K近邻算法中的分类决策规则往往是多数表决，即由输入示例的K个邻近的训练实例中的多数类决定输入实例的类。

</#include<map>
#include<vector>
#include<stdio.h>
#include<cmath>
#include<cstdlib>
#include<algorithm>
#include<fstream>using namespace std;typedef char tLabel;
typedef double tData;
typedef pair<int, double>  PAIR;
const int colLen = 2;
const int rowLen = 12;
ifstream fin;
ofstream fout;class KNN
{
private:tData dataSet[rowLen][colLen];tLabel labels[rowLen];tData testData[colLen];int k;map<int, double> map_index_dis;map<tLabel, int> map_label_freq;double get_distance(tData *d1, tData *d2);
public:KNN(int k);void get_all_distance();void get_max_freq_label();struct CmpByValue{bool operator() (const PAIR& lhs, const PAIR& rhs){return lhs.second < rhs.second;}};};KNN::KNN(int k)
{this->k = k;fin.open("C:\\Users\\zws\\Desktop\\K近邻\\data.txt");if (!fin){cout << "can not open the file data.txt" << endl;exit(1);}/* input the dataSet */for (int i = 0; i<rowLen; i++){for (int j = 0; j<colLen; j++){fin >> dataSet[i][j];}fin >> labels[i];}cout << "please input the test data :" << endl;/* inuput the test data */for (int i = 0; i<colLen; i++)cin >> testData[i];}/*
* calculate the distance between test data and dataSet[i]
*/
double KNN::get_distance(tData *d1, tData *d2)
{double sum = 0;for (int i = 0; i<colLen; i++){sum += pow((d1[i] - d2[i]), 2);}//    cout<<"the sum is = "<<sum<<endl;return sqrt(sum);
}/*
* calculate all the distance between test data and each training data
*/
void KNN::get_all_distance()
{double distance;int i;for (i = 0; i<rowLen; i++){distance = get_distance(dataSet[i], testData);//<key,value> => <i,distance>map_index_dis[i] = distance;}//traverse the map to print the index and distancemap<int, double>::const_iterator it = map_index_dis.begin();while (it != map_index_dis.end()){cout << "index = " << it->first << " distance = " << it->second << endl;it++;}
}/*
* check which label the test data belongs to to classify the test data
*/
void KNN::get_max_freq_label()
{//transform the map_index_dis to vec_index_disvector<PAIR> vec_index_dis(map_index_dis.begin(), map_index_dis.end());//sort the vec_index_dis by distance from low to high to get the nearest datasort(vec_index_dis.begin(), vec_index_dis.end(), CmpByValue());for (int i = 0; i<k; i++){cout << "the index = " << vec_index_dis[i].first << " the distance = " << vec_index_dis[i].second << " the label = " << labels[vec_index_dis[i].first] << " the coordinate ( " << dataSet[vec_index_dis[i].first][0] << "," << dataSet[vec_index_dis[i].first][1] << " )" << endl;//calculate the count of each labelmap_label_freq[labels[vec_index_dis[i].first]]++;}map<tLabel, int>::const_iterator map_it = map_label_freq.begin();tLabel label;int max_freq = 0;//find the most frequent labelwhile (map_it != map_label_freq.end()){if (map_it->second > max_freq){max_freq = map_it->second;label = map_it->first;}map_it++;}cout << "The test data belongs to the " << label << " label" << endl;
}int main()
{int k;cout << "please input the k value : " << endl;cin >> k;KNN knn(k);knn.get_all_distance();knn.get_max_freq_label();system("pause");return 0;
}

测试结果：