* 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:
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.
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.
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.
漢諾塔！！！
Simulate the movement of the Towers of Hanoi puzzle Bonus is possible for using animation
eg. if n = 2 A→B A→C B→C
if n = 3 A→C A→B C→B A→C B→A B→C A→C
