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/*
* 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>
* @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}.
*/
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;
}
}
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