From the Publisher Focus on 2D in Direct3D? teaches you all of the tools and tips you ll need to dive right in and begin creating your own games. If you have some knowledge of C or C++ and have been searching for a guide that will take your 2D programming into the third Dimension, then search no more! In this book you ll learn the skills you ll need to move from the 2D API to Direct3D. Written from the point of view of a 2D programmer, Focus on 2D in Direct3D presents the fundamentals of the Direct3D API in an easy-to-use-and-understand format. Get ready to jump into the world of Direct3D!
上传时间： 2015-09-01
上传用户：ve3344
support vector classification machine ％ soft margin ％ uses "kernel.m" ％ ％ xtrain: (Ltrain,N) with Ltrain: number of points N: Dimension ％ ytrain: (Ltrain,1) containing class labels (-1 or +1) ％ xrun: (Lrun,N) with Lrun: number of points N: Dimension ％ atrain: alpha coefficients (from svcm_train on xtrain and ytrain) ％ btrain: offest coefficient (from svcm_train on xtrain and ytrain) ％ ％ ypred: predicted y (Lrun,1) containing class labels (-1 or +1) ％ margin: (signed) separation from the separating hyperplane (Lrun,1
标签： classification support machine Ltrain
上传时间： 2015-09-04
上传用户：问题问题
function y_cum = cum2x (x,y, maxlag, nsamp, overlap, flag) ％CUM2X Cross-covariance ％ y_cum = cum2x (x,y,maxlag, samp_seg, overlap, flag) ％ x,y - data vectors/matrices with identical Dimensions ％ if x,y are matrices, rather than vectors, columns are ％ assumed to correspond to independent realizations, ％ overlap is set to 0, and samp_seg to the row Dimension. ％ maxlag - maximum lag to be computed [default = 0] ％ samp_seg - samples per segment [default = data_length] ％ overlap - percentage overlap of segments [default = 0] ％ overlap is clipped to the allowed range of [0,99].
标签： cum2x y_cum Cross-covariance function
上传时间： 2015-09-08
上传用户：xieguodong1234
Fractal Explorer GUI-based program for exploring and studying the most common form of fractals, chaotic systems and fractional Dimension systems
标签： GUI-based exploring Explorer fractals
上传时间： 2013-11-25
上传用户：ljmwh2000
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
Creates a Gaussian mixture model with specified architecture.MIX = GMM(DIM, NCENTRES, COVARTYPE) takes the Dimension of the space DIM, the number of centres in the mixture model and the type of the mixture model, and returns a data structure MIX.
标签： architecture COVARTYPE specified Gaussian
上传时间： 2016-04-28
上传用户：dyctj
Probabilistic Principal Components Analysis. [VAR, U, LAMBDA] = PPCA(X, PPCA_DIM) computes the principal ％ component subspace U of Dimension PPCA_DIM using a centred covariance matrix X. The variable VAR contains the off-subspace variance (which is assumed to be spherical), while the vector LAMBDA contains the variances of each of the principal components. This is computed using the eigenvalue and eigenvector decomposition of X.
标签： Probabilistic Components Principal Analysis
上传时间： 2016-04-28
上传用户：qb1993225
％ EM algorithm for k multiDimensional Gaussian mixture estimation ％ ％ Inputs: ％ X(n,d) - input data, n=number of observations, d=Dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％ ％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％
标签： multiDimensional estimation algorithm Gaussian
上传时间： 2013-12-03
上传用户：我们的船长
