根据一下要求，利用Python写完整代码:1、从网上爬取500个文字文档，采用合适的方法对文档进行编码 2、利用K-means算法对500个文档进行聚类分析 3、观察分析聚类结果 注意：不能调用现有类库中已经封装好的K-means算法

首先，我们需要安装`requests`, `BeautifulSoup4` 和 `jieba` 库来抓取网页内容并进行文本处理，以及自定义K-means算法。这里我们不会直接使用Python的内置K-means，因为题目要求不使用现有的库，我们将手动实现K-means算法。

# 安装所需库
!pip install requests beautifulsoup4 jieba

import requests
from bs4 import BeautifulSoup
import re
import random
import numpy as np
from collections import Counter

# 自定义K-means算法
def kmeans(data, k, max_iterations=100):
    centroids = random.sample(data, k)
    clusters = [[] for _ in range(k)]
    
    for _ in range(max_iterations):
        # 分配每个点到最近的质心
        for point in data:
            distances = [np.linalg.norm(point - c) for c in centroids]
            cluster_index = np.argmin(distances)
            clusters[cluster_index].append(point)

        old_centroids = list(centroids)
        
        # 更新质心
        for i, cluster in enumerate(clusters):
            if len(cluster) > 0:
                centroids[i] = np.mean(cluster, axis=0)
        
        # 检查是否达到收敛条件
        if all(old_c == new_c for old_c, new_c in zip(old_centroids, centroids)):
            break
    
    return centroids, clusters

# 爬取网页并提取文字
def fetch_text(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    text = soup.get_text()
    # 过滤掉无用字符，并分词
    words = re.findall(r'\b\w+\b', text.lower())
    return words

# 获取500个文档
urls = ["https://example.com/doc_{}.html".format(i) for i in range(1, 501)]
documents = [fetch_text(url)[:500] for url in urls]

# 文本编码
encoded_documents = [[ord(c) for c in doc] for doc in documents]

# 聚类分析
k = 5  # 假设我们有5个主题
centroids, clusters = kmeans(encoded_documents, k)

# 观察分析聚类结果
for i, cluster in enumerate(clusters):
    print(f"Cluster {i+1}:")
    print(" ".join([chr(word) for word in cluster[0]]))  # 打印第一个文档作为代表
    print("\n")

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