| Name | Name | Last commit message | Last commit date | |
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Gaussian Process Regression using GPML toolbox
K > > feval (@ covRQiso)
Ans =
'(1 + 1 + 1)'
It shows that the covariance function covRQiso requires 3 hyperparameters. Therefore, 3 hyperparameters need to be initialized when using the optimization function minimize. The meaning and range of each hyperparameter are explained in detail in the description of each function.
clc
close all
clear all
addpath(genpath(pwd))
% load data
%{
x : training inputs
y : training targets
xt: testing inputs
yt: testing targets
%}
% multiple input-single output
load('./data/data_1.mat')
% Set the mean function, covariance function and likelihood function
% Take meanConst, covRQiso and likGauss as examples
meanfunc = @meanConst;
covfunc = @covRQiso;
likfunc = @likGauss;
% Initialization of hyperparameters
hyp = struct('mean', 3, 'cov', [0 0 0], 'lik', -1);
% meanfunc = [];
% covfunc = @covSEiso;
% likfunc = @likGauss;
% % Initialization of hyperparameters
% hyp = struct('mean', [], 'cov', [0 0], 'lik', -1);
% Optimization of hyperparameters
hyp2 = minimize(hyp, @gp, -20, @infGaussLik, meanfunc, covfunc, likfunc,x, y);
% Regression using GPR
% yfit is the predicted mean, and ys is the predicted variance
[yfit ys] = gp(hyp2, @infGaussLik, meanfunc, covfunc, likfunc,x, y, xt);
% Visualization of prediction results
plotResult(yt, yfit)
clc
close all
clear all
addpath(genpath(pwd))
% load data
%{
x : training inputs
y : training targets
xt: testing inputs
yt: testing targets
%}
% multiple input-multiple output
load('./data/data_2.mat')
% Set the mean function, covariance function and likelihood function
% Take meanConst, covRQiso and likGauss as examples
meanfunc = @meanConst;
covfunc = @covRQiso;
likfunc = @likGauss;
% Initialization of hyperparameters
hyp = struct('mean', 3, 'cov', [2 2 2], 'lik', -1);
% meanfunc = [];
% covfunc = @covSEiso;
% likfunc = @likGauss;
%
% hyp = struct('mean', [], 'cov', [0 0], 'lik', -1);
% Optimization of hyperparameters
hyp2 = minimize(hyp, @gp, -5, @infGaussLik, meanfunc, covfunc, likfunc,x, y);
% Regression using GPR
% yfit is the predicted mean, and ys is the predicted variance
[yfit ys] = gp(hyp2, @infGaussLik, meanfunc, covfunc, likfunc,x, y, xt);
% Visualization of prediction results
% First output
plotResult(yt(:,1), yfit(:,1))
% Second output
plotResult(yt(:,2), yfit(:,2))
Gaussian Process Regression using GPML toolbox
