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Java example source code file (BaseClusteringAlgorithm.java)

This example Java source code file (BaseClusteringAlgorithm.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

arraylist, baseclusteringalgorithm, clustersetinfo, completed, executorservice, generating, indarray, iterationhistory, iterationinfo, list, logger, optimisationstrategy, random, serializable, threading, threads, util

The BaseClusteringAlgorithm.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    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 org.deeplearning4j.clustering.algorithm;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import java.util.concurrent.ExecutorService;

import org.apache.commons.lang3.ArrayUtils;
import org.deeplearning4j.clustering.algorithm.iteration.IterationHistory;
import org.deeplearning4j.clustering.algorithm.iteration.IterationInfo;
import org.deeplearning4j.clustering.algorithm.strategy.ClusteringStrategy;
import org.deeplearning4j.clustering.algorithm.strategy.ClusteringStrategyType;
import org.deeplearning4j.clustering.algorithm.strategy.OptimisationStrategy;
import org.deeplearning4j.clustering.cluster.Cluster;
import org.deeplearning4j.clustering.cluster.ClusterSet;
import org.deeplearning4j.clustering.cluster.ClusterUtils;
import org.deeplearning4j.clustering.cluster.Point;
import org.deeplearning4j.clustering.cluster.info.ClusterSetInfo;
import org.deeplearning4j.util.MultiThreadUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 *
 * adapted to ndarray matrices
 * 
 * @author Adam Gibson
 * @author Julien Roch
 *
 */
public class BaseClusteringAlgorithm implements ClusteringAlgorithm, Serializable {

	private static final long serialVersionUID	= 338231277453149972L;
	private static final Logger log = LoggerFactory.getLogger(BaseClusteringAlgorithm.class);

	private ClusteringStrategy clusteringStrategy;
	private IterationHistory iterationHistory;
	private int	currentIteration = 0;
	private ClusterSet	clusterSet;
	private List<Point>	initialPoints;
	private transient ExecutorService exec;

	protected BaseClusteringAlgorithm(ClusteringStrategy clusteringStrategy) {
		this.clusteringStrategy = clusteringStrategy;
		this.exec = MultiThreadUtils.newExecutorService();
	}

	public static BaseClusteringAlgorithm setup(ClusteringStrategy clusteringStrategy) {
		return new BaseClusteringAlgorithm(clusteringStrategy);
	}

	public ClusterSet applyTo(List<Point> points) {
		resetState(points);
		initClusters();
		iterations();
		return clusterSet;
	}

	private void resetState(List<Point> points) {
		this.iterationHistory = new IterationHistory();
		this.currentIteration = 0;
		this.clusterSet = null;
		this.initialPoints = points;
	}

	/** Run clustering iterations until a termination condition is hit.
	 * This is done by first classifying all points, and then updating cluster centers based on those classified points
	 */
	private void iterations() {
		int iterationCount = 0;
		while (!clusteringStrategy.getTerminationCondition().isSatisfied(iterationHistory) ||
				iterationHistory.getMostRecentIterationInfo().isStrategyApplied()) {
			currentIteration++;
			removePoints();
			classifyPoints();
			applyClusteringStrategy();
			log.info("Completed clustering iteration {}", ++iterationCount);
		}
	}

	protected void classifyPoints() {
		//Classify points. This also adds each point to the ClusterSet
		ClusterSetInfo clusterSetInfo = ClusterUtils.classifyPoints(clusterSet, initialPoints, exec);
		//Update the cluster centers, based on the points within each cluster
		ClusterUtils.refreshClustersCenters(clusterSet, clusterSetInfo, exec);
		iterationHistory.getIterationsInfos().put(currentIteration, new IterationInfo(currentIteration, clusterSetInfo));
	}

	/**Initialize the cluster centers at random
	 */
	protected void initClusters() {
		log.info("Generating initial clusters");
		List<Point> points = new ArrayList<>(initialPoints);

		//Initialize the ClusterSet with a single cluster center (based on position of one of the points chosen randomly)
		Random random = new Random();
		clusterSet = new ClusterSet(clusteringStrategy.getDistanceFunction());
		clusterSet.addNewClusterWithCenter(points.remove(random.nextInt(points.size())));
		int initialClusterCount = clusteringStrategy.getInitialClusterCount();

		//dxs: distances between each point and nearest cluster to that point
		INDArray dxs = Nd4j.create(points.size());
		dxs.addi(Double.MAX_VALUE);

		//Generate the initial cluster centers, by randomly selecting a point between 0 and max distance
		//Thus, we are more likely to select (as a new cluster center) a point that is far from an existing cluster
		while (clusterSet.getClusterCount() < initialClusterCount && points.size() > 0) {
			dxs = ClusterUtils.computeSquareDistancesFromNearestCluster(clusterSet, points, dxs, exec);
			double r = random.nextFloat() * dxs.maxNumber().doubleValue();
			for (int i = 0; i < dxs.length(); i++) {
				if (dxs.getDouble(i) >= r) {
					clusterSet.addNewClusterWithCenter(points.remove(i));
					dxs = Nd4j.create(ArrayUtils.remove(dxs.data().asDouble(), i));
					break;
				}
			}
		}

		ClusterSetInfo initialClusterSetInfo = ClusterUtils.computeClusterSetInfo(clusterSet);
		iterationHistory.getIterationsInfos().put(currentIteration, new IterationInfo(currentIteration, initialClusterSetInfo));
	}

	protected void applyClusteringStrategy() {
		if (!isStrategyApplicableNow())
			return;

		ClusterSetInfo clusterSetInfo = iterationHistory.getMostRecentClusterSetInfo();
		if (!clusteringStrategy.isAllowEmptyClusters()) {
			int removedCount = removeEmptyClusters(clusterSetInfo);
			if( removedCount>0 ) {
				iterationHistory.getMostRecentIterationInfo().setStrategyApplied(true);
				
				if (clusteringStrategy.isStrategyOfType(ClusteringStrategyType.FIXED_CLUSTER_COUNT) && clusterSet.getClusterCount() < clusteringStrategy.getInitialClusterCount()) {
					int splitCount = ClusterUtils.splitMostSpreadOutClusters(clusterSet, clusterSetInfo, clusteringStrategy.getInitialClusterCount() - clusterSet.getClusterCount(),
							exec);
					if( splitCount>0 )
						iterationHistory.getMostRecentIterationInfo().setStrategyApplied(true);
				}
			}
		}
		if (clusteringStrategy.isStrategyOfType(ClusteringStrategyType.OPTIMIZATION))
			optimize();
	}

	protected void optimize() {
		ClusterSetInfo clusterSetInfo = iterationHistory.getMostRecentClusterSetInfo();
		OptimisationStrategy optimization = (OptimisationStrategy) clusteringStrategy;
		boolean applied = ClusterUtils.applyOptimization(optimization, clusterSet, clusterSetInfo, exec);
		iterationHistory.getMostRecentIterationInfo().setStrategyApplied(applied);
	}

	private boolean isStrategyApplicableNow() {
		return clusteringStrategy.isOptimizationDefined() && iterationHistory.getIterationCount() != 0
				&& clusteringStrategy.isOptimizationApplicableNow(iterationHistory);
	}

	protected int removeEmptyClusters(ClusterSetInfo clusterSetInfo) {
		List<Cluster> removedClusters = clusterSet.removeEmptyClusters();
		clusterSetInfo.removeClusterInfos(removedClusters);
		return removedClusters.size();
	}

	protected void removePoints() {
		clusterSet.removePoints();
	}

}

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