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/292964/3936866
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/279486/4136347
m gmix_pred.m
function [Yhat,Pmod] = gmix_pred(M,pt,x,Y,Seq,maxx)
%GMIX_PRED Make a partial curve prediction with a Gaussian Mixture model.
%
% IMPORTANT:
% This function does not work for n > 1, where n=leng
www.eeworm.com/read/268397/4252998
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/434858/1867917
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/393163/2487813
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/160391/5571138
m cooper_yoo.m
% Do the example in Cooper and Yoo, "Causal discovery from a mixture of experimental and
% observational data", UAI 99, p120
N = 2;
dag = zeros(N);
A = 1; B = 2;
dag(A,B) = 1;
ns = 2*ones(1,N)
www.eeworm.com/read/367152/9779862
m gtm_rspg1.m
% gtm_rspg1 - Modified version of gtm_rspg1 from the GTM toolbox
function llh = gtm_rspg1(beta, D, mode, orient)
%
% Log-likelihood and component responsibilities over a Gaussian mixture
%
% Mo
www.eeworm.com/read/204456/15339321
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/360995/10070096
m mog_extend.m
function w = mog_extend(w,x,n,maxiter)
%MOG_EXTEND Extend a MoG with one cluster
%
% W = MOG_EXTEND(W,X,[],MAXITER)
%
% Extend a Mixture of Gaussians model W to data X with one extra cluster
% for
www.eeworm.com/read/451547/7461981
m mog_extend.m
function w = mog_extend(w,x,n,maxiter)
%MOG_EXTEND Extend a MoG with one cluster
%
% W = MOG_EXTEND(W,X,[],MAXITER)
%
% Extend a Mixture of Gaussians model W to data X with one extra cluster
% for