A* sudo sudo/* B* adduser script adduser C* rmuser script rmuser E* tout tout/* F* dumdum dumdum G* lostfile lostfile H* Mkfl.localsys Makefile.localsys I* spacegripe spacegripe J* sendmail.cf sendmail.cf N* remote remote.c O* distributed conrol distrib/* P* hosts and name server makerevhosts Q* xargs xargs/*
標(biāo)簽： adduser script rmuser sudo
上傳時(shí)間： 2016-03-29
上傳用戶：gxrui1991
Intro/: Directory containing introductory examples. HelloWorld.c A simple program that draws a box and writes "Hello World" in HelloWorld.f it. data The data file for the introductory progressive example. Lines.c Reads the data from file "data" and plots just the curve with Lines.f no labels, viewport or anything indicating quantity or units. Viewport.c Restricts the graph to a viewport and frames the viewport, Viewport.f leaving the remainder of the area for labels, etc. CharLbls.c Adds labels for the chart title, X-axis title, and Y-axis CharLbls.f title. Tics.c Adds tic marks to the viewport edges, but since clipping was Tics.f not set correctly, tics extend outside the viewport. Clip.c Sets clipping such that tic marks are clipped at the viewport Clip.f boundaries. TicLabels.c Adds numeric tic labels to the graph this is the final TicLabels.f installment of the progressive example.
標(biāo)簽： introductory HelloWorld containing Directory
上傳時(shí)間： 2016-03-29
上傳用戶：exxxds
學(xué)生基本注冊(cè)信息管理系統(tǒng),（1）用戶名與密碼的輸入，再進(jìn)行登陸驗(yàn)證，連續(xù)登錄三次不成功，退出。 （2）建立一個(gè)學(xué)生基本注冊(cè)信息的鏈表。 （3）選擇要進(jìn)行的操作：a、注冊(cè)，建立一個(gè)鏈表的新結(jié)點(diǎn)，并以 新結(jié)點(diǎn)作為鏈表的表頭 b、查詢，分為按學(xué)號(hào)、姓名、性別、年齡、專業(yè)、班級(jí)和年級(jí)查詢 ，并顯示所查到的信息 c、修改，用新的信息替換以前的信息 d、刪除，為了保護(hù)鏈表的表頭，用一個(gè)結(jié)構(gòu)類似堆棧的指針實(shí)現(xiàn)，此處排除了刪除棧頂元素的特殊情況 e、排序，用起泡排序算法實(shí)現(xiàn)將學(xué)號(hào)從小到大排序 f、退出
標(biāo)簽： 信息管理系統(tǒng) 用戶 密碼 輸入
上傳時(shí)間： 2016-03-30
上傳用戶：ecooo
在ARM LPC21系列處理器上，令LED數(shù)碼管顯示0-F字符，同時(shí)控制LED1、LED2、LED3、LED4顯示對(duì)應(yīng)的16進(jìn)行值的程序
上傳時(shí)間： 2013-12-15
上傳用戶：zhengzg
圖的深度遍歷，輸出結(jié)果為（紅色為鍵盤輸入的數(shù)據(jù)，權(quán)值都置為1）： 輸入頂點(diǎn)數(shù)和弧數(shù):8 9 輸入8個(gè)頂點(diǎn). 輸入頂點(diǎn)0:a 輸入頂點(diǎn)1:b 輸入頂點(diǎn)2:c 輸入頂點(diǎn)3:d 輸入頂點(diǎn)4:e 輸入頂點(diǎn)5:f 輸入頂點(diǎn)6:g 輸入頂點(diǎn)7:h 輸入9條弧. 輸入弧0:a b 1 輸入弧1:b d 1 輸入弧2:b e 1 輸入弧3:d h 1 輸入弧4:e h 1 輸入弧5:a c 1 輸入弧6:c f 1 輸入弧7:c g 1 輸入弧8:f g 1 深度優(yōu)先遍歷: a b d h e c f g 程序結(jié)束.
標(biāo)簽：
上傳時(shí)間： 2016-04-04
上傳用戶：lht618
在win2000sp4 + VM6基本穩(wěn)定。 原理不多說了，自己看代碼吧，我也早就發(fā)過了驅(qū)動(dòng)的代碼了，現(xiàn)在的就是一個(gè)完整的應(yīng)用。希望能夠?qū)Υ蠹矣幸稽c(diǎn)幫助，但是不要用在不該用的場所。 使用方法將： dd1壓縮包里面是驅(qū)動(dòng)源碼 console壓縮包里面是控制臺(tái)源碼 hide.exe是最終產(chǎn)品 使用方法： 1、將hide.exe復(fù)制到系統(tǒng)目錄 2、運(yùn)行cmd 3、hide -h 查看幫助 hide -i 安裝驅(qū)動(dòng) hide -u 卸載驅(qū)動(dòng) hide -f -a filename 添加一個(gè)隱藏文件
上傳時(shí)間： 2013-12-12
上傳用戶：liglechongchong
On-Line MCMC Bayesian Model Selection This demo demonstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽： demonstrates sequential Selection Bayesian
上傳時(shí)間： 2016-04-07
上傳用戶：lindor
k-meansy算法源代碼。This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen.
標(biāo)簽： code implementing directory algorithm
上傳時(shí)間： 2016-04-07
上傳用戶：shawvi
華恒科技 HHCF5249-R3 技術(shù)手冊(cè) 第一章 產(chǎn)品簡介 第二章 軟件系統(tǒng) 第三章 硬件系統(tǒng) 第四章 機(jī)械特性 第五章 底板的硬件設(shè)計(jì) 第六章 售后服務(wù)及技術(shù)支持 附錄 附錄A 初始化 附錄B LINUX 常見術(shù)語 附錄C 常用LINUX 命令 附錄D GCC 與GDB 附錄E MAKEFILE 附錄F UCLINUX 系統(tǒng)分析 uClinux 簡介 uClinux 小型化的做法 uClinux 的開發(fā)環(huán)境 uClinux 的內(nèi)存管理 工具及內(nèi)核 附錄G 圖形界面（GUI）接口函數(shù)API 附錄H 參考資料
標(biāo)簽： HHCF 5249 華恒科技 產(chǎn)品簡介
上傳時(shí)間： 2013-12-24
上傳用戶：a6697238
This demo nstrates how to use the sequential Monte Carlo algorithm with reversible jump MCMC steps to perform model selection in neural networks. We treat both the model dimension (number of neurons) and model parameters as unknowns. The derivation and details are presented in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Sequential Bayesian Estimation and Model Selection Applied to Neural Networks . Technical report CUED/F-INFENG/TR 341, Cambridge University Department of Engineering, June 1999. After downloading the file, type "tar -xf version2.tar" to uncompress it. This creates the directory version2 containing the required m files. Go to this directory, load matlab5 and type "smcdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標(biāo)簽： sequential reversible algorithm nstrates
上傳時(shí)間： 2014-01-18
上傳用戶：康郎
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