/*
* Copyright 2013 Alibaba.com All right reserved. This software is the
* confidential and proprietary information of Alibaba.com ("Confidential
* Information"). You shall not disclose such Confidential Information and shall
* use it only in accordance with the terms of the license agreement you entered
* into with Alibaba.com.
*/
package com.alibaba.simpleimage.analyze.search.cluster.impl;
import java.util.List;
import com.alibaba.simpleimage.analyze.search.cluster.Clusterable;
import com.alibaba.simpleimage.analyze.search.util.ClusterUtils;
/**
* 类MeansClusterBuilder.java的实现描述：TODO 类实现描述
*
* @author axman 2013-7-24 上午11:25:16
*/
public class MeansClusterBuilder extends AbstractClusterBuilder {
public MeansClusterBuilder(){
super();
}
protected Clusterable[] assignClusters(Clusterable[] clusters, final List<? extends Clusterable> values) {
assignClustersByDistance(clusters, values);
return clusters;
}
protected void assignClustersByDistance(Clusterable[] clusters, List<? extends Clusterable> values) {
// long start = 0;
// long end = 0;
// long cost = 0;
// start = System.currentTimeMillis();
for (int j = 0; j < values.size(); j++) {
// start = System.currentTimeMillis();
Clusterable val = values.get(j);
// end = System.currentTimeMillis();
// start = System.currentTimeMillis();
Clusterable nearestCluster = null;
float minDistance = Float.MAX_VALUE;
for (int i = 0; i < clusters.length; i++) {
Clusterable cluster = clusters[i];
float distance = ClusterUtils.getEuclideanDistance(val, cluster);
if (distance < minDistance) {
nearestCluster = cluster;
minDistance = distance;
}
}
// end = System.currentTimeMillis();
((Cluster) nearestCluster).addItem(val);
// end = System.currentTimeMillis();
// cost += (end - start);
/*
* if(j % 1000 == 0) { System.out.println(j + " --- " + cost); cost = 0; }
*/
}
}
protected Clusterable[] getNewClusters(Clusterable[] clusters) {
for (int i = 0; i < clusters.length; i++) {
if (((Cluster) clusters[i]).getItems().size() > 0) {
clusters[i] = new Cluster(((Cluster) clusters[i]).getClusterMean(), i);
}
}
return clusters;
}
public static void main(String args[]) {
// Random random = new Random(System.currentTimeMillis());
// int numPoints = 400000;
// List<Clusterable> points = new ArrayList<Clusterable>(numPoints);
// for ( int i = 0; i < numPoints; i++ ){
// int x = random.nextInt(1000) - 500;
// int y = random.nextInt(1000) - 500;
// points.add(new Point((float)x,(float)y));
//
// }
// KClusterer clusterer = new KMeansClusterer();
// Cluster[] clusters = clusterer.cluster(points,10);
// System.out.println("--------- cluster info --------------");
// for ( Cluster c : clusters ){
// System.out.println(c.getItems().size());
// }
}
public static boolean hasBadValue(float[] values) {
for (float value : values) {
if (!(value < 1 && value > -1)) {
return true;
}
}
return false;
}
}