Beispiel #1
0
 /**
  * 执行KNN算法,获取测试元组的类别
  *
  * @param list 训练数据集
  * @param test 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>> list, List<Double> test, int k) {
   PriorityQueue<KNNNode> pq = new PriorityQueue<KNNNode>(k, comparator);
   for (int i = 0; i < k; i++) {
     List<Double> trainTuple = list.get(i);
     Double c = trainTuple.get(0);
     KNNNode node = new KNNNode(i, getDistance(test, trainTuple), c);
     pq.add(node);
   }
   for (int i = 0; i < list.size(); i++) {
     List<Double> t = list.get(i);
     double distance = getDistance(test, t);
     KNNNode top = pq.peek();
     if (top.getDistance() > distance) {
       pq.remove();
       pq.add(new KNNNode(i, distance, t.get(0)));
     }
   }
   return getMostClass(pq);
 }
Beispiel #2
0
 /**
  * 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<Double, Integer> classCount = new HashMap<Double, Integer>();
   for (int i = 0; i < pq.size(); i++) {
     KNNNode node = pq.remove();
     double 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();
 }