1.有三根桿子A,B,C。A桿上有若干碟子 2.每次移動一塊碟子,小的只能疊在大的上面 3.把所有碟子從A桿全部移到C桿上 經過研究發現，漢諾塔的破解很簡單，就是按照移動規則向一個方向移動金片： 如3階漢諾塔的移動：A→C,A→B,C→B,A→C,B→A,B→C,A→C 此外，漢諾塔問題也是程序設計中的經典遞歸問題
上傳時間： 2016-07-25
上傳用戶：gxrui1991
1. 下列說法正確的是 （ ） A. Java語言不區分大小寫 B. Java程序以類為基本單位 C. JVM為Java虛擬機JVM的英文縮寫 D. 運行Java程序需要先安裝JDK 2. 下列說法中錯誤的是 ( ) A. Java語言是編譯執行的 B. Java中使用了多進程技術 C. Java的單行注視以//開頭 D. Java語言具有很高的安全性 3. 下面不屬于Java語言特點的一項是( ) A. 安全性 B. 分布式 C. 移植性 D. 編譯執行 4. 下列語句中，正確的項是 ( ) A . int $e,a,b=10 B. char c,d=’a’ C. float e=0.0d D. double c=0.0f
上傳時間： 2017-01-04
上傳用戶：netwolf
These are all the utilities you need to generate MPEG-I movies on a UNIX box with full motion video and stereo sound. For more information on this unusual application of Linux, look in the docs directory or go to www.freeyellow.com/members4/heroine
標簽： utilities generate MPEG-I movies
上傳時間： 2013-12-18
上傳用戶：onewq
TIMER.ASM ********* [ milindhp@tifrvax.tifr.res.in ] Set Processor configuration word as = 0000 0000 1010 b. a] -MCLR tied to VDD (internally). b] Code protection off. c] WDT disabled. d] Internal RC oscillator [4 MHZ].
標簽： configuration Processor milindhp tifrvax
上傳時間： 2015-05-24
上傳用戶：wqxstar
In addition to all the people who contributed to the first edition, we would like to thank the following individuals for their generous help in writing this edition. Very special thanks go to Jory Prather for verifying the code samples as well as fixing them for consistency. Thanks to Dave Thaler, Brian Zill, and Rich Draves for clarifying our IPv6 questions, Mohammad Alam and Rajesh Peddibhotla for help with reliable multicasting, and Jeff Venable for his contributions on the Network Location Awareness functionality. Thanks to Vadim Eydelman for his Winsock expertise. And finally we would like to thank the .NET Application Frameworks team (Lance Olson, Mauro Ottaviani, and Ron Alberda) for their help with our questions about .NET Sockets.
標簽： the contributed addition to
上傳時間： 2015-12-17
上傳用戶：dongqiangqiang
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa. For example, >> project.name = MyProject >> project.id = 1234 >> project.param.a = 3.1415 >> project.param.b = 42 becomes with str=xml_format(project, off ) "<project> <name>MyProject</name> <id>1234</id> <param> <a>3.1415</a> <b>42</b> </param> </project>" On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
標簽： converts Toolbox complex logical
上傳時間： 2016-02-12
上傳用戶：a673761058
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標簽： Rao-Blackwellised conditional filtering particle
上傳時間： 2013-12-17
上傳用戶：zhaiyanzhong
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
In this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標簽： Rao-Blackwellised conditional filtering particle
上傳時間： 2013-12-14
上傳用戶：小儒尼尼奧
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
