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.
標簽： introductory HelloWorld containing Directory
上傳時間： 2016-03-29
上傳用戶：exxxds
學生基本注冊信息管理系統,（1）用戶名與密碼的輸入，再進行登陸驗證，連續登錄三次不成功，退出。 （2）建立一個學生基本注冊信息的鏈表。 （3）選擇要進行的操作：a、注冊，建立一個鏈表的新結點，并以 新結點作為鏈表的表頭 b、查詢，分為按學號、姓名、性別、年齡、專業、班級和年級查詢 ，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，為了保護鏈表的表頭，用一個結構類似堆棧的指針實現，此處排除了刪除棧頂元素的特殊情況 e、排序，用起泡排序算法實現將學號從小到大排序 f、退出
上傳時間： 2016-03-30
上傳用戶：ecooo
在ARM LPC21系列處理器上，令LED數碼管顯示0-F字符，同時控制LED1、LED2、LED3、LED4顯示對應的16進行值的程序
上傳時間： 2013-12-15
上傳用戶：zhengzg
圖的深度遍歷，輸出結果為（紅色為鍵盤輸入的數據，權值都置為1）： 輸入頂點數和弧數:8 9 輸入8個頂點. 輸入頂點0:a 輸入頂點1:b 輸入頂點2:c 輸入頂點3:d 輸入頂點4:e 輸入頂點5:f 輸入頂點6:g 輸入頂點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 深度優先遍歷: a b d h e c f g 程序結束.
標簽：
上傳時間： 2016-04-04
上傳用戶：lht618
在win2000sp4 + VM6基本穩定。 原理不多說了，自己看代碼吧，我也早就發過了驅動的代碼了，現在的就是一個完整的應用。希望能夠對大家有一點幫助，但是不要用在不該用的場所。 使用方法將： dd1壓縮包里面是驅動源碼 console壓縮包里面是控制臺源碼 hide.exe是最終產品 使用方法： 1、將hide.exe復制到系統目錄 2、運行cmd 3、hide -h 查看幫助 hide -i 安裝驅動 hide -u 卸載驅動 hide -f -a filename 添加一個隱藏文件
上傳時間： 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.
標簽： demonstrates sequential Selection Bayesian
上傳時間： 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.
標簽： code implementing directory algorithm
上傳時間： 2016-04-07
上傳用戶：shawvi
華恒科技 HHCF5249-R3 技術手冊 第一章 產品簡介 第二章 軟件系統 第三章 硬件系統 第四章 機械特性 第五章 底板的硬件設計 第六章 售后服務及技術支持 附錄 附錄A 初始化 附錄B LINUX 常見術語 附錄C 常用LINUX 命令 附錄D GCC 與GDB 附錄E MAKEFILE 附錄F UCLINUX 系統分析 uClinux 簡介 uClinux 小型化的做法 uClinux 的開發環境 uClinux 的內存管理 工具及內核 附錄G 圖形界面（GUI）接口函數API 附錄H 參考資料
上傳時間： 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.
標簽： sequential reversible algorithm nstrates
上傳時間： 2014-01-18
上傳用戶：康郎
This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters.
標簽： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
