demo_GMM01.m 2.75 KB
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function demo_GMM01
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% Gaussian mixture model (GMM) parameters estimation.
%
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% 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.
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%
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% @article{Calinon16JIST,
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%   author="Calinon, S.",
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%   title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
%   journal="Intelligent Service Robotics",
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%		publisher="Springer Berlin Heidelberg",
%		doi="10.1007/s11370-015-0187-9",
%		year="2016",
%		volume="9",
%		number="1",
%		pages="1--29"
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% }
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% 
% 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 <http://www.gnu.org/licenses/>.
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addpath('./m_fcts/');

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%% Parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model.nbStates = 5; %Number of states in the GMM
model.nbVar = 2; %Number of variables [x1,x2]
nbData = 200; %Length of each trajectory
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nbSamples = 5; %Number of demonstrations
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%% Load handwriting data
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
demos=[];
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load('data/2Dletters/G.mat');
%nbSamples = length(demos);
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Data=[];
for n=1:nbSamples
	s(n).Data = spline(1:size(demos{n}.pos,2), demos{n}.pos, linspace(1,size(demos{n}.pos,2),nbData)); %Resampling
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	%s(n).Data = interp1(1:size(demos{n}.pos,2), demos{n}.pos', linspace(1,size(demos{n}.pos,2),nbData));
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	Data = [Data s(n).Data]; 
end


%% Parameters estimation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model = init_GMM_kmeans(Data, model);
model = EM_GMM(Data, model);


%% Plots
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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figure('position',[10,10,700,500]); hold on; axis off;
plot(Data(1,:),Data(2,:),'.','markersize',8,'color',[.5 .5 .5]);
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plotGMM(model.Mu, model.Sigma, [.8 0 0],.5);
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axis equal; set(gca,'Xtick',[]); set(gca,'Ytick',[]);

%print('-dpng','graphs/demo_GMM01.png');
%pause;
%close all;