A* sudo sudo/* B* adduser script adduser C* rmuser script rmuser E* tout tout/* F* dumdum dumdum G* lostfile lostfile H* Mkfl.localsys Makefile.localsys I* spacegripe spacegripe J* sendmail.cf sendmail.cf N* remote remote.c O* distributed conrol distrib/* P* hosts and name server makerevhosts Q* xargs xargs/*
标签： adduser script rmuser sudo
上传时间： 2016-03-29
上传用户：gxrui1991
Intro/: Directory containing introductory examples. HelloWorld.c A simple program that draws a box and writes "Hello World" in HelloWorld.f it. data The data file for the introductory progressive example. Lines.c Reads the data from file "data" and plots just the curve with Lines.f no labels, viewport or anything indicating quantity or units. Viewport.c Restricts the graph to a viewport and frames the viewport, Viewport.f leaving the remainder of the area for labels, etc. CharLbls.c Adds labels for the chart title, X-axis title, and Y-axis CharLbls.f title. Tics.c Adds tic marks to the viewport edges, but since clipping was Tics.f not set correctly, tics extend outside the viewport. Clip.c Sets clipping such that tic marks are clipped at the viewport Clip.f boundaries. TicLabels.c Adds numeric tic labels to the graph this is the final TicLabels.f installment of the progressive example.
标签： introductory HelloWorld containing Directory
上传时间： 2016-03-29
上传用户：exxxds
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
k-meansy算法源代码。This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen.
标签： code implementing directory algorithm
上传时间： 2016-04-07
上传用户：shawvi
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
a screen handling program to provide a flashing message. You will have to design a screen layout for where messages are placed on the screen. You will also have to consider when to delay the program in order to give the user time to read the messages. That is, the program will use the curses library, signals and the sleep function.
标签： screen handling flashing program
上传时间： 2016-05-04
上传用户：chongcongying
Using LabVIEW 8.5 Language to control Tektronix AWG520. This program can select gain, sample rate, frequency and offset.
标签： Tektronix Language LabVIEW control
上传时间： 2013-11-26
上传用户：invtnewer
This is a simple GPS tracer developed for Window Mobile 2005/2003 on Compact Framework 2.0 SDK. So first of all, you need VisualStudio 2005 and Windows Mobile CE 5 SDK. You can develop it on emulator devices or on a real device. As you can see in that photo, I developed that application on a read device: the great Asus MyPal 636N.
标签： Framework developed Compact Mobile
上传时间： 2016-08-11
上传用户：gxf2016
