winmine这是程序是用PyQt4实现的,完全模拟windows扫雷游戏。 为了使没有安装python和PyQt4的人能够直接运行游戏,我已经用PyInstaller生成了winmine.exe。 如果已经安装python和PyQt4,那么直接双击winmine.pyw就可以运行程序了。
2021-06-05 18:58:08 6.4MB Python PyQt winmine mine
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扫雷游戏设计 扫雷游戏针对对象设计的原始码和文档,2015年本科毕业设计毕业论文。 文件夹组织 src文件夹为该游戏的源代码文件; 发布文件夹为该游戏的打包文件,可直接点击运行游戏; 资产文件夹里包含毕业论文,详细针对对象设计(UML)和相关参考文献; 课程设计包含的知识点 Java面向对象编程; 软件工程面向对象分析,面向对象设计,软件测试; 数据结构中递归算法设计的思想。 游戏运行 安装java虚拟机的系统可以直接运行发布文件夹下的Game.jar程序。
2021-05-10 16:43:56 5.4MB 系统开源
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nuxt-web-desgin 构建设置 # install dependencies $ yarn install # serve with hot reload at localhost:3000 $ yarn dev # build for production and launch server $ yarn build $ yarn start # generate static project $ yarn generate 有关工作原理的详细说明,请查看 。
2021-04-03 17:08:14 5.75MB HTML
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Decision trees are particularly promising in symbolic representation and reasoning due to their comprehensible nature, which resembles the hierarchical process of human decision making. However, their drawbacks, caused by the single-tree structure, cannot be ignored. A rigid decision path may cause the majority class to overwhelm other class when dealing with imbalanced data sets, and pruning removes not only superfluous nodes, but also subtrees. The proposed learning algorithm, flexible hybrid decision forest (FHDF), mines information implicated in each instance to form logical rules on the basis of a chain rule of local mutual information, then forms different decision tree structures and decision forests later. The most credible decision path from the decision forest can be selected to make a prediction. Furthermore, functional dependencies (FDs), which are extracted from the whole data set based on association rule analysis, perform embeddedattribute selection to remove nodes rather than subtrees, thus helping to achieve different levels of knowledge representation and improve model comprehension in the framework of semi-supervised learning. Naive Bayes replaces the leaf nodes at the bottom of the tree hierarchy, where the conditional independence assumption may hold. This techniquereduces the potential for overfitting and overtraining and improves the prediction quality and generalization. Experimental results on UCI data sets demonstrate the efficacy of the proposed approach.
2021-03-28 17:07:16 269KB decision forest; naive Bayes;
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Java实现扫雷小游戏源代码,直接拿走运行
2021-03-16 18:08:55 89KB java