TFIND searches for one or more strings (boolean AND) in a text file. TFIND reports all lines where the string(s) were found (or NOT found by option). The search can be limited to a field in a fixed field (i.e. column oriented) list. An extended search mode is available, where only letters and digits are relevant. Other options: case sensitive search, alternative errorlevel with number of hits, header line with file name, LFN, custom prefix
標簽： TFIND searches boolean reports
上傳時間： 2016-01-24
上傳用戶：lindor
Introduction A shared library is a collection of functions that are available for use by one or more applications running on a system. On Windows operating systems, the library is compiled into a dynamic link library (.dll) file. At run-time, the library is loaded into memory and made accessible to all applications.
標簽： Introduction collection available functions
上傳時間： 2014-01-26
上傳用戶：2467478207
An object based tree widget, emulating the one found in microsoft windows, | | with persistence using cookies. Works in IE 5+, Mozilla and konqueror 3.
標簽： persistence emulating microsoft windows
上傳時間： 2016-01-30
上傳用戶：qiao8960
step by step移植LCD驅動，這是一個很好的說明文檔，適用于初學者
上傳時間： 2013-12-23
上傳用戶：hullow
JavaBeans is one of the most important developments in Java™ since its inception. It is Java s component architecture, which allows components built with Java to be used in graphical programming environments.
標簽： Java developments JavaBeans important
上傳時間： 2016-02-01
上傳用戶：從此走出陰霾
web chat , which is one of the famous module of openfire. deploy on the web.
上傳時間： 2014-01-15
上傳用戶：二驅蚊器
broad cast could send the message to all the user from the im server, which is one module of the openfire,
上傳時間： 2014-01-21
上傳用戶：英雄
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
NOIS2 step by step for Linux
上傳時間： 2014-11-22
上傳用戶：comua
one scaner soure code
上傳時間： 2014-01-08
上傳用戶：waizhang
