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: VBA_projectMoG.m
    path of file: ./repos/VBA-toolbox/modules/classification/GMM
    
    the code of the file until where you have to start completion: function [handles] = VBA_projectMoG(posterior,out,y)
% display clustering results on 2D data eigenspace
% function [handles] = plotResults(y,xi,eta,F,theta,K_opt,options)
% This function projects the MoG components onto the first two
% eigencomponents of the data. This 2D eigenspace is then tesselated
% according to which component is the most likely at each point in space.
% This allows for a rough (visual) determination of the components'
% boundaries, in the 2D data eigenspace.
% IN:
%   - posterior: structure containing the following fields:
%       .muEta: pxK prior mean of the components 1st-order moment
%       .SigmaEta: Kx1 cell array of posterior pxp covariance matrices of

    
function [handles] = VBA_projectMoG(posterior,out,y)
% display clustering results on 2D data eigenspace
% function [handles] = plotResults(y,xi,eta,F,theta,K_opt,options)
% This function projects the MoG components onto the first two
% eigencomponents of the data. This 2D eigenspace is then tesselated
% according to which component is the most likely at each point in space.
% This allows for a rough (visual) determination of the components'
% boundaries, in the 2D data eigenspace.
% IN:
%   - posterior: structure containing the following fields:
%       .muEta: pxK prior mean of the components 1st-order moment
%       .SigmaEta: Kx1 cell array of posterior pxp covariance matrices of
%       the components 1st-order moment
%       .a_gamma: Kx1 shape parameter of the posterior on the components'
%       2nd-order moment
%       .a_gamma: Kx1 scale parameter of the posterior on the components'
%       2nd-order moment
%       .d: Kx1 vector of posterior counts for each component
%       .Z: nxK matrix containing the estimated probability, for each
%       voxel, of belonging to each of the clusters
%   - out:
%       .dt: elapsed time (in sec)
%       .dim: final dimension of the model
%       .options: (filled in) options for MoG VB model inversion
%       .suffStat: internal sufficient statistics
%       .date: date (vector format)
%       .F: history of Free Energy across VB iterations
%       .it: # VB iterations until convergence
%   - y: the pxn data matrix, where n is the number of 'observations' to be
%   classified, and p is the dimension of each of these observations
% OUT:
%   - handles: the handles of the output graphical objects

[p,n] = size(y);
K = size(posterior.muEta,2);
if p < 2
    handles = [];
    return
end