Revert last commits

checkstyle.xml

@@ -205,9 +205,7 @@
         <module name="FinalClass"/>
         <module name="HideUtilityClassConstructor"/>
         <module name="InterfaceIsType"/>
-        <module name="VisibilityModifier">
-            <property name="protectedAllowed" value="true"/>
-        </module>
+        <module name="VisibilityModifier"/>
 
 
         <!-- Miscellaneous other checks.                   -->

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

-package be.cylab.java.wowa.training;
-
-import java.util.Arrays;
-import java.util.List;
-import java.util.Random;
-
-/**
- * Abstract class Solution.
- */
-public abstract class AbstractSolution
-        implements Comparable<AbstractSolution>, Cloneable {
-
-    protected double[] weights_w;
-    protected double[] weights_p;
-    protected double distance = Double.POSITIVE_INFINITY;
-
-    /**
-     * SolutionDistance constructor. Needs weight number as parameter
-     *
-     * @param weights_number
-     */
-    public AbstractSolution(final int weights_number) {
-        this.weights_w = new double[weights_number];
-        this.weights_p = new double[weights_number];
-        for (int i = 0; i < weights_number; i++) {
-            this.weights_w[i] = Math.random();
-            this.weights_p[i] = Math.random();
-        }
-        this.normalize();
-    }
-
-    /**
-     * Constructor solution distance. Made for test to initialize the Random
-     * Generator
-     *
-     * @param weights_number int
-     * @param seed           int
-     */
-
-    public AbstractSolution(final int weights_number, final int seed) {
-        Random rnd = new Random(seed);
-        this.weights_w = new double[weights_number];
-        this.weights_p = new double[weights_number];
-        for (int i = 0; i < weights_number; i++) {
-            this.weights_w[i] = rnd.nextDouble();
-            this.weights_p[i] = rnd.nextDouble();
-        }
-        this.normalize();
-    }
-
-    abstract void computeScoreTo(List<double[]> data, double[] expected);
-
-    /**
-     * @return
-     */
-    @Override
-    public final String toString() {
-        return "SolutionDistance{"
-                + "weights_w=" + Arrays.toString(weights_w)
-                + ", weights_p=" + Arrays.toString(weights_p)
-                + ", distance=" + Math.abs(distance)
-                + '}';
-    }
-
-    /**
-     * Change a random gene in a weight vector.
-     * The gene is changed is the probability is higher than a random double
-     *
-     * @param probability
-     */
-    final void randomlyMutateWithProbability(final double probability) {
-        double tos = Math.random();
-        if (tos > probability) {
-            // do nothing
-            return;
-        }
-
-        //new weight value and position
-        double new_weight_value = Math.random();
-        int random_index = Utils.randomInteger(0, this.weights_p.length - 1);
-        //Select w or p weights
-        int weight_selection = Utils.randomInteger(0, 1);
-        if (weight_selection == 0) {
-            this.weights_w[random_index] = new_weight_value;
-        } else {
-            this.weights_p[random_index] = new_weight_value;
-        }
-        this.normalize();
-
-    }
-
-    /**
-     * @param solution
-     * @return int
-     */
-    @Override
-    public final int compareTo(final AbstractSolution solution) {
-        if (this.getDistance() > solution.getDistance()) {
-            return 1;
-        } else if (this.getDistance() < solution.getDistance()) {
-            return -1;
-        } else {
-            return 0;
-        }
-    }
-
-    /**
-     * Function to normalize SolutionDistance weights.
-     * Weights must be between 0 and 1 and the sum of the weight in a vector
-     * must be equal to 1
-     */
-    final void normalize() {
-        this.weights_w = Utils.normalizeWeights(this.weights_w);
-        this.weights_p = Utils.normalizeWeights(this.weights_p);
-    }
-
-    /**
-     * @return
-     */
-    public final double getDistance() {
-        return distance;
-    }
-
-    /**
-     * @param distance
-     */
-    public final void setDistance(final double distance) {
-        return;
-    }
-
-    /**
-     * @return
-     */
-    public final double[] getWeightsW() {
-        return this.weights_w;
-    }
-
-    /**
-     * @return
-     */
-    public final double[] getWeightsP() {
-        return this.weights_p;
-    }
-
-    /**
-     * @param weights_w
-     */
-    final void setWeightsW(final double[] weights_w) {
-        this.weights_w = weights_w;
-    }
-
-    /**
-     * @param weights_p
-     */
-    final void setWeightsP(final double[] weights_p) {
-        this.weights_p = weights_p;
-    }
-
-
-    /**
-     * Copy.
-     *
-     * @return
-     * @throws java.lang.CloneNotSupportedException if clone is not supported
-     */
-    public final SolutionDistance clone() throws CloneNotSupportedException {
-        return (SolutionDistance) super.clone();
-    }
-
-}

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

-package be.cylab.java.wowa.training;
-
-import info.debatty.java.aggregation.WOWA;
-
-import java.util.List;
-
-/**
- * Child class Solution.
- * Fitness criterion is AUC
- */
-public class SolutionAUC extends AbstractSolution {
-    /**
-     * Default constructor.
-     * @param weight_number
-     */
-    public SolutionAUC(final int weight_number) {
-        super(weight_number);
-    }
-
-    final void computeScoreTo(
-            final List<double[]> data,
-            final double[] expected) {
-        this.distance = 0;
-        double[] aggregated_values = new double[data.size()];
-        WOWA wowa = new WOWA(this.weights_w, this.weights_p);
-        this.distance = Utils.computeAUC(this, data, expected);
-    }
-}

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

@@ -2,13 +2,19 @@ package be.cylab.java.wowa.training;
 
 import info.debatty.java.aggregation.WOWA;
 
+import java.util.Arrays;
 import java.util.List;
+import java.util.Random;
 
 /**
  * Class of Solution. Solution with distance performance evaluation criterion
  */
-public class SolutionDistance extends AbstractSolution {
+public class SolutionDistance
+        implements Comparable<SolutionDistance>, Cloneable {
 
+    private double[] weights_w;
+    private double[] weights_p;
+    private double distance = Double.POSITIVE_INFINITY;
 
     /**
      * SolutionDistance constructor. Needs weight number as parameter
@@ -16,19 +22,45 @@ public class SolutionDistance extends AbstractSolution {
      * @param weights_number
      */
     public SolutionDistance(final int weights_number) {
-        super(weights_number);
+        this.weights_w = new double[weights_number];
+        this.weights_p = new double[weights_number];
+        for (int i = 0; i < weights_number; i++) {
+            this.weights_w[i] = Math.random();
+            this.weights_p[i] = Math.random();
+        }
+        this.normalize();
     }
 
     /**
-     * Constructor used for tests.
-     * @param weights_number
-     * @param seed
+     * Constructor solution distance. Made for test to initialize the Random
+     * Generator
+     *
+     * @param weights_number int
+     * @param seed           int
      */
+
     public SolutionDistance(final int weights_number, final int seed) {
-        super(weights_number, seed);
+        Random rnd = new Random(seed);
+        this.weights_w = new double[weights_number];
+        this.weights_p = new double[weights_number];
+        for (int i = 0; i < weights_number; i++) {
+            this.weights_w[i] = rnd.nextDouble();
+            this.weights_p[i] = rnd.nextDouble();
+        }
+        this.normalize();
     }
 
-
+    /**
+     * @return
+     */
+    @Override
+    public final String toString() {
+        return "SolutionDistance{"
+                + "weights_w=" + Arrays.toString(weights_w)
+                + ", weights_p=" + Arrays.toString(weights_p)
+                + ", distance=" + distance
+                + '}';
+    }
 
     /**
      * Compute the solution fitness score.
@@ -55,4 +87,99 @@ public class SolutionDistance extends AbstractSolution {
 
     }
 
+    /**
+     * Change a random gene in a weight vector.
+     * The gene is changed is the probability is higher than a random double
+     * @param probability
+     */
+    final void randomlyMutateWithProbability(final double probability) {
+        double tos = Math.random();
+        if (tos > probability) {
+            // do nothing
+            return;
+        }
+
+        //new weight value and position
+        double new_weight_value = Math.random();
+        int random_index = Utils.randomInteger(0, this.weights_p.length - 1);
+        //Select w or p weights
+        int weight_selection = Utils.randomInteger(0, 1);
+        if (weight_selection == 0) {
+            this.weights_w[random_index] = new_weight_value;
+        } else {
+            this.weights_p[random_index] = new_weight_value;
+        }
+        this.normalize();
+
+    }
+
+    /**
+     * @param solution
+     * @return int
+     */
+    @Override
+    public final int compareTo(final SolutionDistance solution) {
+        if (this.getDistance() > solution.getDistance()) {
+            return 1;
+        } else if (this.getDistance() < solution.getDistance()) {
+            return -1;
+        } else {
+            return 0;
+        }
+    }
+
+    /**
+     *Function to normalize SolutionDistance weights.
+     * Weights must be between 0 and 1 and the sum of the weight in a vector
+     * must be equal to 1
+     */
+    final void normalize() {
+        this.weights_w = Utils.normalizeWeights(this.weights_w);
+        this.weights_p = Utils.normalizeWeights(this.weights_p);
+    }
+
+    /**
+     * Copy.
+     *
+     * @return
+     * @throws java.lang.CloneNotSupportedException if clone is not supported
+     */
+    public final SolutionDistance clone() throws CloneNotSupportedException {
+        return (SolutionDistance) super.clone();
+    }
+
+    /**
+     * @return
+     */
+    public final double getDistance() {
+        return distance;
+    }
+
+    /**
+     * @return
+     */
+    public final double[] getWeightsW() {
+        return this.weights_w;
+    }
+
+    /**
+     * @return
+     */
+    public final double[] getWeightsP() {
+        return this.weights_p;
+    }
+
+    /**
+     * @param weights_w
+     */
+    final void setWeightsW(final double[] weights_w) {
+        this.weights_w = weights_w;
+    }
+
+    /**
+     * @param weights_p
+     */
+    final void setWeightsP(final double[] weights_p) {
+        this.weights_p = weights_p;
+    }
 }

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

@@ -152,7 +152,7 @@ public final class Utils {
      * @return
      */
     public static  double computeAUC(
-            final AbstractSolution solution,
+            final SolutionDistance solution,
             final List<double[]> data,
             final double[] expected) {
         if (data.size() != expected.length) {
@@ -172,7 +172,7 @@ public final class Utils {
      * @return
      */
     public static List<RocCoordinates> computeRocPoints(
-            final AbstractSolution solution,
+            final SolutionDistance solution,
             final List<double[]> data,
             final double[] expected,
             final boolean save_on_csv) {
@@ -199,7 +199,7 @@ public final class Utils {
      * @return
      */
     private static double[] computeWOWAScoreWithData(
-            final AbstractSolution solution,
+            final SolutionDistance solution,
             final List<double[]> data) {
         double[] score = new double[data.size()];
         WOWA wowa = new WOWA(solution.getWeightsW(), solution.getWeightsP());