Bugfixes

epde/operators/common/fitness.py

@@ -444,8 +444,9 @@ def apply(self, objective: SoEq, arguments: dict, force_out_of_place: bool = Fal
                 print(f'solution[..., eq_idx] {solution[..., eq_idx].shape}, eq_idx {eq_idx}')
                 referential_data = global_var.tensor_cache.get((eq.main_var_to_explain, (1.0,)))
                 discr = solution[..., eq_idx] - referential_data.reshape(solution[..., eq_idx].shape)
-                discr = np.multiply(discr, self.g_fun_vals.reshape(discr.shape)) / np.std(discr)
-                rl_error = np.sqrt(np.mean(discr ** 2))
+                discr = np.multiply(discr, self.g_fun_vals.reshape(discr.shape))
+                # rl_error = np.sqrt(np.mean(discr ** 2))
+                rl_error = np.sum(np.abs(discr)) / np.sum(np.abs(referential_data.reshape(solution[..., eq_idx].shape))) * 100
 
                 print(f'fitness error is {rl_error}, while loss addition is {float(loss_add)}')
                 lp = rl_error + self.params['pinn_loss_mult'] * float(

epde/operators/common/right_part_selection.py

@@ -52,7 +52,6 @@ def apply(self, objective : Equation, arguments : dict):
             objective.reset_state(True)
             min_fitness = np.inf
             weights_internal = np.zeros_like(objective.structure)
-            objective.weights_internal_evald = False
             min_idx = 0
             if not any(term.contains_variable(objective.main_var_to_explain) and term.contains_deriv(objective.main_var_to_explain) for term in objective.structure):
                 objective.restore_property(mandatory_family=objective.main_var_to_explain, deriv=True)

epde/operators/multiobjective/moeadd_specific.py

@@ -453,7 +453,7 @@ def apply(self, objective : ParetoLevels, arguments : dict):
                 self.suboperators['chromosome_fitness'].apply(objective=candidate,
                                                               arguments=subop_args['chromosome_fitness'])
                 objective.history.add(system)
-                print(candidate.obj_fun)
+                # print(candidate.obj_fun)
             objective.initial_placing()
 
             # TODO: consider carefully, where normalizer init shall be held. If here, only the initial values are employed

epde/supplementary.py

@@ -350,6 +350,8 @@ def minmax_normalize(matrix):
         return 2 * (matrix - matrix.min()) / (matrix.max() - matrix.min()) - 1
     else:
         for i in np.arange(matrix.shape[0]):
-            matrix[i] = 2 * (matrix[i] - matrix[i].min()) / (matrix[i].max() - matrix[i].min()) - 1
-
+            if matrix[i].max() != matrix[i].min():
+                matrix[i] = 2 * (matrix[i] - matrix[i].min()) / (matrix[i].max() - matrix[i].min()) - 1
+            else:
+                matrix[i] = np.zeros_like(matrix[i])
         return matrix