- default is PC

- readme file improved

Author: forgi86

GLIS_BO_analysis.py

@@ -17,8 +17,7 @@
     algo = 'GLIS'
     #algo = 'BO'
 
-    machine = 'PC'
-    #machine = 'PI'
+    machine = 'PI' # PC or PI
     eps_calc = 1.0
     iter_max_plot = 500
 
@@ -153,16 +152,18 @@ def xref_fun_def(t):
         ax.legend(loc='upper right')
 
     axes[0].set_xlim((0, iter_max_plot))
-    axes[0].set_ylim((-1, 19))
+    axes[0].set_ylim((-1, 25))
 
     fig_name = f"ITER_{algo}_{machine}.pdf"
     fig_path = os.path.join(FIG_FOLDER, fig_name)
     fig.savefig(fig_path, bbox_inches='tight')
 
 
     # In[Recompute optimum]
+    #print(Y_best_val)
+
     J_opt = f_x(x_opt, eps_calc=results['eps_calc'])
-    print(J_opt)
+    #print(J_opt)
 
     # In[Optimization computation time]
 

GLIS_BO_main.py

@@ -15,7 +15,7 @@
 
 if __name__ == '__main__':
 
-    np.random.seed(10)
+    np.random.seed(2)
     # initial random points
 
     # optimization parameters
@@ -28,8 +28,8 @@
 
     # method = 'BO'
     method = "GLIS"
-    machine = 'PC'
-    #machine = 'PI'
+    machine = 'PI' # PC or PI
+
 
     dict_x0 = {
         'QDu_scale': 0.1,

objective_function.py

@@ -149,10 +149,12 @@ def f_x(x, eps_calc=1.0, seed_val=None):
         # J_perf = 2e1
         # J_fit = 2e1
 
-    print(f"N_eval: {N_eval}, J_perf:{J_perf:.2f}, J_calc:{J_calc:.2f}, J_emergency:{J_emergency:.2f}")
+    J_cl = np.log(J_perf) + np.log(1 + J_calc) + np.log(1+J_emergency)
+
+    print(f"N_eval: {N_eval}, J_perf:{J_perf:.2f}, J_calc:{J_calc:.2f}, J_emergency:{J_emergency:.2f}, J_cl:{J_cl:.2f}")
     N_eval += 1
 
-    return np.log(J_perf) + np.log(1 + J_calc + J_emergency)  # + J_fit
+    return J_cl  # + J_fit
 
 
 if __name__ == '__main__':