Space Invaders

关联规则挖掘算法 Python实现Apriori算法(Association rule mining algorithm Python implementation Apriori algorithm)

This is the implementation for the following paper:[Learning Sparse Gaussian Graphical Models with Overlapping Blocks](http://papers.nips.cc/paper/6097-learning-sparse-gaussian-graphical-models-with-overlapping-blocks.pdf)‎, Mohammad Javad Hosseini and Su-In Lee. Neural Information Processing Systems (NIPS), 2016.### Running#### SoftwareYou should have R and the R package quic installed on your machine. The file main.py shows an example and will get you started. It uses MILE data (AMLcancer dataset) as input. We have selected 500 genes, consisting of 488 highest varying genes in MILE and 12 genes highly associated with AML:FLT3, NPM1, CEBPA, KIT, N-RAS, MLL, WT1, IDH1/2, TET2, DNMT3A, and ASXL1. Please see data/genes.mat.

Python_VLPR车牌识别

说明：  svr 回归预测，用于石油管道的腐蚀性预测(SVR regression prediction)

网络爬虫-企业信息爬取-python scrapy实现, cookie存储. 爬取企业注册信息, 比如支付类的公司, 保存到excel中

说明：  自动抢购京东抢购商品，如抢显卡、手机。登录后，复制商品ID，修改到配置文件后，到时间自动抢购(Automatic rush to buy Jingdong rush to buy goods, such as graphics card and mobile phone)

基于MXNet深度学习框架，定义CNN模型，训练手写数字识别数据，得到较高的准确率。这是一个基础的深度学习分类任务，值得借鉴。(Based on MXNet deep learning framework, CNN model is defined and handwritten digit recognition data is trained to obtain higher accuracy. This is a basic task of deep learning classification, which is worth learning from.)

参考资料为清华大学出版社的数值分析，作者为李庆杨、王能超、易大义。 运行后可以输入任意点，程序会根据这些点实现插值函数的构造以及绘图。同时提供了一些预设点和龙格函数的点集构造方法。实现的插值方法有：牛顿插值、拉格朗日插值、三次样条插值。 在运行前必须安装numpy库和matplotlib库。

一个使用python的pygame开发的游戏。游戏《外星人入侵》是一个20世纪的经典游戏，非常好玩，真的很好玩啊 一个使用python的pygame开发的游戏。游戏《外星人入侵》是一个20世纪的经典游戏，非常好玩，真的很好玩啊 一个使用python的pygame开发的游戏。游戏《外星人入侵》是一个20世纪的经典游戏，非常好玩，真的很好玩啊

说明：  SSD: Single Shot MultiBox Object Detector, in PyTorch A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang, and Alexander C. Berg. The official and original Caffe code can be found here. Table of Contents Installation Datasets Train Evaluate Performance Demos Future Work Reference