协同过滤算法参考代码

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代码有问题，运行出错，寻找解决办法，是不是数据集的错误呢？

#include<iostream>
#include<queue>
#include<cmath>
#include<cassert>
#include<cstdlib>
#include<fstream>
#include<sstream>
#include<vector>
#include<algorithm>

using namespace std;

const int ITERM_SIZE=1682;
const int USER_SIZE=943;
const int V=15; //ITERM的最近邻居数
const int S=10; //USER的最近邻居数

struct MyPair{
int id;
double value;
MyPair(int i=0,double v=0):id(i),value(v){}
};

struct cmp{
bool operator() (const MyPair & obj1,const MyPair & obj2)const{
return obj1.value < obj2.value;
}
};

double rate[USER_SIZE][ITERM_SIZE]; //评分矩阵
MyPair nbi[ITERM_SIZE][V]; //存放每个ITERM的最近邻居
MyPair nbu[USER_SIZE][S]; //存放每个USER的最近邻居
double rate_avg[USER_SIZE]; //每个用户的平均评分

//从文件中读入评分矩阵
int readRate(string filename){
ifstream ifs;
ifs.open(filename.c_str());
if(!ifs){
cerr<<"error:unable to open input file "<<filename<<endl;
return -1;
}
string line;
while(getline(ifs,line)){
string str1,str2,str3;
istringstream strstm(line);
strstm>>str1>>str2>>str3;
int userid=atoi(str1.c_str());
int itermid=atoi(str2.c_str());
double rating=atof(str3.c_str());
rate[userid-1][itermid-1]=rating;
//line.clear();
}
ifs.close();
return 0;
}

//计算每个用户的平均评分
void getAvgRate(){
for(int i=0;i<USER_SIZE;++i){
double sum=0;
for(int j=0;j<ITERM_SIZE;++j)
sum+=rate[i][j];
rate_avg[i]=sum/ITERM_SIZE;
}
}

//计算两个向量的皮尔森相关系数
double getSim(const vector<double> &vec1,const vector<double> &vec2){
int len=vec1.size();
assert(len==vec2.size());
double sum1=0;
double sum2=0;
double sum1_1=0;
double sum2_2=0;
double sum=0;
for(int i=0;i<len;i++){
sum+=vec1[i]*vec2[i];
sum1+=vec1[i];
sum2+=vec2[i];
sum1_1+=vec1[i]*vec1[i];
sum2_2+=vec2[i]*vec2[i];
}
double ex=sum1/len;
double ey=sum2/len;
double ex2=sum1_1/len;
double ey2=sum2_2/len;
double exy=sum/len;
double sdx=sqrt(ex2-ex*ex);
double sdy=sqrt(ey2-ey*ey);
assert(sdx!=0 && sdy!=0);
double sim=(exy-ex*ey)/(sdx*sdy);
return sim;
}

//计算每个ITERM的最近邻
void getNBI(){
for(int i=0;i<ITERM_SIZE;++i){
vector<double> vec1;
priority_queue<MyPair,vector<MyPair>,cmp> neighbour;
for(int k=0;k<USER_SIZE;k++)
vec1.push_back(rate[k][i]);
for(int j=0;j<ITERM_SIZE;j++){
if(i==j)
continue;
vector<double> vec2;
for(int k=0;k<USER_SIZE;k++)
vec2.push_back(rate[k][j]);
double sim=getSim(vec1,vec2);
MyPair p(j,sim);
neighbour.push(p);
}
for(int n=0;n<V;++n){
nbi[i][n]=neighbour.top();
neighbour.pop();
}
}
}

//预测用户对未评分项目的评分值
double getPredict(const vector<double> &user,int index){
double sum1=0;
double sum2=0;
for(int i=0;i<V;++i){
int neib_index=nbi[index][i].id;
double neib_sim=nbi[index][i].value;
sum1+=neib_sim*user[neib_index];
sum2+=fabs(neib_sim);
}
return sum1/sum2;
}

//计算两个用户的相似度
double getUserSim(const vector<double> &user1,const vector<double> &user2){
vector<double> vec1;
vector<double> vec2;
int len=user1.size();
assert(len==user2.size());
for(int i=0;i<len;++i){
if(user1[i]!=0 || user2[i]!=0){
if(user1[i]!=0)
vec1.push_back(user1[i]);
else
vec1.push_back(getPredict(user1,i));
if(user2[i]!=0)
vec2.push_back(user2[i]);
else
vec2.push_back(getPredict(user2,i));
}
}
return getSim(vec1,vec2);
}

//计算每个USER的最近邻
void getNBU(){
for(int i=0;i<USER_SIZE;++i){
vector<double> user1;
priority_queue<MyPair,vector<MyPair>,cmp> neighbour;
for(int k=0;k<ITERM_SIZE;++k)
user1.push_back(rate[i][k]);
for(int j=0;j<USER_SIZE;++j){
if(j==i)
continue;
vector<double> user2;
for(int k=0;k<ITERM_SIZE;++k)
user2.push_back(rate[j][k]);
double sim=getUserSim(user1,user2);
MyPair p(j,sim);
neighbour.push(p);
}
for(int m=0;m<S;++m){
nbu[i][m]=neighbour.top();
neighbour.pop();
}
}
}

//产生推荐，预测某用户对某项目的评分
double predictRate(int user,int iterm){
double sum1=0;
double sum2=0;
for(int i=0;i<S;++i){
int neib_index=nbu[user][i].id;
double neib_sim=nbu[user][i].value;
sum1+=neib_sim*(rate[neib_index][iterm]-rate_avg[neib_index]);
sum2+=fabs(neib_sim);
}
return rate_avg[user]+sum1/sum2;
}

//测试
int main(){
//string file="/home/orisun/DataSet/movie-lens-100k/u.data";
string file="E:\\C++programs1\\ml-100k\\ml-100k\\u.data";
if(readRate(file)!=0){
return -1;
}
getAvgRate();
getNBI();
getNBU();
while(1){
cout<<"please input user index and iterm index which you want predict"<<endl;
int user,iterm;
cin>>user>>iterm;
cout<<predictRate(user,iterm)<<endl;
}
return 0;
}

协同过滤算法参考代码

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原文地址：http://www.cnblogs.com/bluesky1230/p/4544757.html