读取通达信的日线数据到dataframe结构中

说明：  lppl模型 股市崩盘点预测 使用python语言(log-periodic power law)

说明：  实现文件压缩，解压，使用了哈夫曼编码来实现程序(Realize file compression and decompression)

说明：  对python环境的小测试，包含一个制图模块(A small test of Python environment, including a mapping module)

python机器学习算法 赵志勇 深入分析机器学习中的常用算法(python machine learning)

对于视频中动态背景下运动前景部分提取，一种基于单高斯模型和混合高斯模型的动态背景模型提取前景信息。(For moving foreground extraction in dynamic background, a dynamic background model based on single Gaussian model and mixed Gaussian model is proposed to extract foreground information.)

Libsvm is a simple, easy-to-use, and efficient software for SVMclassification and regression. It solves C-SVM classification, nu-SVMclassification, one-class-SVM, epsilon-SVM regression, and nu-SVMregression. It also provides an automatic model selection tool forC-SVM classification. This document explains the use of libsvm.Libsvm is available at http://www.csie.ntu.edu.tw/~cjlin/libsvmPlease read the COPYRIGHT file before using libsvm.Table of Contents=================- Quick Start- Installation and Data Format- `svm-train" Usage- `svm-predict" Usage- `svm-scale" Usage- Tips on Practical Use- Examples- Precomputed Kernels - Library Usage- Java Version- Building Windows Binaries- Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc.- MATLAB/OCTAVE Interface- Python Interface-

说明：  sins/gnss组合导航仿真代码，主要功能包括：场景生成、惯性传感器Allan方差分析、磁传感器标定、定姿算法、松组合算法设计等(The sauce code is for the simulation of sins/gnss integrated navigation system, mainly including the scenario generating, Allan variance analysis, attitude determination algorithm and the SINS/GNSS loosely coupled integrated navigation algorithm.)

Python编程基础，包括客户端、服务器、域名系统、高级网络编程等知识。(This is a document that concludes Python Program for basic knowledge.)

说明：  opencv中的一段 snake的代码, opencv snake,(A piece of Snake code in opencv, opencv snake,)

(中文版)深度学习(Deep Learning)是由Yoshua Bengio推出的机器学习领域的教科书，以开源的形式免费在网络上提供，主要面向学习机器学习的学生和研究者，以及没有机器学习背景但希望能快速地掌握这方面知识并在他们的产品中使用的软件工程师。(Deep Learning by Yoshua Bengio launched a textbook in the field of machine Learning, in the form of open source provided for free on the Internet, mainly for Learning machine Learning of students and researchers, and the lack of machine Learning background but hope to be able to quickly grasp the knowledge and use of software engineer in their products.)