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Java example source code file (ClusterSet.java)

This example Java source code file (ClusterSet.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, cluster, clusterset, comparator, double, hashmap, list, map, pair, point, pointclassification, serializable, string, util

The ClusterSet.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.cluster;

import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

import org.deeplearning4j.berkeley.Pair;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.Accumulation;
import org.nd4j.linalg.factory.Nd4j;

import java.io.Serializable;

public class ClusterSet implements Serializable {

	private String	distanceFunction;
	private List<Cluster> clusters;
	private Map<String, String>	pointDistribution;

	public ClusterSet() {
		this(null);
	}

	public ClusterSet(String distanceFunction) {
		this.distanceFunction = distanceFunction;
		this.clusters = Collections.synchronizedList(new ArrayList<Cluster>());
		this.pointDistribution = Collections.synchronizedMap(new HashMap<String, String>());
	}
	
	public Cluster addNewClusterWithCenter(Point center) {
		Cluster newCluster = new Cluster(center, distanceFunction);
		getClusters().add(newCluster);
		setPointLocation(center, newCluster);
		return newCluster;
	}

	public PointClassification classifyPoint(Point point) {
		return classifyPoint(point, true);
	}
	
	public void classifyPoints(List<Point> points) {
		classifyPoints(points, true);
	}

	public void classifyPoints(List<Point> points, boolean moveClusterCenter) {
		for (Point point : points)
			classifyPoint(point, moveClusterCenter);
	}

	public PointClassification classifyPoint(Point point, boolean moveClusterCenter) {
		Pair<Cluster, Double> nearestCluster = nearestCluster(point);
		Cluster newCluster = nearestCluster.getFirst();
		boolean locationChange = isPointLocationChange(point, newCluster);
		addPointToCluster(point, newCluster, moveClusterCenter);
		return new PointClassification(nearestCluster.getFirst(), nearestCluster.getSecond(), locationChange);
	}
	
	private boolean isPointLocationChange(Point point, Cluster newCluster) {
		if( !getPointDistribution().containsKey(point.getId()) )
			return true;
		return !getPointDistribution().get(point.getId()).equals(newCluster.getId());
	}
	
	private void addPointToCluster(Point point, Cluster cluster, boolean moveClusterCenter) {
		cluster.addPoint(point, moveClusterCenter);
		setPointLocation(point, cluster);
	}
	
	private void setPointLocation(Point point, Cluster cluster) {
		pointDistribution.put(point.getId(), cluster.getId());
	}

	
	public Pair<Cluster, Double> nearestCluster(Point point) {

		Cluster nearestCluster = null;
		double minDistance = Float.MAX_VALUE;

		double currentDistance;
		for (Cluster cluster : getClusters()) {
			currentDistance = cluster.getDistanceToCenter(point);
			if (currentDistance < minDistance) {
				minDistance = currentDistance;
				nearestCluster = cluster;
			}
		}

		return new Pair<Cluster, Double>(nearestCluster, minDistance);

	}

	public double getDistance(Point m1, Point m2) {
		return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createAccum(distanceFunction,m1.getArray(),m2.getArray())).currentResult().doubleValue();
    }

	public double getDistanceFromNearestCluster(Point point) {
		return nearestCluster(point).getSecond();
	}

	public String getClusterCenterId(String clusterId) {
		Point clusterCenter = getClusterCenter(clusterId);
		return clusterCenter == null ? null : clusterCenter.getId();
	}

	public Point getClusterCenter(String clusterId) {
		Cluster cluster = getCluster(clusterId);
		return cluster == null ? null : cluster.getCenter();
	}

	public Cluster getCluster(String id) {
		for (int i = 0, j = clusters.size(); i < j; i++)
			if (id.equals(clusters.get(i).getId()))
				return clusters.get(i);
		return null;
	}

	public int getClusterCount() {
		return getClusters() == null ? 0 : getClusters().size();
	}

	public void removePoints() {
		for (Cluster cluster : getClusters())
			cluster.removePoints();
	}

	public List<Cluster> getMostPopulatedClusters(int count) {
		List<Cluster> mostPopulated = new ArrayList(clusters);
		Collections.sort(mostPopulated, new Comparator<Cluster>() {
			public int compare(Cluster o1, Cluster o2) {
				return new Integer(o1.getPoints().size()).compareTo(new Integer(o2.getPoints().size()));
			}
		});
		return mostPopulated.subList(0, count);
	}

	public List<Cluster> removeEmptyClusters() {
		List<Cluster> emptyClusters = new ArrayList();
		for (Cluster cluster : clusters)
			if (cluster.isEmpty())
				emptyClusters.add(cluster);
		clusters.removeAll(emptyClusters);
		return emptyClusters;
	}

	public List<Cluster> getClusters() {
		return clusters;
	}

	public void setClusters(List<Cluster> clusters) {
		this.clusters = clusters;
	}

	public String getAccumulation() {
		return distanceFunction;
	}

	public void setAccumulation(String distanceFunction) {
		this.distanceFunction = distanceFunction;
	}

	public Map<String, String> getPointDistribution() {
		return pointDistribution;
	}

	public void setPointDistribution(Map<String, String> pointDistribution) {
		this.pointDistribution = pointDistribution;
	}

}

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