function mix1 = init_MoG1(x)% mix1 = init_MoG1(x)%% Initialises a 1-d, 1-component Gaussian mixture model % for learning using the Variational Bayes framework.%% Called from 'init_MoG'.%%% -----------% Input% -----------%% Necessary parameters%% x                The data vector%%%% -----------% Output% -----------%% MIX1 is a data structure with the following fields:%% type             'g'% m                The number of components (=1)%%                  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% eta_0            If HMM, Dirichlet parameters for trans.%                  matrix%%                  In the field posts:% lambda           Dirichlet parameters  for mixing coeffs% b,c              Gamma parameters for precisions% mm,v             Normal parameters for means% eta              If HMM, Dirichlet parameters for trans.%                  matrix%%                  Expected posterior values:% pi               Mixing coefficients (=1)% centres          Means% precs            Precisions% gammas           Component responsibilities (=1)%%% -------------------------------------------------------%% Original code by Will Penny% Paper: Variational Bayes for 1-dimensional Mixture Models%        Technical Report (www.robots.ox.ac.uk/~parg)%% Modified by Rizwan Choudrey for use in vbICA model% Thesis: Variational Methods for Bayesian Independent%         Component Analysis (www.robots.ox.ac.uk/~parg)global CHECK_PROGRESSprint_estimates=0;N=length(x);mx=mean(x);vx=var(x);bx=1/vx;% PRIORS% Set component precision priorsmean_precision=bx;var_precision=bx/N;b_0=var_precision/mean_precision;c_0=(mean_precision^2)/var_precision;% Set component mean priorsm_0=mx;v_0=(0.3*(max(x)-min(x)))^2; %SHOULD BE OF STANDARD ERROR FORMtau_0=1/v_0;% POSTERIORSb=b_0;c=c_0;mm=m_0;tau=tau_0;lambda=N;if CHECK_PROGRESS  plotMoG(mm,1./(b.*c),lambda./sum(lambda))  drawnowend% Put variables into data structuremix1.type = 'g';mix1.m=1;mix1.priors.lambda_0=N;mix1.priors.m_0=m_0;mix1.priors.tau_0=tau_0;mix1.priors.b_0=b_0;mix1.priors.c_0=c_0;mix1.posts.lambda=N;mix1.posts.mm=mm;mix1.posts.tau=tau;mix1.posts.b=b;mix1.posts.c=c;mix1.pi=1;mix1.centres=mm;mix1.variances=1/(b*c);mix1.pi=lambda./sum(lambda);mix1.centres=mm;mix1.variances=1./(b.*c);mix1.gammas = ones(size(x));