BestMatchingUnit.java

/*
 * Artificial Intelligence for Humans
 * Volume 3: Deep Learning and Neural Networks
 * Java Version
 * http://www.aifh.org
 * http://www.jeffheaton.com
 *
 * Code repository:
 * https://github.com/jeffheaton/aifh
 *
 * Copyright 2014-2015 by Jeff Heaton
 *
 * Licensed 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.
 *
 * For more information on Heaton Research copyrights, licenses
 * and trademarks visit:
 * http://www.heatonresearch.com/copyright
 */
package com.heatonresearch.aifh.som;

import Jama.Matrix;
import com.heatonresearch.aifh.AIFHError;

/**
 * The "Best Matching Unit" or BMU is a very important concept in the training
 * for a SOM. The BMU is the output neuron that has weight connections to the
 * input neurons that most closely match the current input vector. This neuron
 * (and its "neighborhood") are the neurons that will receive training.
 *
 * This class also tracks the worst distance (of all BMU's). This gives some
 * indication of how well the network is trained, and thus becomes the "error"
 * of the entire network.
 */
public class BestMatchingUnit {
    /**
     * The owner of this class.
     */
    private final SelfOrganizingMap som;

    /**
     * What is the worst BMU distance so far, this becomes the error for the
     * entire SOM.
     */
    private double worstDistance;

    /**
     * Construct a BestMatchingUnit class.  The training class must be provided.
     * @param som The SOM to evaluate.
     */
    public BestMatchingUnit(final SelfOrganizingMap som) {
        this.som = som;
    }

    /**
     * Calculate the best matching unit (BMU). This is the output neuron that
     * has the lowest Euclidean distance to the input vector.
     *
     * @param input
     *            The input vector.
     * @return The output neuron number that is the BMU.
     */
    public int calculateBMU(final double[] input) {
        int result = 0;

        if( input.length>this.som.getInputCount() ) {
            throw new AIFHError("Can't train SOM with input size of " + this.som.getInputCount()
                    + " with input data of count "
                    + input.length);
        }

        // Track the lowest distance so far.
        double lowestDistance = Double.MAX_VALUE;

        for (int i = 0; i < this.som.getOutputCount(); i++) {
            final double distance = calculateEuclideanDistance(this.som.getWeights(), input,
                    i);

            // Track the lowest distance, this is the BMU.
            if (distance < lowestDistance) {
                lowestDistance = distance;
                result = i;
            }
        }

        // Track the worst distance, this is the error for the entire network.
        if (lowestDistance > this.worstDistance) {
            this.worstDistance = lowestDistance;
        }

        return result;
    }

    /**
     * Calculate the Euclidean distance for the specified output neuron and the
     * input vector.  This is the square root of the squares of the differences
     * between the weight and input vectors.
     *
     * @param matrix
     *            The matrix to get the weights from.
     * @param input
     *            The input vector.
     * @param outputNeuron
     *            The neuron we are calculating the distance for.
     * @return The Euclidean distance.
     */
    public double calculateEuclideanDistance(final Matrix matrix,
                                             final double[] input, final int outputNeuron) {
        double result = 0;

        // Loop over all input data.
        for (int i = 0; i < input.length; i++) {
            final double diff = input[i]
                    - matrix.get(outputNeuron,i);
            result += diff * diff;
        }
        return Math.sqrt(result);
    }

    /**
     * @return What is the worst BMU distance so far, this becomes the error
     * for the entire SOM.
     */
    public double getWorstDistance() {
        return this.worstDistance;
    }

    /**
     * Reset the "worst distance" back to a minimum value.  This should be
     * called for each training iteration.
     */
    public void reset() {
        this.worstDistance = Double.MIN_VALUE;
    }
}