Fix some style issues

src/main/java/be/cylab/java/wowa/training/Trainer.java

@@ -6,11 +6,15 @@ import java.util.Collections;
 import java.util.List;
 import java.util.logging.Level;
 
+/**
+ * Class Trainer.
+ * Learn wowa weights based on training dataset
+ */
 public class Trainer {
 
     private final TrainerParameters parameters;
 
-    public Trainer(TrainerParameters parameters) {
+    public Trainer(final TrainerParameters parameters) {
         this.parameters = parameters;
     }
 
@@ -19,7 +23,7 @@ public class Trainer {
      * @param expected
      * @return
      */
-    public SolutionDistance run(final List<double[]> data,
+    final public SolutionDistance run(final List<double[]> data,
                                 final double[] expected
     ) {
         List<SolutionDistance> current_population =
@@ -46,8 +50,8 @@ public class Trainer {
             }
 
             //We found a new best solution
-            if (best_solution_of_current_population.getDistance() <
-                    best_solution.getDistance()) {
+            if (best_solution_of_current_population.getDistance()
+                    < best_solution.getDistance()) {
                 best_solution = best_solution_of_current_population;
             }
 
@@ -74,18 +78,18 @@ public class Trainer {
     }
 
     /**
-     * @param numberOfWeights
-     * @param populationSize
+     * @param number_of_weights
+     * @param population_size
      * @return
      */
     private List<SolutionDistance> generateInitialPopulation(
-            int numberOfWeights,
-            int populationSize
+            final int number_of_weights,
+            final int population_size
     ) {
         List<SolutionDistance> population = new ArrayList<>();
-        for (int i = 0; i < populationSize; i++) {
-            SolutionDistance solution = new SolutionDistance(numberOfWeights);
-            for (int j = 0; j < numberOfWeights; j++) {
+        for (int i = 0; i < population_size; i++) {
+            SolutionDistance solution = new SolutionDistance(number_of_weights);
+            for (int j = 0; j < number_of_weights; j++) {
                 solution.weights_p[j] = Math.random();
                 solution.weights_w[j] = Math.random();
             }
@@ -102,9 +106,9 @@ public class Trainer {
      * @return
      */
     private List<SolutionDistance> computeDistances(
-            List<SolutionDistance> solutions,
-            List<double[]> data,
-            double[] expected
+            final List<SolutionDistance> solutions,
+            final List<double[]> data,
+            final double[] expected
     ) {
         for (SolutionDistance solution : solutions) {
             solution.computeScoreTo(data, expected);
@@ -120,9 +124,9 @@ public class Trainer {
      * @return
      */
     private List<SolutionDistance> rouletteWheelSelection(
-            List<SolutionDistance> solutions,
-            List<SolutionDistance> selected_elements,
-            int count
+            final List<SolutionDistance> solutions,
+            final List<SolutionDistance> selected_elements,
+            final int count
     ) {
         double max = Utils.findMaxDistance(solutions);
         double min = Utils.findMinDistance(solutions);
@@ -158,9 +162,9 @@ public class Trainer {
      * @return
      */
     private List<SolutionDistance> tournamentSelection(
-            List<SolutionDistance> solutions,
-            List<SolutionDistance> selected_elements,
-            int count
+            final List<SolutionDistance> solutions,
+            final List<SolutionDistance> selected_elements,
+            final int count
     ) {
 
         while (selected_elements.size() < count) {
@@ -191,13 +195,13 @@ public class Trainer {
     /**
      * @param solutions
      * @param count
-     * @param selectionMethod
+     * @param selection_method
      * @return
      */
     private List<SolutionDistance> selectParents(
-            List<SolutionDistance> solutions,
-            int count,
-            int selectionMethod
+            final List<SolutionDistance> solutions,
+            final int count,
+            final int selection_method
     ) {
         List<SolutionDistance> selected_parents = new ArrayList<>();
         //Select the two best current solutions
@@ -211,13 +215,13 @@ public class Trainer {
         solutions.remove(0);
         solutions.remove(1);
 
-        if (selectionMethod == TrainerParameters.SELECTION_METHOD_RWS) {
+        if (selection_method == TrainerParameters.SELECTION_METHOD_RWS) {
 
             return this.rouletteWheelSelection(solutions,
                     selected_parents,
                     count - 2
             );
-        } else if (selectionMethod == TrainerParameters.SELECTION_METHOD_TOS) {
+        } else if (selection_method == TrainerParameters.SELECTION_METHOD_TOS) {
             return this.tournamentSelection(solutions,
                     selected_parents,
                     count - 2
@@ -269,8 +273,8 @@ public class Trainer {
      * @param beta
      * @return
      */
-    private List<SolutionDistance> reproduce(SolutionDistance dad,
-                                             SolutionDistance mom,
+    private List<SolutionDistance> reproduce(final SolutionDistance dad,
+                                             final SolutionDistance mom,
                                              List<SolutionDistance> solutions,
                                              int cutPosition,
                                              double beta

src/main/java/be/cylab/java/wowa/training/Utils.java

 package be.cylab.java.wowa.training;
 
-import javax.rmi.CORBA.Util;
 import java.util.List;
 import java.util.Random;