ssential C# 7.0 is a well-organized, no-fluff guide to C# 7.0 for programmers at all levels of experience. Reflecting the most important C# features from 3.0 through 7.0 and including modern programming patterns, it will help you write code that’s simple, powerful, robust, secure, and maintainable. Author Mark Michaelis is a world-class C# expert: a long-time Microsoft MVP and Regional Director who also has served on Microsoft’s C# design review team. He presents a comprehensive tutorial and reference for the entire language, including expert coverage of key C# 7.0 enhancements, C# 7.0’s use with .NET Core/.NET Standard, and cross-platform compilation. He illustrates key C# constructs with succinct examples, and presents best-practice coding guidelines. To help you maintain existing code, separate indexes provide version-specific answers for C# 5.0, 6.0, and 7.0, and visual icons show when each language innovation was introduced. Make the most of C# 7.0 enhancements, including tuples, deconstructors, pattern matching, local functions, and ref returns Work efficiently with C# data types, operators, control flow, methods, and parameters Write more robust code with C# object-oriented constructs Implement reliable, effective exception handling Reduce code complexity with generics, delegates, lambda expressions, and events Leverage advanced dynamic and declarative programming techniques Query diverse data collections using LINQ with query expressions Create custom collections that operate against business objects Access .NET collections via collection interfaces and standard query operators Master multithreading and synchronization, including the async/await paradigm Optimize performance and interoperability with P/Invoke and unsafe code Run your code on Linux or macOS with C# 7.0 cross-platform compilation Includes C# 7.1, 7.2, and 7.3 language enhancements This guide offers you a complete foundation for successful development with modern versions of the C# language in any project or environment. Table of Contents Chapter 1 Introducing C# Chapter 2 Data Types Chapter 3 More with Data Types Chapter 4 Operators and Control Flow Chapter 5 Methods and Parameters Chapter 6 Classes Chapter 7 Inheritance Chapter 8 Interfaces Chapter 9 Value Types Chapter 10 Well-Formed Types Chapter 11 Exception Handling Chapter 12 Generics Chapter 13 Delegates and Lambda Expressions Chapter 14 Events

粗糙集工具rosetta,有效处理数据挖掘，数据分割等问题

论文研究-企业财务危机预警Rough-ANN模型的建立及其应用.pdf,  基于Rough集理论和ANN(人工神经网络)方法建立了企业财务危机预警的Rough-ANN模型,对企业财务危机指标的约简、财务危机知识的神经网络动态学习与推理、财务危机的预测和评定等功能进行了集成,并就企业财务危机预警问题进行了实证分析.其结果表明,该模型有效、可行,为企业财务危机的动态预警提供了一条新的途径.

这是一个基于粗糙集的分类程序，目前在网络中为数不多用Matlab编写的，希望对大家有用

一个很好的Rough set软件，可以进行属性约简，属性值约简，规则提起，离散化等处理。还带有很多UCI数据集。

rough集的提出在不确定决策领域起到了重大作用，其在各个领域将有重大突破性发展。

粗糙集属性约简代码python

RSES实现了粗糙集理论的好软件，它有效的解决了具有不完整和不确定性的问题

神经控制微分方程用于不规则时间序列[ ， ] 基于对受控微分方程的深入理解的数学理论，我们演示了如何构建以下模型： 直接作用于不规则采样的部分观测的多元时间序列。 可能会进行记忆有效的伴随反向传播训练-即使是跨观测也是如此。 展示最先进的性能。 使用现有工具（尤其是PyTorch和库）可以轻松实现和评估它们。 图书馆 参见 。 例子 我们鼓励您看一下 ，它演示了如何使用该库来训练Neural CDE模型来预测螺旋的手征性。 有关如何处理可变长度输入，不规则采样或丢失数据的演示，另请参见 ，所有这些都可以在不更改模型的情况下轻松处理。 一个自包含的简短示例： import torch import torchcde # Create some data batch , length , input_channels = 1 , 10 , 2 hidden_channels = 3 t

用rough sets来做属性约简 用matlab编写 多个程序 物有所值