C++ 朴素贝叶斯模型（Naive Bayesian Model，NBM）实现， 西瓜实验数据集 基于周志华老师机器学习

其他 2018-10-30 23:01:39 阅读次数: 0

C++ 朴素贝叶斯模型（Naive Bayesian Model，NBM）实现， 西瓜实验数据集 基于周志华老师机器学习

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标注

学习朴素贝叶斯算法得了解一些基本知识，比如全概率公式和贝叶斯公式。大学基本都学过不在赘述。

数据样本

编号 色泽 根蒂 敲声 纹理 脐部 触感 密度 含糖率 好瓜
1 2 2 2 1 3 1 0.697 0.46 1
2 3 2 3 1 3 1 0.744 0.376 1
3 3 2 2 1 3 1 0.634 0.264 1
4 2 2 3 1 3 1 0.608 0.318 1
5 1 2 2 1 3 1 0.556 0.215 1
6 2 1 2 1 2 2 0.403 0.237 1
7 3 1 2 2 2 2 0.481 0.149 1
8 3 1 2 1 2 1 0.437 0.211 1
9 3 1 3 2 2 1 0.666 0.091 0
10 2 3 1 1 1 2 0.243 0.267 0
11 1 3 1 3 1 1 0.245 0.057 0
12 1 2 2 3 1 2 0.343 0.099 0
13 2 1 2 2 3 1 0.639 0.161 0
14 1 1 3 2 3 1 0.657 0.198 0
15 3 1 2 1 2 2 0.36 0.37 0
16 1 2 2 3 1 1 0.593 0.042 0
17 2 2 3 2 2 1 0.719 0.103 0

表格含义

色泽 1-3代表 浅白 青绿 乌黑
根蒂 1-3代表 稍蜷 蜷缩 硬挺
敲声 1-3代表 清脆 浊响 沉闷
纹理 1-3代表 清晰 稍糊 模糊
脐部 1-3代表 平坦 稍凹 凹陷
好瓜 1代表 是 0 代表 不是

算法定义

朴素贝叶斯算法定义如下：
这里写图片描述
这里写图片描述

代码块

//bayesian.h
#pragma once
//定义训练数据  
#define M 17  
#define N 9  
/*
*色泽 1—3代表 浅白 青绿 乌黑
*根蒂 1-3代表 稍蜷 蜷缩 硬挺
*敲声1-3代表 清脆 浊响 沉闷
*纹理 1-3代表 清晰 稍糊 模糊         
*脐部1-3代表 平坦 稍凹 凹陷          
*触感 1-2 代表 硬滑 软粘            
*好瓜 1代表是 0 代表不是                             
*/  
double A[M][N]= {  
    {2,2,2,1,3,1,0.697,0.460,1},//  1
    {3,2,3,1,3,1,0.744,0.376,1},//  2
    {3,2,2,1,3,1,0.634,0.264,1},//  3
    {2,2,3,1,3,1,0.608,0.318,1},//  4
    {1,2,2,1,3,1,0.556,0.215,1},//  5
    {2,1,2,1,2,2,0.403,0.237,1},//  6
    {3,1,2,2,2,2,0.481,0.149,1},//  7
    {3,1,2,1,2,1,0.437,0.211,1},//  8
    {3,1,3,2,2,1,0.666,0.091,0},//  9
    {2,3,1,1,1,2,0.243,0.267,0},//  10
    {1,3,1,3,1,1,0.245,0.057,0},//  11
    {1,2,2,3,1,2,0.343,0.099,0},//  12
    {2,1,2,2,3,1,0.639,0.161,0},//  13
    {1,1,3,2,3,1,0.657,0.198,0},//  14
    {3,1,2,1,2,2,0.360,0.370,0},//  15
    {1,2,2,3,1,1,0.593,0.042,0},//  16
    {2,2,3,2,2,1,0.719,0.103,0} //  17

};  

struct Px1  
{  
    double x1;  
    double y;  
    double p_x1y;  
};  

struct Px2  
{  
    double x2;  
    double y;  
    double p_x2y;  
};  

struct Px3  
{  
    double x3;  
    double y;  
    double p_x3y;  
};  
struct Px4  
{  
    double x4;  
    double y;  
    double p_x4y;  
};  
struct Px5  
{  
    double x5;  
    double y;  
    double p_x5y;  
};  
struct Px6  
{  
    double x6;  
    double y;  
    double p_x6y;  
};  
struct Px7  
{  
    double x7;  
    double y;  
    double p_x7y;  
};  
struct Px8  
{  
    double x8;  
    double y;  
    double p_x8y;  
};  
// struct MeAVa 
// {  
//  double mean;  
//  double stdev;  
// };  

double p[2];  
Px1 px1[6];  
Px2 px2[6];
Px3 px3[6];
Px4 px4[6];
Px5 px5[6];
Px6 px6[6];
Px7 px7[2];
Px8 px8[2];  

//bayesian .cpp
#include "bayesian.h"
#include <iostream>  
#include <set>  
#include <vector>  
#include <numeric>
#include <algorithm>
#include <iomanip>
//#include <math.h>
#include <cmath>
using namespace std;  
//好瓜密度概率计算
double m_MeansAndAver(double x)
{

    double resultSet[17];
    double p;
    for (int i = 0; i < M; i++)
    {
        resultSet[i]=A[i][6];
    }
    double sum = std::accumulate(std::begin(resultSet), std::begin(resultSet)+8, 0.0);
    double mean=  sum /8; //均值
    double accum  = 0.0;
    std::for_each (std::begin(resultSet), std::begin(resultSet)+8, [&](const double d) {
        accum  += (d-mean)*(d-mean);
    });

    double stdev = sqrt(accum/(7)); //方差
//  std::cout<<"--------------------test1-------------------------------"<<stdev<<endl;
//  std::cout<<"均值为"<<mean<<"方差为："<<stdev<<endl;
//  std::cout<<"--------------test---------------"<<endl;
    p = (1/(sqrt(2*3.14)*stdev))*exp(-(pow((x-mean),2)/(2*pow(stdev,2))));
    //px7[0]=p;
    px7[0].p_x7y=p ;
    return p;
}
//坏瓜密度概率计算
double m_w_MeansAndAver(double x)
{

    double resultSet[17];
    double p;
    for (int i = 0; i < M; i++)
    {
        resultSet[i]=A[i][6];
    }
    double sum = std::accumulate(std::begin(resultSet)+8, std::end(resultSet), 0.0);
    double mean=  sum /9; //均值
    double accum  = 0.0;
    std::for_each ( std::begin(resultSet)+8,std::end(resultSet), [&](const double d) {
        accum  += (d-mean)*(d-mean);
    });

    double stdev = sqrt(accum/(8)); //方差
//  std::cout<<"--------------------test2-------------------------------"<<stdev<<endl;
//  std::cout<<"均值为"<<mean<<"方差为："<<stdev<<endl;
//  std::cout<<"--------------test---------------"<<endl;
    p = (1/(sqrt(2*3.14)*stdev))*exp(-(pow((x-mean),2)/(2*pow(stdev,2))));
    px7[1].p_x7y=p ;
    return p;
}
//好瓜含糖量概率计算
double h_MeansAndAver(double x)
{

    double resultSet[17];
    double p;
    for (int i = 0; i < M; i++)
    {
        resultSet[i]=A[i][7];
    }
    double sum = std::accumulate(std::begin(resultSet), std::begin(resultSet)+8, 0.0);
    double mean=  sum /8; //均值
    double accum  = 0.0;
    std::for_each (std::begin(resultSet), std::begin(resultSet)+8, [&](const double d) {
        accum  += (d-mean)*(d-mean);
    });

    double stdev = sqrt(accum/(7)); //方差
//  std::cout<<"--------------------test3--------------------------------"<<stdev<<endl;
//  std::cout<<"均值为"<<mean<<"方差为："<<stdev<<endl;
//  std::cout<<"--------------test---------------"<<endl;
    p = (1/(sqrt(2*3.14)*stdev))*exp(-(pow((x-mean),2)/(2*pow(stdev,2))));
    px8[0].p_x8y=p;
    return p;
}

//坏瓜含糖量概率计算
double h_w_MeansAndAver(double x)
{

    double resultSet[17];
    double p;
    for (int i = 0; i < M; i++)
    {
        resultSet[i]=A[i][7];
    }
    double sum = std::accumulate(std::begin(resultSet)+8, std::end(resultSet), 0.0);
    double mean=  sum /9; //均值
    double accum  = 0.0;
    std::for_each (std::begin(resultSet)+8,  std::end(resultSet), [&](const double d) {
        accum  += (d-mean)*(d-mean);
    });

    double stdev = sqrt(accum/(8)); //方差
//  std::cout<<"--------------------test4--------------------------------"<<stdev<<endl;
//  std::cout<<"均值为"<<mean<<"方差为："<<stdev<<endl;
//  std::cout<<"--------------test---------------"<<endl;
    p = (1/(sqrt(2*3.14)*stdev))*exp(-(pow((x-mean),2)/(2*pow(stdev,2))));
    px8[1].p_x8y=p;
    return p;
}
//计算先验概率和条件概率  
void calP()  
{
    //计算先验  
    //double p[2];  
    int i, j, k;  
    multiset<double> m_x1, m_x2,m_x3, m_x4,m_x5, m_x6,m_x7, m_x8, m_y;//多重集容器  
    multiset<double>::iterator pos1;  

    set<double> x1, x2,x3, x4,x5, x6,x7, x8, y;//集合容器  
    set<double>::iterator pos2, pos3;  

    //运用多重集容器和集合容器  
    for(i = 0; i < M; i++) 
    {
        m_x1.insert(A[i][0]);  
        m_x2.insert(A[i][1]);
        m_x3.insert(A[i][2]);
        m_x4.insert(A[i][3]);
        m_x5.insert(A[i][4]);
        m_x6.insert(A[i][5]);
        m_x7.insert(A[i][6]);
        m_x8.insert(A[i][7]);
        m_y.insert(A[i][8]);  

        x1.insert(A[i][0]);  
        x2.insert(A[i][1]);
        x3.insert(A[i][2]);
        x4.insert(A[i][3]);
        x5.insert(A[i][4]);
        x6.insert(A[i][5]);
        x7.insert(A[i][6]);
        x8.insert(A[i][7]);
        y.insert(A[i][8]); 

    }  


    p[0] = m_y.count(1) / (double)M;    //p(Y = 1)  
    p[1] = m_y.count(0) / (double)M;    //p(Y = 2)  
    cout << endl << "************先验***********" << endl; 
//p[0]代表好瓜所占的比例  p[1]代表坏瓜所占的比例
    cout << "p(Y = 1) = " << p[0] << endl;  
    cout << "p(Y = 0) = " << p[1] << endl; 

//计算条件概率  
    cout << endl;  
    cout << "*********条件概率********" << endl;  
    //  int px1_num = 3 * 2;  
    //  int px2_num = 3 * 2;  
//p(x1 | y)概率
    j=0; 
    for(pos2 = y.begin(); pos2 != y.end(); pos2++)  
    { 
        for(pos3 = x1.begin(); pos3 != x1.end(); pos3++)  
        {       
            px1[j].y = *pos2;  
            px1[j].x1 = *pos3;  

            int count_x1y = 0;  
            for(k = 0; k < M; k++)  
            {  
                if(A[k][0] == px1[j].x1 && A[k][8] == px1[j].y)  
                    count_x1y++;  
            }  
            px1[j].p_x1y = count_x1y / (double)m_y.count(px1[j].y);//计算p(x1 | y)的概率  
            j++;  
        }  
    }  

    cout << "p(x1 | y):" << endl;  
    for(j = 0; j < 6; j++)  
    {  
        cout << px1[j].x1 << " " <<  px1[j].y << " " << px1[j].p_x1y << endl;  
    }  
//p(x2|y)概率
    j=0;  
    for(pos2 = y.begin(); pos2 != y.end(); pos2++)  
    {  
        for(pos3 = x2.begin(); pos3 != x2.end(); pos3++)  
        {  
            px2[j].y = *pos2;  
            px2[j].x2 = *pos3;  

            int count_x2y = 0;  
            for(k = 0; k < M; k++)  
            {  
                if(A[k][1] == px2[j].x2 && A[k][8] == px2[j].y)  
                    count_x2y++;  
            }  
            px2[j].p_x2y = count_x2y / (double)m_y.count(px2[j].y);//计算p(x2 | y)的概率  
            j++;  
        }  
    }  

    cout << "p(x2 | y):" << endl;  
    for(j = 0; j < 6; j++)  
    {  
        cout << px2[j].x2 << " " <<  px2[j].y << " " << px2[j].p_x2y << endl;  
    }  


//p(x3|y)概率
    j=0;  
    for(pos2 = y.begin(); pos2 != y.end(); pos2++)  
    {  
        for(pos3 = x3.begin(); pos3 != x3.end(); pos3++)  
        {  
            px3[j].y = *pos2;  
            px3[j].x3 = *pos3;  

            int count_x3y = 0;  
            for(k = 0; k < M; k++)  
            {  
                if(A[k][2] == px3[j].x3 && A[k][8] == px3[j].y)  
                    count_x3y++;  
            }  
            px3[j].p_x3y = count_x3y / (double)m_y.count(px3[j].y);//计算p(x2 | y)的概率  
            j++;  
        }  
    }  

    cout << "p(x3 | y):" << endl;  
    for(j = 0; j < 6; j++)  
    {  
        cout << px3[j].x3 << " " <<  px3[j].y << " " << px3[j].p_x3y << endl;  
    }  
//p(x4|y)概率
    j=0;  
    for(pos2 = y.begin(); pos2 != y.end(); pos2++)  
    {  
        for(pos3 = x4.begin(); pos3 != x4.end(); pos3++)  
        {  
            px4[j].y = *pos2;  
            px4[j].x4 = *pos3;  

            int count_x4y = 0;  
            for(k = 0; k < M; k++)  
            {  
                if(A[k][3] == px4[j].x4 && A[k][8] == px4[j].y)  
                    count_x4y++;  
            }  
            px4[j].p_x4y = count_x4y / (double)m_y.count(px4[j].y);//计算p(x4 | y)的概率  
            j++;  
        }  
    }  

    cout << "p(x4 | y):" << endl;  
    for(j = 0; j < 6; j++)  
    {  
        cout << px4[j].x4 << " " <<  px4[j].y << " " << px4[j].p_x4y << endl;  
    }  
//p(x5|y)概率
    j=0;  
    for(pos2 = y.begin(); pos2 != y.end(); pos2++)  
    {  
        for(pos3 = x5.begin(); pos3 != x5.end(); pos3++)  
        {  
            px5[j].y = *pos2;  
            px5[j].x5 = *pos3;  

            int count_x5y = 0;  
            for(k = 0; k < M; k++)  
            {  
                if(A[k][4] == px5[j].x5 && A[k][8] == px5[j].y)  
                    count_x5y++;  
            }  
            px5[j].p_x5y = count_x5y / (double)m_y.count(px5[j].y);//计算p(x5 | y)的概率  
            j++;  
        }  
    }  

    cout << "p(x5 | y):" << endl;  
    for(j = 0; j < 6; j++)  
    {  
        cout << px5[j].x5 << " " <<  px5[j].y << " " << px5[j].p_x5y << endl;  
    }  
//p(x6|y)概率
    j=0;  
    for(pos2 = y.begin(); pos2 != y.end(); pos2++)  
    {  
        for(pos3 = x6.begin(); pos3 != x6.end(); pos3++)  
        {  
            px6[j].y = *pos2;  
            px6[j].x6 = *pos3;  

            int count_x6y = 0;  
            for(k = 0; k < M; k++)  
            {  
                if(A[k][5] == px6[j].x6 && A[k][8] == px6[j].y)  
                    count_x6y++;  
            }  
            px6[j].p_x6y = count_x6y / (double)m_y.count(px6[j].y);//计算p(x6 | y)的概率  
            j++;  
        }  
    }  

    cout << "p(x6 | y):" << endl;  
    for(j = 0; j < 6; j++)  
    {  
        cout << px6[j].x6 << " " <<  px6[j].y << " " << px6[j].p_x6y << endl;  
    }  
//p(x7|y)概率

} 
int main()  
{  
    int i = 0, j = 0;  
    //输出训练数据  
    cout << "***********训练数据************" << endl;  
    for(i = 0; i < M; i++)  
    {  
        for(int j = 0; j < N; j++)  
        {  
            cout << " "<< A[i][j];  
        }  
        cout << endl;  
    } 

    calP();//计算先验和条件概率  
    int s_x1, s_x2, s_x3, s_x4, s_x5, s_x6;
    double  s_x7, s_x8;  
    double result[2];  
    int class_y = 1;  
    cout<< "##########################< 提   示 >##########################"<<endl;
    cout<<setw(10)<<"色泽"<<setw(10)<<"1-3代表"<<setw(10)<<"浅白"<<setw(10)<<"青绿"<<setw(10)<<"乌黑"<<endl;
    cout<<setw(10)<<"根蒂"<<setw(10)<<"1-3代表"<<setw(10)<<"稍蜷"<<setw(10)<<"蜷缩"<<setw(10)<<"硬挺"<<endl;
    cout<<setw(10)<<"敲声"<<setw(10)<<"1-3代表"<<setw(10)<<"清脆"<<setw(10)<<"浊响"<<setw(10)<<"沉闷"<<endl;
    cout<<setw(10)<<"纹理"<<setw(10)<<"1-3代表"<<setw(10)<<"清晰"<<setw(10)<<"稍糊"<<setw(10)<<"模糊"<<endl;
    cout<<setw(10)<<"脐部"<<setw(10)<<"1-3代表"<<setw(10)<<"平坦"<<setw(10)<<"稍凹"<<setw(10)<<"凹陷"<<endl;
    cout<<setw(10)<<"触感"<<setw(10)<<"1-2代表"<<setw(10)<<"硬滑"<<setw(10)<<"软粘"<<endl;
    cout<<"      密度以及含糖量 0<Xi<1 "<<endl;
    cout<<"      请按照以上范围输入"<<endl;
    cout<< "###############################################################"<<endl;
    /************************************************************************/
    /* 
    色泽  1-3代表  浅白  青绿  乌黑
    根蒂  1-3代表  稍蜷  蜷缩  硬挺
    敲声  1-3代表  清脆  浊响  沉闷
    纹理  1-3代表  清晰  稍糊  模糊
    脐部  1-3代表  平坦  稍凹  凹陷
    触感  1-2代表  硬滑  软粘    
    好瓜  1代表 是  0 代表  不是                             
    */
    /************************************************************************/

    cout <<endl<< "##########################< 预   测 >##########################"<<endl; 
    cout <<endl<<"Input:";  
    cin >> s_x1 >> s_x2>> s_x3>> s_x4>> s_x5>> s_x6>> s_x7>> s_x8; 
    cout << "##########<连续属性X7与x8的 p(x7|y)、<p(x8|y)计算结果>##########"<<endl<<endl; 
    cout<<"好瓜密度其概率为："<<m_MeansAndAver(s_x7)<<endl;//当前密度，在是好瓜的情况下可能发生的概率
    cout<<"坏瓜密度的概率"<<m_w_MeansAndAver(s_x7)<<endl;//准确
    cout<<"好瓜其概率为："<<h_MeansAndAver(s_x8)<<endl;//准确
    cout<<"好瓜其概率为："<<h_w_MeansAndAver(s_x8)<<endl<<endl;//准确

    for(i = 0; i < 2; i++)  
    {  
        double s_px_1, s_px_2, s_px_3, s_px_4, s_px_5, s_px_6, s_px_7, s_px_8;  
        for(j = 0; j < 6; j++)  
        {  
            if(s_x1 == px1[j].x1 && px1[j].y == class_y)  
                s_px_1 = px1[j].p_x1y;  
            if(s_x2 == px2[j].x2 && px2[j].y == class_y)
                s_px_2 = px2[j].p_x2y;  
            if(s_x3 == px3[j].x3 && px3[j].y == class_y)
                s_px_3 = px3[j].p_x3y;  
            if(s_x4 == px4[j].x4 && px4[j].y == class_y)
                s_px_4 = px4[j].p_x4y;  
            if(s_x5 == px5[j].x5 && px5[j].y == class_y)
                s_px_5 = px5[j].p_x5y;  
            if(s_x6 == px6[j].x6 && px6[j].y == class_y)
                s_px_6 = px6[j].p_x6y;  
        }  
        s_px_7=px7[i].p_x7y;
        s_px_8=px8[i].p_x8y;
        result[i] = p[i] * s_px_1 * s_px_2*s_px_3* s_px_4* s_px_5* s_px_6*s_px_7*s_px_8;  //p[0]代表好瓜所占的比例  p[1]代表坏瓜所占的比例
        class_y--;  
    }  
    cout << "###########################<分类结果>###########################"<<endl; 
    cout << endl << "all results:";  
    cout <<"可能为好瓜的概率"<< result[0] << "   " <<"可能为坏瓜的概率"<< result[1] << endl<<endl;  //0代表否（不是好瓜），1代表是好瓜，其中result[0]存放好瓜可能概率result[1]坏瓜所占比例
    cout << "###########################<预测结果>###########################"<<endl<<endl;  
    i =0;
    if(result[i] < result[i+1])  //如果坏瓜概率>好瓜概率
        {  
            class_y = 0; 
            cout << "属性为：("<< s_x1 << "," << s_x2 << "," << s_x3 << "," << s_x4 << "," 
                << s_x5 << "," << s_x6<< "," << s_x7<< "," << s_x8 << ")所属的类是：" << class_y<< "-----------坏瓜"<<endl<<endl;  
        }  
    else                        //好瓜概率>坏瓜概率
    {
        class_y=1;
        cout << "属性为：("<< s_x1 << "," << s_x2 << "," << s_x3 << "," << s_x4 << "," 
            << s_x5 << "," << s_x6 << "," << s_x7<< "," << s_x8 << ")所属的类是：" << class_y  <<"-----------好瓜"<< endl<<endl;  
    }
    /*cout << "("<< s_x1 << "," << s_x2 << ")所属的类是：" << class_y + 1 << endl;  */
    system("pause");
    return 0;  
}

“`

分类结果

这里写图片描述

UML 图:

这里写图片描述

源码下载地址

http://download.csdn.net/detail/u011557212/9700532

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