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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.Network;
+import org.apache.commons.math3.ml.neuralnet.twod.NeuronSquareMesh2D;
+import org.apache.commons.math3.ml.distance.DistanceMeasure;
+import org.apache.commons.math3.util.Pair;
+
+/**
+ * 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} */
+ public double[][] computeImage(NeuronSquareMesh2D map,
+ Iterable<double[]> data) {
+ final int nR = map.getNumberOfRows();
+ final int nC = map.getNumberOfColumns();
+
+ final Network net = map.getNetwork();
+ final LocationFinder finder = new LocationFinder(map);
+
+ // Hit bins.
+ final int[][] hit = new int[nR][nC];
+ // Error bins.
+ final double[][] error = new double[nR][nC];
+
+ for (double[] sample : data) {
+ final Pair<Neuron, Neuron> p = MapUtils.findBestAndSecondBest(sample, map, distance);
+ final Neuron best = p.getFirst();
+
+ 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.getSecond())) {
+ // 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;
+ }
+}