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From er...@apache.org
Subject 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 11:55:26 GMT
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.

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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