java + mysql Ztree 点击树前面+号，就加载当前节点下的第一级子节点

目录如下: 1、系统分析概念(Basic concepts of System Analysis)； 2、系统分析方法介绍(Overview of Inductive Methods) 3、故障树分析基本概念（Fault Tree Analysis-Basic Concepts） 4、故障树基本元素(The Basic Elements of a Fault Tree) 5、建立故障树基础（Fault Tree Construction Fundamentals） 6、概率定理-事件的数学描述(Probability Theory-The Mathematical Description of Events) 7、布尔代数及故障树分析应用(Boolean Algebra and Application to Fault Tree Analysis) 8、压力舱示例(The Pressure Tank Example) 9、三速电动机示例（The Three Motor Example） 10、概率与统计分析(Probabilistic and Statistical Analyses) 11、故障树评价技术(Fault Tree Evaluation Techniques) 12、故障树评价计算代码(Fault Tree Evaluation Computer Codes)

主要介绍了element-ui tree结构实现增删改自定义功能代码，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

Dan Gusfield的字符串处理的经典书籍Algorithms on Strings, Trees and Sequences，完整版，550页。由djvu格式转换过来的，比较清楚，但是不能搜索和复制文字。

C# COMBOX 树下拉,多选下拉等功能

HTML5树状图 使用带有纯JavaScriptHTML5的树形图的图形化绘制，没有任何其他依赖项。 专为提高速度和效率而设计。 现场示例可以在这里看到：

看了一下有好多朋友需要USB Tree View 源码，上一个博客工程只给了编译好的dll，还有通过AutoIt 调用的源码，好多朋友可能不熟悉autoit，今天把USB Tree View 修改版源码也分享给大家，希望能给USB开发的朋友带来帮助。 该版本是命令行输出形式，删除了原版本的GUI界面，需要修改为GUI界面的朋友可以自行修改。我的资源如果对你有帮助的话希望能支持点赞，谢谢！

## A C++11 implementation of the B-Tree part of "The Case for Learned Index Structures" A research **proof of concept** that implements the B-Tree section of [The Case for Learned Index Structures](https://arxiv.org/pdf/1712.01208.pdf) paper in C++. The general design is to have a single lookup structure that you can parameterize with a KeyType and a ValueType, and an overflow list that keeps new inserts until you retrain. There is a value in the constructor of the RMI that triggers a retrain when the overflow array reaches a certain size. The basic API: ```c++ // [first/second]StageParams are network parameters int maxAllowedError = 256; int maxBufferBeforeRetrain = 10001; auto modelIndex = RecursiveModelIndex recursiveModelIndex(firstStageParams, secondStageParams, maxAllowedError, maxBufferBeforeRetrain); for (int ii = 0; ii < 10000; ++ii) { modelIndex.insert(ii, ii * 2); } // Since we still have one more insert before retraining, retrain before searching... modelIndex.train(); auto result = modelIndex.find(5); if (result) { std::cout << "Yay! We got: " << result.get().first << ", " << result.get().second << std::endl; } else { std::cout << "Value not found." << std::endl; // This shouldn't happen in the above usage... } ``` See [src/main.cpp](src/main.cpp) for a usage example where it stores scaled log normal data. ### Dependencies - [nn_cpp](https://github.com/bcaine/nn_cpp) - Eigen based minimalistic C++ Neural Network library - [cpp-btree](https://code.google.com/archive/p/cpp-btree/) - A fast C++ implementation of a B+ Tree ### TODO: - Lots of code cleanup - Profiling of where the slowdowns are. On small tests, the cpp_btree lib beats it by 10-100x - Eigen::TensorFixed in nn_cpp would definitel

Chucky: A Succinct Cuckoo Filter for LSM-Tree Niv Dayan, Moshe Twitto Pliops

zTree移动端实验,移动端使用tree