code stringlengths 1 1.05M | repo_name stringlengths 6 83 | path stringlengths 3 242 | language stringclasses 222
values | license stringclasses 20
values | size int64 1 1.05M |
|---|---|---|---|---|---|
<!DOCTYPE html>
<html lang="cn">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Document</title>
</head>
<body>
<div>Hello Cangjie!</div>
<p></p>
<script>
let xhr = new XMLHttpRequest()
xhr.open("POST", "/Hello", true)
... | 2301_80674151/Cangjie-Examples_4666 | HTTPServer/index.html | HTML | apache-2.0 | 687 |
<!doctype html>
<meta charset="utf-8">
<title>登录 / 借阅系统</title>
<style>
body{font-family:Arial,Helvetica,sans-serif;font-size:14px}
table{border-collapse:collapse;margin-top:6px}
td,th{border:1px solid #999;padding:4px 8px}
button{margin:0 2px;padding:2px 6px}
.hide{display:none}
input[type=text],input[type... | 2301_79679684/JIT-Cangjie-examples | hanzongao/web/index.html | HTML | apache-2.0 | 5,699 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Document</title>
<link rel="stylesheet" href="./main.css">
</head>
<body>
<h1>Coffee Order</h1>
<ul id="orders">
<!-- <li class="order" id="1">
... | 2301_79679684/JIT-Cangjie-examples | songqvlv/CoffeeOrder/src/public/index.html | HTML | apache-2.0 | 1,811 |
* {
padding: 0;
margin: 10px;
}
#orders {
list-style-type: none;
margin: 0;
}
#orders .order {
background-color: #c4c4c4;
padding: 5px;
}
#orders .nonedit {
display: none;
}
#orders .edit {
display: none;
}
button {
padding: 5px 10px;
} | 2301_79679684/JIT-Cangjie-examples | songqvlv/CoffeeOrder/src/public/main.css | CSS | apache-2.0 | 277 |
$(function () {
let $name = $('#name')
let $drink = $('#drink')
let $orders = $('#orders')
function addOrder(order) {
$orders.append(`<li class="order" id="${order.id}">
<p>
Name: <span class="name">${order.name}</span>
<input type="text" class="na... | 2301_79679684/JIT-Cangjie-examples | songqvlv/CoffeeOrder/src/public/script.js | JavaScript | apache-2.0 | 4,073 |
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>与鲨鱼AI对话</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body {
font-family: Arial, sans-serif;
min-... | 2301_80381209/tks_kiro_kjdlxstr | ai-chat.html | HTML | unknown | 17,137 |
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>钓鱼游戏</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body {
font-family: Arial, sans-serif;
min-hei... | 2301_80381209/tks_kiro_kjdlxstr | choose.html | HTML | unknown | 91,737 |
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>结果展示</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body {
font-family: Arial, sans-serif;
min-hei... | 2301_80381209/tks_kiro_kjdlxstr | pro.html | HTML | unknown | 6,045 |
// DeepSeek API 代理服务器
// 运行方式: node proxy-server.js
const http = require('http');
const https = require('https');
const DEEPSEEK_API_KEY = 'sk-1c2957f7804a4dc4bde7756acfcaec86';
const PORT = 3000;
const server = http.createServer((req, res) => {
// 设置CORS头
res.setHeader('Access-Control-Allow-Origin', '*');
... | 2301_80381209/tks_kiro_kjdlxstr | proxy-server.js | JavaScript | unknown | 3,266 |
// 测试 npm 是否安装成功的简单脚本
console.log("=== npm 安装测试 ===\n");
// 获取 npm 版本
const { execSync } = require('child_process');
try {
const npmVersion = execSync('npm --version', { encoding: 'utf-8' }).trim();
console.log(`✅ npm 已安装成功！`);
console.log(`📦 npm 版本: ${npmVersion}`);
const nodeVersion = execSyn... | 2301_80381209/tks_kiro_kjdlxstr | test-npm.js | JavaScript | unknown | 648 |
<?php
function getFileNames($filePath) {
$fileNames = [];
if (file_exists($filePath)) {
$file = fopen($filePath, 'r');
if ($file) {
while (($line = fgets($file))!== false) {
$fileNames[] = trim($line);
}
fclose($file);
}
... | 2301_80867077/awd_waf_5_span | Difference.php | PHP | unknown | 1,576 |
<?php
function scanDirectory($directory, $outputFile) {
// 检查输出文件所在目录是否存在，如果不存在则创建
$fileSpanDir = dirname($outputFile);
if (!is_dir($fileSpanDir)) {
mkdir($fileSpanDir, 0777, true);
}
$fileHandle = fopen($outputFile, 'w'); // 直接以写入模式打开文件，如果文件存在则会被覆盖
scanRecursive($directory, $fil... | 2301_80867077/awd_waf_5_span | file_span_new.php | PHP | unknown | 1,086 |
<?php
function scanDirectory($directory, $outputFile) {
// 检查输出文件所在目录是否存在，如果不存在则创建
$fileSpanDir = dirname($outputFile);
if (!is_dir($fileSpanDir)) {
mkdir($fileSpanDir, 0777, true);
}
// 检查输出文件是否存在，如果不存在则创建
if (!file_exists($outputFile)) {
touch($outputFile);
}
... | 2301_80867077/awd_waf_5_span | file_span_old.php | PHP | unknown | 1,078 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Different Files</title>
<style>
table {
width: 100%;
border-collapse: collapse;
}
table, th, td {
... | 2301_80867077/awd_waf_5_span | span.php | PHP | unknown | 1,095 |
"""undoom-pdf-mcp: PDF转换工具MCP服务器
这是一个基于MCP (Model Context Protocol) 的PDF转换工具服务器，
集成了PDF转图片、Office文件转PDF等功能。
"""
__version__ = "0.2.3"
__author__ = "undoom"
__email__ = "kaikaihuhu666@163.com" | 2301_80863610/undoom_pdf_mcp | undoom_pdf_mcp/__init__.py | Python | mit | 265 |
#!/usr/bin/env python3
"""
__main__.py - 支持 python -m undoom_pdf_mcp 运行
"""
import asyncio
import sys
def main():
"""同步入口点函数"""
# 导入异步main函数
from .main import main as async_main
# 运行异步main函数
try:
asyncio.run(async_main())
except KeyboardInterrupt:
print("\n服务器已停止")
... | 2301_80863610/undoom_pdf_mcp | undoom_pdf_mcp/__main__.py | Python | mit | 521 |
#!/usr/bin/env python3
"""
PDF转换工具MCP服务器
集成PDF转JPG、PDF批量转图片、Office文件转PDF等功能
"""
import asyncio
import json
import os
import tempfile
import gc
from typing import Any, Dict, List, Optional
from pathlib import Path
import fitz # PyMuPDF
from PIL import Image
try:
import win32com.client
WIN32_AVAILABLE = True
e... | 2301_80863610/undoom_pdf_mcp | undoom_pdf_mcp/main.py | Python | mit | 29,544 |
"""抖音数据分析 MCP 服务器包"""
from .douyin_mcp_server import DouyinMCPServer, main, cli_main
__version__ = "0.1.2"
__all__ = ["DouyinMCPServer", "main", "cli_main"] | 2301_80863610/undoom-douyin-data-analysis | undoom_douyin_data_analysis/__init__.py | Python | mit | 178 |
#!/usr/bin/env python3
"""
抖音数据分析 MCP 服务器
基于原始的抖音作品分析工具开发的 MCP 服务器版本
提供数据采集、分析和导出功能
"""
import asyncio
import json
import logging
import os
import time
from datetime import datetime
from typing import Any, Dict, List, Optional
from urllib.parse import quote
import pandas as pd
from bs4 import BeautifulSoup
from colle... | 2301_80863610/undoom-douyin-data-analysis | undoom_douyin_data_analysis/douyin_mcp_server.py | Python | mit | 41,756 |
<?php
// 获取客户端IP地址
function get_client_ip() {
$ipaddress = '';
// 首先检查 REMOTE_ADDR
if (isset($_SERVER['REMOTE_ADDR'])) {
$ipaddress = $_SERVER['REMOTE_ADDR'];
} elseif (isset($_SERVER['HTTP_CLIENT_IP'])) {
$ipaddress = $_SERVER['HTTP_CLIENT_IP'];
} elseif (isset($_SERVER['HTTP_X_FOR... | 2301_80867077/ip_span | ip_span.php | PHP | unknown | 4,228 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>IP Check Files</title>
<meta http-equiv="refresh" content="10"> <!-- 页面每 10 秒自动刷新 -->
</head>
<body>
<h1>IP Check Files with Content 1</h1>
<table border="1">
<thead>
<tr>
<th>IP Address</th>
... | 2301_80867077/ip_span | start.php | PHP | unknown | 2,482 |
"""Undoom Uninstaller MCP - A Windows program uninstaller MCP server."""
__version__ = "0.1.7"
__author__ = "Undoom"
__email__ = "kaikaihuhu666@163.com"
from .server import main, cli_main
__all__ = ["main", "cli_main"] | 2301_80863610/undoom_Uninstaller_mcp | undoom_uninstaller_mcp/__init__.py | Python | mit | 221 |
"""配置管理模块"""
import os
from typing import Dict, List, Tuple
import winreg
# 注册表路径配置
REGISTRY_PATHS: List[Tuple[int, str]] = [
(winreg.HKEY_LOCAL_MACHINE, r"SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall"),
(winreg.HKEY_LOCAL_MACHINE, r"SOFTWARE\WOW6432Node\Microsoft\Windows\CurrentVersion\Uninstall"),
... | 2301_80863610/undoom_Uninstaller_mcp | undoom_uninstaller_mcp/config.py | Python | mit | 824 |
"""程序管理核心模块"""
import os
import winreg
import subprocess
from typing import List, Dict, Optional, Tuple
from datetime import datetime
from .config import REGISTRY_PATHS
from .utils import (
get_directory_size,
format_size,
format_install_date,
get_drive_letter,
safe_remove_directory,
get_co... | 2301_80863610/undoom_Uninstaller_mcp | undoom_uninstaller_mcp/program_manager.py | Python | mit | 9,205 |
"""报告生成模块"""
import os
from typing import List, Dict
from datetime import datetime
from .program_manager import ProgramInfo
from .utils import format_size, escape_markdown, truncate_text
class ReportGenerator:
"""报告生成器"""
@staticmethod
def generate_markdown_table(programs: List[ProgramInfo], title:... | 2301_80863610/undoom_Uninstaller_mcp | undoom_uninstaller_mcp/report_generator.py | Python | mit | 10,109 |
"""MCP服务器主模块 - 重构版本"""
import asyncio
import json
from typing import List
from mcp.server.models import InitializationOptions
from mcp.server import NotificationOptions, Server
from mcp.types import Tool, TextContent
import mcp.types as types
from .program_manager import ProgramManager, ProgramInfo
from .report_gene... | 2301_80863610/undoom_Uninstaller_mcp | undoom_uninstaller_mcp/server.py | Python | mit | 13,312 |
"""工具函数模块"""
import os
import shutil
from typing import Optional, Tuple, List
from datetime import datetime
def format_size(size: int) -> str:
"""格式化文件大小
Args:
size: 文件大小（字节）
Returns:
格式化后的大小字符串
"""
if size == 0:
return "N/A"
units = ['B', 'KB', 'MB'... | 2301_80863610/undoom_Uninstaller_mcp | undoom_uninstaller_mcp/utils.py | Python | mit | 4,662 |
#!/usr/bin/env python3
"""
本地MCP服务器测试入口
"""
import asyncio
import sys
import os
# 添加当前目录到Python路径
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def main():
"""本地测试入口点"""
try:
# 导入服务器模块
from system_monitor_mcp.server import main as async_main
# 运行异步主函数
... | 2301_80863610/system-monitor | main.py | Python | mit | 675 |
#!/usr/bin/env python3
"""
MCP测试代码 - 基于JSON配置生成
"""
import asyncio
import sys
import os
# 添加当前目录到Python路径
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def main():
"""MCP测试入口点"""
try:
# 导入服务器模块
from system_monitor_mcp.server import main as async_main
print("启... | 2301_80863610/system-monitor | mcp_test.py | Python | mit | 723 |
#!/usr/bin/env python3
"""
System Monitor MCP Server - 包初始化文件
"""
__version__ = "1.1.2"
__author__ = "Undoom"
__description__ = "System Monitor MCP Server - 系统监控MCP服务器"
from .server import MCPServer
from .server import main
# 为了兼容性，也导出SystemMonitorMCP别名
SystemMonitorMCP = MCPServer | 2301_80863610/system-monitor | system_monitor_mcp/__init__.py | Python | mit | 333 |
#!/usr/bin/env python3
"""
System Monitor MCP Server - 包入口点
"""
import asyncio
import sys
import os
import argparse
def main():
"""Entry point for the package"""
parser = argparse.ArgumentParser(description='System Monitor MCP Server')
parser.add_argument('--version', action='version', version='system-mon... | 2301_80863610/system-monitor | system_monitor_mcp/__main__.py | Python | mit | 1,503 |
#!/usr/bin/env python3
"""
System Monitor MCP Server - 数据收集模块
"""
import platform
import time
from typing import Dict, Any
import psutil
from .utils import format_uptime
async def collect_system_info() -> Dict[str, Any]:
"""收集系统基本信息"""
# CPU信息
cpu_freq = psutil.cpu_freq()
cpu_info = {
'physi... | 2301_80863610/system-monitor | system_monitor_mcp/collectors.py | Python | mit | 6,829 |
#!/usr/bin/env python3
"""
System Monitor MCP Server - 主服务器模块
"""
import asyncio
import json
import logging
import sys
from typing import Dict, Any, List, Optional
from .collectors import (
collect_system_info,
collect_cpu_info,
collect_memory_info,
collect_disk_info,
collect_network_info,
col... | 2301_80863610/system-monitor | system_monitor_mcp/server.py | Python | mit | 14,939 |
#!/usr/bin/env python3
"""
System Monitor MCP Server - 工具函数模块
"""
import logging
import os
import sys
from typing import Dict, Any
def setup_logging(level=logging.INFO):
"""设置日志配置"""
# 创建日志目录
log_dir = os.path.join(os.path.expanduser("~"), ".system_monitor_mcp", "logs")
os.makedirs(log_dir, exist_ok=T... | 2301_80863610/system-monitor | system_monitor_mcp/utils.py | Python | mit | 2,149 |
import math
import random
from collections import defaultdict
def euclidean_distance(point1, point2):
if len(point1) != len(point2):
raise ValueError("Points must have the same dimensions")
squared_distance = 0
for i in range(len(point1)):
squared_distance += (point1[i] - point2[i]) *... | 2301_80822435/machine-learning-course | assignment1/assignment3/2班44.py | Python | mit | 4,931 |
import math
import operator
from collections import Counter
def euclidean_distance(point1, point2):
if len(point1) != len(point2):
raise ValueError("Points must have the same dimensions")
squared_distance = 0
for i in range(len(point1)):
squared_distance += (point1[i] - point2[i]) ** ... | 2301_80822435/machine-learning-course | assignment1/assignment4/2班44.py | Python | mit | 10,796 |
import math
import random
def Kmeans(data, k, epsilon=1e-4, max_iterations=100):
# 辅助函数：计算两个向量的欧氏距离
def euclidean_distance(a, b):
return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b)))
# 辅助函数：将样本分配到最近的聚类中心
def assign_cluster(x, c):
min_distance = float('inf')
cluster_index =... | 2301_80822435/machine-learning-course | assignment3/1班01.py | Python | mit | 2,119 |
import random
import math
import matplotlib.pyplot as plt
def assign_cluster(x, centroids):
"""
将样本 x 分配到最近的簇中心
"""
min_dist = float('inf')
idx = 0
for i, c in enumerate(centroids):
dist = math.dist(x, c)
if dist < min_dist:
min_dist = dist
idx = i
re... | 2301_80822435/machine-learning-course | assignment3/1班02.py | Python | mit | 2,172 |
import math
import random
def assign_cluster(x, c):
"""
将样本x分配到最近的质心c
参数:
x: 一个数据点 (列表或元组)
c: 质心列表 [c1, c2, ..., ck]，每个质心是一个与x维度相同的点
返回:
cluster_index: 最近质心的索引
min_distance: 到最近质心的距离
"""
min_distance = float('inf')
cluster_index = -1
for ... | 2301_80822435/machine-learning-course | assignment3/1班03.py | Python | mit | 6,091 |
import random
import math
def euclidean_distance(point1, point2):
if len(point1) != len(point2):
raise ValueError("点的维度必须相同")
squared_distance = 0
for i in range(len(point1)):
squared_distance += (point1[i] - point2[i]) ** 2
return math.sqrt(squared_distance)
def assign_cluster(x, cent... | 2301_80822435/machine-learning-course | assignment3/1班04.py | Python | mit | 2,271 |
import math
import random
def assign_cluster(x, c):
min_dist = float('inf')
cluster_idx = 0
for i, center in enumerate(c):
# 计算欧氏距离
dist = math.sqrt(sum([(a - b)**2 for a, b in zip(x, center)]))
if dist < min_dist:
min_dist = dist
cluster_idx = i
return c... | 2301_80822435/machine-learning-course | assignment3/1班05.py | Python | mit | 1,793 |
import random
def assign_cluster(x, centers):
min_dist_sq = float('inf')
best_cluster_idx = 0
# 计算样本到每个聚类中心的欧氏距离平方
for idx, center in enumerate(centers):
dist_sq = sum((xi - ci) ** 2 for xi, ci in zip(x, center))
if dist_sq < min_dist_sq:
min_dist_sq = dist_sq
be... | 2301_80822435/machine-learning-course | assignment3/1班13.py | Python | mit | 2,511 |
import math
import random
def assign_cluster(x, centers):
"""
将样本分配到最近的聚类中心
x: 单个样本（列表或元组）
centers: 聚类中心列表（每个元素为样本格式）
return: 最近聚类中心的索引
"""
min_dist = float('inf')
cluster_idx = 0
for i, c in enumerate(centers):
# 计算欧氏距离
dist = math.sqrt(sum([(a - b) **2 for a, b in ... | 2301_80822435/machine-learning-course | assignment3/1班18.py | Python | mit | 2,311 |
import math
import random
def assign_cluster(x, centers):
"""
将样本x分配到最近的聚类中心
参数:
x: 单个样本（列表/元组，如[1,2,3]）
centers: 聚类中心列表（每个元素为样本格式）
返回:
最近聚类中心的索引（整数）
"""
min_distance = float('inf') # 初始化最小距离为无穷大
best_cluster = 0 # 初始化最佳聚类索引
for i, center in enu... | 2301_80822435/machine-learning-course | assignment3/1班22.py | Python | mit | 3,179 |
import math
import random
def euclidean_distance(point1, point2):
#计算欧几里得距离
return math.sqrt(sum((a - b) ** 2 for a, b in zip(point1, point2)))
def assign_cluster(X, centers):
# X: list of lists, 数据点列表 centers: list of lists, 聚类中心列表 labels: 每个样本所属的簇索引
labels = []
for point in X:
min_dist =... | 2301_80822435/machine-learning-course | assignment3/1班23.py | Python | mit | 2,012 |
import random
import math
def assign_cluster(x, c):
min_dist = float('inf')
cluster_idx = 0
for i, center in enumerate(c):
# 计算欧氏距离
dist = math.sqrt(sum([(a - b) ** 2 for a, b in zip(x, center)]))
if dist < min_dist:
min_dist = dist
cluster_idx = i
retur... | 2301_80822435/machine-learning-course | assignment3/1班28.py | Python | mit | 2,233 |
import numpy as np
import matplotlib.pyplot as plt
# 聚类的类
class JuLei:
def __init__(self, ge_shu=2, zui_da_ci_shu=100, cha_zhi=0.0001):
self.ge_shu = ge_shu # 要聚成几类
self.zui_da_ci_shu = zui_da_ci_shu # 最多迭代多少次
self.cha_zhi = cha_zhi # 中心点变化小于这个就停
self.zhong_xin = None # 存放中心点
... | 2301_80822435/machine-learning-course | assignment3/1班29.py | Python | mit | 3,204 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
class KMeans:
def __init__(self, k=3, max_iters=100, tol=1e-4, random_state=None):
"""
KMeans聚类算法
参数:
k: 聚类数量
max_iters: 最大迭代次数
tol: 收敛阈值（质心变化小于该值时停止迭代）
random... | 2301_80822435/machine-learning-course | assignment3/1班31.py | Python | mit | 6,294 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
class KMeans:
def __init__(self, k=3, max_iters=100, tol=1e-4):
self.k = k
self.max_iters = max_iters
self.tol = tol
self.centroids = None
self.labels = None
def fit(self,... | 2301_80822435/machine-learning-course | assignment3/1班32.py | Python | mit | 2,467 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
class KMeans:
def __init__(self, k=3, max_iters=100, random_state=42):
self.k = k
self.max_iters = max_iters
self.random_state = random_state
self.centroids = None
self.labels = None
... | 2301_80822435/machine-learning-course | assignment3/1班34.py | Python | mit | 3,002 |
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
def assign_cluster(x, c):
distances = np.linalg.norm(x[:, np.newaxis] - c, axis=2)
return np.argmin(distances, axis=1)
def Kmeans(data, k, epsilon=1e-4, iteration=100):
... | 2301_80822435/machine-learning-course | assignment3/1班73.py | Python | mit | 1,515 |
import math
import random
def assign_cluster(x, c):
min_dist = float('inf')
cluster_idx = 0
for i, center in enumerate(c):
dist_sq = 0.0
for xi, ci in zip(x, center):
dist_sq += (xi - ci) ** 2
dist = math.sqrt(dist_sq)
if dist < min_dist:
min_dist =... | 2301_80822435/machine-learning-course | assignment3/2班37.py | Python | mit | 2,999 |
import random
import math
from collections import defaultdict
def euclidean_distance(p1, p2):
if len(p1) != len(p2):
raise ValueError("Points must have the same number of dimensions")
sum_squares = 0
for i in range(len(p1)):
sum_squares += (p1[i] - p2[i]) ** 2
return math.sqrt(sum_squa... | 2301_80822435/machine-learning-course | assignment3/2班39.py | Python | mit | 3,654 |
import math
import random
def assign_cluster(x, c):
distances = []
for center in c:
dist = math.sqrt(sum((x[i] - center[i])**2 for i in range(len(x))))
distances.append(dist)
return distances.index(min(distances))
def Kmeans(data, k, epsilon=1e-4, iteration=100):
if not data or k <= 0 ... | 2301_80822435/machine-learning-course | assignment3/2班40.py | Python | mit | 2,271 |
import math
import random
def assign_cluster(x, centers):
min_dist = float('inf')
cluster_idx = 0
for i, c in enumerate(centers):
# 计算欧氏距离
dist = math.sqrt(sum([(a - b) ** 2 for a, b in zip(x, c)]))
if dist < min_dist:
min_dist = dist
cluster_idx = i
retur... | 2301_80822435/machine-learning-course | assignment3/2班41.py | Python | mit | 2,479 |
import math
import random
def Kmeans(data, k, epsilon=1e-5, iteration=100):
"""
K均值聚类主函数
:param data: 输入数据，格式为列表嵌套列表，每个子列表是一个样本向量（如[[x1,y1], [x2,y2], ...]）
:param k: 聚类数量
:param epsilon: 收敛阈值（centroids变化量小于该值则停止迭代）
:param iteration: 最大迭代次数（防止无限循环）
:return: (clusters, centroids)
... | 2301_80822435/machine-learning-course | assignment3/2班42.py | Python | mit | 3,577 |
import math
import random
random.seed(42)
def assign_cluster(x, c):
min_dist = float('inf') # 初始化最小距离为无穷大
cluster_idx = 0 # 初始化聚类索引
for idx, center in enumerate(c):
dist = math.sqrt(sum((xi - ci) ** 2 for xi, ci in zip(x, center)))
if dist < min_dist:
min_dist = dist
... | 2301_80822435/machine-learning-course | assignment3/2班43.py | Python | mit | 3,694 |
import numpy as np
class KMeans:
def __init__(self, k=3, max_iters=100, tol=1e-4):
self.k = k
self.max_iters = max_iters
self.tol = tol
def fit(self, X):
np.random.seed(42)
# Step 1：随机选择 k 个点作为初始聚类中心
random_idx = np.random.choice(len(X), self.k, replace=False)
... | 2301_80822435/machine-learning-course | assignment3/2班45号.py | Python | mit | 1,563 |
import random
def assign_cluster(x, centers):
min_dist_sq = float('inf')
cluster_idx = 0 # 最近聚类中心的索引
for i, center in enumerate(centers):
# 计算欧氏距离的平方(避免开方运算，提高效率)
dist_sq = sum((xi - ci) ** 2 for xi, ci in zip(x, center))
# 更新最小距离和对应索引
if dist_sq < min_dist_sq:... | 2301_80822435/machine-learning-course | assignment3/2班46.py | Python | mit | 3,380 |
import math
import random
from collections import defaultdict
def euclidean_distance(point1, point2):
"""
计算两个点之间的欧几里得距离
"""
if len(point1) != len(point2):
raise ValueError("Points must have the same dimensions")
squared_distance = 0
for i in range(len(point1)):
squared_dis... | 2301_80822435/machine-learning-course | assignment3/2班47.py | Python | mit | 4,607 |
import random # 导入随机模块
import math # 导入数学模块
def assign_cluster(x, c):
min_dist = float('inf') # 初始化最小距离为无穷大
best_idx = 0 # 初始化最佳质心索引
for i, center in enumerate(c): # 遍历所有质心
dist = math.sqrt(sum((a - b) ** 2 for a, b in zip(x, center))) # 计算欧几里得距离
if dist < min_dist: ... | 2301_80822435/machine-learning-course | assignment3/2班49.py | Python | mit | 2,229 |
import random
import math
def assign_cluster(x, c):
"""
将数据点x分配到最近的聚类中心
参数:
x: 数据点（列表或元组）
c: 聚类中心列表
返回:
最近的聚类中心的索引
"""
min_dist = float('inf')
cluster_idx = 0
for i, center in enumerate(c):
# 计算欧氏距离
dist = math.sqrt(sum((x[j] - cen... | 2301_80822435/machine-learning-course | assignment3/2班50.py | Python | mit | 2,920 |
"""
手动实现K均值聚类算法
只使用Python标准库
"""
import random
import math
def euclidean_distance(point1, point2):
"""
计算两个点之间的欧氏距离
参数:
point1: 第一个点（列表或元组）
point2: 第二个点（列表或元组）
返回:
欧氏距离
"""
if len(point1) != len(point2):
raise ValueError("两个点的维度必须相同")
sum_squ... | 2301_80822435/machine-learning-course | assignment3/2班51.py | Python | mit | 4,303 |
import random
import math
from typing import List, Tuple # 从 typing 中导入类型提示，便于代码可读性
# 1. 计算欧氏距离的平方（避免不必要的 sqrt）
def euclidean_sq(a: List[float], b: List[float]) -> float:
# 对两个向量对应元素求差的平方并求和，得到距离的平方
return sum((x - y) ** 2 for x, y in zip(a, b))
# 2. 为单个样本分配最近的簇
... | 2301_80822435/machine-learning-course | assignment3/2班54.py | Python | mit | 4,575 |
import math
import random
def calc_dist(p1, p2):
return math.sqrt(sum((a - b) ** 2 for a, b in zip(p1, p2)))
def find_nearest(pt, centers):
dists = [calc_dist(pt, c) for c in centers]
return dists.index(min(dists))
def kmeans(data, k, eps=1e-4, max_iter=100):
centers = random.sample(data, k)
hist... | 2301_80822435/machine-learning-course | assignment3/2班55.py | Python | mit | 2,978 |
import random
import math
def assign_cluster(x, centroids):
min_distance = float('inf')
closest_centroid = 0
for i, centroid in enumerate(centroids):
distance = 0.0
for j in range(len(x)):
distance += (x[j] - centroid[j]) ** 2
distance = math.sqrt(distance)
if ... | 2301_80822435/machine-learning-course | assignment3/2班56.py | Python | mit | 1,917 |
import random
import math
from typing import List, Tuple
def assign_cluster(point: List[float], centers: List[List[float]]) -> int:
#将数据点分配到最近的聚类中心
return min(range(len(centers)),
key=lambda i: sum((p - c) ** 2 for p, c in zip(point, centers[i])))
def kmeans(data: List[List[float]], k: int, eps... | 2301_80822435/machine-learning-course | assignment3/2班57.py | Python | mit | 2,041 |
import random
import math
def euclidean_distance(x, y):
"""计算两点之间的欧式距离"""
return math.sqrt(sum((a - b) ** 2 for a, b in zip(x, y)))
def assign_cluster(x, centers):
"""将样本 x 分配给最近的中心，返回其索引"""
distances = [euclidean_distance(x, c) for c in centers]
return distances.index(min(distances))
... | 2301_80822435/machine-learning-course | assignment3/2班58.py | Python | mit | 1,748 |
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
from sklearn.datasets import make_blobs
import random
class KMeans:
def __init__(self, k=3, max_iters=100, tol=1e-4, random_state=None):
self.k = k
self.max_it... | 2301_80822435/machine-learning-course | assignment3/2班59.py | Python | mit | 5,934 |
import math
import random
def assign_cluster(x, c):
"""
将样本x分配到最近的聚类中心
参数:
x: 单个样本(列表或元组)
c: 聚类中心列表(每个元素为列表或元组)
返回:
最近聚类中心的索引
"""
min_dist = float('inf')
cluster_idx = 0
for i, center in enumerate(c):
# 计算欧氏距离
dist = math.sqrt(sum((xi - ci)**2 for... | 2301_80822435/machine-learning-course | assignment3/2班61.py | Python | mit | 2,529 |
import random
import math
def assign_cluster(x, c):
min_dist = float('inf') # 初始最小距离设为无穷大
cluster_idx = 0 # 初始聚类索引设为0
for i, centroid in enumerate(c):
# 计算x与当前质心的欧氏距离（多维向量距离公式）
dist = 0.0
for xi, ci in zip(x, centroid):
dist += (xi - ci) ** 2
dist = mat... | 2301_80822435/machine-learning-course | assignment3/2班63.py | Python | mit | 3,827 |
import random
import math
def assign_cluster(x, centroids):
min_dist = float('inf')
cluster_idx = 0
for idx, centroid in enumerate(centroids):
# 计算数据点x与质心centroid的欧氏距离
dist = 0.0
for xi, ci in zip(x, centroid):
dist += (xi - ci) ** 2
dist = math... | 2301_80822435/machine-learning-course | assignment3/2班64.py | Python | mit | 3,685 |
import random
import math
def assign_cluster(x, c):
"""
将样本x分配到最近的簇中心
参数:
x: 单个样本向量 (list 或 tuple)
c: 簇中心列表，每个元素是一个簇中心向量 (list of lists)
返回:
int: 最近簇的索引
"""
min_distance = float('inf')
cluster_index = 0
for i, center in enumerate(c):
# 计算欧几... | 2301_80822435/machine-learning-course | assignment3/2班65.py | Python | mit | 3,038 |
import math
import random
def kmeans_plus_plus_init(data, k):
centroids = []
# 1. 随机选择第一个聚类中心
centroids.append(random.choice(data))
# 2. 选择剩余的 k-1 个聚类中心
for _ in range(1, k):
# 计算每个数据点到最近聚类中心的距离
distances = []
for point in data:
min_distance = f... | 2301_80822435/machine-learning-course | assignment3/2班66.py | Python | mit | 6,111 |
import random
import math
def assign_cluster(x, c):
min_distance = float("inf") #初始化最小距离
cluster_index = 0
for i, center in enumerate(c):
#计算欧氏距离
distance = math.sqrt(sum((xi - ci) ** 2 for xi, ci in zip(x, center)))
if distance < min_distance:
min_distance = distance
... | 2301_80822435/machine-learning-course | assignment3/2班67.py | Python | mit | 2,266 |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['axes.unicode_minus'] = False
def assign_cluster(x, c):
distances = np.linalg.norm(c - x, axis=1)
y = np.argmin(distances)
return y
def Kmeans(data, k, epsilon=1... | 2301_80822435/machine-learning-course | assignment3/2班68.py | Python | mit | 1,650 |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
def assign_cluster(x, c):
"""
将样本 x 分配到最近的聚类中心
"""
distances = np.linalg.norm(x[:, np.newaxis] - c, axis=2) # shape: (n_samples, K)
y = np.argmin(distances, axis=1)
return y
def Kmean(data, K, epsilon=... | 2301_80822435/machine-learning-course | assignment3/2班70.py | Python | mit | 1,862 |
import math
import numpy as np
from collections import Counter
from operator import itemgetter
class KNN:
def __init__(self, k=3, task='classification'):
"""
初始化 KNN 模型
参数:
k: 近邻数量
task: 任务类型，'classification' 或 'regression'
"""
self.k = k
se... | 2301_80822435/machine-learning-course | assignment4/1班01.py | Python | mit | 3,396 |
import math
import matplotlib.pyplot as plt
# ===================== 距离函数 =====================
def euclidean_distance(x1, x2):
return math.sqrt(sum((a - b) ** 2 for a, b in zip(x1, x2)))
# ===================== KNN 预测函数 =====================
def knn_predict(train_X, train_y, x, k=3):
distances = []
for xi... | 2301_80822435/machine-learning-course | assignment4/1班02.py | Python | mit | 1,706 |
import math
from collections import Counter
from operator import itemgetter
class KNN:
"""
K近邻算法实现（仅使用Python标准库）
适用于分类问题
"""
def __init__(self, k=5, weights='uniform', metric='euclidean'):
"""
初始化KNN分类器
参数:
k: 邻居数量，默认5
weights: 权... | 2301_80822435/machine-learning-course | assignment4/1班03.py | Python | mit | 5,563 |
import numpy as np
from collections import Counter
class KNN:
def __init__(self, k=3):
self.k = k
self.X_train = None
self.y_train = None
def fit(self, X, y):
self.X_train = np.array(X)
self.y_train = np.array(y)
def euclidean_distance(self, x1, x2):
retur... | 2301_80822435/machine-learning-course | assignment4/1班04.py | Python | mit | 1,713 |
import math
from collections import Counter
def euclidean_distance(x1, x2):
"""计算两个样本之间的欧氏距离"""
return math.sqrt(sum([(a - b) **2 for a, b in zip(x1, x2)]))
def knn_predict(X_train, y_train, x_test, k=3):
"""
单个样本的k近邻预测
参数:
X_train: 训练样本特征（列表，每个元素为样本的特征向量）
y_train: 训练样本标签（列表，与X_tra... | 2301_80822435/machine-learning-course | assignment4/1班05.py | Python | mit | 1,637 |
import math
def euclidean_distance(point1, point2):
return math.sqrt(sum((a - b) ** 2 for a, b in zip(point1, point2)))
def knn_predict(training_data, training_labels, test_point, k=3):
if len(training_data) != len(training_labels):
raise ValueError("训练数据和标签长度不一致")
if k > len(training_data)... | 2301_80822435/machine-learning-course | assignment4/1班10.py | Python | mit | 2,008 |
import math
from collections import Counter
def euclidean_distance(x1, x2):
if len(x1) != len(x2):
raise ValueError("两个样本的维度必须一致")
squared_diff_sum = sum((a - b) ** 2 for a, b in zip(x1, x2))
return math.sqrt(squared_diff_sum)
def KNN(X_train, y_train, X_test, k=5, task="classification", distance_... | 2301_80822435/machine-learning-course | assignment4/1班13.py | Python | mit | 1,966 |
import math
from collections import Counter
def euclidean_distance(x1, x2):
"""计算两个样本之间的欧氏距离"""
return math.sqrt(sum([(a - b) **2 for a, b in zip(x1, x2)]))
def knn_predict(train_data, train_labels, x, k):
"""
单个样本的k近邻预测
train_data: 训练样本列表（每个样本为可迭代对象）
train_labels: 训练样本对应的标签列表
x: 待预测样本
... | 2301_80822435/machine-learning-course | assignment4/1班18.py | Python | mit | 1,814 |
import math
from collections import Counter
def _distance(x1, x2):
"""计算两个样本的欧氏距离（内部辅助函数）"""
return math.sqrt(sum((a - b) **2 for a, b in zip(x1, x2)))
def knn_predict(sample, train_data, train_labels, k):
"""
单个样本的k近邻预测
参数:
sample: 待预测样本（如[1.2, 3.4]）
train_data: 训练样本列表（每个元素为样本）
... | 2301_80822435/machine-learning-course | assignment4/1班22.py | Python | mit | 1,919 |
import math
def euclidean_distance(point1, point2):
"""手动计算欧几里得距离"""
if len(point1) != len(point2):
raise ValueError("点的维度不一致")
squared_sum = 0
for i in range(len(point1)):
squared_sum += (point1[i] - point2[i]) ** 2
return math.sqrt(squared_sum)
class KNN:
def __init__(se... | 2301_80822435/machine-learning-course | assignment4/1班23.py | Python | mit | 2,293 |
import numpy as np
from sklearn.model_selection import KFold
def distance(a, b, metric='euclidean'):
if metric == 'euclidean':
return np.sqrt(np.sum((a - b) ** 2))
elif metric == 'manhattan':
return np.sum(np.abs(a - b))
else:
raise ValueError(f"不支持的距离度量: {metric}。请使用 'euclidea... | 2301_80822435/machine-learning-course | assignment4/1班28.py | Python | mit | 4,421 |
import numpy as np
import matplotlib.pyplot as plt
# k近邻算法类
class KJinLin:
def __init__(self, k=3):
self.k = k # 选几个邻居
self.shu_ju = None # 存训练数据
self.biao_qian = None # 存训练标签
# 训练函数（其实就是存数据）
def xun_lian(self, xun_lian_shu_ju, xun_lian_biao_qian):
self.shu_ju = xun_lian... | 2301_80822435/machine-learning-course | assignment4/1班29.py | Python | mit | 3,072 |
import math
from collections import Counter
from operator import itemgetter
class KNN:
def __init__(self, k=3, task='classification'):
self.k = k
self.task = task
self.X_train = None
self.y_train = None
def fit(self, X, y):
self.X_train = X
self.y_train = y
... | 2301_80822435/machine-learning-course | assignment4/1班30.py | Python | mit | 2,424 |
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
import seaborn as sns
class KNN:
def __init__(... | 2301_80822435/machine-learning-course | assignment4/1班31.py | Python | mit | 11,100 |
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
class kNN:
def __init__(self, k=3):
self.k = k
self.X_train = None
self.y_train = None
def fit(self, X, y):
"""训练kNN模型（只是存储数据）"""
... | 2301_80822435/machine-learning-course | assignment4/1班32.py | Python | mit | 2,547 |
from collections import Counter
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from sklearn.datasets import load_iris
class KNN:
def __init__(self, k=3):
self.k = k
self.X_train = None
self.y_train = None
d... | 2301_80822435/machine-learning-course | assignment4/1班34.py | Python | mit | 3,094 |
import math
import random
from collections import Counter
def euclidean_distance(x1, x2):
if len(x1) != len(x2):
raise ValueError("两个样本的特征维度必须一致")
dist_sq = sum((a - b) ** 2 for a, b in zip(x1, x2))
return math.sqrt(dist_sq)
def knn_predict(x_test, X_train, y_train, k=3, task="classification"):
... | 2301_80822435/machine-learning-course | assignment4/2班37.py | Python | mit | 3,511 |
import random
import math
from collections import Counter
class KNN:
def __init__(self, k=3):
if k <= 0:
raise ValueError("K must be a positive integer.")
self.k = k
self.x_train = None
self.y_train = None
def _euclidean_distance(self, p1, p2):
return math.s... | 2301_80822435/machine-learning-course | assignment4/2班39.py | Python | mit | 3,035 |
import math
from collections import Counter
def knn_predict(train_data, train_labels, test_point, k=3):
if len(train_data) != len(train_labels):
raise ValueError("训练数据和标签的数量不匹配")
if k <= 0:
raise ValueError("k值必须大于0")
all_distances_info = []
for i, point in enumerate(train_data):
... | 2301_80822435/machine-learning-course | assignment4/2班40.py | Python | mit | 2,687 |
import math
from collections import Counter
def euclidean_distance(x1, x2): # 计算欧氏距离
if len(x1) != len(x2):
raise ValueError("两个样本的特征维度必须一致")
return math.sqrt(sum([(a - b) ** 2 for a, b in zip(x1, x2)]))
def knn_predict(x, train_data, train_labels, k):
# 检查输入有效性
if len(train_data) != len(trai... | 2301_80822435/machine-learning-course | assignment4/2班41.py | Python | mit | 2,042 |
import math
import heapq
from collections import Counter
def knn_predict(
x_test,
x_train,
y_train,
k=3,
distance_type='euclidean',
task='classification'
):
"""
k近邻算法预测函数
:param x_test: 单个测试样本（列表/元组，如[x1, x2, ...]）
:param x_train: 训练集特征（列表嵌套列表，如[[x1,y1], [x2,y2], ...]）
... | 2301_80822435/machine-learning-course | assignment4/2班42.py | Python | mit | 3,718 |
import math
import random
from collections import Counter
random.seed(42)
def calc_euclidean_dist(s1, s2):
# 统一校验样本格式与特征类型
for s, name in [(s1, "样本1"), (s2, "样本2")]:
if not isinstance(s, list):
raise TypeError(f"{name}需为列表类型")
if not all(isinstance(val, (int, float)) for val in s)... | 2301_80822435/machine-learning-course | assignment4/2班43.py | Python | mit | 4,283 |
import numpy as np
from collections import Counter
class KNN:
def __init__(self, k=3):
self.k = k
def fit(self, X, y):
self.X_train = X
self.y_train = y
def predict(self, X):
return np.array([self._predict_one(x) for x in X])
def _predict_one(self, x):
# 计算该样本与所有训... | 2301_80822435/machine-learning-course | assignment4/2班45号.py | Python | mit | 1,138 |