pyiwn-1.0.tar

MainApp启动器 main(): 创建主界面类对象 显示界面 ------------------- PictureMainFrame主界面 init()界面初始化操作: 指定游戏窗体大小 指定游戏标题 指定游戏窗口的显示位置 指定游戏窗口大小为固定大小 addCompoment()添加组件方法： 完成按钮区的组件设置 完成游戏状态去的组件设置 addPicturePreview()添加拼图游戏预览与拼图界面: 完成右侧的 预览图片的设置 完成坐标的 拼图图片的设置(MainApp starter Main (): Create the main interface class object Display interface ----------------------------- PictureMainFrame Home Interface Init () interface initialization operation: Specify Game Form Size Specify the title of the game Specify the display position of the game window Specify the size of the game window to be fixed AddCompoment () Add Component Method: Complete the component settings in the button area Component settings to complete game status Add PicturePreview () Add a jigsaw preview and jigsaw interface: Complete the settings for the preview image on the right Complete the setting of coordinate mosaic pictures)

Fuzzy Systems. Introduction.

说明：  强化学习网页浏览示例，非常好的示例代码。强化学习快速入门(Reinforcement learning web browsing example, very good example code)

刘金琨老师的《滑模变结构控制MATLAB仿真 》(The content of this folder is matlab programming engineering)

基于python和opencv的车牌识别,包括车牌提取和字符识别 1,plate.py为提取和识别图片里面的车牌号码 2,切割和识别第一步车牌图片里面的字符,提取特征(License plate recognition based on Python and opencv, including license plate extraction and character recognition 1. Plate.py is used to extract and identify the license plate number in the picture. 2. Cutting and recognizing the characters in the first step license plate image and extracting features)

说明：  一款效果炫酷的html5和css3图片3d翻转多米诺动画特效插件。(A cool effect of HTML 5 and CSS3 images 3D flip domino animation special effects plug-in.)

说明：  This is a sample project that I am working on

模拟地震合成记录，仅供信号分析课程参考。程序执行完毕后，生成两个xls文件，Ricker.xls中是雷克子波，Seis.xls中是地震合成记录。(Simulated seismic synthesis record, reference only to the signal analysis course. Program execution is completed, generating two XLS files, Ricker.xls is Ricker wave, Seis.xls is a synthetic seismogram.)

说明：  使用SVM分类器来预测乳腺癌病人的预后(特征选择；分类器构建)，评价模型时使用无被交叉验证，性能评价指标包括准确率，AUC，灵敏度，特异度。学会最基本的机器学习方法。可查看分发给大家的代码，以后遇到类似的问题，可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; classifier construction), and the model was used without cross-validation. Performance evaluation indicators included accuracy, AUC, sensitivity, and specificity. Learn the most basic machine learning methods. You can view the code distributed to everyone, and later encounter similar problems, similar ideas and code can be used.)

delphi中关于控件dbgrideh每列点击后进行排序的方法(About the method of sorting control dbgrideh after clicking on each column)