代码搜索：MIXTURE

找到约 1,805 项符合「MIXTURE」的源代码

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www.eeworm.com/read/400576/11573484

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
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www.eeworm.com/read/400576/11573509

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
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www.eeworm.com/read/158037/11648217

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
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www.eeworm.com/read/158037/11648294

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
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www.eeworm.com/read/158037/11648313

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
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www.eeworm.com/read/259241/11812496

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) % % data(
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www.eeworm.com/read/259241/11812505

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, data,
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www.eeworm.com/read/259241/11812607

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) % % data(
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www.eeworm.com/read/150760/12266184

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
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www.eeworm.com/read/128468/14295363

m pnormal.m

function pnormal(MI,SIGMA,I,afill,r,col) % pnormal(MI,SIGMA,I,afill,r,col) % % PNORAML vizualizes mixture of normal distributions in 2D space. % Each normal distribution is determined by a pair of
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