KNN最近邻基于欧几里德距离的JAVA算法实现

本算法只适合学习使用，可以大致了解一下KNN算法的原理。

算法作了如下的假定与简化处理：

1.小规模数据集

2.假设所有数据及类别都是数值类型的

3.直接根据数据规模设定了k值

4.对原训练集进行测试

KNN实现代码如下：

package KNN;
/**
* KNN结点类，用来存储最近邻的k个元组相关的信息
* @author Rowen
* @qq 443773264
* @mail [email protected]
* @blog blog.csdn.net/luowen3405
* @data 2011.03.25
*/
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;
    }
}

package KNN; /** * KNN结点类，用来存储最近邻的k个元组相关的信息 * @author Rowen * @qq 443773264 * @mail [email protected] * @blog blog.csdn.net/luowen3405 * @data 2011.03.25 */ 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; } }

package KNN;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.PriorityQueue;

/**
* KNN算法主体类
* @author Rowen
* @qq 443773264
* @mail [email protected]
* @blog blog.csdn.net/luowen3405
* @data 2011.03.25
*/
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<KNNNode> comparator = new Comparator<KNNNode>() {
        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<Integer> getRandKNum(int k, int max) {
        List<Integer> rand = new ArrayList<Integer>(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<Double> d1, List<Double> 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<List<Double>> datas, List<Double> testData, int k) {
        PriorityQueue<KNNNode> pq = new PriorityQueue<KNNNode>(k, comparator);
        List<Integer> randNum = getRandKNum(k, datas.size());
        for (int i = 0; i < k; i++) {
            int index = randNum.get(i);
            List<Double> 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<Double> 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<KNNNode> pq) {
        Map<String, Integer> classCount = new HashMap<String, Integer>();
        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();
    }
}

package KNN; import java.util.ArrayList; import java.util.Comparator; import java.util.HashMap; import java.util.List; import java.util.Map; import java.util.PriorityQueue; /** * KNN算法主体类 * @author Rowen * @qq 443773264 * @mail [email protected] * @blog blog.csdn.net/luowen3405 * @data 2011.03.25 */ 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<KNNNode> comparator = new Comparator<KNNNode>() { 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<Integer> getRandKNum(int k, int max) { List<Integer> rand = new ArrayList<Integer>(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<Double> d1, List<Double> 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<List<Double>> datas, List<Double> testData, int k) { PriorityQueue<KNNNode> pq = new PriorityQueue<KNNNode>(k, comparator); List<Integer> randNum = getRandKNum(k, datas.size()); for (int i = 0; i < k; i++) { int index = randNum.get(i); List<Double> 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<Double> 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<KNNNode> pq) { Map<String, Integer> classCount = new HashMap<String, Integer>(); 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(); } }

package KNN;
import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.util.ArrayList;
import java.util.List;
/**
* KNN算法测试类
* @author Rowen
* @qq 443773264
* @mail [email protected]
* @blog blog.csdn.net/luowen3405
* @data 2011.03.25
*/
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<List<Double>> datas, String path){
        try {
            BufferedReader br = new BufferedReader(new FileReader(new File(path)));
            String data = br.readLine();
            List<Double> l = null;
            while (data != null) {
                String t[] = data.split(" ");
                l = new ArrayList<Double>();
                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<List<Double>> datas = new ArrayList<List<Double>>();
            List<List<Double>> testDatas =  new ArrayList<List<Double>>();
            t.read(datas, datafile);
            t.read(testDatas, testfile);
            KNN knn = new KNN();
            for (int i = 0; i < testDatas.size(); i++) {
                List<Double> 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();
        }
    }
}

package KNN; import java.io.BufferedReader; import java.io.File; import java.io.FileReader; import java.util.ArrayList; import java.util.List; /** * KNN算法测试类 * @author Rowen * @qq 443773264 * @mail [email protected] * @blog blog.csdn.net/luowen3405 * @data 2011.03.25 */ 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<List<Double>> datas, String path){ try { BufferedReader br = new BufferedReader(new FileReader(new File(path))); String data = br.readLine(); List<Double> l = null; while (data != null) { String t[] = data.split(" "); l = new ArrayList<Double>(); 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<List<Double>> datas = new ArrayList<List<Double>>(); List<List<Double>> testDatas = new ArrayList<List<Double>>(); t.read(datas, datafile); t.read(testDatas, testfile); KNN knn = new KNN(); for (int i = 0; i < testDatas.size(); i++) { List<Double> 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(); } } }

训练数据文件：

1.0 1.1 1.2 2.1 0.3 2.3 1.4 0.5 1
1.7 1.2 1.4 2.0 0.2 2.5 1.2 0.8 1
1.2 1.8 1.6 2.5 0.1 2.2 1.8 0.2 1
1.9 2.1 6.2 1.1 0.9 3.3 2.4 5.5 0
1.0 0.8 1.6 2.1 0.2 2.3 1.6 0.5 1
1.6 2.1 5.2 1.1 0.8 3.6 2.4 4.5 0

1.0 1.1 1.2 2.1 0.3 2.3 1.4 0.5 1 1.7 1.2 1.4 2.0 0.2 2.5 1.2 0.8 1 1.2 1.8 1.6 2.5 0.1 2.2 1.8 0.2 1 1.9 2.1 6.2 1.1 0.9 3.3 2.4 5.5 0 1.0 0.8 1.6 2.1 0.2 2.3 1.6 0.5 1 1.6 2.1 5.2 1.1 0.8 3.6 2.4 4.5 0

1.0 1.1 1.2 2.1 0.3 2.3 1.4 0.5
1.7 1.2 1.4 2.0 0.2 2.5 1.2 0.8
1.2 1.8 1.6 2.5 0.1 2.2 1.8 0.2
1.9 2.1 6.2 1.1 0.9 3.3 2.4 5.5
1.0 0.8 1.6 2.1 0.2 2.3 1.6 0.5
1.6 2.1 5.2 1.1 0.8 3.6 2.4 4.5

1.0 1.1 1.2 2.1 0.3 2.3 1.4 0.5 1.7 1.2 1.4 2.0 0.2 2.5 1.2 0.8 1.2 1.8 1.6 2.5 0.1 2.2 1.8 0.2 1.9 2.1 6.2 1.1 0.9 3.3 2.4 5.5 1.0 0.8 1.6 2.1 0.2 2.3 1.6 0.5 1.6 2.1 5.2 1.1 0.8 3.6 2.4 4.5

程序运行结果：

测试元组: 1.0  1.1 1.2 2.1 0.3 2.3 1.4 0.5 类别为: 1
测试元组: 1.7 1.2 1.4 2.0 0.2 2.5 1.2 0.8 类别为: 1
测试元组: 1.2 1.8 1.6 2.5 0.1 2.2 1.8 0.2 类别为: 1
测试元组: 1.9 2.1 6.2 1.1 0.9 3.3 2.4 5.5 类别为: 0
测试元组: 1.0 0.8 1.6 2.1 0.2 2.3 1.6 0.5 类别为: 1
测试元组: 1.6 2.1 5.2 1.1 0.8 3.6 2.4 4.5 类别为: 0