*--- --- --- --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，但當做的每一處矯正或改進時，請將改進 方案，及修改部分發給本人 (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 (4) …… */ /*-----------------------------------------------*/ /*參考書《計算機編譯原理——編譯程序構造實踐》 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
一個基于數據挖掘的圖書智能銷售系統，具有預測功能，同時能對客戶進行數據挖掘分析。 .net平臺，sql2005環境，必須安裝sql205 BI
上傳時間： 2016-03-15
上傳用戶：Thuan
人工智能的一個工具軟件，較為經典，BI常用的推薦工具
上傳時間： 2016-03-30
上傳用戶：四只眼
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
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
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.
標簽： algorithms problems Several trivial
上傳時間： 2014-01-20
上傳用戶：royzhangsz
復數計算器：1、設計的任務要求 (1) 所設計的復數計算器可以進行+、-、*、+=、-=、*=、++、--、>=、<=、==、!=運算符，其中，>=、<=是針對復數的模進行計算。 (2) 設計輸入重載函數，要求能接收從鍵盤輸入a+bi形式的復數，在程序中可以識別出實部虛部并正確賦值。 (3) 設計計算器測試程序，對加減法進行測試，要求在兩位數以內進行，對乘法進行測試，乘法要求為一位數的計算。
上傳時間： 2016-04-28
上傳用戶：chenjjer
本實訓是有關線性表的順序存儲結構的應用，在本實訓的實例程序中，通過C語言中提供的數組來存儲兩個已知的線性表，然后利用數組元素的下標來對線性表進行比較。通過對本實訓的學習，可以理解線性表在順序存儲結構下的操作方法。 在實訓中，我們設A=（a1,a2,…,an）和B=（b1,b2,…,bm）是兩個線性表，其數據元素的類型是整型。若n=m,且ai=bi,則稱A=B 若ai=bi，而aj<bj,則稱A<B；除此以外，均稱A>B。設計一比較大小的程序。
上傳時間： 2014-01-14
上傳用戶：www240697738
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
標簽： simulator Simulator different Create
上傳時間： 2016-07-02
上傳用戶：wfl_yy
