GetSkeletonCurve2

一个基于VC++源码编写的磁盘信息查看程序，界面相当友好，其实界面已经超越了友好，是专业，软件功能更像是一个磁盘管理软件，可动态加载各个硬盘的使用情况，包括可移动磁盘，单击任意磁盘可进入详细的信息检测及显示页，并可以对系统的磁盘信息进行监测，相当专业。

说明：  粒子群算法结合遗传算法的机器人路径规划算法(Particle swarm optimization)

说明：  SYSTEM C from the ground up 2nd

documents

Debug增强版 Aogosoft.com网友怜香为Debug扩充了新功能的程序，就是这个啦,我Debug用得不多，不敢乱说，大家自己试吧。-Debug enhance version. One Aogosoft.com employee expand functionality for Debug. Try it!

Windows下的小游戏程序,运行以后就在屏幕上走来走去,很可爱哦.-Windows small game, the running of the future on the screen around, a very lovely oh.

bool AABB::intersects(const AABB &p_aabb) const { if (position.x >= (p_aabb.position.x + p_aabb.size.x)) return false;

说明：  介绍各种目标检测侧与跟踪算法，分析其主要思想和主要适用场景(Introduce various target detection side and tracking algorithms, analyze their main ideas and main applicable scenarios.)

告诉你如何构建更强劲的应用而不用乏味的手写代码；回答多数开发者甚至不会问的问题；演示如何使用JSF和Tiles自动构建一致的用户界面；提供提示及“如何...”信息，使你很快上手；解释如何集成JSF和数据库，使用目录服务，无线应用以及Web 服务；传授最好的经验和好习惯如使用样式表和消息绑定；覆盖全部JSF标记和如何创建新的标记库，英文，原名称：Core JavaServer Faces-tell you how to build a more robust application instead of the dull handwritten code; Answered most developers do not even ask the question; Demonstrate how to use the JSF and Tiles automatically build a consistent user interface; For the presentation and "How to ..." message and you will soon get started; As explained He JSF and database integration, the use of directory services, wireless applications and Web services; the best teaching experience and good habits such as the use of style sheets and news binding; coverage for all JSF tags and how to create a new library markings, English, the original name : Core JavaServer Faces

K-means算法是基于划分的思想，因此算法易于理解且实现方法简单易行，但需要人工选择初始的聚类数目即算法是带参数的。类的数目确定往往非常复杂和具有不确定性，因此需要专业的知识和行业经验才能较好的确定。而且因为初始聚类中心的选择是随机的，因此会造成部分初始聚类中心相似或者处于数据边缘，造成算法的迭代次数明显增加，甚至会因为个别数据而造成聚类失败的现象。(K-means algorithm is based on the idea of partitioning, so the algorithm is easy to understand and the implementation method is simple and feasible, but it requires manual selection of the initial number of clusters, that is, the algorithm is with parameters. The number of classes is often very complex and uncertain, so professional knowledge and industry experience are needed to better determine. Moreover, because the selection of initial clustering centers is random, some initial clustering centers will be similar or at the edge of data, resulting in a significant increase in the number of iterations of the algorithm, and even the phenomenon of clustering failure due to individual data.)