《机器学习与数据挖掘》实验七

「爱情、让人受尽委屈。」 2023-10-07 21:50 37阅读 0赞

> 实验题目： 实现拉普拉斯修正的朴素贝叶斯分类器 
> 
> 实验目的： 掌握朴素贝叶斯分类器的原理及应用 
> 
> 实验环境（硬件和软件） Anaconda/Jupyter notebook/Pycharm 
> 
> 实验内容：
> 
> 编码实现拉普拉斯修正的朴素贝叶斯分类器，基于给定的训练数据，对测试样本进行判别。
> 
> **要求：**
> 
> **一、**已经给定部分代码，补充完整的代码，需要补充代码的地方已经用红色字体标注，包括：
> 
> **（****1****）****\#****补充计算条件概率的代码****-1****；**
> 
> **（****2****）****\#****补充计算条件概率的代码****-2****；**
> 
> **（****3****）****\#****补充预测代码；**
> 
> **二、**将补充完整的代码提交，并提交实验结果；（**也可以自己重写这部分的代码提交**）

import numpy as np
    
    def loaddata():
        X = np.array([[1, 'S'], [1, 'M'], [1, 'M'], [1, 'S'],
                      [1, 'S'], [2, 'S'], [2, 'M'], [2, 'M'],
                      [2, 'L'], [2, 'L'], [3, 'L'], [3, 'M'],
                      [3, 'M'], [3, 'L'], [3, 'L']])
        y = np.array([-1, -1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, -1])
        return X, y
    
    # 训练、计算各个概率值
    def Train(trainset, train_labels):
        # 数据量
        m = trainset.shape[0]
        # 特征数
        n = trainset.shape[1]
        # 先验概率,key是类别值，value是类别的概率值
        prior_probability = {}
        # 条件概率，key的构造：类别，特征，特征值,value是
        conditional_probability = {}
    
        # 类别的可能取值
        labels = set(train_labels)
        # 计算先验概率,此时没有计算总数据量m
        for label in labels:
            prior_probability[label] = len(train_labels[train_labels == label]) + 1
        print('prior_probabilit =', prior_probability)
    
        # 计算条件概率
        for i in range(m):
            for j in range(n):
                # key的构造：类别，特征，特征值
                key = str(train_labels[i]) + ',' + str(j) + ',' + str(trainset[i][j])
                if key in conditional_probability:
                    conditional_probability[key] += 1
                else:
                    conditional_probability[key] = 1
        print('conditional_probability = ', conditional_probability)
    
        # 因字典在循环时不能改变，故定义新字典来保存值
        conditional_probability_final = {}
        for key in conditional_probability:
            # 取出当前的类别
            label = key.split(',')[0]
            key1 = key.split(',')[1]
            Ni = len(set(trainset[:, int(key1)]))
            print(Ni)
            conditional_probability_final[key] = (conditional_probability[key] + 1) / (prior_probability[int(label)] + Ni)
    
        # 最终先验概率（除以总数据量m）
        for label in labels:
            prior_probability[label] = prior_probability[label] / (m + len(labels))
    
        return prior_probability, conditional_probability_final, labels
    
    
    # 定义预测函数
    def predict(data):
        result = {}
        # 循环标签
        for label in train_labels_set:
            temp = 1.0
            for j in range(len(data)):
                key = str(label) + ',' + str(j) + ',' + str(data[j])
                # 条件概率连乘
                temp = temp * conditional_probability[key]
            # 在乘上先验概率
            result[label] = temp * prior_probability[label]
        print('result =', result)
        # 排序返回标签值
        return sorted(result.items(), key=lambda x: x[1], reverse=True)[0][0]
    
    
    X, y = loaddata()
    prior_probability, conditional_probability, train_labels_set = Train(X, y)
    print('conditional_probability = ', conditional_probability)
    r_label = predict([2, 'S'])
    print(' r_label =', r_label)

> ![0bc6245cb5a2491abe023cdc90d5c4b0.png][]
> 
> 实验截图

[0bc6245cb5a2491abe023cdc90d5c4b0.png]: https://img-blog.csdnimg.cn/0bc6245cb5a2491abe023cdc90d5c4b0.png