function r = estimateAttractorPath(DataIn, model, r)
% Estimation of an attractor path from a task-parameterized GMM and a set of candidate frames.
%
% Author: Sylvain Calinon, 2014
% http://programming-by-demonstration.org/SylvainCalinon
%
% This source code is given for free! In exchange, I would be grateful if you cite
% the following reference in any academic publication that uses this code or part of it:
%
% @inproceedings{Calinon14ICRA,
% author="Calinon, S. and Bruno, D. and Caldwell, D. G.",
% title="A task-parameterized probabilistic model with minimal intervention control",
% booktitle="Proc. {IEEE} Intl Conf. on Robotics and Automation ({ICRA})",
% year="2014",
% month="May-June",
% address="Hong Kong, China",
% pages="3339--3344"
% }
in = 1:size(DataIn,1);
out = in(end)+1:model.nbVar;
%% Estimation of the attractor path by Gaussian mixture regression,
%% by using the GMM resulting from the product of linearly transformed Gaussians
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[r.Mu, r.Sigma] = productTPGMM0(model, r.p); %See Eq. (6.0.5), (6.0.6) and (6.0.7) in doc/TechnicalReport.pdf
r.Priors = model.Priors;
r.nbStates = model.nbStates;
[r.currTar, r.currSigma] = GMR(r, DataIn, in, out); %See Eq. (3.0.2) to (3.0.5) in doc/TechnicalReport.pdf