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<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(); } }
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<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(); } } }