Python

elastic search 的python测试运行(Python test run of elastic search)

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：  对中海庭给的高精地图数据进行清洗，简易代码(data processing of HD map data)

利用随机森林算法实现最佳特征选择，当面对众多特征值如何选择问题 该算法可以有效实现，选择最佳特征集，帮助后面分类算法提供更加特征集

LSTM入门学习，正弦波序列预测         for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):             h_t, c_t = self.lstm1(input_t, (h_t, c_t))             h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))             output = self.linear(h_t2)  # output.shape:[batch,1]             outputs = [output]  # outputs.shape:[[batch,1],...[batch,1]], list composed of n [batch,1],         for i in range(future):  # if we should predict the future             h_t, c_t = self.lstm1(output, (h_t, c_t))             h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))             output = self.linear(h_t2)  # output.shape:[batch,1]             outputs = [output]  # outputs.shape:[[batch,1],...[batch,1]], list composed of n [batch,1],         outputs = torch.stack(outputs, 1).squeeze(2)  # shape after stack:[batch, n, 1], shape after squeeze: [batch,n]         return outputs

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)

说明：  深度学习处理图片，适合学习仔细研究等等等等(Python Programming introductory books, suitable for beginners to learn, recommended)

计算多材料单元平均应力应变,用于提取计算等效弹性模量 泊松系数(Calculate the average stress-strain of multi-material elements)

DLIB FOR PYTHON Development

OpenCV是一个基于BSD许可（开源）发行的跨平台计算机视觉库，可以运行在Linux、Windows、Android和Mac OS操作系统上。(OpenCV is a cross-platform computer vision library based on BSD license (open source), which can run on Linux, Windows, Android and MacOS operating systems.)