编程设计一个基于条件风险最小的Bayes分类器

矫情吗；* 2024-04-06 13:35 14阅读 0赞

### 编程设计一个基于条件风险最小的Bayes分类器： ###

要求：

1.  混淆矩阵维度可任意设定
2.  先验概率基于训练样本集自动求得
3.  样本属性数量可任意输入设定

#### 朴素贝叶斯求条件风险最小公式： ####

![在这里插入图片描述][ec2c7838588e4ad0afb8cd43ff28db53.png]

#### 训练数据集： ####

![在这里插入图片描述][c7ba08bf56f3442a909f25f0de4c6328.png]

#### 代码： ####

import numpy as np
    import pandas as pd
    
    XY = pd.read_excel('d1.xls')
    dset = np.array(XY)
    def calc_ent(dset):
        r = np.shape(dset)[0]
        c = np.shape(dset)[1]
        l_count = {
        }
        for i in range(r):
            label = dset[i][c-1]
            if label not in l_count:
                l_count[label] = 0
            l_count[label] += 1
        for i in l_count.keys():
            l_count[i] = l_count[i]/r
        return l_count
    
    def pXy(dset, X, y):
        row = np.shape(dset)[0]  # 行数
        clm = np.shape(dset)[1]  # 列数
    
        dlect = np.empty([0, clm], dtype=type(dset))
        for i in range(row):
            if (dset[i][clm - 1] == y):
                dlect = np.append(dlect, [dset[i]], axis=0)
        PXy = 1
        num = 0
        n = len(dlect)
        for j in range(clm - 1):
            for i in range(n):
                if (isinstance(dlect[i][j], str)):
                    if (dlect[i][j] == X[j]):
                        num += 1
                else:
                    s = str(dlect[i][j])
                    if (s == X[j]):
                        num += 1
            PXy = PXy * (num / n)
            # print(f"{X[j]}num的值为{num}")
            num = 0
        return PXy
    
    X = np.array(['大' ,'红','圆'])
    py = calc_ent(dset)
    bn = len(py)
    hx = np.empty([bn,bn], dtype = int)
    for j in range(bn):
        for i in range(bn):
            if(i == j):
                hx[j][i] = 0
            else:
                hx[j][i] = 1
    
    R_y_X = 0
    j = 0
    for i in range(bn):
        for key in py.keys():
            if(j == i):
                keyi = key
            R_y_X += hx[i][j] * py[key] * pXy(dset, X, key)
            j += 1
        if(i == 0):
            min_RyX = R_y_X
            min_key = keyi
        else:
            if(R_y_X < min_RyX):
                min_RyX = R_y_X
                min_key = keyi
        print(f"{X}被分为{keyi}的条件风险是：{R_y_X}")
        R_y_X = 0
        j = 0
    print("\n")
    print(f"所以{X}被分为{min_key}的条件风险最小，为{min_RyX}")

##### 运行结果： #####

![在这里插入图片描述][90d3e71eab724ecda2ed6f34e9f16efb.png]

##### 注：仅供学习参考，不要抄袭！ #####

[ec2c7838588e4ad0afb8cd43ff28db53.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/a65296d48c134c61b6603b7fe8696953.png
[c7ba08bf56f3442a909f25f0de4c6328.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/bc28a7596f9e40ed8b141e097cddf190.png
[90d3e71eab724ecda2ed6f34e9f16efb.png]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/06/0d60088a7efb49048b0da3196a8c79e7.png