| /* |
| * 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.math4.ml.neuralnet.twod.util; |
| |
| import java.util.List; |
| import org.apache.commons.math4.ml.neuralnet.MapRanking; |
| import org.apache.commons.math4.ml.neuralnet.Neuron; |
| import org.apache.commons.math4.ml.neuralnet.twod.NeuronSquareMesh2D; |
| import org.apache.commons.math4.ml.distance.DistanceMeasure; |
| import org.apache.commons.math4.exception.NumberIsTooSmallException; |
| |
| /** |
| * Visualization of high-dimensional data projection on a 2D-map. |
| * The method is described in |
| * <blockquote> |
| * <em>Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps</em> |
| * <br> |
| * by Elias Pampalk, Andreas Rauber and Dieter Merkl. |
| * </blockquote> |
| * @since 3.6 |
| */ |
| 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}. |
| */ |
| @Override |
| 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); |
| final MapRanking rank = new MapRanking(map.getNetwork(), distance); |
| |
| // Histogram bins. |
| final double[][] histo = new double[nR][nC]; |
| |
| for (double[] sample : data) { |
| final List<Neuron> sorted = rank.rank(sample); |
| for (int i = 0; i < smoothingBins; i++) { |
| final LocationFinder.Location loc = finder.getLocation(sorted.get(i)); |
| final int row = loc.getRow(); |
| final int col = loc.getColumn(); |
| histo[row][col] += (smoothingBins - i) * membershipNormalization; |
| } |
| } |
| |
| return histo; |
| } |
| } |