C#斗地主

这是一个利用指针返回多个函数值的c++源码，可以直接运行(This is an advantage of more than one function returns a pointer value c++ source code can be directly run)

说明：  第一步：将该目录所有文件覆盖到石器客户端目录； 第二步：将授权文件license.yz放置到文件夹 saplus； 第三步：打开目录saplussaplus.ini 修改 assa根目录= （如情缘：assa根目录=assa； 第四步：打开 64位系统请导入我后再操作.reg 并点击 确定； 第五步：重启电脑； 第六步：打开客户端自带的ASSA即可（如有错误请参考第三步assa根目录名称是否有错）；(Step 1: overwrite all files in the directory to the stone tool client directory Step 2: place license.yz in the folder saplus Step 3: open the directory saplus saplus.ini and modify the Assa root directory = (for example, romance: Assa root = Assa) Step 4: open 64 bit system, please import me and then operate. REG and click OK Step 5: restart the computer Step 6: open the Assa provided by the client (if there is any error, please refer to step 3 whether the root directory name of Assa is wrong))

采用首次适应法、最佳适应法或最差适应法，编写一内存分配和回收模拟程序。 动态地随机产生新的“内存分配”或“内存回收”请求，再按照你选定的分配算法修改这个数组。由于这个实验的重点在于内存分配，所以不考虑与某内存区相关的进程情况。(used first to adapt, adapt best or the worst adaptation law, the preparation of a memory allocation and recovery simulation program. Dynamic random to create a new "memory allocation" or "memory recovery" request, then you have chosen in accordance with the allocation algorithm to change the array. As the focus of this experiment is memory allocation, it is not considered relevant to a certain memory area of the process.)

STM32 的DALI库,keil uversion5 , 标准的DALI协议，从机模式，符合IEC101，102，207协议

Basic Analysis and Security Engine (BASE): BASE

通过MFC实现上位机与下位机的串口通讯，可自配置。(Through MFC to achieve the host computer and the next bit machine serial communication, self configuration.)

C#调用ｔｃｌ核的DLL库，可以在C3中直接调用TCL，非常好用。-called C# DLLs tcl nuclear arsenals, C3 can directly call TCL, very handy.

基于arcobjects与才c#.net的GIS应用开发书上的第四单元源码(Based on the fourth unit source arcobjects and only c# .net of GIS application development book)

说明：  AR程序设计5，扫描图片可以出现三维模型，并操作(AR programming 5, scanning pictures can appear three-dimensional model, and operation)

利用聚类分析法改进的多Agent协作强化学习方法(Using cluster analysis to improve collaborative multi-Agent Reinforcement Learning Method)