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main.m
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main.m
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% Efficient Spatio-Temporal Gaussian Process learning via Kalman Filtering
%
% Copyright (C) 2017, University of Padova
% Andrea Carron , carrona@ethz.ch
% Marco Todescato, mrc.todescato@gmail.com
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% This program 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 this program. If not, see <http://www.gnu.org/licenses/>.
close all
clear
clc
% add current (sub)folder to the path
addpath(genpath('./'))
% load necesary parameters
loadParameters
% generate data
[F,Y,noiseVar] = loadDataSet(Params.data);
% nonparametric posterior GP
[postMeanNp, postCovNp, exeTimeNp] = nonparametricEstimation(Params.data, Params.np, Y, noiseVar);
% nonparametric predicted GP
[predMeanNp, predCovNp, exeTimeNpPred] = nonparametricPrediction(Params.data, Params.np, Y, noiseVar);
% GPKF (gaussian process kalman filter) estimate
[postMeanKf, postCovKf, exeTimeKf, ~] = gpkfEstimation(Params.data, Params.gpkf, Y, noiseVar);
% GPKF (gaussian process kalman filter) prediction
[predMeanKf, predCovKf, exeTimeKfPred] = gpkfPrediction(Params.data, Params.gpkf, Y, noiseVar);
%% plotting some results
plotResults