用人工智能算法写一个垃圾邮件处理的代码

好的，下面是一个简单的垃圾邮件处理代码，使用朴素贝叶斯算法：

import os
import io
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from collections import Counter

# 加载停用词
stop_words = set(stopwords.words('english'))

# 读取垃圾邮件和正常邮件数据
spam_dir = 'spam/'
ham_dir = 'ham/'

spam_files = os.listdir(spam_dir)
ham_files = os.listdir(ham_dir)

spam_data = []
for file in spam_files:
    with io.open(spam_dir + file, 'r', encoding='utf-8', errors='ignore') as f:
        spam_data.append(f.read())

ham_data = []
for file in ham_files:
    with io.open(ham_dir + file, 'r', encoding='utf-8', errors='ignore') as f:
        ham_data.append(f.read())

# 计算词频
spam_words = []
for email in spam_data:
    words = word_tokenize(email)
    words = [word.lower() for word in words if word.isalpha() and word.lower() not in stop_words]
    spam_words += words

ham_words = []
for email in ham_data:
    words = word_tokenize(email)
    words = [word.lower() for word in words if word.isalpha() and word.lower() not in stop_words]
    ham_words += words

spam_word_counts = Counter(spam_words)
ham_word_counts = Counter(ham_words)

# 计算概率
spam_word_probs = {}
ham_word_probs = {}

for word in spam_word_counts:
    spam_word_probs[word] = (spam_word_counts[word] + 1) / (len(spam_words) + len(set(spam_words) | set(ham_words)))

for word in ham_word_counts:
    ham_word_probs[word] = (ham_word_counts[word] + 1) / (len(ham_words) + len(set(spam_words) | set(ham_words)))

# 预测新邮件
def predict(email):
    words = word_tokenize(email)
    words = [word.lower() for word in words if word.isalpha() and word.lower() not in stop_words]

    spam_prob = 1
    ham_prob = 1

    for word in words:
        if word in spam_word_probs:
            spam_prob *= spam_word_probs[word]
        else:
            spam_prob *= 1 / (len(spam_words) + len(set(spam_words) | set(ham_words)))

        if word in ham_word_probs:
            ham_prob *= ham_word_probs[word]
        else:
            ham_prob *= 1 / (len(ham_words) + len(set(spam_words) | set(ham_words)))

    if spam_prob > ham_prob:
        return 'spam'
    else:
        return 'ham'

该代码将所有垃圾邮件和正常邮件加载到内存中，并计算每个单词在垃圾邮件和正常邮件中出现的频率。然后，它使用朴素贝叶斯算法计算每个单词属于垃圾邮件或正常邮件的概率，并使用这些概率来预测新邮件的类别。