朴素贝叶斯算法-非模型

#include <iostream>
#include <set>
#include <vector>
using namespace std;

//定义训练数据
#define M 3
#define N 15

//为了计算简单，对A2={S, M, L},令S=1, M=2, L=3；
//Y={1， -1}，令为Y={1， 2}
int A[M][N]= {
    {1 , 1 , 1 , 1 , 1 , 2 , 2 , 2 , 2 , 2 , 3 , 3 , 3 , 3 , 3},
    {1 , 2 , 2 , 1 , 1 , 1 , 2 , 2 , 3 , 3 , 3 , 2 , 2 , 3 , 3},
    {2 , 2 , 1 , 1 , 2 , 2 , 2 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 2}
};

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

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

double p[2];
Px1 px1[6];
Px2 px2[6];

//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<int> m_x1, m_x2, m_y;//多重集容器
    multiset<int>::iterator pos1;

    set<int> x1, x2, y;//集合容器
    set<int>::iterator pos2, pos3;

    //运用多重集容器和集合容器
    for(i = 0; i < N; i++)
    {
        m_x1.insert(A[0][i]);
        m_x2.insert(A[1][i]);
        m_y.insert(A[2][i]);
        x1.insert(A[0][i]);
        x2.insert(A[1][i]);
        y.insert(A[2][i]);
    }
    p[0] = m_y.count(1) / (double)N;    //p(Y = 1)
    p[1] = m_y.count(2) / (double)N;    //p(Y = 2)
    cout << endl << "************先验***********" << endl;
    cout << "p(Y = 1) = " << p[0] << endl;
    cout << "p(Y = 2) = " << 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;

    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 < N; k++)
            {
                if(A[0][k] == px1[j].x1 && A[2][k] == 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;
    }
    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 < N; k++)
            {
                if(A[1][k] == px2[j].x2 && A[2][k] == 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;
    }
}
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++)
    {
        if(i == 0) cout << "X1: ";
        else if(i == 1) cout << "X2: ";
        else if(i == 2) cout << " Y: ";
        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;
    double result[2];
    int class_y = 1;
    cout << "*************预测***************" << endl;
    cout << endl << endl << "Input:";
    cin >> s_x1 >> s_x2;

    for(i = 0; i < 2; i++)
    {
        double s_px_1, s_px_2;
        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;
        }
        result[i] = p[i] * s_px_1 * s_px_2;
        class_y++;
    }

    cout << endl << "all results:";
    cout << result[0] << " " << result[1] << endl;
    for(i = 0; i <2; i++)
    {
        class_y = i;
        if(result[i] < result[i+1])
        {
            class_y = i+1;
        }
    }
    cout << "("<< s_x1 << "," << s_x2 << ")所属的类是：" << class_y + 1 << endl;

    return 0;
}

参考：https://blog.csdn.net/IDMer/article/details/48809677