MPC_Cube

可以创建并获取IIS站点，以及常用的iis操作

一个小的钢琴模拟程序，用vb开发，支持MIDI端口(minipiano)

说明：  求解带时间窗的纯电动车的路径规划问题，采用节约算法求解(Solve the path planning problem of pure electric vehicles with time windows, using a conservation algorithm)

说明：  01背包样例，帮助初学者搞懂样板案例，提升学习算法兴趣(01 knapsack sample, help beginners understand the sample case, enhance interest in learning algorithm)

说明：  牛顿法计算潮流，利用牛拉法基于MATLAB计算电力系统稳态潮流(Newton method is used to calculate power flow, and Newton method is used to calculate steady power flow of power system based on MATLAB)

一个基于MFC操作excel表格的应用程序。根据需求生成一张刻度与数据对应的excel表(MFC-based operation excel spreadsheet application. Excel sheet with a scale corresponding to the data generated on demand)

使用替换wsock32.dll文件来跟踪windows socket api函数的程序- The use replaces the wsock32.dll document to track windows socket the api function the procedure

format DataDrivenTest data (calendar.res) not initialized.

AnimateWindow使用方法源码,例程程序结合易语言系统核心支持库，通过调用API函数，实现了窗口从侧面移出的功能。(AnimateWindow use source code, routine procedures core system combines easy language support library , by calling the API function , realized the side out of the window function.)

tf–idf, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.[1]:8 It is often used as a weighting factor in information retrieval and text mining. The tf-idf value increases proportionally to the number of times a word appears in the document, but is offset by the frequency of the word in the corpus, which helps to control for the fact that some words are generally more common than others. Variations of the tf–idf weighting scheme are often used by search engines as a central tool in scoring and ranking a document s relevance given a user query. tf–idf can be successfully used for stop-words filtering in various subject fields including text summarization and classification. One of the simplest ranking functions is computed by summing the tf–idf for each query term many more sophisticated ranking functions are variants of this simple model.