org.apache.commons.math3.ml.clustering.Clusterer Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of commons-math3 Show documentation
Show all versions of commons-math3 Show documentation
The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.clustering;
import java.util.Collection;
import java.util.List;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.MathIllegalArgumentException;
import org.apache.commons.math3.ml.distance.DistanceMeasure;
/**
* Base class for clustering algorithms.
*
* @param the type of points that can be clustered
* @since 3.2
*/
public abstract class Clusterer {
/** The distance measure to use. */
private DistanceMeasure measure;
/**
* Build a new clusterer with the given {@link DistanceMeasure}.
*
* @param measure the distance measure to use
*/
protected Clusterer(final DistanceMeasure measure) {
this.measure = measure;
}
/**
* Perform a cluster analysis on the given set of {@link Clusterable} instances.
*
* @param points the set of {@link Clusterable} instances
* @return a {@link List} of clusters
* @throws MathIllegalArgumentException if points are null or the number of
* data points is not compatible with this clusterer
* @throws ConvergenceException if the algorithm has not yet converged after
* the maximum number of iterations has been exceeded
*/
public abstract List extends Cluster> cluster(Collection points)
throws MathIllegalArgumentException, ConvergenceException;
/**
* Returns the {@link DistanceMeasure} instance used by this clusterer.
*
* @return the distance measure
*/
public DistanceMeasure getDistanceMeasure() {
return measure;
}
/**
* 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());
}
}