[07/10] [math] MATH-1270

[er...@apache.org]

MATH-1270

SOFM visualization: Smoothed data histogram.


Project: http://git-wip-us.apache.org/repos/asf/commons-math/repo
Commit: http://git-wip-us.apache.org/repos/asf/commons-math/commit/012c15ce
Tree: http://git-wip-us.apache.org/repos/asf/commons-math/tree/012c15ce
Diff: http://git-wip-us.apache.org/repos/asf/commons-math/diff/012c15ce

Branch: refs/heads/MATH_3_X
Commit: 012c15ce4d83817217430adb0b3750c92f07ab3e
Parents: d7f6c8d
Author: Gilles <er...@apache.org>
Authored: Fri Sep 11 00:55:00 2015 +0200
Committer: Gilles <er...@apache.org>
Committed: Fri Sep 11 00:55:00 2015 +0200

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 .../twod/util/SmoothedDataHistogram.java        | 96 ++++++++++++++++++++
 1 file changed, 96 insertions(+)
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http://git-wip-us.apache.org/repos/asf/commons-math/blob/012c15ce/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/SmoothedDataHistogram.java
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diff --git a/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/SmoothedDataHistogram.java b/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/SmoothedDataHistogram.java
new file mode 100644
index 0000000..c8a6d84
--- /dev/null
+++ b/src/main/java/org/apache/commons/math3/ml/neuralnet/twod/util/SmoothedDataHistogram.java
@@ -0,0 +1,96 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.commons.math3.ml.neuralnet.twod.util;
+
+import org.apache.commons.math3.ml.neuralnet.MapUtils;
+import org.apache.commons.math3.ml.neuralnet.Neuron;
+import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D;
+import org.apache.commons.math3.ml.distance.DistanceMeasure;
+import org.apache.commons.math3.exception.NumberIsTooSmallException;
+
+/**
+ * Visualization of high-dimensional data projection on a 2D-map.
+ * The method is described in
+ * <quote>
+ *  <em>Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps</em>
+ *  <br>
+ *  by Elias Pampalk, Andreas Rauber and Dieter Merkl.
+ * </quote>
+ */
+public class SmoothedDataHistogram implements MapDataVisualization {
+    /** Smoothing parameter. */
+    private final int smoothingBins;
+    /** Distance. */
+    private final DistanceMeasure distance;
+    /** Normalization factor. */
+    private final double membershipNormalization;
+
+    /**
+     * @param smoothingBins Number of bins.
+     * @param distance Distance.
+     */
+    public SmoothedDataHistogram(int smoothingBins,
+                                 DistanceMeasure distance) {
+        this.smoothingBins = smoothingBins;
+        this.distance = distance;
+
+        double sum = 0;
+        for (int i = 0; i < smoothingBins; i++) {
+            sum += smoothingBins - i;
+        }
+        
+        this.membershipNormalization = 1d / sum;
+    }
+
+    /**
+     * {@inheritDoc}
+     *
+     * @throws NumberIsTooSmallException if the size of the {@code map}
+     * is smaller than the number of {@link SmoothedDataHistogram(int,DistanceMeasure)
+     * smoothing bins}.
+     */
+    public double[][] computeImage(NeuronSquareMesh2D map,
+                                   Iterable<double[]> data) {
+        final int nR = map.getNumberOfRows();
+        final int nC = map.getNumberOfColumns();
+
+        final int mapSize = nR * nC;
+        if (mapSize < smoothingBins) {
+            throw new NumberIsTooSmallException(mapSize, smoothingBins, true);
+        }
+
+        final LocationFinder finder = new LocationFinder(map);
+
+        // Histogram bins.
+        final double[][] histo = new double[nR][nC];
+
+        for (double[] sample : data) {
+            final Neuron[] sorted = MapUtils.sort(sample,
+                                                  map.getNetwork(),
+                                                  distance);
+            for (int i = 0; i < smoothingBins; i++) {
+                final LocationFinder.Location loc = finder.getLocation(sorted[i]);
+                final int row = loc.getRow();
+                final int col = loc.getColumn();
+                histo[row][col] += (smoothingBins - i) * membershipNormalization;
+            }
+        }
+
+        return histo;
+    }
+}