| /* |
| * 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.Network; |
| import org.apache.commons.math4.ml.neuralnet.twod.NeuronSquareMesh2D; |
| import org.apache.commons.math4.ml.distance.DistanceMeasure; |
| |
| /** |
| * Computes the topographic error histogram. |
| * Each bin will contain the number of data for which the first and |
| * second best matching units are not adjacent in the map. |
| * @since 3.6 |
| */ |
| public class TopographicErrorHistogram implements MapDataVisualization { |
| /** Distance. */ |
| private final DistanceMeasure distance; |
| /** Whether to compute relative bin counts. */ |
| private final boolean relativeCount; |
| |
| /** |
| * @param relativeCount Whether to compute relative bin counts. |
| * If {@code true}, the data count in each bin will be divided by the total |
| * number of samples mapped to the neuron represented by that bin. |
| * @param distance Distance. |
| */ |
| public TopographicErrorHistogram(boolean relativeCount, |
| DistanceMeasure distance) { |
| this.relativeCount = relativeCount; |
| this.distance = distance; |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| public double[][] computeImage(NeuronSquareMesh2D map, |
| Iterable<double[]> data) { |
| final int nR = map.getNumberOfRows(); |
| final int nC = map.getNumberOfColumns(); |
| |
| final LocationFinder finder = new LocationFinder(map); |
| final Network net = map.getNetwork(); |
| final MapRanking rank = new MapRanking(net, distance); |
| |
| // Hit bins. |
| final int[][] hit = new int[nR][nC]; |
| // Error bins. |
| final double[][] error = new double[nR][nC]; |
| |
| for (double[] sample : data) { |
| final List<Neuron> p = rank.rank(sample, 2); |
| final Neuron best = p.get(0); |
| |
| final LocationFinder.Location loc = finder.getLocation(best); |
| final int row = loc.getRow(); |
| final int col = loc.getColumn(); |
| hit[row][col] += 1; |
| |
| if (!net.getNeighbours(best).contains(p.get(1))) { |
| // Increment count if first and second best matching units |
| // are not neighbours. |
| error[row][col] += 1; |
| } |
| } |
| |
| if (relativeCount) { |
| for (int r = 0; r < nR; r++) { |
| for (int c = 0; c < nC; c++) { |
| error[r][c] /= hit[r][c]; |
| } |
| } |
| } |
| |
| return error; |
| } |
| } |