net.sourceforge.javaocr.plugin.cluster.AbstractBaseCluster Maven / Gradle / Ivy

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/*
 * Copyright (c) 2003-2012, Ronald B. Cemer , Konstantin Pribluda, William Whitney, Andrea De Pasquale
 *
 *
 *
 *  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.
 */

package net.sourceforge.javaocr.plugin.cluster;

import java.util.List;

/**
 * base cluster functionality. provides mx
 *
 * @author Konstantin Pribluda
 */
public abstract class AbstractBaseCluster implements Cluster {
    private double[] sum;
    private double[] mx;
    private int amountSamples;
    private int dimensions;

    public AbstractBaseCluster() {
    }

    protected AbstractBaseCluster(int dimensions) {
        this.dimensions = dimensions;
        sum = new double[dimensions];
    }

    protected AbstractBaseCluster(double[] mx) {
        this.mx = mx;
        this.dimensions = mx.length;
    }

    /**
     * lazily calculate and return expectation cluster
     *
     * @return expectation vector
     */
    public double[] center() {
        if (mx == null) {
            mx = new double[getDimensions()];
            for (int i = 0; i < getDimensions(); i++) {
                mx[i] = getAmountSamples() == 0 ? 0 : sum[i] / getAmountSamples();
            }
        }
        return mx;
    }

    public int getAmountSamples() {
        return amountSamples;
    }

    public double[] getMx() {
        return mx;
    }

    public void setMx(double[] mx) {
        this.mx = mx;
        if (mx != null) {
            setDimensions(mx.length);
        }
    }

    public void setDimensions(int dimensions) {
        this.dimensions = dimensions;
    }


    public int getDimensions() {
        return dimensions;
    }

    public void setAmountSamples(int amountSamples) {
        this.amountSamples = amountSamples;
    }

    /**
     * training means coputing sum of values. mx shall be reset and sampler countet up
     *
     * @param features
     */
    public void train(double[] features) {

        amountSamples++;
        // reset mx and variance
        mx = null;
        for (int i = 0; i < getDimensions(); i++) {
            sum[i] += features[i];
        }
    }

    public double[] getSum() {
        return sum;
    }

    public void setSum(double[] sum) {
        this.sum = sum;
    }

    /**
     * calculate maximal distance for group from center
     * @param samples sample group, sample size shall correspond to cluster dimensions
     * @return
     */
    public double radius(List samples) {
        double max = 0;
        for(double[] sample: samples) {
            final double dist = distance(sample);
            if(dist > max)
                max = dist;
        }
        return max;
    }
}