Java实现 基于密度的局部离群点检测------lof算法

package com.bigdata.ml.outlier;
 
import java.util.ArrayList;
import java.util.List;
 

public class DataNode {
	private String nodeName; // 样本点名
	private double[] dimensioin; // 样本点的维度
	private double kDistance; // k-距离
	private List<DataNode> kNeighbor = new ArrayList<DataNode>();// k-领域
	private double distance; // 到给定点的欧几里得距离
	private double reachDensity;// 可达密度
	private double reachDis;// 可达距离
 
	private double lof;// 局部离群因子
 
	public DataNode() {
 
	}
 
	public DataNode(String nodeName, double[] dimensioin) {
		this.nodeName = nodeName;
		this.dimensioin = dimensioin;
	}
 
	public String getNodeName() {
		return nodeName;
	}
 
	public void setNodeName(String nodeName) {
		this.nodeName = nodeName;
	}
 
	public double[] getDimensioin() {
		return dimensioin;
	}
 
	public void setDimensioin(double[] dimensioin) {
		this.dimensioin = dimensioin;
	}
 
	public double getkDistance() {
		return kDistance;
	}
 
	public void setkDistance(double kDistance) {
		this.kDistance = kDistance;
	}
 
	public List<DataNode> getkNeighbor() {
		return kNeighbor;
	}
 
	public void setkNeighbor(List<DataNode> kNeighbor) {
		this.kNeighbor = kNeighbor;
	}
 
	public double getDistance() {
		return distance;
	}
 
	public void setDistance(double distance) {
		this.distance = distance;
	}
 
	public double getReachDensity() {
		return reachDensity;
	}
 
	public void setReachDensity(double reachDensity) {
		this.reachDensity = reachDensity;
	}
 
	public double getReachDis() {
		return reachDis;
	}
 
	public void setReachDis(double reachDis) {
		this.reachDis = reachDis;
	}
 
	public double getLof() {
		return lof;
	}
 
	public void setLof(double lof) {
		this.lof = lof;
	}
 
}

package com.bigdata.ml.outlier;
 
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
 

public class OutlierNodeDetect {
	private static int INT_K = 5;//正整数K
 
	// 1.找到给定点与其他点的欧几里得距离
	// 2.对欧几里得距离进行排序，找到前5位的点，并同时记下k距离
	// 3.计算每个点的可达密度
	// 4.计算每个点的局部离群点因子
	// 5.对每个点的局部离群点因子进行排序，输出。
	public List<DataNode> getOutlierNode(List<DataNode> allNodes) {
 
		List<DataNode> kdAndKnList = getKDAndKN(allNodes);
		calReachDis(kdAndKnList);
		calReachDensity(kdAndKnList);
		calLof(kdAndKnList);
		//降序排序
		Collections.sort(kdAndKnList, new LofComparator());
 
		return kdAndKnList;
	}
 
	
	private void calLof(List<DataNode> kdAndKnList) {
		for (DataNode node : kdAndKnList) {
			List<DataNode> tempNodes = node.getkNeighbor();
			double sum = 0.0;
			for (DataNode tempNode : tempNodes) {
				double rd = getRD(tempNode.getNodeName(), kdAndKnList);
				sum = rd / node.getReachDensity() + sum;
			}
			sum = sum / (double) INT_K;
			node.setLof(sum);
		}
	}
 
	
	private void calReachDensity(List<DataNode> kdAndKnList) {
		for (DataNode node : kdAndKnList) {
			List<DataNode> tempNodes = node.getkNeighbor();
			double sum = 0.0;
			double rd = 0.0;
			for (DataNode tempNode : tempNodes) {
				sum = tempNode.getReachDis() + sum;
			}
			rd = (double) INT_K / sum;
			node.setReachDensity(rd);
		}
	}
	
	
	private void calReachDis(List<DataNode> kdAndKnList) {
		for (DataNode node : kdAndKnList) {
			List<DataNode> tempNodes = node.getkNeighbor();
			for (DataNode tempNode : tempNodes) {
				//获取tempNode点的k-距离
				double kDis = getKDis(tempNode.getNodeName(), kdAndKnList);
				//reachdis(p,o)=max{ k-distance(o),d(p,o)}
				if (kDis < tempNode.getDistance()) {
					tempNode.setReachDis(tempNode.getDistance());
				} else {
					tempNode.setReachDis(kDis);
				}
			}
		}
	}
 
	
	private double getKDis(String nodeName, List<DataNode> nodeList) {
		double kDis = 0;
		for (DataNode node : nodeList) {
			if (nodeName.trim().equals(node.getNodeName().trim())) {
				kDis = node.getkDistance();
				break;
			}
		}
		return kDis;
 
	}
 
	
	private double getRD(String nodeName, List<DataNode> nodeList) {
		double kDis = 0;
		for (DataNode node : nodeList) {
			if (nodeName.trim().equals(node.getNodeName().trim())) {
				kDis = node.getReachDensity();
				break;
			}
		}
		return kDis;
 
	}
	
	
	private List<DataNode> getKDAndKN(List<DataNode> allNodes) {
		List<DataNode> kdAndKnList = new ArrayList<DataNode>();
		for (int i = 0; i < allNodes.size(); i++) {
			List<DataNode> tempNodeList = new ArrayList<DataNode>();
			DataNode nodeA = new DataNode(allNodes.get(i).getNodeName(), allNodes
					.get(i).getDimensioin());
			//1,找到给定点NodeA与其他点NodeB的欧几里得距离，并记录在NodeB点的distance变量中。
			for (int j = 0; j < allNodes.size(); j++) {
				DataNode nodeB = new DataNode(allNodes.get(j).getNodeName(), allNodes
						.get(j).getDimensioin());
				//计算NodeA与NodeB的欧几里得距离(distance)
				double tempDis = getDis(nodeA, nodeB);
				nodeB.setDistance(tempDis);
				tempNodeList.add(nodeB);
			}
 
			//2,对所有NodeB点中的欧几里得距离（distance）进行升序排序。
			Collections.sort(tempNodeList, new DistComparator());
			for (int k = 1; k < INT_K; k++) {
				//3,找到NodeB点的前5位的欧几里得距离点，并记录到到NodeA的kNeighbor变量中。
				nodeA.getkNeighbor().add(tempNodeList.get(k));
				if (k == INT_K - 1) {
					//4,找到NodeB点的第5位距离，并记录到NodeA点的kDistance变量中。
					nodeA.setkDistance(tempNodeList.get(k).getDistance());
				}
			}
			kdAndKnList.add(nodeA);
		}
 
		return kdAndKnList;
	}
	
	
	private double getDis(DataNode A, DataNode B) {
		double dis = 0.0;
		double[] dimA = A.getDimensioin();
		double[] dimB = B.getDimensioin();
		if (dimA.length == dimB.length) {
			for (int i = 0; i < dimA.length; i++) {
				double temp = Math.pow(dimA[i] - dimB[i], 2);
				dis = dis + temp;
			}
			dis = Math.pow(dis, 0.5);
		}
		return dis;
	}
 
	
	class DistComparator implements Comparator<DataNode> {
		public int compare(DataNode A, DataNode B) {
			//return A.getDistance() - B.getDistance() < 0 ? -1 : 1;
			if((A.getDistance()-B.getDistance())<0)   
                return -1;  
            else if((A.getDistance()-B.getDistance())>0)  
                return 1;  
            else return 0;  
		}
	}
 
	
	class LofComparator implements Comparator<DataNode> {
		public int compare(DataNode A, DataNode B) {
			//return A.getLof() - B.getLof() < 0 ? 1 : -1;
			if((A.getLof()-B.getLof())<0)   
                return 1;  
            else if((A.getLof()-B.getLof())>0)  
                return -1;  
            else return 0;  
		}
	}
 
	public static void main(String[] args) {
		
		java.text.DecimalFORMat   df   =new   java.text.DecimalFormat("#.####");  
 
		ArrayList<DataNode> dpoints = new ArrayList<DataNode>();
 
		double[] a = { 2, 3 };
		double[] b = { 2, 4 };
		double[] c = { 1, 4 };
		double[] d = { 1, 3 };
		double[] e = { 2, 2 };
		double[] f = { 3, 2 };
 
		double[] g = { 8, 7 };
		double[] h = { 8, 6 };
		double[] i = { 7, 7 };
		double[] j = { 7, 6 };
		double[] k = { 8, 5 };
 
		double[] l = { 100, 2 };// 孤立点
 
		double[] m = { 8, 20 };
		double[] n = { 8, 19 };
		double[] o = { 7, 18 };
		double[] p = { 7, 17 };
		double[] q = { 8, 21 };
 
		dpoints.add(new DataNode("a", a));
		dpoints.add(new DataNode("b", b));
		dpoints.add(new DataNode("c", c));
		dpoints.add(new DataNode("d", d));
		dpoints.add(new DataNode("e", e));
		dpoints.add(new DataNode("f", f));
 
		dpoints.add(new DataNode("g", g));
		dpoints.add(new DataNode("h", h));
		dpoints.add(new DataNode("i", i));
		dpoints.add(new DataNode("j", j));
		dpoints.add(new DataNode("k", k));
 
		dpoints.add(new DataNode("l", l));
 
		dpoints.add(new DataNode("m", m));
		dpoints.add(new DataNode("n", n));
		dpoints.add(new DataNode("o", o));
		dpoints.add(new DataNode("p", p));
		dpoints.add(new DataNode("q", q));
 
		OutlierNodeDetect lof = new OutlierNodeDetect();
 
		List<DataNode> nodeList = lof.getOutlierNode(dpoints);
 
		for (DataNode node : nodeList) {
			System.out.println(node.getNodeName() + "  " + df.format(node.getLof()));
		}
 
	}
}