MATLAB/Simulink code update

Matlab_scripts/gp_casadi_test.m

@@ -0,0 +1,83 @@
+clear all
+close all
+clc
+%%%%%%%%%%%%
+addpath('/home/radu/Media/MATLAB/casadi-linux-matlabR2014b-v3.5.5')
+import casadi.*
+
+%% Generate GP data
+size = 500; n_samples = 15;
+X = linspace(-2, 2, size);
+Y = 3 * X .^2;
+
+% Add noise to the output
+mean = 0; std = 0.5;
+noise = mean + std.*randn(1, n_samples);
+
+idx_samples = randperm(size, n_samples);
+
+X_sampled = X(idx_samples);
+Y_sampled = Y(idx_samples);
+
+Y_sampled = Y_sampled + noise;
+
+figure; hold on; 
+plot(X, Y);
+scatter(X_sampled, Y_sampled);
+
+tbl_gpr_in = array2table([X_sampled', Y_sampled']);
+tbl_gpr_in.Properties.VariableNames = {'X', 'Y'};
+
+tic;
+model = fitrgp(tbl_gpr_in, 'Y', 'KernelFunction', 'ardsquaredexponential', ...
+                'FitMethod', 'sr', 'PredictMethod', 'fic', 'Standardize', 1);
+toc;
+
+%% Predict stuff
+[yhat_test, sigma_test] = predict(model, X');
+std_test = sqrt(sigma_test);
+
+% prepare it for the fill function
+x_ax    = X';
+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, 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
+
+%% Save the model
+save('test_gpr_model.mat', 'model')
+
+%% CasADi optimization problem
+cs_model = test_gpCallback('model');
+cs_x = MX.sym('x');
+cs_y = 2 * cs_model(cs_x) + 5;
+f = Function('f', {cs_x}, {cs_y});
+
+
+nlp_prob = struct('f', f(cs_x), 'x', cs_x);
+
+
+opts = struct;
+opts.ipopt.max_iter = 2000;
+opts.ipopt.hessian_approximation = 'limited-memory';
+%opts.ipopt.print_level =1;%0,3
+opts.print_time = 0;
+opts.ipopt.acceptable_tol =1e-8;
+opts.ipopt.acceptable_obj_change_tol = 1e-6;
+
+solver = nlpsol('solver', 'ipopt', nlp_prob,opts);
+
+res = solver('lbx', -2, 'ubx', 2);
+
+res
+