kingsupernova's Stars
iovxw/rssbot
Lightweight Telegram RSS notification bot. 用于消息通知的轻量级 Telegram RSS 机器人
0voice/from_coder_to_expert
2021年最新总结，从程序员到CTO，从专业走向卓越，分享大牛企业内部pdf与PPT
d2l-ai/d2l-zh
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
programthink/books
【编程随想】收藏的电子书清单（多个学科，含下载链接）
ab77/netflix-proxy
Smart DNS proxy to watch Netflix
233boy/v2ray
最好用的 V2Ray 一键安装脚本 & 管理脚本
nailperry-zd/The-Economist
The Economist 经济学人，持续更新
Alvin9999/new-pac
翻墙-科学上网、自由上网、免费科学上网、免费翻墙、油管youtube、fanqiang、软件、VPN、一键翻墙浏览器，vps一键搭建翻墙服务器脚本/教程，免费shadowsocks/ss/ssr/v2ray/goflyway账号/节点，翻墙梯子，电脑、手机、iOS、安卓、windows、Mac、Linux、路由器翻墙、科学上网、youtube视频下载、美区apple id共享账号
FunctionClub/SSR-Bash-Python
一个SSR多用户控制脚本
CYP0630/Term-Project-Indoor-Localization
EEE413 Computer Network Term Project; Yupeng.Cao, 1302533;
pozyxLabs/Pozyx-Python-library
The Python library to work with the pozyx accurate indoor positioning system
kyeongsoo/indoor_localization
Research on indoor localization
Laurae2/Indoor_Prediction
Indoor User Movement Prediction from RSS data Data Set
ibmdbanalytics/ibmdbpy
A Pandas-like SQL-wrapper for in-database analytics with IBM Db2.
xianhu/LearnPython
以撸代码的形式学习Python
christophertull/taxi-trajectory-prediction
Predict the destination of taxis in Porto, Portugal. https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i
pushkar/ABAGAIL
The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.
nose-devs/nose
nose is nicer testing for python
jmschrei/pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
hmmlearn/hmmlearn
Hidden Markov Models in Python, with scikit-learn like API
rougier/matplotlib-tutorial
Matplotlib tutorial for beginner
owenashurst/agar.io-clone
Agar.io clone written with Socket.IO and HTML5 canvas
aosabook/500lines
500 Lines or Less
Yixiaohan/show-me-the-code
Python 练习册，每天一个小程序
libspatialindex/libspatialindex
C++ implementation of R*-tree, an MVR-tree and a TPR-tree with C API
roy-ht/editdistance
Fast implementation of the edit distance(Levenshtein distance)
ThemisB/LCSS
Implementation of LCSS Algorithm
michaelmior/mvptree
A fork of D Grant Starkweather's multiple vantage point tree library
XuJin1992/The-Research-And-Implementation-Of-Data-Mining-For-Geological-Data
Data mining and knowledge discovery, refers to discover knowledge from huge amounts of data, has a broad application prospect.When faced with geological data, however, even the relatively mature existing models, there are defects performance and effect.Investigate its reason, mainly because of the inherent characteristics of geological data, high dimension, unstructured, more relevance, etc., in the data model, indexing structure knowledge representation, storage, mining, etc., is far more complicated than the traditional data. The geological data of the usual have raster, vector and so on, this paper pays attention to raster data processing.Tobler theorem tells us: geography everything associated with other things, but closer than far stronger correlation.Spatial correlation characteristics of geological data, the author of this paper, by establishing a spatial index R tree with spatial pattern mining algorithms as the guiding ideology, through the raster scanning method materialized space object space between adjacent relationship, transaction concept, thus the space with a pattern mining into the traditional association rules mining, and then take advantage of commonly used association rules to deal with some kind of geological data, to find association rules of interest. Using the simulation program to generate the geological data of the experiment, in the process of experiment, found a way to use R tree indexing can significantly speed up the generating spatial transaction set, at the same time, choose the more classic Apriori algorithm and FP - growth algorithm contrast performance, results show that the FP - growth algorithm is much faster than the Apriori algorithm, analyses the main reasons why the Apriori algorithm to generate a large number of candidate itemsets.In this paper, the main work is as follows: (1) In order to speed up the neighborhood search, choose to establish R tree spatial index, on the basis of summarizing the common scenarios to apply spatial indexing technology and the advantages and disadvantages. (2) Based on the analysis of traditional association rule mining algorithm and spatial association rule mining algorithm on the basis of the model based on event center space with pattern mining algorithm was described, and puts forward with a rule mining algorithm based on raster scanning, the algorithm by scanning for the center with a grid of R - neighborhood affairs set grid, will study data mining into the traditional data mining algorithm. (3) In the process of spatial index R tree insert, in order to prevent insertion to split after the leaf node, leading to a recursive has been split up destroy the one-way traverse, is put forward in the process of looking for insert position that records if full node number is M (M number) for each node up to insert nodes, first to divide to avoid after layers of recursive splitting up, speed up the R tree insertion efficiency. (4) On the basis of spatial transaction set preprocessing, realize the Apriori algorithm and FP-growth algorithm two kinds of classic association rule mining algorithm, performance contrast analysis.