代码搜索：MIXTURE

找到约 1,805 项符合「MIXTURE」的源代码

代码结果 1,805
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m unsuni.m

function [MI,SIGMA,Pk,I,solution,t]=unsuni(X,K,tmax,randinit,t,MI,SIGMA,Pk) % UNSUNI EM algorithm, mixture of Gaussians, diag. cov. matrix. % [MI,SIGMA,Pk,I,solution,t]=unsuni(X,K,tmax,randinit,t,MI
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www.eeworm.com/read/429878/8784094

htm gmmem.htm

Netlab Reference Manual gmmem gmmem Purpose EM algorithm for Gaussian mixture model. Synopsis [mix, options, e
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www.eeworm.com/read/319794/13442785

m mixmodel1d.m

function mix1 = mixmodel1d(data,comps,source_type,tol,max_steps, ... DRAW) % mix1 = mixmodel1d(data,comps,source_type,tol,max_steps,DRAW) % % Train a 1-dimensional mixture model or HMM using the
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www.eeworm.com/read/343227/11962868

m mix_par.m

function [mu, Sigma, w] = mix_par (X, gamma, DIAG_COV, QUIET) %mix_par Reestimate mixture parameters. % Use: [mu,Sigma,w] = mix_par(X,gamma,DIAG_COV). % Note that mix_par can also be used for re-es
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www.eeworm.com/read/253950/12174107

htm gmmem.htm

Netlab Reference Manual gmmem gmmem Purpose EM algorithm for Gaussian mixture model. Synopsis [mix, options, e
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www.eeworm.com/read/150905/12250277

htm gmmem.htm

Netlab Reference Manual gmmem gmmem Purpose EM algorithm for Gaussian mixture model. Synopsis [mix, options, e
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www.eeworm.com/read/465320/1520833

c gms_gprune.c

/** * @file gms_gprune.c * @author Akinobu LEE * @date Thu Feb 17 15:05:08 2005 * * * @brief Gaussian Mixture Selection のための Gaussian pruning を脱いたモノフォンHMMの纷换 * * * * @
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www.eeworm.com/read/465297/1521512

c rdhmmdef_mpdf.c

/** * @file rdhmmdef_mpdf.c * * * @brief HTK %HMM 年盗ファイルの粕み哈み¨ガウス寒圭尸邵 * * * * @brief Read HTK %HMM definition file: Gaussian mixture PDF * * * @author Akinobu L
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www.eeworm.com/read/319794/13442781

m learn_mix1d.m

function src = learn_mix1d(src,x,x_sq,tol,max_steps) % src = learn_mix1d(src,x,x_sq,tol,max_steps) % % Train a 1-dimensional mixture model using the % Variational Bayes framework. % % Called from 'mi
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www.eeworm.com/read/319794/13442784

m initialise_mix1d.m

function mix1 = initialise_mix1d(x,m,source_type,init_method,priors) % mix1 = initialise_mix1d(x,m,source_type,init_method,priors) % % Initialises a 1-dimensional mixture model for % learning using th
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