KNN.rar_K-NEAREST ALGORITHM_K._KNN java_java k-neighbor

import java.io.BufferedReader; import java.io.FileReader; import java.util.ArrayList; import java.util.Comparator; import java.util.HashMap; import java.util.PriorityQueue; public class KNN { private static int trainnum = 351; private static int attributenum = 34; private ArrayList<Knode> trainset; private static String dataroute = "D:\\周宇的文档\\学习\\Eagle-Lab\\实验\\KNN实验\\ionosphere.data"; private static int K = 1; //neighbor number private static int labelnum = 2; public void readdata() { trainset = new ArrayList<Knode>(); try { FileReader fr = new FileReader(dataroute); BufferedReader br = new BufferedReader(fr); String line = br.readLine(); while(line != null) //read data from file { String[] tokens = line.split(","); double[] attributes = new double[attributenum]; for(int i = 0;i < attributenum;i++) attributes[i] = Double.parseDouble(tokens[i]); int classlabel = -1; if(tokens[attributenum].equals("b")) classlabel = 0; else if(tokens[attributenum].equals("g")) classlabel = 1; else classlabel = -1; Knode aknode = new Knode(classlabel, attributes); trainset.add(aknode); line = br.readLine(); } br.close(); fr.close(); } catch(Exception e) { System.out.println(e.toString()); } } public void predictLabel() { for(int i = 0;i < trainnum;i++) { PriorityQueue<Knode> priorityqueue = new PriorityQueue<Knode>(K); for(int j = 0;j < trainnum;j++) { trainset.get(j).setDistance(distance(trainset.get(i),trainset.get(j))); if(j != i) priorityqueue.add(trainset.get(j)); } int[] count = new int[labelnum]; for(int k = 0;k < K;k++) { Knode aneighbor = (Knode)priorityqueue.poll(); count[aneighbor.getClassLabel()]++; } trainset.get(i).setPredictLabel(getMax(count)); } } private int getMax(int[] count) { int max = 0; int index = -1; for(int i = 0;i < count.length;i++) { if(max < count[i]) { max = count[i]; index = i; } } return index; } private double distance(Knode a, Knode b) { double sum = 0.0; for(int i = 0;i < attributenum;i++) { sum += Math.pow(a.getAttribute(i) - b.getAttribute(i), 2); } return Math.sqrt(sum); } public void printTrainData() { int ZZ = 0; int ZO = 0; int OZ = 0; int OO = 0; for(int i = 0;i < trainset.size();i++) { if(trainset.get(i).getClassLabel() == 0) { if(trainset.get(i).getPredictLabel() == 0) ZZ++; else ZO++; } else { if(trainset.get(i).getPredictLabel() == 0) OZ++; else OO++; } } System.out.println(ZZ + " " + ZO + " " + OZ + " " + OO); } public static void main(String[] args) { KNN knn = new KNN(); knn.readdata(); knn.predictLabel(); knn.printTrainData(); } }