资源列表
DataStructTest
- K-means文本聚类方法（IDEA项目包） 下载就能运行-K-means clustering method text (IDEA project package) will be able to download Run
Wind-Speed-Combined-Prediction
- 针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat 算法对短期风速时间序列进行db3 小波三层分解与重构，得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性，对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测，对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。-In order to improve short-term wind speed pr
pachongBDTB
- Python 爬去百度贴吧中一个贴子的内容，运用Urllib2和re模块，并对爬取的内容进行修改，去掉网页中的各种标签。-Python crawls the contents of a post in Baidu Post Bar, using Urllib2 and re modules, and crawl the contents of the amendment, remove the various pages of the label.
beautifulsoup4test1
- 爬取糗事百科，运用BeautifulSoup模块对爬取内容进行处理。-Crawling embarrassing encyclopedia, using BeautifulSoup module to crawl content processing.
pachongtest2
- 运用python爬取知乎日报的内容，对知乎日报网页中的每一个子链接进行爬取，并对内容进行修改，运用re,urllib2,BeautifulSoup模块。-Use python to crawl the contents of daily news, to know every page in the daily sub-links to crawl, and to modify the content, the use of re, urllib2, BeautifulSoup module.
cnbeta
- 运用python爬取cnbeta的最新内容，运用到了scarpy模块。-The use of python crawl cnbeta the latest content, the use of the scarpy module.
K-means
- k-means简单实现，实现了k近邻的实现，以图像的形式显示出来，简单实用-k-means simple to achieve achieve a k neighbors realized and presented in the form of an image, simple and practical
beautifulsoup4-4.0.0b3.tar
- Beautiful Soup提供一些简单的、python式的函数用来处理导航、搜索、修改分析树等功能。-Beautiful Soup offers some simple, python-like functions to handle navigation, search, modify the parse tree and so on.
Kalman
- Kalman算法示例，java实现通过Klman对GPS点进行过滤，实现平滑-Kalman algorithm example, trajectory filtering by Kalman filtering useing java
DIGG-all
- 图生成器DEGG，能够自动生成各种属性的图，注意是graph，而不是image。-Map generator DEGG, can automatically generate various figures attributes, note the graph, rather than the image.
icwsm14-T4-code
- 利用开源SNAP网络处理工具进行网络分析，是已经发表过的论文中用到的，目的主要是为了推广SNAP工具包。-SNAP network processing using an open source tool for network analysis, already published in the paper used, the main purpose is to promote SNAP toolkit.
lof
- LOF（局部异常因子）是用于识别基于密度的局部异常值的算法-It uses local outlier mining method to count the Local Outlier Factor(LOF) of the outlier candidated object。