MATH-1158 Adapt "examples" code to the new sampler API.
Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/e3668946 Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/e3668946 Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/e3668946 Branch: refs/heads/develop Commit: e366894658914c0f2fb41cac003392b0dfa6e4b7 Parents: b577805 Author: Gilles <er...@apache.org> Authored: Sat Mar 26 13:00:08 2016 +0100 Committer: Gilles <er...@apache.org> Committed: Sun Mar 27 23:45:32 2016 +0200 ---------------------------------------------------------------------- .../userguide/ClusterAlgorithmComparison.java | 76 +++++++++++++------- .../userguide/RealDistributionComparison.java | 13 ++-- .../math4/userguide/sofm/ChineseRings.java | 20 +++--- 3 files changed, 66 insertions(+), 43 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/commons-math/blob/e3668946/src/userguide/java/org/apache/commons/math4/userguide/ClusterAlgorithmComparison.java ---------------------------------------------------------------------- diff --git a/src/userguide/java/org/apache/commons/math4/userguide/ClusterAlgorithmComparison.java b/src/userguide/java/org/apache/commons/math4/userguide/ClusterAlgorithmComparison.java index b85b2ef..b33c3cf 100644 --- a/src/userguide/java/org/apache/commons/math4/userguide/ClusterAlgorithmComparison.java +++ b/src/userguide/java/org/apache/commons/math4/userguide/ClusterAlgorithmComparison.java @@ -34,6 +34,8 @@ import java.util.List; import javax.swing.JComponent; import javax.swing.JLabel; +import org.apache.commons.math4.distribution.RealDistribution; +import org.apache.commons.math4.distribution.UniformRealDistribution; import org.apache.commons.math4.distribution.NormalDistribution; import org.apache.commons.math4.geometry.euclidean.twod.Vector2D; import org.apache.commons.math4.ml.clustering.CentroidCluster; @@ -45,9 +47,9 @@ import org.apache.commons.math4.ml.clustering.DoublePoint; import org.apache.commons.math4.ml.clustering.FuzzyKMeansClusterer; import org.apache.commons.math4.ml.clustering.KMeansPlusPlusClusterer; import org.apache.commons.math4.random.RandomAdaptor; -import org.apache.commons.math4.random.RandomGenerator; import org.apache.commons.math4.random.SobolSequenceGenerator; -import org.apache.commons.math4.random.Well19937c; +import org.apache.commons.math4.rng.UniformRandomProvider; +import org.apache.commons.math4.rng.RandomSource; import org.apache.commons.math4.util.FastMath; import org.apache.commons.math4.util.Pair; import org.apache.commons.math4.userguide.ExampleUtils.ExampleFrame; @@ -59,12 +61,16 @@ import org.apache.commons.math4.userguide.ExampleUtils.ExampleFrame; */ public class ClusterAlgorithmComparison { - public static List<Vector2D> makeCircles(int samples, boolean shuffle, double noise, double factor, final RandomGenerator random) { + public static List<Vector2D> makeCircles(int samples, + boolean shuffle, + double noise, + double factor, + UniformRandomProvider rng) { if (factor < 0 || factor > 1) { throw new IllegalArgumentException(); } - NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9); + RealDistribution.Sampler dist = new NormalDistribution(0.0, noise).createSampler(rng); List<Vector2D> points = new ArrayList<Vector2D>(); double range = 2.0 * FastMath.PI; @@ -78,14 +84,18 @@ public class ClusterAlgorithmComparison { } if (shuffle) { - Collections.shuffle(points, new RandomAdaptor(random)); + // Collections.shuffle(points, new RandomAdaptor(rng)); // XXX TODO + Collections.shuffle(points); // XXX temporary workaround } return points; } - public static List<Vector2D> makeMoons(int samples, boolean shuffle, double noise, RandomGenerator random) { - NormalDistribution dist = new NormalDistribution(random, 0.0, noise, 1e-9); + public static List<Vector2D> makeMoons(int samples, + boolean shuffle, + double noise, + UniformRandomProvider rng) { + RealDistribution.Sampler dist = new NormalDistribution(0.0, noise).createSampler(rng); int nSamplesOut = samples / 2; int nSamplesIn = samples - nSamplesOut; @@ -105,23 +115,26 @@ public class ClusterAlgorithmComparison { } if (shuffle) { - Collections.shuffle(points, new RandomAdaptor(random)); + // Collections.shuffle(points, new RandomAdaptor(rng)); // XXX TODO + Collections.shuffle(points); // XXX temporary workaround } return points; } - public static List<Vector2D> makeBlobs(int samples, int centers, double clusterStd, - double min, double max, boolean shuffle, RandomGenerator random) { + public static List<Vector2D> makeBlobs(int samples, + int centers, + double clusterStd, + double min, + double max, + boolean shuffle, + UniformRandomProvider rng) { + RealDistribution.Sampler uniform = new UniformRealDistribution(min, max).createSampler(rng); + RealDistribution.Sampler gauss = new NormalDistribution(0.0, clusterStd).createSampler(rng); - NormalDistribution dist = new NormalDistribution(random, 0.0, clusterStd, 1e-9); - - double range = max - min; Vector2D[] centerPoints = new Vector2D[centers]; for (int i = 0; i < centers; i++) { - double x = random.nextDouble() * range + min; - double y = random.nextDouble() * range + min; - centerPoints[i] = new Vector2D(x, y); + centerPoints[i] = new Vector2D(uniform.sample(), uniform.sample()); } int[] nSamplesPerCenter = new int[centers]; @@ -135,13 +148,13 @@ public class ClusterAlgorithmComparison { List<Vector2D> points = new ArrayList<Vector2D>(); for (int i = 0; i < centers; i++) { for (int j = 0; j < nSamplesPerCenter[i]; j++) { - Vector2D point = new Vector2D(dist.sample(), dist.sample()); - points.add(point.add(centerPoints[i])); + points.add(centerPoints[i].add(generateNoiseVector(gauss))); } } if (shuffle) { - Collections.shuffle(points, new RandomAdaptor(random)); + // Collections.shuffle(points, new RandomAdaptor(rng)); // XXX TODO + Collections.shuffle(points); // XXX temporary workaround } return points; @@ -162,11 +175,15 @@ public class ClusterAlgorithmComparison { return points; } - public static Vector2D generateNoiseVector(NormalDistribution distribution) { + public static Vector2D generateNoiseVector(RealDistribution.Sampler distribution) { return new Vector2D(distribution.sample(), distribution.sample()); } - public static List<DoublePoint> normalize(final List<Vector2D> input, double minX, double maxX, double minY, double maxY) { + public static List<DoublePoint> normalize(final List<Vector2D> input, + double minX, + double maxX, + double minY, + double maxY) { double rangeX = maxX - minX; double rangeY = maxY - minY; List<DoublePoint> points = new ArrayList<DoublePoint>(); @@ -190,7 +207,7 @@ public class ClusterAlgorithmComparison { int nSamples = 1500; - RandomGenerator rng = new Well19937c(0); + UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_19937_C, 0); List<List<DoublePoint>> datasets = new ArrayList<List<DoublePoint>>(); datasets.add(normalize(makeCircles(nSamples, true, 0.04, 0.5, rng), -1, 1, -1, 1)); @@ -200,11 +217,16 @@ public class ClusterAlgorithmComparison { List<Pair<String, Clusterer<DoublePoint>>> algorithms = new ArrayList<Pair<String, Clusterer<DoublePoint>>>(); - algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=2)", new KMeansPlusPlusClusterer<DoublePoint>(2))); - algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=3)", new KMeansPlusPlusClusterer<DoublePoint>(3))); - algorithms.add(new Pair<String, Clusterer<DoublePoint>>("FuzzyKMeans\n(k=3, fuzzy=2)", new FuzzyKMeansClusterer<DoublePoint>(3, 2))); - algorithms.add(new Pair<String, Clusterer<DoublePoint>>("FuzzyKMeans\n(k=3, fuzzy=10)", new FuzzyKMeansClusterer<DoublePoint>(3, 10))); - algorithms.add(new Pair<String, Clusterer<DoublePoint>>("DBSCAN\n(eps=.1, min=3)", new DBSCANClusterer<DoublePoint>(0.1, 3))); + algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=2)", + new KMeansPlusPlusClusterer<DoublePoint>(2))); + algorithms.add(new Pair<String, Clusterer<DoublePoint>>("KMeans\n(k=3)", + new KMeansPlusPlusClusterer<DoublePoint>(3))); + algorithms.add(new Pair<String, Clusterer<DoublePoint>>("FuzzyKMeans\n(k=3, fuzzy=2)", + new FuzzyKMeansClusterer<DoublePoint>(3, 2))); + algorithms.add(new Pair<String, Clusterer<DoublePoint>>("FuzzyKMeans\n(k=3, fuzzy=10)", + new FuzzyKMeansClusterer<DoublePoint>(3, 10))); + algorithms.add(new Pair<String, Clusterer<DoublePoint>>("DBSCAN\n(eps=.1, min=3)", + new DBSCANClusterer<DoublePoint>(0.1, 3))); GridBagConstraints c = new GridBagConstraints(); c.fill = GridBagConstraints.VERTICAL; http://git-wip-us.apache.org/repos/asf/commons-math/blob/e3668946/src/userguide/java/org/apache/commons/math4/userguide/RealDistributionComparison.java ---------------------------------------------------------------------- diff --git a/src/userguide/java/org/apache/commons/math4/userguide/RealDistributionComparison.java b/src/userguide/java/org/apache/commons/math4/userguide/RealDistributionComparison.java index 315659b..5d4e015 100644 --- a/src/userguide/java/org/apache/commons/math4/userguide/RealDistributionComparison.java +++ b/src/userguide/java/org/apache/commons/math4/userguide/RealDistributionComparison.java @@ -46,8 +46,6 @@ import org.apache.commons.math4.distribution.ParetoDistribution; import org.apache.commons.math4.distribution.RealDistribution; import org.apache.commons.math4.distribution.TDistribution; import org.apache.commons.math4.distribution.WeibullDistribution; -import org.apache.commons.math4.random.MersenneTwister; -import org.apache.commons.math4.random.RandomGenerator; import org.apache.commons.math4.util.FastMath; import org.apache.commons.math4.userguide.ExampleUtils.ExampleFrame; @@ -242,14 +240,13 @@ public class RealDistributionComparison { container.add(comp, c); c.gridx++; - RandomGenerator rng = new MersenneTwister(0); comp = createComponent("Levy", 0, 3, new String[] { "c=0.5", "c=1", "c=2", "c=4", "c=8" }, - new LevyDistribution(rng, 0, 0.5), - new LevyDistribution(rng, 0, 1), - new LevyDistribution(rng, 0, 2), - new LevyDistribution(rng, 0, 4), - new LevyDistribution(rng, 0, 8)); + new LevyDistribution(0, 0.5), + new LevyDistribution(0, 1), + new LevyDistribution(0, 2), + new LevyDistribution(0, 4), + new LevyDistribution(0, 8)); container.add(comp, c); c.gridy++; http://git-wip-us.apache.org/repos/asf/commons-math/blob/e3668946/src/userguide/java/org/apache/commons/math4/userguide/sofm/ChineseRings.java ---------------------------------------------------------------------- diff --git a/src/userguide/java/org/apache/commons/math4/userguide/sofm/ChineseRings.java b/src/userguide/java/org/apache/commons/math4/userguide/sofm/ChineseRings.java index 77cc75a..2c942a9 100644 --- a/src/userguide/java/org/apache/commons/math4/userguide/sofm/ChineseRings.java +++ b/src/userguide/java/org/apache/commons/math4/userguide/sofm/ChineseRings.java @@ -20,6 +20,8 @@ package org.apache.commons.math4.userguide.sofm; import org.apache.commons.math4.geometry.euclidean.threed.Vector3D; import org.apache.commons.math4.geometry.euclidean.threed.Rotation; import org.apache.commons.math4.random.UnitSphereRandomVectorGenerator; +import org.apache.commons.math4.rng.UniformRandomProvider; +import org.apache.commons.math4.rng.RandomSource; import org.apache.commons.math4.distribution.RealDistribution; import org.apache.commons.math4.distribution.UniformRealDistribution; @@ -57,11 +59,13 @@ public class ChineseRings { final UnitSphereRandomVectorGenerator unit = new UnitSphereRandomVectorGenerator(2); - final RealDistribution radius1 + final UniformRandomProvider rng = RandomSource.create(RandomSource.WELL_19937_C); + + final RealDistribution.Sampler radius1 = new UniformRealDistribution(radiusRing1 - halfWidthRing1, - radiusRing1 + halfWidthRing1); - final RealDistribution widthRing1 - = new UniformRealDistribution(-halfWidthRing1, halfWidthRing1); + radiusRing1 + halfWidthRing1).createSampler(rng); + final RealDistribution.Sampler widthRing1 + = new UniformRealDistribution(-halfWidthRing1, halfWidthRing1).createSampler(rng); for (int i = 0; i < numPointsRing1; i++) { final double[] v = unit.nextVector(); @@ -72,11 +76,11 @@ public class ChineseRings { widthRing1.sample()); } - final RealDistribution radius2 + final RealDistribution.Sampler radius2 = new UniformRealDistribution(radiusRing2 - halfWidthRing2, - radiusRing2 + halfWidthRing2); - final RealDistribution widthRing2 - = new UniformRealDistribution(-halfWidthRing2, halfWidthRing2); + radiusRing2 + halfWidthRing2).createSampler(rng); + final RealDistribution.Sampler widthRing2 + = new UniformRealDistribution(-halfWidthRing2, halfWidthRing2).createSampler(rng); for (int i = 0; i < numPointsRing2; i++) { final double[] v = unit.nextVector();
