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Java example source code file (QuantizationError.java)

This example Java source code file (QuantizationError.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

distancemeasure, iterable, locationfinder, mapdatavisualization, neuron, quantizationerror

The QuantizationError.java Java example source code

 * 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,
 * 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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