Refactoring: change double[] to List<Double>

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

@@ -13,7 +13,7 @@ public abstract class AbstractSolution
 
     protected List<Double> weights_w;
     protected List<Double> weights_p;
-    protected double fitness_score = Double.POSITIVE_INFINITY;
+    protected Double fitness_score = Double.POSITIVE_INFINITY;
 
     /**
      * SolutionDistance constructor. Needs weight number as parameter
@@ -49,7 +49,8 @@ public abstract class AbstractSolution
         this.normalize();
     }
 
-    abstract void computeScoreTo(List<double[]> data, double[] expected);
+    abstract void computeScoreTo(List<List<Double>> data,
+                                 List<Double> expected);
 
     /**
      * @return

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

@@ -41,18 +41,19 @@ public class SolutionDistance extends AbstractSolution {
      * @param data
      * @param expected
      */
-    final void computeScoreTo(final List<double[]> data,
-                              final double[] expected
+    final void computeScoreTo(final List<List<Double>> data,
+                              final List<Double> expected
     ) {
 
         this.fitness_score = 0;
         for (int i = 0; i < data.size(); i++) {
-            double[] vector = data.get(i);
-            double target_value = expected[i];
+            List<Double> vector = data.get(i);
+            Double target_value = expected.get(i);
             WOWA wowa = new WOWA(
                     Utils.convertListDoubleToArrayDouble(this.weights_w),
                     Utils.convertListDoubleToArrayDouble(this.weights_p));
-            double aggregated_value = wowa.aggregate(vector);
+            Double aggregated_value = wowa.aggregate(
+                    Utils.convertListDoubleToArrayDouble(vector));
             this.fitness_score += Math.pow(target_value - aggregated_value, 2);
         }
         this.fitness_score = Math.sqrt(this.fitness_score);

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

@@ -37,8 +37,8 @@ public class Trainer {
     public final AbstractSolution run(
             final String data_file_name,
             final String expected_file_name) {
-        List<double[]> data = Utils.convertJsonToDataForTrainer(data_file_name);
-        double[] expected
+        List<List<Double>> data = Utils.convertJsonToDataForTrainer(data_file_name);
+        List<Double> expected
                 = Utils.convertJsonToExpectedForTrainer(expected_file_name);
         return this.run(data, expected);
     }
@@ -49,8 +49,8 @@ public class Trainer {
      * @return
      */
     public final AbstractSolution run(
-            final List<double[]> data,
-            final double[] expected) {
+            final List<List<Double>> data,
+            final List<Double> expected) {
 
         List<AbstractSolution> current_population
                 = this.generateInitialPopulationAndComputeDistances(
@@ -184,8 +184,8 @@ public class Trainer {
      */
     final List<AbstractSolution> computeDistances(
             final List<AbstractSolution> solutions,
-            final List<double[]> data,
-            final double[] expected) {
+            final List<List<Double>> data,
+            final List<Double> expected) {
 
         for (AbstractSolution solution : solutions) {
             solution.computeScoreTo(data, expected);
@@ -477,10 +477,10 @@ public class Trainer {
      */
     private List<AbstractSolution> generateInitialPopulationAndComputeDistances(
             final int population_size,
-            final List<double[]> data,
-            final double[] expected) {
+            final List<List<Double>> data,
+            final List<Double> expected) {
 
-        int number_of_weights = data.get(0).length;
+        int number_of_weights = data.get(0).size();
         List<AbstractSolution> initial_population = new ArrayList<>();
         if (this.parameters.getPopulationInitializationMethod()
                 == TrainerParameters.POPULATION_INITIALIZATION_RANDOM) {
@@ -505,8 +505,8 @@ public class Trainer {
      */
     final List<AbstractSolution> performReproduction(
             final List<AbstractSolution> population,
-            final List<double[]> data,
-            final double[] expected) {
+            final List<List<Double>> data,
+            final List<Double> expected) {
 
         List<AbstractSolution> parents = this.selectParents(population,
                 this.getParameters().getNumberOfParents(),

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

@@ -126,13 +126,13 @@ final class Utils {
      * @param filename
      * @return
      */
-    public static List<double[]> convertJsonToDataForTrainer(
+    public static List<List<Double>> convertJsonToDataForTrainer(
             final String filename) {
         Genson genson = new Genson();
         String data_json = Utils.readFileToString(filename);
-        List<double[]> data = genson.deserialize(
+        List<List<Double>> data = genson.deserialize(
                 data_json,
-                new GenericType<List<double[]>>() {
+                new GenericType<List<List<Double>>>() {
                 });
         return data;
     }
@@ -143,13 +143,14 @@ final class Utils {
      * @param filename
      * @return
      */
-    public static double[] convertJsonToExpectedForTrainer(
+    public static List<Double> convertJsonToExpectedForTrainer(
             final String filename) {
 
         Genson genson = new Genson();
         String expected_json = Utils.readFileToString(filename);
-
-        double[] expected = genson.deserialize(expected_json, double[].class);
+        List<Double> expected = genson.deserialize(expected_json,
+                new GenericType<List<Double>>() {
+        });
         return expected;
     }