群智能算法源代码 HGWO(DE-GWO)

python开发的demo，实现将地理数据从高德坐标转换为wgs84坐标(Conversion of Goethe coordinates to WGS84 coordinates)

工厂模式 这是一个的代码 ，利用起来非常牛逼，得到写的(Factory model ,Figure 2 shows the approach, and you might recognize the abstract factory pattern)

说明：  使用协同过滤的推荐系统测试代码Predicting movie ratings using collaborative filtering and latent concept extractors based on matrix decomposition methods like SVD and CUR on the movielens-100k dataset The project explores techniques for predicting unknown user ratings based on a large dataset of users and movie ratings.(Recommender-SystemsWe used 6 different recommender methods to predict user ratings across the data sets. Namely we set up Collaborative Filtering along with its baseline variant and Matrix decomposition techniques like Singular Value Decomposition and CUR. Moreover, we also implemented dimensionality reduction in these matrix decomposition techniques which preserved 90% of the energy so as to increase the computational efficiency while keeping the accuracy similar for these methods.)

用python所编写的Rtree的实例，实现Rtree的创建，查询，删除(Examples python with written Rtree achieve Rtree create, query, delete)

简单动物识别系统的知识表示与推理 （1）以动物识别系统的产生式规则为例 （2）建造规则库和综合数据库，并能对他们进行增加、删除和修改操作 （3）基于建立的规则库和综合数据库，进行推理(Knowledge representation and reasoning in simple animal recognition system (1) take the production rule of animal identification system as an example. (2) build rule base and integrated database, and add, delete and modify them. (3) reasoning based on the established rule base and integrated database.)

说明：  讲解Python的基本使用方法，包括数据结构，生成器，迭代器和网络编程(Explain the basic usage of Python)

说明：  使用Python语言画出一个简单的五彩心形图案(Draw colorful heart-shaped pattern with Python)

说明：  无监督训练的生成对抗网络程序，python版本，生成有关光照不同的图片(Unsupervised training generated against the network program, python version, to generate different pictures about the light)

在python中实现简单的光线追迹，定义光线，场景等(python for ray tracing)

基于树莓派B+版本的车牌识别设计，辨识部分以树莓派3B+/3B为核心处理器，基于Linux平台，通过python3.5和OpenCV来对含有车牌信息的图片，进行高斯去噪、灰度化、边缘检测、二值化、闭操作、字符分割、字符识别匹配等一系列图像处理操作最终实现车牌识别这个系统。(Based on the design of license plate recognition of raspberry pie B+version, the recognition part takes raspberry pie 3B+/3B as the core processor, and based on Linux platform, uses Python 3.5 and OpenCV to process the image containing license plate information, and carries out a series of image processing operations such as Gaussian denoising, graying, edge detection, binarization, closed operation, character segmentation, character recognition matching, etc. Finally, the system of license plate recognition is realized)