You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_mix.m
    path of file: ./repos/spm12/toolbox/mixture
    
    the code of the file until where you have to start completion: function [mix] = spm_mix (y,m,verbose)
% Fit a multivariate Gaussian Mixture model using VB
% FORMAT [mix] = spm_mix (y,m,verbose)
%
% y          [N x d] data matrix containing N samples of d-dim data
% m          Number of mixture components
% verbose    Set to 1 to see evolution of free energy, 0 otherwise
%            (default=1)
%
% mix        Returned model
%
%--------------------------------------------------------------------------
% The fields in mix are:
%
% m                The number of components
% fm               The negative free energy. This decomposes into
%                  fm=acc-kl_proportions-kl_covs-kl_centres
%
% acc              model accuracy
% kl_proportions   complexity penalty for cluster proportions
% kl_covs          complexity penalty for cluster covariances
% kl_centres       complexity penalty for cluster centres
%
% Fields:
%
% lambda           Post mixers, q(pi|D) = D(lambda)
% gamma            [m x N] matrix of belonging probabilities
% state(s).a       Post precisions, q(Gamma|D)=W(a,B)
% state(s).B   
% state(s).C       Post covariance
% state(s).m       Post mean, q(mu|D)=N(m_s,beta_s Gamma_s)
% state(s).beta
% state(s).prior   Estimated mixing proportions
%
%                  In the field prior:
%
% lambda_0         Prior mixers, p(pi) = D(lambda_0)

    
function [mix] = spm_mix (y,m,verbose)
% Fit a multivariate Gaussian Mixture model using VB
% FORMAT [mix] = spm_mix (y,m,verbose)
%
% y          [N x d] data matrix containing N samples of d-dim data
% m          Number of mixture components
% verbose    Set to 1 to see evolution of free energy, 0 otherwise
%            (default=1)
%
% mix        Returned model
%
%--------------------------------------------------------------------------
% The fields in mix are:
%
% m                The number of components
% fm               The negative free energy. This decomposes into
%                  fm=acc-kl_proportions-kl_covs-kl_centres
%
% acc              model accuracy
% kl_proportions   complexity penalty for cluster proportions
% kl_covs          complexity penalty for cluster covariances
% kl_centres       complexity penalty for cluster centres
%
% Fields:
%
% lambda           Post mixers, q(pi|D) = D(lambda)
% gamma            [m x N] matrix of belonging probabilities
% state(s).a       Post precisions, q(Gamma|D)=W(a,B)
% state(s).B   
% state(s).C       Post covariance
% state(s).m       Post mean, q(mu|D)=N(m_s,beta_s Gamma_s)
% state(s).beta
% state(s).prior   Estimated mixing proportions
%
%                  In the field prior:
%
% lambda_0         Prior mixers, p(pi) = D(lambda_0)
% a_0,B_0          Prior precisions, p(Gamma)=W(a_0,B_0)
% m_0,beta_0       Prior means, p(mu)=N(m_0,beta_0 Gamma_s)
%
%__________________________________________________________________________
% Copyright (C) 2007-2014 Wellcome Trust Centre for Neuroimaging

% Will Penny 
% $Id: spm_mix.m 5962 2014-04-17 12:47:43Z spm $

% This code implements the algorithm in:
%
% See Attias, H. (2000) A Variational
% Bayesian framework for Graphical Models, NIPS.
%
% See also:
%
% W.D. Penny (2001) Variational Bayes for d-dimensional Gaussian mixture models 
% Wellcome Department of Imaging Neuroscience, University College London. 
%
% for Negative Free Energy expression and validation.


if nargin < 3
    verbose=1;
end