KNN的C++实现_knn c++库

#include"stdafx.h"
#include<iostream>  
#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("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 distance  
	map<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_dis  
	vector<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 data  
	sort(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 label  
		map_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 label  
	while (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;
}

data.txt数据如下：

0.0 1.1 A  
1.0 1.0 A  
2.0 1.0 B  
0.5 0.5 A  
2.5 0.5 B  
0.0 0.0 A  
1.0 0.0 A   
2.0 0.0 B  
3.0 0.0 B  
0.0 -1.0 A  
1.0 -1.0 A  
2.0 -1.0 B