ * @return
+ */
public List<SolutionDistance> performReproduction(List<SolutionDistance> population, List<double[]> data, double[] expected) {
List<SolutionDistance> parents = this.selectParents(population, this.getParameters().getNumberParents(), this.getParameters().getSelectionMethod());
List<SolutionDistance> new_generation = this.doReproduction(parents);
@@ -237,6 +322,10 @@ public class Trainer {
return this.computeDistances(mutated, data, expected);
}
+ /**
+ *
+ * @param child
+ */
private void checkAndCorrectNullWeightVector(SolutionDistance child) {
}
diff --git a/src/main/java/be/cylab/java/wowa/training/Utils.java b/src/main/java/be/cylab/java/wowa/training/Utils.java
index 27a5b87d080efa8b99cbe6de10ecb32e0fe4a06e..3449769396a4a0e306116f7153b770e893252e2f 100644
--- a/src/main/java/be/cylab/java/wowa/training/Utils.java
+++ b/src/main/java/be/cylab/java/wowa/training/Utils.java
@@ -6,7 +6,11 @@ import java.util.List;
import java.util.Random;
public class Utils {
-
+ /**
+ *
+ * @param weights
+ * @return
+ */
public static double[] normalizeWeights(final double[] weights) {
double sum_weight = Utils.sumArrayElements(weights);
double[] weightsNormalized = new double[weights.length];
@@ -16,6 +20,11 @@ public class Utils {
return weightsNormalized;
}
+ /**
+ *
+ * @param solutions
+ * @return
+ */
public static double findMaxDistance(final List<SolutionDistance> solutions) {
double max = Double.NEGATIVE_INFINITY;
for (SolutionDistance solution : solutions) {
@@ -26,6 +35,11 @@ public class Utils {
return max;
}
+ /**
+ *
+ * @param solutions
+ * @return
+ */
public static double findMinDistance(final List<SolutionDistance> solutions) {
double min = Double.POSITIVE_INFINITY;
for (SolutionDistance solution : solutions) {
@@ -36,6 +50,11 @@ public class Utils {
return min;
}
+ /**
+ *
+ * @param solutions
+ * @return
+ */
public static double sumTotalDistance(final List<SolutionDistance> solutions) {
double sum = 0;
for (SolutionDistance solution : solutions) {
@@ -44,6 +63,11 @@ public class Utils {
return sum;
}
+ /**
+ *
+ * @param array
+ * @return
+ */
public static double sumArrayElements(final double[] array) {
float sum = 0;
for (double weight : array) {
@@ -52,6 +76,12 @@ public class Utils {
return sum;
}
+ /**
+ *
+ * @param min
+ * @param max
+ * @return
+ */
public static int randomInteger(final int min, final int max) {
if (min >= max) {
throw new IllegalArgumentException("Max must be greater then min");