贝叶斯分类器c++源代码（含测试数据）

#include <iostream>////////////适合计算属性值是离散的分类 #include <fstream> #include <sstream> #include "BayesianClassifier.h" using namespace std; const int Shuxing=13;//属性总数 ifstream f; vector<OriginalData> trainData; //存放训练数据 vector<OriginalData> testData; //存放测试数据 double A[3]; //先验概率 int m; //存放每一类型，每种属性中某数值的概率 map<double, double> C1_map[Shuxing]; map<double, double> C2_map[Shuxing]; map<double, double> C3_map[Shuxing]; //从文件中读取数值 void DataRead(vector<OriginalData> &data, const char* fileName) { f.open(fileName); int ZHjiang; if (fileName[0] == 'w') ZHjiang = TrainNum; else ZHjiang = TestNum; string line; OriginalData wine; for (int i = 0; i < ZHjiang; i++) { f >> line;//一行字符 while (line.find(',') > 0 && line.find(',') < line.length()) { line[line.find(',')] = ' '; } istringstream stream(line);//将stream绑定到读取的行 stream >> wine.A1 >> wine.A2 >> wine.A3 >> wine.A4 >> wine.A5 >> wine.A6 >> wine.A7 >> wine.A8 >> wine.A9 >> wine.A10 >> wine.A11 >> wine.A12 >> wine.A13 >> wine.A14;//读取数据传给wine data.push_back(wine); } f.close(); } void bayes() { int count1 = 0, count2 = 0, count3 = 0; int i; for(i = 0; i < TrainNum ; i++) { if(trainData[i].A1 == 1) { count1 ++; } if(trainData[i].A1 == 2) { count2 ++; } if(trainData[i].A1 == 3) { count3 ++; }//统计三类数据,各自求和 } A[0] = (double)count1/(double)TrainNum; //求先验概率 A[1] = (double)count2/(double)TrainNum; A[2] = (double)count3/(double)TrainNum; map<double, double>::iterator pipei; for(i = 0 ; i < TrainNum; i++) { if(trainData[i].A1 == 1) //求P(Xk|C1) 中Xk的个数 { int j=0; for(;j< 13 ;j++) { double temp = *(&trainData[i].A2+j); pipei = C1_map[j].find(temp); if(pipei == C1_map[j].end()) { C1_map[j].insert(map<double, double>::value_type(temp,1)); } else { double j = pipei->second; pipei->second = j + 1; } } } if(trainData[i].A1 == 2) //求P(Xk|C2) 中Xk的个数 { int j = 0; for(;j< 13 ;j++) { double temp = *(&trainData[i].A2+j); pipei = C2_map[j].find(temp); if(pipei == C2_map[j].end()) { C2_map[j].insert(map<double, double>::value_type(temp,1)); } else { double j = pipei->second; pipei->second = j + 1; } } } if(trainData[i].A1 == 3) //求P(Xk|C3) 中Xk的个数 { int j = 0; for(;j< 13 ;j++) { double temp = *(&trainData[i].A2+j); pipei = C3_map[j].find(temp); if(pipei == C3_map[j].end()) { C3_map[j].insert(map<double, double>::value_type(temp,1)); } else { double j = pipei->second; pipei->second = j + 1; } } } } //概率 for(i = 0; i < Shuxing; i++) { for(pipei=C1_map[i].begin(); pipei!=C1_map[i].end(); ++pipei) { double num = pipei->second; pipei->second = (double)num/(double)count1; } for(pipei=C2_map[i].begin(); pipei!=C2_map[i].end(); ++pipei) { double num = pipei->second; pipei->second = (double)num/(double)count2; } for(pipei=C3_map[i].begin(); pipei!=C3_map[i].end(); ++pipei) { double num = pipei->second; pipei->second = (double)num/(double)count3; } } } void houyan()//计算后验分布,找出最大值 { int i,j,k; double p[3]; for(i = 0; i<TestNum; i++) { double pXC[3]={0,0,0}; for(j = 0; j < 3; j++) { map<double, double>::iterator pipei; //计算p(X|C1) for(k = 0; k < Shuxing; k++) { pipei = C1_map[k].find(*(&testData[i].A2+k)); if(pipei != C1_map[k].end()) { pXC[0] =pXC[0] + pipei->second; } } p[0] = A[0] * pXC[0]; //计算p(X|C2) for(k = 0; k < Shuxing; k++) { pipei = C2_map[k].find(*(&testData[i].A2+k)); if(pipei != C2_map[k].end()) { pXC[1] =pXC[1] + pipei->second; } } p[1] = A[1]*pXC[1]; //计算p(X|C3) for(k = 0; k < Shuxing; k++) { pipei = C3_map[k].find(*(&testData[i].A2+k)); if(pipei != C3_map[k].end()) { pXC[2] =pXC[2] + pipei->second; } } p[2] = A[2]*pXC[2]; } //找出最大值 if(p[0] > p[1] && p[0] >p[2]) { cout<<p[0]<<" "<<1<<endl; if(testData[i].A1==1) m++; } else { if(p[1] > p[2]) { cout<<p[1]<<" "<<2<<endl; if(testData[i].A1==2) m++; } else { cout<<p[2]<<" "<<3<<endl; if(testData[i].A1==3) m++; } } } } void main() { double tp,fp; cout<<"概率最大值 "<<"所属类别"<<endl; DataRead(trainData,"wine.data"); bayes(); DataRead(testData,"test.data"); houyan(); tp=(double)m/51; fp=1-tp; cout<<"正确率为："<<tp*100<<"%"<<endl; cout<<"错误率为："<<fp*100<<"%"<<endl; system("pause"); }