帮我生成一个用朴素贝叶斯方法分类垃圾邮件的代码

当然可以，以下是一个简单的朴素贝叶斯分类器的代码：

import os
import math

class NaiveBayesClassifier:
    def __init__(self, spam_dir, ham_dir):
        self.spam_dir = spam_dir
        self.ham_dir = ham_dir
        self.vocabulary = set()
        self.spam_word_count = {}
        self.ham_word_count = {}
        self.spam_total_words = 0
        self.ham_total_words = 0
        self.spam_total_files = 0
        self.ham_total_files = 0

    def train(self):
        for filename in os.listdir(self.spam_dir):
            with open(os.path.join(self.spam_dir, filename), 'r', encoding='utf8', errors='ignore') as file:
                words = file.read().split()
                self.spam_total_files += 1
                self.spam_total_words += len(words)
                for word in words:
                    self.vocabulary.add(word)
                    if word not in self.spam_word_count:
                        self.spam_word_count[word] = 1
                    else:
                        self.spam_word_count[word] += 1

        for filename in os.listdir(self.ham_dir):
            with open(os.path.join(self.ham_dir, filename), 'r', encoding='utf8', errors='ignore') as file:
                words = file.read().split()
                self.ham_total_files += 1
                self.ham_total_words += len(words)
                for word in words:
                    self.vocabulary.add(word)
                    if word not in self.ham_word_count:
                        self.ham_word_count[word] = 1
                    else:
                        self.ham_word_count[word] += 1

    def classify(self, message):
        spam_score = math.log(self.spam_total_files / (self.spam_total_files + self.ham_total_files))
        ham_score = math.log(self.ham_total_files / (self.spam_total_files + self.ham_total_files))

        for word in message.split():
            if word in self.vocabulary:
                spam_score += math.log((self.spam_word_count.get(word, 0) + 1) / (self.spam_total_words + len(self.vocabulary)))
                ham_score += math.log((self.ham_word_count.get(word, 0) + 1) / (self.ham_total_words + len(self.vocabulary)))

        return 'spam' if spam_score > ham_score else 'ham'


# Example usage
classifier = NaiveBayesClassifier('path/to/spam/folder', 'path/to/ham/folder')
classifier.train()
print(classifier.classify('Hello, this is a spam email'))
print(classifier.classify('Hello, this is a legitimate email'))

以上代码是一个简单的垃圾邮件分类器，它使用了朴素贝叶斯分类算法。在训练阶段，它会读取垃圾邮件和正常邮件的文件夹，并统计每个单词在垃圾邮件和正常邮件中出现的次数。在分类阶段，它会根据每个单词在垃圾邮件和正常邮件中出现的概率，计算出一个垃圾邮件得分和一个正常邮件得分，并返回得分较高的那个标签。