代码搜索：MIXTURE

找到约 1,805 项符合「MIXTURE」的源代码

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m mixgauss_prob.m

function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) % EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians % function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma
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m mixgauss_prob.m

function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) % EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians % function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma
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www.eeworm.com/read/469416/6976078

m mixgauss_prob.m

function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) % EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians % function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma
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www.eeworm.com/read/397115/8066545

m som_probability_gmm.m

function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) %SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model. % % [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) % % [K,P] = som_e
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www.eeworm.com/read/331448/12827354

m som_probability_gmm.m

function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) %SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model. % % [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) % % [K,P] = som_e
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m som_probability_gmm.m

function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) %SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model. % % [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) % % [K,P] = som_e
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www.eeworm.com/read/141739/12988563

m som_probability_gmm.m

function [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) %SOM_PROBABILITY_GMM Probabilities based on a gaussian mixture model. % % [pd,Pdm,pmd] = som_probability_gmm(D, sM, K, P) % % [K,P] = som_e
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www.eeworm.com/read/140851/13058324

m mixgauss_prob.m

function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) % EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians % function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma
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www.eeworm.com/read/138798/13211380

m mixgauss_prob.m

function [B, B2] = mixgauss_prob(data, mu, Sigma, mixmat, unit_norm) % EVAL_PDF_COND_MOG Evaluate the pdf of a conditional mixture of Gaussians % function [B, B2] = eval_pdf_cond_mog(data, mu, Sigma
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www.eeworm.com/read/319794/13442783

m init_mop.m

function mix1 = init_MoP(x,m,source_type,priors) % mix1 = init_MoP(x,m,source_type,priors) % % Initialises a 1-dimensional positive mixture model % for learning using the Variational Bayes framework.
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