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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;
/**
* Computes the quantization error histogram.
* Each bin will contain the average of the distances between samples
* mapped to the corresponding unit and the weight vector of that unit.
* @since 3.6
*/
public class QuantizationError implements MapDataVisualization {
/** Distance. */
private final DistanceMeasure distance;
/**
* @param distance Distance.
*/
public QuantizationError(DistanceMeasure distance) {
this.distance = distance;
}
/** {@inheritDoc} */
public double[][] computeImage(NeuronSquareMesh2D map,
Iterable<double[]> data) {
final int nR = map.getNumberOfRows();
final int nC = map.getNumberOfColumns();
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 Neuron best = MapUtils.findBest(sample, map, distance);
final LocationFinder.Location loc = finder.getLocation(best);
final int row = loc.getRow();
final int col = loc.getColumn();
hit[row][col] += 1;
error[row][col] += distance.compute(sample, best.getFeatures());
}
for (int r = 0; r < nR; r++) {
for (int c = 0; c < nC; c++) {
final int count = hit[r][c];
if (count != 0) {
error[r][c] /= count;
}
}
}
return error;
}
}
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