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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!
標(biāo)簽：
the
Publisher
you
teaches
上傳時(shí)間：
2015-09-01
上傳用戶：ve3344
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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
標(biāo)簽：
classification
support
machine
Ltrain
上傳時(shí)間：
2015-09-04
上傳用戶：問題問題
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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].
標(biāo)簽：
cum2x
y_cum
Cross-covariance
function
上傳時(shí)間：
2015-09-08
上傳用戶：xieguodong1234
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Fractal Explorer
GUI-based program for exploring and studying the most common form of fractals, chaotic systems and fractional dimension systems
標(biāo)簽：
GUI-based
exploring
Explorer
fractals
上傳時(shí)間：
2013-11-25
上傳用戶：ljmwh2000
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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(biāo)簽：
demonstrates
sequential
Selection
Bayesian
上傳時(shí)間：
2016-04-07
上傳用戶：lindor
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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(biāo)簽：
sequential
reversible
algorithm
nstrates
上傳時(shí)間：
2014-01-18
上傳用戶：康郎
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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.
標(biāo)簽：
reversible
algorithm
the
nstrates
上傳時(shí)間：
2014-01-08
上傳用戶：cuibaigao
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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.
標(biāo)簽：
architecture
COVARTYPE
specified
Gaussian
上傳時(shí)間：
2016-04-28
上傳用戶：dyctj
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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.
標(biāo)簽：
Probabilistic
Components
Principal
Analysis
上傳時(shí)間：
2016-04-28
上傳用戶：qb1993225
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％ 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
％
標(biāo)簽：
multidimensional
estimation
algorithm
Gaussian
上傳時(shí)間：
2013-12-03
上傳用戶：我們的船長
