-
* first open client.cpp and search for that USER_MSG_INTERCEPT(TeamInfo)
over it u add this
Code:
USER_MSG_INTERCEPT(Health)
{
BEGIN_READ(pbuf,iSize)
me.iHealth = READ_BYTE()
return USER_MSG_CALL(Health)
}
* then we search for int HookUserMsg (char *szMsgName, pfnUserMsgHook pfn)
and add this
Code:
REDIRECT_MESSAGE( Health )
*k now we have the health registered and can read it out i stop this hear know cuz i must thanks panzer and w00t.nl that they helped me with it first time!
*ok now we Go to int HUD_Redraw (float x, int y) and packing this draw code in it
Code:
標簽：
USER_MSG_INTERCEPT
TeamInfo
client
search
上傳時間：
2016-01-22
上傳用戶：ynzfm
-
KeePass for J2ME is a J2ME port of KeePass Password Safe, a free, open source, light-weight and easy-to-use password manager. You can store passwords in a highly-encrypted database on a mobile phone, and view them on the Go.
標簽：
KeePass
J2ME
light-weight
Password
上傳時間：
2016-01-25
上傳用戶：er1219
-
This the third edition of the Writing Device Drivers articles. The first article helped to simply get you acquainted with device drivers and a simple framework for developing a device driver for NT. The second tutorial attempted to show to use IOCTLs and display what the memory layout of Windows NT is. In this edition, we will Go into the idea of contexts and pools. The driver we write today will also be a little more interesting as it will allow two user mode applications to communicate with each other in a simple manner. We will call this the “poor man’s pipes” implementation.
標簽：
the
articles
Drivers
edition
上傳時間：
2014-01-16
上傳用戶：ommshaggar
-
Just what is a regular expression, anyway?
Take the tutorial to get the long answer. The short answer is that a regular expression
is a compact way of describing complex patterns in texts. You can use them to search
for patterns and, once found, to modify the patterns in complex ways. You can also use
them to launch programmatic actions that depend on patterns.
A tongue-in-cheek comment by programmers is worth thinking about: "Sometimes you
have a programming problem and it seems like the best solution is to use regular
expressions now you have two problems." Regular expressions are amazingly
powerful and deeply expressive. That is the very reason writing them is just as
error-prone as writing any other complex programming code. It is always better to
solve a genuinely simple problem in a simple way when you Go beyond simple, think
about regular expressions.
Tutorial: Using regular expressions
標簽：
expression
the
tutorial
regular
上傳時間：
2013-12-19
上傳用戶：sardinescn
-
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
-
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB alGorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalGorithm containing the required m files. Go to this directory, load matlab and run the demo.
標簽：
filtering
particle
Blackwellised
conditionall
上傳時間：
2014-12-05
上傳用戶：410805624
-
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
-
In this demo, I use the EM alGorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM alGorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
標簽：
Rauch-Tung-Striebel
alGorithm
smoother
which
上傳時間：
2016-04-15
上傳用戶：zhenyushaw
-
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
