From 9aa60eaf2f55213901e8f5834a0fead061d7241c Mon Sep 17 00:00:00 2001
From: "a.croix" <croix.alexandre@gmail.com>
Date: Tue, 19 Feb 2019 17:30:15 +0100
Subject: [PATCH] Fix style issues
---
.../java/wowa/training/SolutionDistance.java | 24 ++++-
.../be/cylab/java/wowa/training/Trainer.java | 96 +++++++++----------
2 files changed, 66 insertions(+), 54 deletions(-)
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 b1041f4..35e4741 100644
--- a/src/main/java/be/cylab/java/wowa/training/SolutionDistance.java
+++ b/src/main/java/be/cylab/java/wowa/training/SolutionDistance.java
@@ -1,19 +1,26 @@
package be.cylab.java.wowa.training;
import info.debatty.java.aggregation.WOWA;
-import sun.nio.cs.ext.MacHebrew;
import java.util.Arrays;
import java.util.List;
+/**
+ * Class of Solution.
+ * Solution with distance performance evaluation criterion
+ */
public class SolutionDistance implements Comparable<SolutionDistance> {
- public double[] weights_w;
- public double[] weights_p;
+ private double[] weights_w;
+ private double[] weights_p;
private double distance = Double.POSITIVE_INFINITY;
-
- public SolutionDistance(int weights_number) {
+ /**
+ * SolutionDistance constructor.
+ * Needs weight number as parameter
+ * @param weights_number
+ */
+ public SolutionDistance(final int weights_number) {
this.weights_w = new double[weights_number];
this.weights_p = new double[weights_number];
}
@@ -97,4 +104,11 @@ public class SolutionDistance implements Comparable<SolutionDistance> {
return distance;
}
+ public double[] getWeightsW() {
+ return this.weights_w;
+ }
+
+ public double[] getWeightsP() {
+ return this.weights_p;
+ }
}
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 f862739..da7ebfd 100644
--- a/src/main/java/be/cylab/java/wowa/training/Trainer.java
+++ b/src/main/java/be/cylab/java/wowa/training/Trainer.java
@@ -72,7 +72,7 @@ public class Trainer {
final List<SolutionDistance> solutions
) {
SolutionDistance best_solution =
- new SolutionDistance(solutions.get(0).weights_p.length);
+ new SolutionDistance(solutions.get(0).getWeightsP().length);
for (SolutionDistance solution : solutions) {
if (solution.getDistance() < best_solution.getDistance()) {
@@ -95,8 +95,8 @@ public class Trainer {
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();
+ solution.getWeightsP()[j] = Math.random();
+ solution.getWeightsW()[j] = Math.random();
}
solution.normalize();
population.add(solution);
@@ -241,9 +241,9 @@ public class Trainer {
* @return
*/
private List<SolutionDistance> doReproduction(
- List<SolutionDistance> solutions
+ final List<SolutionDistance> solutions
) {
- int nbr_weights = solutions.get(0).weights_p.length;
+ int nbr_weights = solutions.get(0).getWeightsP().length;
//we add children to the current list of solutions
while (solutions.size() < this.parameters.getPopulationSize()) {
@@ -274,49 +274,49 @@ public class Trainer {
* @param dad
* @param mom
* @param solutions
- * @param cutPosition
+ * @param cut_position
* @param beta
* @return
*/
private void reproduce(final SolutionDistance dad,
final SolutionDistance mom,
- List<SolutionDistance> solutions,
- int cutPosition,
- double beta
+ final List<SolutionDistance> solutions,
+ final int cut_position,
+ final double beta
) {
- double p_new1W = dad.weights_w[cutPosition] - beta
- * (dad.weights_w[cutPosition] - mom.weights_w[cutPosition]);
- double p_new2W = mom.weights_w[cutPosition] + beta
- * (dad.weights_w[cutPosition] - mom.weights_w[cutPosition]);
+ double p_new1_W = dad.getWeightsW()[cut_position] - beta
+ * (dad.getWeightsW()[cut_position] - mom.getWeightsW()[cut_position]);
+ double p_new2_W = mom.getWeightsW()[cut_position] + beta
+ * (dad.getWeightsW()[cut_position] - mom.getWeightsW()[cut_position]);
- double p_new1P = dad.weights_p[cutPosition] - beta
- * (dad.weights_p[cutPosition] - mom.weights_p[cutPosition]);
- double p_new2P = mom.weights_p[cutPosition] + beta
- * (dad.weights_p[cutPosition] - mom.weights_p[cutPosition]);
+ double p_new1_P = dad.getWeightsP()[cut_position] - beta
+ * (dad.getWeightsP()[cut_position] - mom.getWeightsP()[cut_position]);
+ double p_new2_P = mom.getWeightsP()[cut_position] + beta
+ * (dad.getWeightsP()[cut_position] - mom.getWeightsP()[cut_position]);
- SolutionDistance child1 = new SolutionDistance(dad.weights_p.length);
- SolutionDistance child2 = new SolutionDistance(dad.weights_p.length);
+ SolutionDistance child1 = new SolutionDistance(dad.getWeightsP().length);
+ SolutionDistance child2 = new SolutionDistance(dad.getWeightsP().length);
- for (int i = 0; i < cutPosition; i++) {
- child1.weights_w[i] = dad.weights_w[i];
- child1.weights_p[i] = dad.weights_p[i];
+ for (int i = 0; i < cut_position; i++) {
+ child1.getWeightsW()[i] = dad.getWeightsW()[i];
+ child1.getWeightsP()[i] = dad.getWeightsP()[i];
- child2.weights_w[i] = mom.weights_w[i];
- child2.weights_p[i] = mom.weights_p[i];
+ child2.getWeightsW()[i] = mom.getWeightsW()[i];
+ child2.getWeightsP()[i] = mom.getWeightsP()[i];
}
- child1.weights_w[cutPosition] = p_new1W;
- child2.weights_w[cutPosition] = p_new2W;
- child1.weights_p[cutPosition] = p_new1P;
- child2.weights_p[cutPosition] = p_new2P;
+ child1.getWeightsW()[cut_position] = p_new1_W;
+ child2.getWeightsW()[cut_position] = p_new2_W;
+ child1.getWeightsP()[cut_position] = p_new1_P;
+ child2.getWeightsP()[cut_position] = p_new2_P;
- int nbre_weights = dad.weights_w.length;
- for (int i = cutPosition + 1; i < nbre_weights; i++) {
- child1.weights_w[i] = mom.weights_w[i];
- child1.weights_p[i] = mom.weights_p[i];
+ int nbre_weights = dad.getWeightsW().length;
+ for (int i = cut_position + 1; i < nbre_weights; i++) {
+ child1.getWeightsW()[i] = mom.getWeightsW()[i];
+ child1.getWeightsP()[i] = mom.getWeightsP()[i];
- child2.weights_w[i] = dad.weights_w[i];
- child2.weights_p[i] = dad.weights_p[i];
+ child2.getWeightsW()[i] = dad.getWeightsW()[i];
+ child2.getWeightsP()[i] = dad.getWeightsP()[i];
}
//Check and correct only if we used a QuasiRandom generation
child1.normalize();
@@ -331,7 +331,7 @@ public class Trainer {
* @return
*/
private List<SolutionDistance> randomlyMutateGenes(
- List<SolutionDistance> solutions
+ final List<SolutionDistance> solutions
) {
double probability = this.getParameters().getMutationRate() / 100;
@@ -357,15 +357,15 @@ public class Trainer {
* @param expected
* @return
*/
- public List<SolutionDistance> generateInitialPopulationAndComputeDistances(
- int population_size,
- List<double[]> data,
- double[] expected
+ private List<SolutionDistance> generateInitialPopulationAndComputeDistances(
+ final int population_size,
+ final List<double[]> data,
+ final double[] expected
) {
- int numberOfWeights = data.get(0).length;
+ int number_of_weights = data.get(0).length;
List<SolutionDistance> initial_population =
this.generateInitialPopulation(
- numberOfWeights,
+ number_of_weights,
population_size
);
return this.computeDistances(initial_population, data, expected);
@@ -378,9 +378,9 @@ public class Trainer {
* @return
*/
private List<SolutionDistance> performReproduction(
- List<SolutionDistance> population,
- List<double[]> data,
- double[] expected
+ final List<SolutionDistance> population,
+ final List<double[]> data,
+ final double[] expected
) {
List<SolutionDistance> parents = this.selectParents(population,
this.getParameters().getNumberOfParents(),
@@ -392,12 +392,10 @@ public class Trainer {
return this.computeDistances(mutated, data, expected);
}
- /**
- * @param child
- */
- private void checkAndCorrectNullWeightVector(SolutionDistance child) {
+ /*
+ private void checkAndCorrectNullWeightVector(final SolutionDistance child) {
}
-
+*/
}
--
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