MATH-1563: Introducing new implementation of GA functionality (WIP).

commons-math-examples/examples-ga/examples-ga-math-functions-adaptive/src/main/java/org/apache/commons/math4/examples/ga/mathfunctions/adaptive/AdaptiveMathFunctionOptimizer.java

@@ -42,13 +42,29 @@
  * genetic algorithm.
  */
 public final class AdaptiveMathFunctionOptimizer {
+
     /** length of chromosome. **/
     private static final int CHROMOSOME_LENGTH_PER_DIMENSION = 12;
-
     /** instance of logger. **/
     private final Logger logger = LoggerFactory.getLogger(AdaptiveMathFunctionOptimizer.class);
 
-    public void optimize(int dimension,
+    /**
+     * Optimizes the population.
+     * @param dimension                               problem dimension
+     * @param minCrossoverRate                        minimum crossover rate
+     * @param maxCrossoverRate                        maximum crossover rate
+     * @param minMutationRate                         minimum mutation rate
+     * @param maxMutationRate                         maximum mutation rate
+     * @param elitismRate                             elitism rate
+     * @param tournamentSize                          tournament size for tournament
+     *                                                selection
+     * @param generationCountWithUnchangedBestFitness number of generation to be
+     *                                                evolved with best unchanged
+     *                                                fitness for convergence
+     * @param populationSize                          size of population
+     * @return returns best chromosome
+     */
+    public Chromosome<Coordinate> optimize(int dimension,
             double minCrossoverRate,
             double maxCrossoverRate,
             double minMutationRate,
@@ -75,7 +91,7 @@ public void optimize(int dimension,
         // best chromosome from the final population
         final Chromosome<Coordinate> bestFinal = finalPopulation.getFittestChromosome();
 
-        logger.info(bestFinal.toString());
+        return bestFinal;
     }
 
     private static Population<Coordinate> getInitialPopulation(int dimension, int populationSize) {

commons-math-examples/examples-ga/examples-ga-math-functions-adaptive/src/main/java/org/apache/commons/math4/examples/ga/mathfunctions/adaptive/StandAlone.java

@@ -16,6 +16,15 @@
  */
 package org.apache.commons.math4.examples.ga.mathfunctions.adaptive;
 
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.PrintWriter;
+import java.io.UnsupportedEncodingException;
+import java.nio.charset.Charset;
+
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
 import picocli.CommandLine;
 import picocli.CommandLine.Command;
 import picocli.CommandLine.Option;
@@ -27,43 +36,40 @@ public class StandAlone implements Runnable {
     /** number of dimension. **/
     @Option(names = "-d", paramLabel = "DIMENSION", required = true, description = "Dimension of problem domain.")
     private int dimension;
-
     /** size of tournament. **/
     @Option(names = "-t", paramLabel = "TOURNAMENT_SIZE", required = true, description = "Tournament size.")
     private int tournamentSize;
-
     /** size of population. **/
     @Option(names = "-p", paramLabel = "POPULATION_SIZE", required = true, description = "Size of population.")
     private int populationSize;
-
     /** minimum rate of crossover. **/
     @Option(names = "-c", paramLabel = "MIN_CROSSOVER_RATE",
             description = "Crossover rate (default: ${DEFAULT-VALUE}).")
     private double minCrossoverRate = 0;
-
     /** maximum rate of crossover. **/
     @Option(names = "-C", paramLabel = "MAX_CROSSOVER_RATE",
             description = "Crossover rate (default: ${DEFAULT-VALUE}).")
     private double maxCrossoverRate = 1.0;
-
     /** minimum rate of mutation. **/
     @Option(names = "-m", paramLabel = "MIN_MUTATION_RATE",
             description = "Minimum Mutation rate (default: ${DEFAULT-VALUE}).")
     private double minMutationRate = 0;
-
     /** maximum rate of mutation. **/
     @Option(names = "-M", paramLabel = "MAX_MUTATION_RATE",
             description = "Maximum Mutation rate (default: ${DEFAULT-VALUE}).")
     private double maxMutationRate = 0.1;
-
     /** rate of elitism. **/
     @Option(names = "-e", paramLabel = "ELITISM_RATE", description = "Elitism rate (default: ${DEFAULT-VALUE}).")
     private double elitismRate = 0.25;
-
     /** number of generations with unchanged best fitness. **/
     @Option(names = "-g", paramLabel = "GENERATIONS_EVOLVED_WITH_UNCHANGED_BEST_FITNESS",
             description = "No of generations evolved with unchanged best fitness (default: ${DEFAULT-VALUE}).")
     private int generationsEvolvedWithUnchangedBestFitness = 50;
+    /** indicates the absolute file path where output would be stored. **/
+    @Option(names = {"-o", "--output"}, required = true, paramLabel = "OUTPUT_FILE_PATH", arity = "1")
+    private String output;
+    /** instance of logger. **/
+    private final Logger logger = LoggerFactory.getLogger(StandAlone.class);
 
     public static void main(String[] args) {
         CommandLine.run(new StandAlone(), args);
@@ -79,10 +85,18 @@ public void run() {
         // validate all input options.
         validateInput();
 
-        final AdaptiveMathFunctionOptimizer optimizer = new AdaptiveMathFunctionOptimizer();
-
-        optimizer.optimize(dimension, minCrossoverRate, maxCrossoverRate, minMutationRate, maxMutationRate, elitismRate,
-                tournamentSize, generationsEvolvedWithUnchangedBestFitness, populationSize);
+        try (PrintWriter writer = new PrintWriter(new File(output), Charset.defaultCharset().name())) {
+            writer.println("Optimization Result:");
+            final AdaptiveMathFunctionOptimizer optimizer = new AdaptiveMathFunctionOptimizer();
+            writer.println(optimizer
+                    .optimize(dimension, minCrossoverRate, maxCrossoverRate, minMutationRate, maxMutationRate,
+                            elitismRate, tournamentSize, generationsEvolvedWithUnchangedBestFitness, populationSize)
+                    .toString());
+        } catch (FileNotFoundException e) {
+            logger.error("Error while writing to file", e);
+        } catch (UnsupportedEncodingException e) {
+            logger.error("Encoding not supported for writing to output file", e);
+        }
 
     }
 
@@ -121,5 +135,9 @@ private void validateInput() {
             throw new IllegalArgumentException(
                     "Number of generations evolved with unchanged best fitness should be >= 1.");
         }
+        File outFile = new File(output);
+        if (outFile.exists() && !outFile.delete()) {
+            throw new IllegalArgumentException("Existing output file could not be deleted.");
+        }
     }
 }

commons-math-examples/examples-ga/examples-ga-math-functions/src/main/java/org/apache/commons/math4/examples/ga/mathfunctions/MathFunctionOptimizer.java

@@ -40,6 +40,7 @@
  */
 
 public final class MathFunctionOptimizer {
+
     /** length of chromosome. **/
     private static final int CHROMOSOME_LENGTH_PER_DIMENSION = 12;
     /** instance of logger. **/
@@ -55,8 +56,9 @@ public final class MathFunctionOptimizer {
      * @param generationCountWithUnchangedBestFitness no of generation evolved with
      *                                                unchanged best fitness
      * @param populationSize                          size of population
+     * @return returns best chromosome
      */
-    public void optimize(int dimension,
+    public Chromosome<Coordinate> optimize(int dimension,
             double crossoverRate,
             double mutationRate,
             double elitismRate,
@@ -81,7 +83,7 @@ public void optimize(int dimension,
         // best chromosome from the final population
         final Chromosome<Coordinate> bestFinal = finalPopulation.getFittestChromosome();
 
-        logger.info(bestFinal.toString());
+        return bestFinal;
     }
 
     private static Population<Coordinate> getInitialPopulation(int dimension, int populationSize) {
@@ -101,14 +103,14 @@ private static Population<Coordinate> getInitialPopulation(int dimension, int po
                             final BinaryChromosome<Coordinate> binaryChromosome =
                                     (BinaryChromosome<Coordinate>) chromosome;
                             final long length = binaryChromosome.getLength();
-                            final List<Double> coordinates = new ArrayList<>();
+                            final List<Double> dimValues = new ArrayList<>();
 
                             for (int j = 0; j < length; j += 12) {
                                 final String dimensionStrValue = binaryChromosome.getStringRepresentation(j, j + 12);
-                                coordinates.add(Integer.parseUnsignedInt(dimensionStrValue, 2) / 100d);
+                                dimValues.add(Integer.parseUnsignedInt(dimensionStrValue, 2) / 100d);
                             }
 
-                            return new Coordinate(coordinates);
+                            return new Coordinate(dimValues);
                         }));
         }
         return population;

commons-math-examples/examples-ga/examples-ga-math-functions/src/main/java/org/apache/commons/math4/examples/ga/mathfunctions/StandAlone.java

@@ -16,7 +16,15 @@
  */
 package org.apache.commons.math4.examples.ga.mathfunctions;
 
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.PrintWriter;
+import java.io.UnsupportedEncodingException;
+import java.nio.charset.Charset;
+
 import org.apache.commons.math4.examples.ga.mathfunctions.legacy.LegacyMathFunctionOptimizer;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 import picocli.CommandLine;
 import picocli.CommandLine.Command;
@@ -28,35 +36,33 @@ public class StandAlone implements Runnable {
     /** number of dimension. **/
     @Option(names = "-d", paramLabel = "DIMENSION", required = true, description = "Dimension of problem domain.")
     private int dimension;
-
     /** size of tournament. **/
     @Option(names = "-t", paramLabel = "TOURNAMENT_SIZE", required = true, description = "Tournament size.")
     private int tournamentSize;
-
     /** size of population. **/
     @Option(names = "-p", paramLabel = "POPULATION_SIZE", required = true, description = "Size of population.")
     private int populationSize;
-
     /** rate of crossover. **/
     @Option(names = "-c", paramLabel = "CROSSOVER_RATE", description = "Crossover rate (default: ${DEFAULT-VALUE}).")
     private double crossoverRate = 1.0;
-
     /** rate of elitism. **/
     @Option(names = "-e", paramLabel = "ELITISM_RATE", description = "Elitism rate (default: ${DEFAULT-VALUE}).")
     private double elitismRate = 0.25;
-
     /** rate of mutation. **/
     @Option(names = "-m", paramLabel = "MUTATION_RATE", description = "Mutation rate (default: ${DEFAULT-VALUE}).")
     private double mutationRate = 0.01;
-
     /** number of generations with unchanged best fitness. **/
     @Option(names = "-g", paramLabel = "GENERATIONS_EVOLVED_WITH_UNCHANGED_BEST_FITNESS",
             description = "No of generations evolved with unchanged best fitness (default: ${DEFAULT-VALUE}).")
     private int generationsEvolvedWithUnchangedBestFitness = 50;
-
     /** indicates whether the algorithm should be legacy or not. **/
     @Option(names = "--legacy", description = "Indicates which version of algorithm to execute current or legacy.")
     private boolean legacy;
+    /** indicates the absolute file path where output would be stored. **/
+    @Option(names = {"-o", "--output"}, required = true, paramLabel = "OUTPUT_FILE_PATH")
+    private String output;
+    /** instance of logger. **/
+    private final Logger logger = LoggerFactory.getLogger(StandAlone.class);
 
     public static void main(String[] args) {
         CommandLine.run(new StandAlone(), args);
@@ -72,14 +78,26 @@ public void run() {
         // validate all input options.
         validateInput();
 
-        if (!legacy) {
-            new MathFunctionOptimizer().optimize(dimension, crossoverRate, mutationRate, elitismRate, tournamentSize,
-                    generationsEvolvedWithUnchangedBestFitness, populationSize);
-        } else {
-            new LegacyMathFunctionOptimizer().optimize(dimension, crossoverRate, mutationRate, elitismRate,
-                    tournamentSize, generationsEvolvedWithUnchangedBestFitness, populationSize);
+        try (PrintWriter writer = new PrintWriter(new File(output), Charset.defaultCharset().name())) {
+            writer.println("Optimization Result:");
+            if (!legacy) {
+                writer.println(
+                        new MathFunctionOptimizer()
+                                .optimize(dimension, crossoverRate, mutationRate, elitismRate, tournamentSize,
+                                        generationsEvolvedWithUnchangedBestFitness, populationSize)
+                                .toString());
+            } else {
+                writer.println(
+                        new LegacyMathFunctionOptimizer()
+                                .optimize(dimension, crossoverRate, mutationRate, elitismRate, tournamentSize,
+                                        generationsEvolvedWithUnchangedBestFitness, populationSize)
+                                .toString());
+            }
+        } catch (FileNotFoundException e) {
+            logger.error("Error while writing to file", e);
+        } catch (UnsupportedEncodingException e) {
+            logger.error("Encoding not supported for writing to output file", e);
         }
-
     }
 
     private void validateInput() {
@@ -105,5 +123,9 @@ private void validateInput() {
             throw new IllegalArgumentException(
                     "Number of generations evolved with unchanged best fitness should be >= 1.");
         }
+        File outFile = new File(output);
+        if (outFile.exists() && !outFile.delete()) {
+            throw new IllegalArgumentException("Existing output file could not be deleted.");
+        }
     }
 }

commons-math-examples/examples-ga/examples-ga-math-functions/src/main/java/org/apache/commons/math4/examples/ga/mathfunctions/legacy/LegacyMathFunctionOptimizer.java

@@ -31,6 +31,7 @@
  * the legacy genetic algorithm.
  */
 public final class LegacyMathFunctionOptimizer {
+
     /** length of chromosome per dimension. **/
     private static final int CHROMOSOME_LENGTH_PER_DIMENSION = 12;
 
@@ -44,8 +45,9 @@ public final class LegacyMathFunctionOptimizer {
      * @param generationCountWithUnchangedBestFitness no of generation evolved with
      *                                                unchanged best fitness
      * @param populationSize                          size of population
+     * @return returns best chromosome
      */
-    public void optimize(int dimension,
+    public Chromosome optimize(int dimension,
             double crossoverRate,
             double mutationRate,
             double elitismRate,
@@ -67,9 +69,7 @@ public void optimize(int dimension,
         // best chromosome from the final population
         final Chromosome bestFinal = finalPopulation.getFittestChromosome();
 
-        //CHECKSTYLE: stop all
-        System.out.println("best=" + bestFinal.toString());
-        //CHECKSTYLE: resume all
+        return bestFinal;
     }
 
     private static Population getInitialPopulation(int dimension, int populationSize, double elitismRate) {