*--- --- --- --声明--- --- --- -----*/ /* VC6.0下运行通过 此程序为本人苦心所做，请您在阅读的时候，尊重本人的 劳动。可以修改，但当做的每一处矫正或改进时，请将改进 方案，及修改部分发给本人 (修改部分请注名明：修改字样) Email: jink2005@sina.com QQ: 272576320 ——初稿完成：06-5-27 jink2005 补充： 程序存在问题： (1) follow集不能处理：U->xVyVz的情况 (2) 因本人偷懒，本程序为加入文法判断，故 输入的文法必须为LL(1)文法 (3) 您可以帮忙扩充：消除左递归，提取公因子等函数 (4) …… */ /*-----------------------------------------------*/ /*参考书《计算机编译原理——编译程序构造实践》 LL(1)语法分析,例1: ERTWF# +*()i# 文法G[E]:(按此格式输入) 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： 编译书5.6例题1 SHMA# adbe# S->aH H->aMd H->d M->Ab M-> A->aM A->e 分析例句：aaabd# */
上传时间： 2016-02-08
上传用户：ayfeixiao
一个基于数据挖掘的图书智能销售系统，具有预测功能，同时能对客户进行数据挖掘分析。 .net平台，sql2005环境，必须安装sql205 BI
上传时间： 2016-03-15
上传用户：Thuan
人工智能的一个工具软件，较为经典，BI常用的推荐工具
上传时间： 2016-03-30
上传用户：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.
标签： demonstrates sequential Selection Bayesian
上传时间： 2016-04-07
上传用户：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.
标签： sequential reversible algorithm nstrates
上传时间： 2014-01-18
上传用户：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.
标签： reversible algorithm the nstrates
上传时间： 2014-01-08
上传用户：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.
标签： algorithms problems Several trivial
上传时间： 2014-01-20
上传用户：royzhangsz
复数计算器：1、设计的任务要求 (1) 所设计的复数计算器可以进行+、-、*、+=、-=、*=、++、--、>=、<=、==、!=运算符，其中，>=、<=是针对复数的模进行计算。 (2) 设计输入重载函数，要求能接收从键盘输入a+bi形式的复数，在程序中可以识别出实部虚部并正确赋值。 (3) 设计计算器测试程序，对加减法进行测试，要求在两位数以内进行，对乘法进行测试，乘法要求为一位数的计算。
上传时间： 2016-04-28
上传用户：chenjjer
本实训是有关线性表的顺序存储结构的应用，在本实训的实例程序中，通过C语言中提供的数组来存储两个已知的线性表，然后利用数组元素的下标来对线性表进行比较。通过对本实训的学习，可以理解线性表在顺序存储结构下的操作方法。 在实训中，我们设A=（a1,a2,…,an）和B=（b1,b2,…,bm）是两个线性表，其数据元素的类型是整型。若n=m,且ai=bi,则称A=B 若ai=bi，而aj<bj,则称A<B；除此以外，均称A>B。设计一比较大小的程序。
上传时间： 2014-01-14
上传用户：www240697738
看n2实例 #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
标签： simulator Simulator different Create
上传时间： 2016-07-02
上传用户：wfl_yy
