KNN （K-最近邻分类）算法源代码 模式识别

KNN （K-最近邻分类）算法源代码 模式识别

模式识别P86

K-近邻法是最近邻法的推广

using namespace std;
#include <iostream> 
#include <cmath> 
#include <fstream> 

#define NATTRS 5 //属性数 
#define MAXSZ 1700 //训练集的最大尺寸 
#define MAXVALUE 10000.0	//最大属性10000 
#define K 5  //定义最邻近值
 
struct vector { 
double attributes[NATTRS]; 
double classlabel; 
}; 
 
struct item { 
double distance; 
double classlabel; 
}; 
 
struct vector trSet[MAXSZ];//全局变量。训练集 
struct item knn[K];//全局变量。K-最邻近集合 
int curTSize = 0; //当前训练集的尺寸 
 
int AddtoTSet(struct vector v) 
{ 
	if(curTSize>=MAXSZ) 
	{ 
		cout<<endl<<"The training set has "<<MAXSZ<<" examples!"<<endl<<endl;  
		return 0; 
	} 
	trSet[curTSize] = v; 
	curTSize++; 
	return 1; 
} 
 
double Distance(struct vector v1,struct vector v2) 
{ 
	double d = 0.0; 
	double tem = 0.0; 
	for(int i = 0;i < NATTRS;i++) 
		tem += (v1.attributes[i]-v2.attributes[i])*(v1.attributes[i]-v2.attributes[i]); 
	d = sqrt(tem); 
	return d; 
} 
 
int max(struct item knn[]) //返回值为项目间的最大距离 
{ 
	int maxNo = 0; 
	if(K > 1)	
	for(int i = 1;i < K;i++) 
		if(knn[i].distance>knn[maxNo].distance)		
	maxNo = i; 
	return maxNo; 
} 
 
double Classify(struct vector v)//决定分到哪一类中 
{ 
	double dd = 0; 
	int maxn = 0; 
	int freq[K]; 
	double mfreqC = 0;//分类出现次数 
	int i; 
	for(i = 0;i < K;i++)
		knn[i].distance = MAXVALUE; 
	for(i = 0;i < curTSize;i++) 
	{ 
		dd = Distance(trSet[i],v); 
		maxn = max(knn);//为新加数据更新训练集 
		if(dd < knn[maxn].distance) 
		{ 
			knn[maxn].distance = dd; 
			knn[maxn].classlabel = trSet[i].classlabel; 
		} 
	} 
	for(i = 0;i < K;i++)//freq[i]显示knn[i].classlabel出现的次数 
		freq[i] = 1; 
	for(i = 0;i < K;i++)  
		for(int j = 0;j < K;j++) 
			if((i!=j)&&(knn[i].classlabel == knn[j].classlabel))	
				freq[i]+=1; 
		for(i=0;i<K;i++)
		cout<<"freq:"<<freq[i]<<endl;
	int mfreq = 1; 
	mfreqC = knn[0].classlabel; 
	for(i = 0;i < K;i++) 
		if(freq[i] > mfreq) 
		{ 
			mfreq = freq[i];//分类出现最大次数 
			mfreqC = knn[i].classlabel; 
		} 
return mfreqC; 
} 
 
int main(){ 
	double classlabel; 
	double c;  
	double n; 
	struct vector trExmp;  
	int i; 
	ifstream filein("C:\\Users\\慰\\Documents\\大学\\大三上\\模式识别\\K近邻\\data.txt"); 
	if(filein.fail()) { cout<<"Can't open data.txt"<<endl; //return; 
	} 
	while(!filein.eof()) 
	{ 
		filein>>c; 
		trExmp.classlabel = c; 
		cout<<"label:"<<trExmp.classlabel<<"|"; 
		
		for(int i = 0;i < NATTRS;i++) 
		{ 
			filein>>n; 
			trExmp.attributes[i] = n; 
			cout<<trExmp.attributes[i]<<" "; 
		} 
		cout<<endl; 
	if(!AddtoTSet(trExmp))		
		break; 
	} 
	filein.close(); 
	
	struct vector testv={{1,18,11,11,0.5513196},17}; 
	classlabel = Classify(testv); 
	cout<<"the classlable of the testv is:"; 
	cout<<classlabel<<endl; 
	for(i = 0;i < K;i++)	
        cout<<knn[i].distance<<"\t"<<knn[i].classlabel<<endl; 
} 


           

截图

data.txt

0 1 1 1 1 1

0 2 2 2 2 2

0 3 3 3 3 3

1 4 4 4 4 4

1 5 5 5 5 5

1 6 6 6 6 6

2 7 7 7 7 7

2 8 8 8 8 8

2 9 9 9 9 9

3 10 10 10 10 10

3 11 11 11 11 11

3 12 12 12 12 12

4 13 13 13 13 13

4 14 14 14 14 14

4 15 15 15 15 15

6 16 16 16 16 16

6 17 17 17 17 17

6 18 18 18 18 18

7 19 19 19 19 19

7 20 20 20 20 20

7 21 21 21 21 21

8 22 22 22 22 22

8 23 23 23 23 23

8 24 24 24 24 24

9 25 25 25 25 25

9 26 26 26 26 26

9 27 27 27 27 27

10 28 28 28 28 28

10 29 29 29 29 29

10 30 30 30 30 30