001/* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 018package org.apache.commons.math3.ml.neuralnet.twod.util; 019 020import org.apache.commons.math3.ml.neuralnet.MapUtils; 021import org.apache.commons.math3.ml.neuralnet.Neuron; 022import org.apache.commons.math3.ml.neuralnet.Network; 023import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D; 024import org.apache.commons.math3.ml.distance.DistanceMeasure; 025import org.apache.commons.math3.util.Pair; 026 027/** 028 * Computes the topographic error histogram. 029 * Each bin will contain the number of data for which the first and 030 * second best matching units are not adjacent in the map. 031 * @since 3.6 032 */ 033public class TopographicErrorHistogram implements MapDataVisualization { 034 /** Distance. */ 035 private final DistanceMeasure distance; 036 /** Whether to compute relative bin counts. */ 037 private final boolean relativeCount; 038 039 /** 040 * @param relativeCount Whether to compute relative bin counts. 041 * If {@code true}, the data count in each bin will be divided by the total 042 * number of samples mapped to the neuron represented by that bin. 043 * @param distance Distance. 044 */ 045 public TopographicErrorHistogram(boolean relativeCount, 046 DistanceMeasure distance) { 047 this.relativeCount = relativeCount; 048 this.distance = distance; 049 } 050 051 /** {@inheritDoc} */ 052 public double[][] computeImage(NeuronSquareMesh2D map, 053 Iterable<double[]> data) { 054 final int nR = map.getNumberOfRows(); 055 final int nC = map.getNumberOfColumns(); 056 057 final Network net = map.getNetwork(); 058 final LocationFinder finder = new LocationFinder(map); 059 060 // Hit bins. 061 final int[][] hit = new int[nR][nC]; 062 // Error bins. 063 final double[][] error = new double[nR][nC]; 064 065 for (double[] sample : data) { 066 final Pair<Neuron, Neuron> p = MapUtils.findBestAndSecondBest(sample, map, distance); 067 final Neuron best = p.getFirst(); 068 069 final LocationFinder.Location loc = finder.getLocation(best); 070 final int row = loc.getRow(); 071 final int col = loc.getColumn(); 072 hit[row][col] += 1; 073 074 if (!net.getNeighbours(best).contains(p.getSecond())) { 075 // Increment count if first and second best matching units 076 // are not neighbours. 077 error[row][col] += 1; 078 } 079 } 080 081 if (relativeCount) { 082 for (int r = 0; r < nR; r++) { 083 for (int c = 0; c < nC; c++) { 084 error[r][c] /= hit[r][c]; 085 } 086 } 087 } 088 089 return error; 090 } 091}