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 % Variational Bayes framework.%%% -----------% Input% -----------%% Necessary parameters%% data         The observation vector% comps        Number of components in mixture% source_type  'g', 'e', 't' - Mixture of Gaussians, %              exponentials or truncated Gaussians%              'hg','he','ht' - HMM versions%%% Optional parameters%% tol       Convergence tolerance          (Default = 1e-5)% max_steps Max number of iteration steps  (Default = 500)% DRAW      Plot pdf at every iteration    (Default = 0)%%% Note:     Exponentials take longer to converge, particularly %           if source pdfs not well described by mixture of   %           exponentials. Therefore, tol=1e-6 for MoE. Truncated %           Gaussians are more robust and have better convergence.%%%% -----------% Output% -----------%% MIX1 is a data structure with the following fields:%% type             'g', 'e', 't' - Mixture of Gaussians, %                  exponentials or truncated Gaussians%                  'hg','he','ht' - HMM versions% m                The number of comp% f_hidd           The negative free energy of hidden vars.% f_params         The negative free energy of the params.% fm               The total negative free energy%%                  In the field priors:% lambda_0         Dirichlet parameters for mixing coeffs% b_0,c_0          Gamma parameters for precisions% m_0,tau_0        Normal parameters for means%%                  In the field posts:% lambda           Dirichlet parameters  for mixing coeffs% b,c              Gamma parameters for precisions% mm,tau           Normal parameters for means%%                  Expected posterior values:% pi               Mixing coefficients% centres          Means % precs            Precisions% gammas           Component probabilities%%% If HMM, also%% pi               Initial state probabilities% P                Calculated trans. probs. for HMM% posts.eta        Dirichlet parameters for P% posts.lambda     Dirichlet parameters for pi%%% --------------------------------------------------------------%% Original code by Rizwan Choudrey % Thesis: Variational Methods for Bayesian Independent%         Component Analysis (www.robots.ox.ac.uk/~parg)addpath ../utilsglobal CHECK_PROGRESS;if nargin<4  tol = 1e-4;endif nargin<5  max_steps = 500;endif nargin<6  CHECK_PROGRESS = 0;else  CHECK_PROGRESS = DRAW;endif (length(source_type)==2)  src_type = source_type(2);else  src_type = source_type;endswitch src_type case 'g'  init_method = 'kmeans';% init_method = 'co_mean'; case 't'  init_method = 'rand'; case 'e'  init_method = 'rand';  tol = 1e-6; % 'e' requires longer convergenceendmix1 = initialise_mix1d(data,comps,source_type,init_method);mix1 = learn_mix1d(mix1,data,data.^2,tol,max_steps);