代码搜索：MIXTURE

找到约 1,805 项符合「MIXTURE」的源代码

代码结果 1,805
热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/229007/14355748

m mltrain1.m

function [ocinv,cinv, Cen, ppri, R] = mltrain1(Pr,Tr,setflag); % Usage: [cinv, Cen, ppri, R] = mltrain(Pr,Tr,setflag); % Given training feature and target labels, determine a Gaussian mixture % mod
·
www.eeworm.com/read/224012/14607900

m em.m

function [Priors, Mu, Sigma, Pix] = EM(Data, Priors0, Mu0, Sigma0) % % This function learns the parameters of a Gaussian Mixture Model % (GMM) using a recursive Expectation-Maximization (EM) algor
·
www.eeworm.com/read/213492/15133826

m melgmm.m

function model=melgmm(X,Alpha,cov_type) % MELGMM Maximizes Expectation of Log-Likelihood for Gaussian mixture. % % Synopsis: % model = melgmm(X,Alpha) % model = melgmm(X,Alpha,cov_type) % % Descri
·
www.eeworm.com/read/213240/15139973

m mog_init.m

function [means,invcovs,priors] = mog_init(x,k,covtype,dataSigma,reg) %MOG_INIT Initialize a MoG % % [MEANS,INVCOVS,PRIORS] = MOG_INIT(X,K,COVTYPE) % % Initialize a mixture of Gaussians on dataset
·
www.eeworm.com/read/213240/15139998

m mog_p.m

function p = mog_P(x,covtype,means,invcovs,priors) %MOG_P Compute the probability density of a Mixture of Gaussians % % P = MOG_P(X,COVTYPE,MEANS,INVCOVS,PRIORS) % % Calculate the probability de
·
www.eeworm.com/read/13871/284286

m mixgauss_init.m

function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type, method) % MIXGAUSS_INIT Initial parameter estimates for a mixture of Gaussians % function [mu, Sigma, weights] = mixgauss_init(M, dat
·
www.eeworm.com/read/13871/284315

m mixgauss_mstep.m

function [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY, varargin) % MSTEP_COND_GAUSS Compute MLEs for mixture of Gaussians given expected sufficient statistics % function [mu, Sigma] = Mstep_cond_gauss
·
www.eeworm.com/read/13871/284321

m mixgauss_em.m

function [mu, Sigma, prior] = mixgauss_em(Y, nc, varargin) % MIXGAUSS_EM Fit the parameters of a mixture of Gaussians using EM % function [mu, Sigma, prior] = mixgauss_em(data, nc, varargin) % % d
·
www.eeworm.com/read/13871/284732

m mhmm_logprob.m

function [loglik, errors] = mhmm_logprob(data, prior, transmat, mu, Sigma, mixmat) % LOG_LIK_MHMM Compute the log-likelihood of a dataset using a (mixture of) Gaussians HMM % [loglik, errors] = log_li
·
www.eeworm.com/read/465307/1521117

dcf monplainm1s3fullcov.dcf

This DCF produces a plain single mixture, three stream, full covariance monophone system hsKind: P covKind: F nStreams: 3 nMixes: 1 1 1 Cont
·