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python算法教程，数据结构和算法实例在python下的应用范例(tutorial of python algorithms, data structures and algorithms instance application examples in python)

电影推荐系统中运用的推荐算法是基于协同过滤算法（Collaborative Filtering Recommendation）。协同过滤是在信息过滤和信息系统中正迅速成为一项很受欢迎的技术。

用Python实现dijkstra算法。使用python 语言在网络图中实现Dijkstra 算法，返回从源点出 发到每个结点的最短距离、路径，并打印出该路由(源点)的forwarding table,(dijkstra algorithm in Python)

用Python实现各种图算法,包括深度优先，广度优先，Dijkstral，Prim，WelshPowell_Coloring等算法，

c++算法,常用得算法可以使你编程简单方便,放心使用,一个简单得hello程序()

利用一维卷积神经网络将叶子进行分类，里面包含的有数据(Classification of leaves using one dimensional convolution neural network)

boxcox函数的python实现，引用该函数可将偏态分布调整为正态分布(Python implementation of box Cox function)

说明：  从数据库获取车辆在一段时间内的所有行驶记录的相关数据，确定所需数据为GPS经纬度坐标和驾驶时长等，QB模型采用MDF的思想,其基本思想为:通过平均直接翻转距离函数定义两条轨迹之间的距离，两条轨迹需要具有相同的经纬度点数，具有相同点数的轨迹最大的优点是对轨迹距离成对计算，且相同轨迹之间具有更高的分辨率，对于轨迹聚类的结果有一定的优化。(Retrieved from the database cars all over a period of time, record the related data, determine the required data for the GPS latitude and longitude coordinates, and the driving time, QB model by adopting the idea of MDF, its basic idea is: flip directly by the average distance function definition of the distance between two trajectories, two tracks will have the same latitude and longitude points, and has the biggest advantages of the same points of trajectory track distance calculation in pairs, and has higher resolution, between the same trajectory for trajectory clustering results have certain optimization.)

Python是一种面向对象、解释型计算机程序设计语言，其应用领域非常广泛，包括数据分析、自然语言处理、机器学习、科学计算以及推荐系统构建等。 本书用Python语言来讲解算法的分析和设计。本书主要关注经典的算法，但同时会为读者理解基本算法问题和解决问题打下很好的基础。(Python is an object-oriented, interpretive computer programming language. It has a wide range of applications, including data analysis, natural language processing, machine learning, scientific computing and recommendation system construction. This book uses Python language to explain the analysis and design of algorithms. This book focuses on classical algorithms, but at the same time it will lay a good foundation for readers to understand basic algorithms and solve problems. The book consists of 11 chapters. The tree, graph, counting problem, inductive recursion, traversal, decomposition and merging, greedy algorithm, complex dependency, Dijkstra algorithm, matching and cutting problem, difficult problem and its dilution are introduced. The book has exercises and reference materials at the end of each chapter, which provides readers with more convenience for self-examination and further stu)

模拟Photoshop中的磁力套索的算法，主要是先用Opencv做梯度相关处理，然后用Djisktra最短路径搜索找到边缘。(Magnetic Lasso implementation using Python)