K最近邻(KNN)算法原理和java实现

原理部分：

请参考：KNN演算法

 

 

代码实现：

 

KNN结点类，用来存储最近邻的k个元组相关的信息

/**
 * KNN结点类，用来存储最近邻的k个元组相关的信息
 */
public class KNNNode {
	private int index; 			// 元组标号
	private double distance; 	// 与测试元组的距离
	private String c; 			// 所属类别
	public KNNNode(int index, double distance, String c) {
		super();
		this.index = index;
		this.distance = distance;
		this.c = c;
	}
	
	
	public int getIndex() {
		return index;
	}
	public void setIndex(int index) {
		this.index = index;
	}
	public double getDistance() {
		return distance;
	}
	public void setDistance(double distance) {
		this.distance = distance;
	}
	public String getC() {
		return c;
	}
	public void setC(String c) {
		this.c = c;
	}
}

 

 

KNN算法主体类

/**
 * KNN算法主体类
 */
public class KNN {
	/**
	 * 设置优先级队列的比较函数，距离越大，优先级越高
	 */
	private Comparator comparator = new Comparator() {
		public int compare(KNNNode o1, KNNNode o2) {
			if (o1.getDistance() >= o2.getDistance()) {
				return 1;
			} else {
				return 0;
			}
		}
	};
	/**
	 * 获取K个不同的随机数
	 * @param k 随机数的个数
	 * @param max 随机数最大的范围
	 * @return 生成的随机数数组
	 */
	public List getRandKNum(int k, int max) {
		List rand = new ArrayList(k);
		for (int i = 0; i < k; i++) {
			int temp = (int) (Math.random() * max);
			if (!rand.contains(temp)) {
				rand.add(temp);
			} else {
				i--;
			}
		}
		return rand;
	}
	/**
	 * 计算测试元组与训练元组之前的距离
	 * @param d1 测试元组
	 * @param d2 训练元组
	 * @return 距离值
	 */
	public double calDistance(List d1, List d2) {
		double distance = 0.00;
		for (int i = 0; i < d1.size(); i++) {
			distance += (d1.get(i) - d2.get(i)) * (d1.get(i) - d2.get(i));
		}
		return distance;
	}
	/**
	 * 执行KNN算法，获取测试元组的类别
	 * @param datas 训练数据集
	 * @param testData 测试元组
	 * @param k 设定的K值
	 * @return 测试元组的类别
	 */
	public String knn(List> datas, List testData, int k) {
		PriorityQueue pq = new PriorityQueue(k, comparator);
		List randNum = getRandKNum(k, datas.size());
		for (int i = 0; i < k; i++) {
			int index = randNum.get(i);
			List currData = datas.get(index);
			String c = currData.get(currData.size() - 1).toString();
			KNNNode node = new KNNNode(index, calDistance(testData, currData), c);
			pq.add(node);
		}
		for (int i = 0; i < datas.size(); i++) {
			List t = datas.get(i);
			double distance = calDistance(testData, t);
			KNNNode top = pq.peek();
			if (top.getDistance() > distance) {
				pq.remove();
				pq.add(new KNNNode(i, distance, t.get(t.size() - 1).toString()));
			}
		}
		
		return getMostClass(pq);
	}
	/**
	 * 获取所得到的k个最近邻元组的多数类
	 * @param pq 存储k个最近近邻元组的优先级队列
	 * @return 多数类的名称
	 */
	private String getMostClass(PriorityQueue pq) {
		Map classCount = new HashMap();
		for (int i = 0; i < pq.size(); i++) {
			KNNNode node = pq.remove();
			String c = node.getC();
			if (classCount.containsKey(c)) {
				classCount.put(c, classCount.get(c) + 1);
			} else {
				classCount.put(c, 1);
			}
		}
		int maxIndex = -1;
		int maxCount = 0;
		Object[] classes = classCount.keySet().toArray();
		for (int i = 0; i < classes.length; i++) {
			if (classCount.get(classes[i]) > maxCount) {
				maxIndex = i;
				maxCount = classCount.get(classes[i]);
			}
		}
		return classes[maxIndex].toString();
	}
}

 

KNN算法测试类

/**
 * KNN算法测试类
 */
public class TestKNN {
	
	/**
	 * 从数据文件中读取数据
	 * @param datas 存储数据的集合对象
	 * @param path 数据文件的路径
	 */
	public void read(List> datas, String path){
		try {
			BufferedReader br = new BufferedReader(new FileReader(new File(path)));
			String data = br.readLine();
			List l = null;
			while (data != null) {
				String t[] = data.split(" ");
				l = new ArrayList();
				for (int i = 0; i < t.length; i++) {
					l.add(Double.parseDouble(t[i]));
				}
				datas.add(l);
				data = br.readLine();
			}
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
	
	/**
	 * 程序执行入口
	 * @param args
	 */
	public static void main(String[] args) {
		TestKNN t = new TestKNN();
		String datafile = new File("").getAbsolutePath() + File.separator + "datafile";
		String testfile = new File("").getAbsolutePath() + File.separator + "testfile";
		try {
			List> datas = new ArrayList>();
			List> testDatas = new ArrayList>();
			t.read(datas, datafile);
			t.read(testDatas, testfile);
			KNN knn = new KNN();
			for (int i = 0; i < testDatas.size(); i++) {
				List test = testDatas.get(i);
				System.out.print("测试元组: ");
				for (int j = 0; j < test.size(); j++) {
					System.out.print(test.get(j) + " ");
				}
				System.out.print("类别为: ");
				System.out.println(Math.round(Float.parseFloat((knn.knn(datas, test, 3)))));
			}
		} catch (Exception e) {
			e.printStackTrace();
		}
	}
}