代码搜索：MIXTURE
找到约 1,805 项符合「MIXTURE」的源代码
代码结果 1,805
www.eeworm.com/read/343227/11962686
m mix_post.m
function [gamma, logl] = mix_post (X, w, mu, Sigma, QUIET)
%mix_post A posteriori probabilities for a gaussian mixture model.
% Use: [gamma,logl] = mix_post (X,w,mu,Sigma) returns the a posteriori
%
www.eeworm.com/read/213240/15140024
m dd_aic.m
function e = dd_aic(w,x)
%DD_AIC compute the Akaike Information Criterion for MoG
%
% E = DD_AIC(W,X)
%
% Compute the Akaike Information Criterion of the Mixture of
% Gaussians. We assume we have
www.eeworm.com/read/251838/4414484
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix
www.eeworm.com/read/251522/4418856
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix
www.eeworm.com/read/225759/4792554
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix
www.eeworm.com/read/215485/4903446
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix
www.eeworm.com/read/197905/5090892
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix
www.eeworm.com/read/347796/3163075
java abstractmixtureweightem.java
package dragon.ir.search.smooth;
import dragon.ir.index.*;
import dragon.ir.query.*;
import java.io.*;
import java.util.ArrayList;
/**
* Abstract EM Algorithm for Mixture Weights Estimat
www.eeworm.com/read/346158/3189478
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix
www.eeworm.com/read/292984/3935718
m mfa.m
% function [Lh,Ph,Mu,Pi,LL]=mfa(X,M,K,cyc,tol);
%
% Maximum Likelihood Mixture of Factor Analysis using EM
%
% X - data matrix
% M - number of mixtures (default 1)
% K - number of factors in each mix