C# BigInteger class. BigInteger.cs is a csharp program. It is the BIgInteger class. It has methods: abs() , FermatLittleTest(int confidence) ,gcd(BigInteger bi) , genCoPrime(int bits, Random rand) , genPseudoPrime(int bits, int confidence, Random rand) , genRandomBits(int bits, Random rand) , isProbablePrime(int confidence) , isProbablePrime() , Jacobi(BigInteger a, BigInteger b) , LucasSequence(BigInteger P, BigInteger Q, BigInteger k, BigInteger n) ,max(BigInteger bi) , min(BigInteger bi) , modInverse(BigInteger modulus) , RabinMillerTest(int confidence) ,
標(biāo)簽： BigInteger class BIgInteger program
上傳時(shí)間： 2013-12-23
上傳用戶：ynzfm
<％@ LANGUAGE="VBSCRIPT" ％> <!--#include file="util.asp" --> <％ Head="您放入購(gòu)物車的物品已經(jīng)全數(shù)退回！" Session("ProductList") = "" ％> <html> <head> <meta http-equiv="Content-Type" content="text/html charset=gb2312"> <STYLE type=text/css>.main { FONT-SIZE: 9pt } .main1 { FONT-SIZE: 14px } </STYLE> <title>清空購(gòu)物車</title> </head> <body topmargin="5" bgcolor="#E6E4C4"> <diiv align="center"><center> <table width="100％" border="0" class="table1" bordercolor="#62ACFF" cellspacing="0" class=main1> <tr> <td width="80％" valign="top"> <p align="center" class=main1><％=Head％></p> <p align="center"> <br><input type="button" value="關(guān)閉" name="B2" onclick="window.close() " style="font-size: 9pt"></td> </tr> </table> </center></div> </body> </html>
標(biāo)簽： lt LANGUAGE VBSCRIPT include
上傳時(shí)間： 2015-11-05
上傳用戶：zhaoq123
問(wèn)題描述：編寫一個(gè)JAVA程序，用面向?qū)ο笤O(shè)計(jì)的方法編寫一個(gè)電話卡的類。包括卡號(hào)、密碼、余額、撥入號(hào)碼等 b）基本要求：類的屬性有卡號(hào)、密碼、余額、撥入號(hào)碼，電話卡的常用操作可以用連接電話方法、返回余額方法與通電話方法來(lái)實(shí)現(xiàn)。 c）方法功能描述： 構(gòu)造方法（PhoneCard(卡號(hào),密碼,余額,撥入號(hào)碼)）可以完成屬性值初始化賦值，并判斷余額，余額為負(fù)就退出系統(tǒng)，請(qǐng)?jiān)跇?gòu)造方法中將初始時(shí)的連接置為false即表示沒(méi)有連接。 卡號(hào)long cardNumber 密碼private int password,余額double balance,撥入號(hào)碼string connectNumber boolean connected(一個(gè)布爾類型變量表示電話卡連接狀態(tài)，初始時(shí)默認(rèn)沒(méi)有連接，值為false，當(dāng)調(diào)用連接電話方法()后，在判斷卡號(hào)和密碼相匹配后值置為true) 連接電話方法(performConnection(卡號(hào),密碼))可以完成檢查卡號(hào)和密碼，它是只有在卡號(hào)和密碼相匹配時(shí)才連接 返回余額方法(getBalance())得到電話卡的余額 通電話方法(performDial())是模擬通過(guò)過(guò)程中，余額會(huì)不斷減少，每調(diào)用此方法，電話卡的余額減少0。5元,打一次電話調(diào)用一次
上傳時(shí)間： 2014-01-20
上傳用戶：1109003457
本文專門講解如何運(yùn)用這種原始套接字，來(lái)模擬I P的一些實(shí)用工具，比如Tr a c e r o u t e和P i n g程序等等。使用原始套接字，亦可對(duì)I P頭信息進(jìn)行實(shí)際的操作。
上傳時(shí)間： 2013-12-24
上傳用戶：wqxstar
*--- --- --- --聲明--- --- --- -----*/ /* VC6.0下運(yùn)行通過(guò) 此程序?yàn)楸救丝嘈乃觯?qǐng)您在閱讀的時(shí)候，尊重本人的 勞動(dòng)。可以修改，但當(dāng)做的每一處矯正或改進(jìn)時(shí)，請(qǐng)將改進(jìn) 方案，及修改部分發(fā)給本人 (修改部分請(qǐng)注名明：修改字樣) Email: jink2005@sina.com QQ: 272576320 ——初稿完成：06-5-27 jink2005 補(bǔ)充： 程序存在問(wèn)題： (1) follow集不能處理：U->xVyVz的情況 (2) 因本人偷懶，本程序?yàn)榧尤胛姆ㄅ袛啵? 輸入的文法必須為L(zhǎng)L(1)文法 (3) 您可以幫忙擴(kuò)充：消除左遞歸，提取公因子等函數(shù) (4) …… */ /*-----------------------------------------------*/ /*參考書《計(jì)算機(jī)編譯原理——編譯程序構(gòu)造實(shí)踐》 LL(1)語(yǔ)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(shí)間： 2016-02-08
上傳用戶：ayfeixiao
一個(gè)基于數(shù)據(jù)挖掘的圖書智能銷售系統(tǒng)，具有預(yù)測(cè)功能，同時(shí)能對(duì)客戶進(jìn)行數(shù)據(jù)挖掘分析。 .net平臺(tái)，sql2005環(huán)境，必須安裝sql205 BI
標(biāo)簽： 數(shù)據(jù)挖掘 圖書 銷售
上傳時(shí)間： 2016-03-15
上傳用戶：Thuan
人工智能的一個(gè)工具軟件，較為經(jīng)典，BI常用的推薦工具
上傳時(shí)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(biāo)簽： demonstrates sequential Selection Bayesian
上傳時(shí)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(shí)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(shí)間： 2014-01-08
上傳用戶：cuibaigao
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