帮我用numpy库写一个基于朴素贝叶斯算法的垃圾邮件分类

当然可以！以下是一个基于朴素贝叶斯算法的垃圾邮件分类器的示例代码，使用了numpy库：

import numpy as np

class NaiveBayes:
    def __init__(self):
        self.vocab = set()  # 词汇表
        self.labels = set()  # 标签集合
        self.label_word_count = {}  # 每个标签下每个单词出现的次数
        self.label_doc_count = {}  # 每个标签下文档数量
        self.doc_count = 0  # 文档总数

    def fit(self, X, y):
        self.doc_count = len(X)
        for i in range(self.doc_count):
            label = y[i]
            self.labels.add(label)
            if label not in self.label_word_count:
                self.label_word_count[label] = {}
            if label not in self.label_doc_count:
                self.label_doc_count[label] = 0
            self.label_doc_count[label] += 1
            for word in X[i]:
                self.vocab.add(word)
                if word not in self.label_word_count[label]:
                    self.label_word_count[label][word] = 0
                self.label_word_count[label][word] += 1

    def predict(self, X):
        y_pred = []
        for doc in X:
            max_prob = -np.inf
            arg_max_label = None
            for label in self.labels:
                prob = np.log(self.label_doc_count[label]) - np.log(self.doc_count)
                for word in doc:
                    if word in self.vocab:
                        prob += np.log(self.label_word_count[label][word] + 1) - np.log(sum(self.label_word_count[label].values()) + len(self.vocab))
                if prob > max_prob:
                    max_prob = prob
                    arg_max_label = label
            y_pred.append(arg_max_label)
        return y_pred

使用方法：

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from nltk.corpus import stopwords

# 加载数据集
newsgroups = fetch_20newsgroups(subset='all')
X, y = newsgroups.data, newsgroups.target

# 分词
stop_words = set(stopwords.words('english'))
vectorizer = CountVectorizer(stop_words=stop_words)
X = [vectorizer.build_analyzer()(doc) for doc in X]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
nb = NaiveBayes()
nb.fit(X_train, y_train)

# 预测并计算准确率
y_pred = nb.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)

这个示例代码使用了sklearn中的20个新闻组数据集，使用CountVectorizer进行分词，然后使用我们实现的朴素贝叶斯分类器进行分类。