wzreedimensional-FDTD

一个833分酒问题的求解,C++编写,简单易读,输出最佳路径解,()

《柔性字符串匹配》的英文版，是目前讲解字符串匹配算法的参考资料中比较全面和前沿的，很有参考价值和收藏价值(" Soft string matching" in English, is the string-matching algorithm on the reference in a more comprehensive and cutting-edge, a good reference value and value to the collection)

追敢法,主要是n阶线形方程组Ax b的列主元高斯消去法的一个特殊情况,为上三角矩阵,通用程序如附件所示()

标准BP神经网络算法程序：动量BP算法程序：％ 调用 TRAINGDX 算法训练 BP 网络 粒子群优化神经网络源程序()

This is certification dumbs for db2 version 9.

PL/SQL Developer V8.0.4.1514 注册机版(PL/SQL Developer V8.0.4.1514 RI Edition)

oracle XML 文档 oracle XML 文档(oracle oracle XML )

此代码是针对数据库教材的源代码，包括求“输入的两个数的最大公约数和最小公倍数”、“输入工资输出工资水平”。程序在Access上得到了验证，也可以移植到其他数据库上。(This code is aimed at teaching book of source code, including database for "input the two Numbers when the largest and smallest LCD", "input output wages." wages Program in the Access is verified, also can be transplanted into other database.)

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：  Spark 机器学习代码，有大量机器学习算法实例。(Spark machine learning code, there are a large number of machine learning algorithm examples.)