实验 4：列表与字典应用

1. 基础：生日悖论分析。如果一个房间有 23 人或以上，那么至少有两 个人的生日相同的概率大于 50%。编写程序，输出在不同随机样本数 量下，23 个人中至少两个人生日相同的概率。

2. 进阶：统计《一句顶一万句》文本中前 10 高频词，生成词云。

3. 拓展：金庸、古龙等武侠小说写作风格分析。输出不少于 3 个金庸（古 龙）作品的最常用 10 个词语，找到其中的相关性，总结其风格。

代码：

import random  
from typing import List  

def birthday_paradox(num_people: int = 23, simulations: int = 10000) -> float:  
    """  
    计算至少两人生日相同的概率  
    :param num_people: 房间人数（默认23）  
    :param simulations: 模拟次数（默认10000次）  
    :return: 概率值  
    """  
    if num_people < 2 or simulations < 1:  
        raise ValueError("人数需≥2，模拟次数需≥1")  

    matches = 0  
    for _ in range(simulations):  
        birthdays = [random.randint(1, 365) for _ in range(num_people)]  
        if len(birthdays) != len(set(birthdays)):  
            matches += 1  
    return matches / simulations  

# 测试不同样本量下的概率  
sample_sizes = [1000, 5000, 10000]  
for size in sample_sizes:  
    prob = birthday_paradox(23, size)  
    print(f"模拟{size}次，概率：{prob:.2%}")  

import jieba
from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as plt
import os

def generate_wordcloud_and_statistics(text_path, stopwords_path, output_path="wordcloud.png"):
    # 检查文件是否存在
    if not os.path.exists(text_path):
        print(f"文件不存在：{text_path}")
        return
    if not os.path.exists(stopwords_path):
        print(f"文件不存在：{stopwords_path}")
        return

    # 读取文本
    with open(text_path, 'r', encoding='utf-8') as file:
        text = file.read()

    # 读取停用词
    with open(stopwords_path, 'r', encoding='utf-8') as file:
        stopwords = set(file.read().split())

    # 分词并过滤停用词
    words = jieba.lcut(text)
    filtered_words = [word for word in words if word not in stopwords and len(word) > 1]

    # 统计词频
    word_counts = Counter(filtered_words)
    top_10_words = word_counts.most_common(10)
    print("前10高频词：")
    for word, count in top_10_words:
        print(f"{word}: {count}")

    # 生成词云
    wc = WordCloud(
        font_path="simhei.ttf",  # 确保使用中文字体
        background_color="white",
        width=800,
        height=600,
        max_words=100
    ).generate(" ".join(filtered_words))

    # 显示词云
    plt.figure(figsize=(10, 8))
    plt.imshow(wc, interpolation='bilinear')
    plt.axis('off')
    plt.title('《一句顶一万句》高频词词云')
    plt.show()

    # 保存词云
    wc.to_file(output_path)
    print(f"词云已保存至：{output_path}")

if __name__ == "__main__":
    # 获取当前脚本所在目录
    current_dir = os.path.dirname(os.path.abspath(__file__))
    text_path = os.path.join(current_dir, "一句顶一万句.txt")
    stopwords_path = os.path.join(current_dir, "stopwords.txt")
    output_path = os.path.join(current_dir, "wordcloud.png")

    generate_wordcloud_and_statistics(text_path, stopwords_path, output_path) 

import jieba
from collections import Counter
import os
from wordcloud import WordCloud
import matplotlib.pyplot as plt

def analyze_author_style(books, stopwords_path):
    """
    分析作者作品的写作风格
    :param books: 书籍文件路径列表
    :param stopwords_path: 停用词文件路径
    :return: 前10高频词列表
    """
    all_words = []
    stopwords = set()

    # 读取停用词
    try:
        with open(stopwords_path, 'r', encoding='utf-8') as file:
            stopwords = set(file.read().split())
    except Exception as e:
        print(f"读取停用词文件时出错：{e}")
        return []

    # 处理每本书
    for book in books:
        try:
            with open(book, 'r', encoding='utf-8') as f:
                text = f.read()
                words = jieba.lcut(text)
                filtered_words = [word for word in words if word not in stopwords and len(word) > 1]
                all_words.extend(filtered_words)
        except Exception as e:
            print(f"处理文件 {book} 时出错：{e}")
            continue

    # 统计词频
    if not all_words:
        return []
    word_counts = Counter(all_words)
    top_10_words = word_counts.most_common(10)
    return top_10_words

def summarize_style(top_words, author):
    """
    根据高频词总结作者风格
    :param top_words: 高频词列表
    :param author: 作者名
    """
    print(f"\n{author}作品前10高频词：")
    for word, count in top_words:
        print(f"{word}: {count}")

    # 根据高频词总结风格
    print(f"\n{author}作品风格总结：")
    if author == "金庸":
        print("金庸的作品中常见武林门派、武功秘籍、江湖恩怨等元素，注重武学体系的构建和侠义精神的宣扬。")
        print("语言华丽流畅，情节宏大多彩，人物形象丰满立体，常以宏大的叙事展现江湖恩怨和家国情怀。")
    elif author == "古龙":
        print("古龙作品以情节曲折、对白机智、人物形象鲜明著称，语言风格独特，富有诗意和哲理。")
        print("侧重个体感受与心理描写，叙事节奏明快，常通过简洁而富有个性的对话来推动情节发展。")
    # 添加词云生成
    generate_wordcloud(top_words, author)

def generate_wordcloud(top_words, author):
    """
    根据高频词生成词云
    :param top_words: 高频词列表
    :param author: 作者名
    """
    # 提取词和频次
    words = [word for word, count in top_words]
    counts = [count for word, count in top_words]
    # 创建一个字典来存储词频信息
    word_freq = {word: count for word, count in zip(words, counts)}
    # 生成词云
    wc = WordCloud(
        font_path="simhei.ttf",  # 确保使用中文字体
        background_color="white",
        width=800,
        height=600,
        max_words=100
    ).generate_from_frequencies(word_freq)

    # 显示词云
    plt.figure(figsize=(10, 8))
    plt.imshow(wc, interpolation='bilinear')
    plt.axis('off')
    plt.title(f'{author}作品词云')
    plt.show()

    # 保存词云
    current_dir = os.path.dirname(os.path.abspath(__file__))
    output_path = os.path.join(current_dir, f"{author}_wordcloud.png")
    wc.to_file(output_path)
    print(f"{author}词云已保存至：{output_path}")

def main():
    # 获取当前脚本所在目录
    current_dir = os.path.dirname(os.path.abspath(__file__))

    # 停用词文件路径
    stopwords_path = os.path.join(current_dir, "stopwords.txt")

    # 金庸作品文件路径
    jin_yong_books = [
        os.path.join(current_dir, "天龙八部.txt"),
        os.path.join(current_dir, "射雕英雄传.txt"),
        os.path.join(current_dir, "神雕侠侣.txt")
    ]

    # 古龙作品文件路径
    gu_long_books = [
        os.path.join(current_dir, "楚留香传奇.txt"),
        os.path.join(current_dir, "绝代双骄.txt"),
        os.path.join(current_dir, "陆小凤传奇.txt")
    ]

    # 分析金庸作品
    jin_yong_top_words = analyze_author_style(jin_yong_books, stopwords_path)
    if jin_yong_top_words:
        summarize_style(jin_yong_top_words, "金庸")

    # 分析古龙作品
    gu_long_top_words = analyze_author_style(gu_long_books, stopwords_path)
    if gu_long_top_words:
        summarize_style(gu_long_top_words, "古龙")

if __name__ == "__main__":
    import os
    main()

运行结果：