function demo_MPPCA01
% Mixture of probabilistic principal component analyzers (MPPCA) encoding.
%
% 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{Calinon16JIST,
% author="Calinon, S.",
% title="A Tutorial on Task-Parameterized Movement Learning and Retrieval",
% journal="Intelligent Service Robotics",
% publisher="Springer Berlin Heidelberg",
% doi="10.1007/s11370-015-0187-9",
% year="2016",
% volume="9",
% number="1",
% pages="1--29"
% }
%
% 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 .
addpath('./m_fcts/');
%% Parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model.nbStates = 4; %Number of states in the GMM
model.nbVar = 4; %Number of variables [x1,x2,x3,x4]
model.nbFA = 1; %Dimension of the subspace (number of principal components)
nbData = 200; %Length of each trajectory
nbSamples = 5; %Number of demonstrations
%% Load handwriting data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
demos=[];
load('data/2Dletters/C.mat'); %Load x1,x2 variables
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
end
demos=[];
load('data/2Dletters/D.mat'); %Load x3,x4 variables
Data=[];
for n=1:nbSamples
s(n).Data = [s(n).Data; spline(1:size(demos{n}.pos,2), demos{n}.pos, linspace(1,size(demos{n}.pos,2),nbData))]; %Resampling
Data = [Data s(n).Data];
end
%% Parameters estimation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
model = init_GMM_kmeans(Data, model);
model0 = EM_GMM(Data, model); %for comparison
model = EM_MPPCA(Data, model);
%% Plots
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure('position',[10,10,1300,500]);
for i=1:2
subplot(1,2,i); hold on; box on;
plot(Data((i-1)*2+1,:),Data(i*2,:),'.','markersize',8,'color',[.7 .7 .7]);
plotGMM(model0.Mu((i-1)*2+1:i*2,:), model0.Sigma((i-1)*2+1:i*2,(i-1)*2+1:i*2,:), [.8 .8 .8], .5);
plotGMM(model.Mu((i-1)*2+1:i*2,:), model.Sigma((i-1)*2+1:i*2,(i-1)*2+1:i*2,:), [.8 0 0], .5);
axis equal; set(gca,'Xtick',[]); set(gca,'Ytick',[]);
xlabel(['x_' num2str((i-1)*2+1)]); ylabel(['x_' num2str(i*2)]);
end
%print('-dpng','graphs/demo_MPPCA01.png');
%pause;
%close all;