function model = init_tensorGMM_kmeans(Data, model)
% Initialization of TP-GMM with k-means.
%
% Writing code takes time. Polishing it and making it available to others takes longer!
% If some parts of the code were useful for your research of for a better understanding
% of the algorithms, please reward the authors by citing the related publications,
% and consider making your own research available in this way.
%
% @article{Calinon15,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% year="2015"
% }
%
% Copyright (c) 2015 Idiap Research Institute, http://idiap.ch/
% Written by Sylvain Calinon, http://calinon.ch/
%
% This file is part of PbDlib, http://www.idiap.ch/software/pbdlib/
%
% PbDlib is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License version 3 as
% published by the Free Software Foundation.
%
% PbDlib is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with PbDlib. If not, see .
nbVar = size(Data,1);
diagRegularizationFactor = 1E-4; %Optional regularization term
DataAll = reshape(Data, size(Data,1)*size(Data,2), size(Data,3)); %Matricization/flattening of tensor
%k-means clustering
[Data_id, Mu] = kmeansClustering(DataAll, model.nbStates);
for i=1:model.nbStates
idtmp = find(Data_id==i);
model.Priors(i) = length(idtmp);
Sigma(:,:,i) = cov([DataAll(:,idtmp) DataAll(:,idtmp)]') + eye(size(DataAll,1))*diagRegularizationFactor;
end
model.Priors = model.Priors / sum(model.Priors);
%Reshape GMM parameters into tensor data
for m=1:model.nbFrames
for i=1:model.nbStates
model.Mu(:,m,i) = Mu((m-1)*nbVar+1:m*nbVar,i);
model.Sigma(:,:,m,i) = Sigma((m-1)*nbVar+1:m*nbVar,(m-1)*nbVar+1:m*nbVar,i);
end
end