*--- --- --- --聲明--- --- --- -----*/ /* VC6.0下運行通過 此程序為本人苦心所做，請您在閱讀的時候，尊重本人的 勞動。可以修改，但當(dāng)做的每一處矯正或改進時，請將改進 方案，及修改部分發(fā)給本人 (修改部分請注名明：修改字樣) Email: jink2005@sina.com QQ: 272576320 ——初稿完成：06-5-27 jink2005 補充： 程序存在問題： (1) follow集不能處理：U->xVyVz的情況 (2) 因本人偷懶，本程序為加入文法判斷，故 輸入的文法必須為LL(1)文法 (3) 您可以幫忙擴充：消除左遞歸，提取公因子等函數(shù) (4) …… */ /*-----------------------------------------------*/ /*參考書《計算機編譯原理——編譯程序構(gòu)造實踐》 LL(1)語法分析,例1: ERTWF# +*()i# 文法G[E]:(按此格式輸入) 1 E -> TR 2 R -> +TR 3 R -> 4 T -> FW 5 W -> * FW 6 W -> 7 F -> (E) 8 F -> i 分析例句：i*(i)# , i+i# 例2： 編譯書5.6例題1 SHMA# adbe# S->aH H->aMd H->d M->Ab M-> A->aM A->e 分析例句：aaabd# */
上傳時間： 2016-02-08
上傳用戶：ayfeixiao
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
上傳時間： 2016-04-07
上傳用戶：lindor
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(biāo)簽： reversible algorithm the nstrates
上傳時間： 2014-01-08
上傳用戶：cuibaigao
The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar -xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "demo_MC" for the demo.
標(biāo)簽： algorithms problems Several trivial
上傳時間： 2014-01-20
上傳用戶：royzhangsz
看n2實例 #Create a simulator object set ns [new Simulator] #Define different colors for data flows #$ns color 1 Blue #$ns color 2 Red #Open the nam trace file set nf [open out-1.nam w] $ns namtrace-all $nf set f0 [open out0.tr w] set f1 [open out1.tr w] #Define a finish procedure proc finish {} { global ns nf $ns flush-trace #Close the trace file close $nf #Execute nam on the trace file exit 0 } #Create four nodes set n0 [$ns node] set n1 [$ns node] set n2 [$ns node] set n3 [$ns node] #Create links between the nodes $ns duplex-link $n0 $n2 1Mb 10ms
標(biāo)簽： simulator Simulator different Create
上傳時間： 2016-07-02
上傳用戶：wfl_yy
SoftTimer.h 利用定時器T2模擬多個軟件定時器 特點： 只占用一個硬件定時器T2，就可以擴展出多達(dá)數(shù)十個以上的軟件定時器 軟件定時器基本定時單位是10ms，定時范圍很寬：0.01～163.84s 軟件定時器數(shù)量可以按需要設(shè)定，每增加一個，只多消耗2個字節(jié)的RAM空間 利用定時器T2的自動重裝特性，能夠?qū)崿F(xiàn)所有軟件定時器的精確定時 所有軟件定時器都工作在14位倒計時方式，用TR和TF位控制，使用極為方便 第0號定時器專門用于Delay()函數(shù)，其它定時器可供用戶程序自由使用
標(biāo)簽： SoftTimer 定時器 模擬 軟件定時器
上傳時間： 2014-01-16
上傳用戶：黑漆漆
linux,Unix,shell編程詳細(xì)說明。本書內(nèi)容全面，包括了awk, sed, tr等常用工具的用法，對于shell初學(xué)者是不可多得的好書。
上傳時間： 2016-09-04
上傳用戶：cccole0605
linux,shell編程。講解詳細(xì)，包括awk,sed,tr等工具的使用。對于初學(xué)者而言，非常有幫助。
上傳時間： 2013-12-17
上傳用戶：www240697738
linux,shell編程。講解詳細(xì)，包括awk,sed,tr等工具的使用。對于初學(xué)者而言，非常有幫助。
上傳時間： 2014-01-01
上傳用戶：小碼農(nóng)lz
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