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From Luc Maisonobe <...@spaceroots.org>
Subject Re: svn commit: r1560953 - in /commons/proper/math/trunk/src: main/java/org/apache/commons/math3/ml/neuralnet/ test/java/org/apache/commons/math3/ml/neuralnet/sofm/
Date Fri, 24 Jan 2014 14:42:34 GMT
Le 24/01/2014 12:55, erans@apache.org a écrit :
> Author: erans
> Date: Fri Jan 24 11:55:25 2014
> New Revision: 1560953
> 
> URL: http://svn.apache.org/r1560953
> Log:
> Enable choice of RNG, so that the unit test can be run with a
> seed that makes it always succeed.

Thanks a lot Gilles !

Luc

> 
> Modified:
>     commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java
>     commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/KohonenTrainingTaskTest.java
>     commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/TravellingSalesmanSolver.java
> 
> Modified: commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java
> URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java?rev=1560953&r1=1560952&r2=1560953&view=diff
> ==============================================================================
> --- commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java
(original)
> +++ commons/proper/math/trunk/src/main/java/org/apache/commons/math3/ml/neuralnet/FeatureInitializerFactory.java
Fri Jan 24 11:55:25 2014
> @@ -21,6 +21,7 @@ import org.apache.commons.math3.distribu
>  import org.apache.commons.math3.distribution.UniformRealDistribution;
>  import org.apache.commons.math3.analysis.UnivariateFunction;
>  import org.apache.commons.math3.analysis.function.Constant;
> +import org.apache.commons.math3.random.RandomGenerator;
>  
>  /**
>   * Creates functions that will select the initial values of a neuron's
> @@ -42,6 +43,25 @@ public class FeatureInitializerFactory {
>       * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
>       * if {@code min >= max}.
>       */
> +    public static FeatureInitializer uniform(final RandomGenerator rng,
> +                                             final double min,
> +                                             final double max) {
> +        return randomize(new UniformRealDistribution(rng, min, max),
> +                         function(new Constant(0), 0, 0));
> +    }
> +
> +    /**
> +     * Uniform sampling of the given range.
> +     *
> +     * @param min Lower bound of the range.
> +     * @param max Upper bound of the range.
> +     * @param rng Random number generator used to draw samples from a
> +     * uniform distribution.
> +     * @return an initializer such that the features will be initialized with
> +     * values within the given range.
> +     * @throws org.apache.commons.math3.exception.NumberIsTooLargeException
> +     * if {@code min >= max}.
> +     */
>      public static FeatureInitializer uniform(final double min,
>                                               final double max) {
>          return randomize(new UniformRealDistribution(min, max),
> 
> Modified: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/KohonenTrainingTaskTest.java
> URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/KohonenTrainingTaskTest.java?rev=1560953&r1=1560952&r2=1560953&view=diff
> ==============================================================================
> --- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/KohonenTrainingTaskTest.java
(original)
> +++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/KohonenTrainingTaskTest.java
Fri Jan 24 11:55:25 2014
> @@ -63,7 +63,10 @@ public class KohonenTrainingTaskTest {
>              new City("i0", 1, 1),
>          };
>  
> -        final TravellingSalesmanSolver solver = new TravellingSalesmanSolver(squareOfCities,
2);
> +        // Seed that allows the unit test to always succeed.
> +        final long seed = 1245632379L;
> +
> +        final TravellingSalesmanSolver solver = new TravellingSalesmanSolver(squareOfCities,
2, seed);
>          // printSummary("before.travel.seq.dat", solver);
>          solver.createSequentialTask(15000).run();
>          // printSummary("after.travel.seq.dat", solver);
> 
> Modified: commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/TravellingSalesmanSolver.java
> URL: http://svn.apache.org/viewvc/commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/TravellingSalesmanSolver.java?rev=1560953&r1=1560952&r2=1560953&view=diff
> ==============================================================================
> --- commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/TravellingSalesmanSolver.java
(original)
> +++ commons/proper/math/trunk/src/test/java/org/apache/commons/math3/ml/neuralnet/sofm/TravellingSalesmanSolver.java
Fri Jan 24 11:55:25 2014
> @@ -49,7 +49,7 @@ import org.apache.commons.math3.distribu
>  public class TravellingSalesmanSolver {
>      private static final long FIRST_NEURON_ID = 0;
>      /** RNG. */
> -    private final RandomGenerator random = new Well44497b();
> +    private final RandomGenerator random;
>      /** Set of cities. */
>      private final Set<City> cities = new HashSet<City>();
>      /** SOFM. */
> @@ -65,6 +65,20 @@ public class TravellingSalesmanSolver {
>       */
>      public TravellingSalesmanSolver(City[] cityList,
>                                      double numNeuronsPerCity) {
> +        this(cityList, numNeuronsPerCity, new Well44497b().nextLong());
> +    }
> +
> +    /**
> +     * @param cityList List of cities to visit in a single travel.
> +     * @param numNeuronsPerCity Number of neurons per city.
> +     * @param seed Seed for the RNG that is used to present the samples
> +     * to the trainer.
> +     */
> +    public TravellingSalesmanSolver(City[] cityList,
> +                                    double numNeuronsPerCity,
> +                                    long seed) {
> +        random = new Well44497b(seed);
> +
>          final double[] xRange = {Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY};
>          final double[] yRange = {Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY};
>  
> @@ -291,7 +305,8 @@ public class TravellingSalesmanSolver {
>          final UnivariateFunction f1 = FunctionUtils.add(h1, new Constant(centre[0]));
>          final UnivariateFunction f2 = FunctionUtils.add(h2, new Constant(centre[1]));
>  
> -        final RealDistribution u = new UniformRealDistribution(-0.05 * radius, 0.05
* radius);
> +        final RealDistribution u
> +            = new UniformRealDistribution(random, -0.05 * radius, 0.05 * radius);
>  
>          return new FeatureInitializer[] {
>              FeatureInitializerFactory.randomize(u, FeatureInitializerFactory.function(f1,
0, 1)),
> 
> 
> 
> 


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