Python网络数据采集.pdf

说明：  针对图像亮度不均匀问题，Li Tao与2005年提出的这个算法。其主要用于提升再低照度或者不均匀光照条件下拍摄到的图像亮度。该算法主要由两部分构成：自适应亮度增强模块和自适应对比度增强。(Li Tao and this algorithm proposed in 2005 for the problem of uneven image brightness. It is mainly used to enhance the brightness of images captured under low illumination or uneven illumination. The algorithm is mainly composed of two parts: adaptive brightness enhancement module and adaptive contrast enhancement.)

说明：  状态估计在IEEE14网络上的加权最小二乘法计算(state estimation for IEEE14 using Weighted Least Squares method)

Python机器学习——预测分析核心算法(MICHAEL BOWLES teaches machine learning at Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as bioinformatics and high-frequency trading. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses at Hacker Dojo are nearly always sold out and receive great feedback from participants.)

批量下载ERA interim数据资料的方法(download ERA interim in batch)

一款简单的ABAQUS施加周期性边界条件的插件(A Simple ABAQUS Plug-in with Periodic Boundary Conditions)

针对早期windows系统SMB协议的攻击 前提时先安装安装python 在命令提示符窗口中首先进入py命令所在的文件夹 直接键入python xx.py后回车确认(Attacks on SMB Protocol of Early Windows System Install Python first First enter the folder where the PY command resides in the command prompt window Type Python xx.py directly and return to confirm)

说明：  python标准库，用于学习python的小伙伴(Python standard library)

说明：  多标签学习算法的相关资料学习，包含数据集和代码(the multi-label knn algorithm ,can use the code in python)

从猎聘网爬取相关招聘信息和评论信息，网络爬虫(又被称为网页蜘蛛，网络机器人，在FOAF社区中间，更经常的称为网页追逐者)，是一种按照一定的规则，自动的抓取万维网信息的程序或者脚本。(From the recruitment network crawling relevant recruitment information.Web crawlers, also known as web spiders, web robots, more commonly known as web chasers in the FOAF community, are programs or scripts that automatically crawl the World Wide Web in accordance with certain rules.)

说明：  利用机器学习中的的线性回归模型，完成对房价的预估(Using the linear regression model in machine learning to complete the prediction of house price)