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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
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 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
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 * See the License for the specific language governing permissions and
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package org.apache.commons.math3.ml.clustering.evaluation;

import java.util.List;

import org.apache.commons.math3.ml.clustering.CentroidCluster;
import org.apache.commons.math3.ml.clustering.Cluster;
import org.apache.commons.math3.ml.clustering.Clusterable;
import org.apache.commons.math3.ml.clustering.DoublePoint;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
import org.apache.commons.math3.ml.distance.EuclideanDistance;

Base class for cluster evaluation methods.
Type parameters:
<T> – type of the clustered points
Since:
3.3/**
 * Base class for cluster evaluation methods.
 *
 * @param <T> type of the clustered points
 * @since 3.3
 */
public abstract class ClusterEvaluator<T extends Clusterable> {

    
The distance measure to use when evaluating the cluster. /** The distance measure to use when evaluating the cluster. */
    private final DistanceMeasure measure;

    
Creates a new cluster evaluator with an EuclideanDistance as distance measure. /**
     * Creates a new cluster evaluator with an {@link EuclideanDistance}
     * as distance measure.
     */
    public ClusterEvaluator() {
        this(new EuclideanDistance());
    }

    
Creates a new cluster evaluator with the given distance measure.
Params:
measure – the distance measure to use/**
     * Creates a new cluster evaluator with the given distance measure.
     * @param measure the distance measure to use
     */
    public ClusterEvaluator(final DistanceMeasure measure) {
        this.measure = measure;
    }

    
Computes the evaluation score for the given list of clusters.
Params:
clusters – the clusters to evaluate
Returns:
the computed score/**
     * Computes the evaluation score for the given list of clusters.
     * @param clusters the clusters to evaluate
     * @return the computed score
     */
    public abstract double score(List<? extends Cluster<T>> clusters);

    
Returns whether the first evaluation score is considered to be better
than the second one by this evaluator.

Specific implementations shall override this method if the returned scores
do not follow the same ordering, i.e. smaller score is better.
Params:
score1 – the first score
score2 – the second score
Returns:
true if the first score is considered to be better, false otherwise/**
     * Returns whether the first evaluation score is considered to be better
     * than the second one by this evaluator.
     * <p>
     * Specific implementations shall override this method if the returned scores
     * do not follow the same ordering, i.e. smaller score is better.
     *
     * @param score1 the first score
     * @param score2 the second score
     * @return {@code true} if the first score is considered to be better, {@code false} otherwise
     */
    public boolean isBetterScore(double score1, double score2) {
        return score1 < score2;
    }

    
Calculates the distance between two Clusterable instances with the configured DistanceMeasure. 
Params:
p1 – the first clusterable
p2 – the second clusterable
Returns:
the distance between the two clusterables/**
     * Calculates the distance between two {@link Clusterable} instances
     * with the configured {@link DistanceMeasure}.
     *
     * @param p1 the first clusterable
     * @param p2 the second clusterable
     * @return the distance between the two clusterables
     */
    protected double distance(final Clusterable p1, final Clusterable p2) {
        return measure.compute(p1.getPoint(), p2.getPoint());
    }

    
Computes the centroid for a cluster.
Params:
cluster – the cluster
Returns:
the computed centroid for the cluster, or null if the cluster does not contain any points/**
     * Computes the centroid for a cluster.
     *
     * @param cluster the cluster
     * @return the computed centroid for the cluster,
     * or {@code null} if the cluster does not contain any points
     */
    protected Clusterable centroidOf(final Cluster<T> cluster) {
        final List<T> points = cluster.getPoints();
        if (points.isEmpty()) {
            return null;
        }

        // in case the cluster is of type CentroidCluster, no need to compute the centroid
        if (cluster instanceof CentroidCluster) {
            return ((CentroidCluster<T>) cluster).getCenter();
        }

        final int dimension = points.get(0).getPoint().length;
        final double[] centroid = new double[dimension];
        for (final T p : points) {
            final double[] point = p.getPoint();
            for (int i = 0; i < centroid.length; i++) {
                centroid[i] += point[i];
            }
        }
        for (int i = 0; i < centroid.length; i++) {
            centroid[i] /= points.size();
        }
        return new DoublePoint(centroid);
    }

}