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MATLAB/Simulink code update

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Radu C. Martin 2021-06-02 10:43:38 +02:00
parent d2179071db
commit d6b69acb17
28 changed files with 956 additions and 266 deletions
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Matlab_scripts/gp_identification.m Normal file
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clear all
close all
clc
%%%%%%%%%%%
load("gpr_carnot.mat");
%% Format the train/test data arrays
tbl_gpr_train = array2table(gpr_train);
tbl_gpr_train.Properties.VariableNames = cellstr(table_cols);
tbl_gpr_train = removevars(tbl_gpr_train,{'u'});
tbl_gpr_train_x = removevars(tbl_gpr_train, {'y'});
tbl_gpr_test = array2table(gpr_test);
tbl_gpr_test.Properties.VariableNames = cellstr(table_cols);
tbl_gpr_test = removevars(tbl_gpr_test,{'u'});
tbl_gpr_test_x = removevars(tbl_gpr_test, {'y'});
%% Train the GP model
OutputName = 'y';
tic;
model = fitrgp(tbl_gpr_train, OutputName, 'KernelFunction', 'ardsquaredexponential', ...
'FitMethod', 'sr', 'PredictMethod', 'fic', 'Standardize', 1);
toc;
%% Validate the model using training data
[yhat_train, sigma_train] = predict(model, tbl_gpr_train_x);
std_train = sqrt(sigma_train);
% prepare it for the fill function
x_ax = (1:size(tbl_gpr_train, 1))';
X_plot = [x_ax; flip(x_ax)];
Y_plot = [yhat_train-1.96.*std_train; flip(yhat_train+1.96.*std_train)];
% plot a line + confidence bands
figure(); hold on;
title("GP performance on training data");
plot(x_ax, tbl_gpr_train.y, 'red', 'LineWidth', 1.2);
plot(x_ax, yhat_train, 'blue', 'LineWidth', 1.2)
fill(X_plot, Y_plot , 1,....
'facecolor','blue', ...
'edgecolor','none', ...
'facealpha', 0.3);
legend({'data','prediction_mean', '95% confidence'},'Location','Best');
hold off
%% Validate the model using test data
[yhat_test, sigma_test] = predict(model, tbl_gpr_test_x);
std_test = sqrt(sigma_test);
% prepare it for the fill function
x_ax = (1:size(tbl_gpr_test, 1))';
X_plot = [x_ax; flip(x_ax)];
Y_plot = [yhat_test-1.96.*std_test; flip(yhat_test+1.96.*std_test)];
% plot a line + confidence bands
figure(); hold on;
title("GP performance on test data");
plot(x_ax, tbl_gpr_test.y, 'red', 'LineWidth', 1.2);
plot(x_ax, yhat_test, 'blue', 'LineWidth', 1.2)
fill(X_plot, Y_plot , 1,....
'facecolor','blue', ...
'edgecolor','none', ...
'facealpha', 0.3);
legend({'data','prediction_mean', '95% confidence'},'Location','Best');
hold off
%% Export the final GP model
save('gpr_model.mat', 'model')