Skip to content

Commit

Permalink
Improved comments no code change in tclustIC
Browse files Browse the repository at this point in the history
  • Loading branch information
@MarcoRianiUNIPR
MarcoRianiUNIPR committed Jun 12, 2024
1 parent b613dba commit 1fb235f
Showing 1 changed file with 33 additions and 33 deletions.
66 changes: 33 additions & 33 deletions toolbox/clustering/tclustIC.m
View file
Original file line number Diff line number Diff line change
Expand Up @@ -31,12 +31,12 @@
%
% cc: values of restriction factor. Vector. A vector specifying
% the values of the restriction factor
% which have to be considered for the covariance matrices.
% which have to be considered for the covariance matrices.
% The default value of cc is [1 2 4 8 16 32 64 128]
% Example - 'cc',[1 2 4 8 128]
% Data Types - double
%
% whichIC: type of information criterion. Character.
% whichIC: type of information criterion. Character.
% Character which specifies which information criteria must
% be computed for each k (number of groups) and each value of
% the restriction factor (c).
Expand Down Expand Up @@ -109,7 +109,7 @@
% proportions of the groups. This option is effective just
% if nsamp is a matrix which contains pre-extracted
% subsamples and k is a scalar. The purpose of this option
% is to enable the user to replicate the results.
% is to enable the user to replicate the results.
% The default value of RandNumbForNini is empty,
% that is random numbers from uniform are used.
% Example - 'RandNumbForNini',''
Expand All @@ -129,7 +129,7 @@
%equalweights : cluster weights in the concentration and assignment steps.
% Logical. A logical value specifying whether cluster weights
% shall be considered in the concentration, assignment steps
% and computation of the likelihood. The default value is false.
% and computation of the likelihood. The default value is false.
% Example - 'equalweights',true
% Data Types - Logical
%
Expand Down Expand Up @@ -164,7 +164,7 @@
% Example - 'plots',1
% Data Types - single | double
%
% numpool : number of pools for parellel computing. Scalar.
% numpool : number of pools for parellel computing. Scalar.
% If numpool > 1, the routine automatically checks if
% the Parallel Computing Toolbox is installed and distributes
% the random starts over numpool parallel processes. If
Expand Down Expand Up @@ -231,7 +231,7 @@
% Example - 'Ysave',1
% Data Types - single | double
%
% UnitsSameGroup : Units with same labels. Numeric vector.
% UnitsSameGroup : Units with same labels. Numeric vector.
% List of the units which must (whenever possible)
% have the same label. For example if
% UnitsSameGroup=[20 26], it means that group which contains
Expand All @@ -247,9 +247,9 @@
% Example - 'UnitsSameGroup',[12 20]
% Data Types - single | double
%
% cshape : constraint to apply to each of the shape matrices.
% Scalar greater or equal than 1.
% This options only works is 'restrtype' is 'deter'.
% cshape : constraint to apply to each of the shape matrices.
% Scalar greater or equal than 1.
% This options only works is 'restrtype' is 'deter'.
% When restrtype is deter the default value of the "shape" constraint (as
% defined below) applied to each group is fixed to
% $c_{shape}=10^{10}$, to ensure the procedure is (virtually)
Expand Down Expand Up @@ -358,7 +358,7 @@
%
% Cerioli, A., Garcia-Escudero, L.A., Mayo-Iscar, A. and Riani M. (2017),
% Finding the Number of Groups in Model-Based Clustering via Constrained
% Likelihoods, "Journal of Computational and Graphical Statistics", pp. 404-416,
% Likelihoods, "Journal of Computational and Graphical Statistics", pp. 404-416,
% https://doi.org/10.1080/10618600.2017.1390469
%
% Copyright 2008-2024.
Expand Down Expand Up @@ -498,13 +498,13 @@
[varargin{:}] = convertStringsToChars(varargin{:});
UserOptions=varargin(1:2:length(varargin));
if ~isempty(UserOptions)


% Check if number of supplied options is valid
if length(varargin) ~= 2*length(UserOptions)
error('FSDA:tclustIC:WrongInputOpt','Number of supplied options is invalid. Probably values for some parameters are missing.');
end

% Check if all the specified optional arguments were present
% in structure options
% Remark: the nocheck option has already been dealt by routine
Expand All @@ -519,13 +519,13 @@


if nargin > 1

% Write in structure 'options' the options chosen by the user
for i=1:2:length(varargin)
options.(varargin{i})=varargin{i+1};
end


restr=options.restrtype;
alpha=options.alpha;
kk=options.kk;
Expand All @@ -536,17 +536,17 @@
nsamp=options.nsamp; % Number of subsets to extract
plots=options.plots; % Plot of the resulting classification
equalweights=options.equalweights; % Specify if assignment must take into account the size of the groups

refsteps=options.refsteps;
reftol=options.reftol;
msg=options.msg; % Scalar which controls the messages displayed on the screen

whichIC=options.whichIC;
cleanpool=options.cleanpool;
numpool=options.numpool;
UnitsSameGroup=options.UnitsSameGroup;
RandNumbForNini=options.RandNumbForNini;
cshape=options.cshape;
cshape=options.cshape;
end

if strcmp(whichIC,'ALL')
Expand Down Expand Up @@ -593,9 +593,9 @@
%% Preapare the pool (if required)

for k=1:length(kk) % loop for different values of k (number of groups)

seqk=kk(k);

% Cnsamp=subsets(nsamp,n,(v+1)*seqk);
%seqk = number of groups to consider
if isscalar(nsamp)
Expand All @@ -612,7 +612,7 @@
else
gRandNumbForNini=RandNumbForNini;
end

parfor (c=1:length(cc) , numpool)
% columns = restr
% rows = number of groups
Expand All @@ -629,7 +629,7 @@
MIXCLA(k,c)=outMixt.MIXCLA;
end
end

if typeIC==0 || typeIC==3
% tclust using classification likelihood
outCla=tclust(Y,seqk,alpha,cc(c),'nsamp',Cnsamp,'plots',0,'msg',0, ...
Expand Down Expand Up @@ -676,14 +676,14 @@
out.CLACLAtable=array2table(CLACLA,'RowNames',rownamesIC,...
'VariableNames',matlab.lang.makeValidName(colnamesIC));
end

% Store whenever possible consistent labels
if ~isempty(UnitsSameGroup)
IDXCLA=ClusterRelabel(IDXCLA,UnitsSameGroup);
end

out.IDXCLA=IDXCLA;

if plots==1
figure
plot1=plot(kk',out.CLACLA,'LineWidth',LineWidth);
Expand All @@ -695,23 +695,23 @@
cmin(j)=cc(posj);
text(xkk(j),0.98,['c=' num2str(cmin(j))],'Units','Normalized')
end

% Set line type and markers
set(plot1,{'LineStyle'},slintyp(1:lcc));
set(plot1,{'Marker'},styp(1:lcc))
xlabel('Number of groups')
set(gca,'xtick',kk)
legend(legstr,'location','best')
end

end

% MIXMIX or MIXCLA
if typeIC>0
if ~isempty(UnitsSameGroup)
IDXMIX=ClusterRelabel(IDXMIX,UnitsSameGroup);
end

out.IDXMIX=IDXMIX;
end

Expand All @@ -722,7 +722,7 @@
% out.(IC) is also given in table format
out.MIXMIXtable=array2table(MIXMIX,'RowNames',rownamesIC,'VariableNames',colnamesIC);
end

if plots==1
figure
plot1=plot(kk',out.MIXMIX,'LineWidth',LineWidth);
Expand All @@ -734,7 +734,7 @@
cmin(j)=cc(posj);
text(xkk(j),0.98,['c=' num2str(cmin(j))],'Units','Normalized')
end

% Set line type and markers
set(plot1,{'LineStyle'},slintyp(1:lcc));
set(plot1,{'Marker'},styp(1:lcc))
Expand All @@ -751,12 +751,12 @@
% out.(IC) is also given in table format
out.MIXCLAtable=array2table(MIXCLA,'RowNames',rownamesIC,'VariableNames',colnamesIC);
end

if plots==1
figure
plot1=plot(kk',out.MIXCLA,'LineWidth',LineWidth);
title('MIXCLA')

% Add labels for the best value of c for each k
cmin=zeros(length(cc),1);
for j=1:length(kk)
Expand Down

0 comments on commit 1fb235f

Please sign in to comment.