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: discover.m
    path of file: ./repos/CBIG/external_packages/matlab/default_packages/DSP
    
    the code of the file until where you have to start completion: function [grpRes indRes] = discover(dat,noClus,noReps,dim)

% [grpRes indRes] = discover(dat,noClus,noReps,dim)
% 
% NOTE: This function is a modified version of Danial Lashikari's code
%       discover.m, which can be found here:
%       http://people.csail.mit.edu/danial/Site/Code.html
%
%       Our modification includes:
%       - Removing two input arguments meanFlag and epsilon. Now meanFlag
%       is always non-zero.
%       - Removing re-running direcClus and choosing the best likelihood
%
% Performs the analysis on the data (variable dat) with number of clusters
% indicated by variable noClus. The analysis is repeated noReps number of
% times with random initializations.
%
% Format of variable dat: 
% The variable has a cell structure. For subject i,
% dat{i} is an N X D dimensional matrix representing the fMRI response of
% the N voxels within the mask for subject i, to D different stimuli or
% time points.
%
% grpRes: clustering results for the concatenated group data
%

    
function [grpRes indRes] = discover(dat,noClus,noReps,dim)

% [grpRes indRes] = discover(dat,noClus,noReps,dim)
% 
% NOTE: This function is a modified version of Danial Lashikari's code
%       discover.m, which can be found here:
%       http://people.csail.mit.edu/danial/Site/Code.html
%
%       Our modification includes:
%       - Removing two input arguments meanFlag and epsilon. Now meanFlag
%       is always non-zero.
%       - Removing re-running direcClus and choosing the best likelihood
%
% Performs the analysis on the data (variable dat) with number of clusters
% indicated by variable noClus. The analysis is repeated noReps number of
% times with random initializations.
%
% Format of variable dat: 
% The variable has a cell structure. For subject i,
% dat{i} is an N X D dimensional matrix representing the fMRI response of
% the N voxels within the mask for subject i, to D different stimuli or
% time points.
%
% grpRes: clustering results for the concatenated group data
%
% indRes: clustering results for individual subject data. indRes has a cell
%         structure where indRes{i} presents the result for subject i (see the
%         structure of variable dat below).
%
% dim:    the actual dimensionality of data. If ny linear constraint that we
%         add to the data reduces dim from the value D. For instance, we
%         the vectors are all zero mean, then dim = D-1. If we remove the
%         mean 
%
% discover also has a number of other default values introduced running the
% clustering code (direcClus.m). For more information look a the help for
% direcClus.


d = size(dat{1},2);

if ~exist('dim')
    dim = d;
end