Cumulitive updates

epde/operators/common/coeff_calculation.py

@@ -81,7 +81,10 @@ def apply(self, objective : Equation, arguments : dict = None):
             for weight_idx in range(len(weights)-1):
                 if weight_idx in nonzero_features_indexes:
                     weights[weight_idx] = valueable_weights[nonzero_features_indexes.index(weight_idx)]
-            weights[-1] = valueable_weights[-1]    
+            weights[-1] = valueable_weights[-1]
+            nonzero_terms_mask = np.array([False if np.isclose(weight, 0) else True for weight in weights])
+            weights = np.array([item if keep else 0 for item, keep in zip(weights, nonzero_terms_mask)])
+            objective.weights_internal = np.array([item if keep else 0 for item, keep in zip(objective.weights_internal, nonzero_terms_mask[:-1])])
             objective.weights_final_evald = True
             objective.weights_final = weights
 

epde/operators/multiobjective/moeadd_specific.py

@@ -349,7 +349,7 @@ def apply(self, objective: ParetoLevels, arguments: dict):
 
         while objective.unplaced_candidates:
             offspring = objective.unplaced_candidates.pop()
-            attempt = 1;
+            attempt = 1
             attempt_limit = self.params['attempt_limit']
             temp_offspring = self.suboperators['chromosome_mutation'].apply(objective=offspring,
                                                                             arguments=subop_args['chromosome_mutation'])
@@ -359,14 +359,12 @@ def apply(self, objective: ParetoLevels, arguments: dict):
                 self.suboperators['chromosome_fitness'].apply(objective=temp_offspring,
                                                               arguments=subop_args['chromosome_fitness'])
 
-                if all([not np.allclose(temp_offspring.obj_fun, solution.obj_fun) for solution in objective.population]):
+                if all([not np.array_equal(temp_offspring.obj_fun, solution.obj_fun) for solution in objective.population]):
                     self.suboperators['pareto_level_updater'].apply(objective=(temp_offspring, objective),
                                                                     arguments=subop_args['pareto_level_updater'])
                     break
                 elif attempt >= attempt_limit:
-                    # print('The algorithm had issues with generating unique offsprings.')
                     temp_offspring.create()
-                    # temp_offspring.reset_state()
                     attempt = 1
                 self.suboperators['chromosome_mutation'].apply(objective=temp_offspring,
                                                                arguments=subop_args['chromosome_mutation'])

epde/operators/multiobjective/mutations.py

@@ -39,11 +39,11 @@ def apply(self, objective : SoEq, arguments : dict): # TODO: add setter for best
 
                 altered_objective.vals.replace_gene(gene_key = eq_key, value = altered_eq)
 
-            for param_key in params_keys:
-                altered_param = self.suboperators['param_mutation'].apply(altered_objective.vals[param_key],
-                                                                          subop_args['param_mutation'])
-                altered_objective.vals.replace_gene(gene_key = param_key, value = altered_param)
-                altered_objective.vals.pass_parametric_gene(key = param_key, value = altered_param)
+        for param_key in params_keys:
+            altered_param = self.suboperators['param_mutation'].apply(altered_objective.vals[param_key],
+                                                                      subop_args['param_mutation'])
+            altered_objective.vals.replace_gene(gene_key = param_key, value = altered_param)
+            altered_objective.vals.pass_parametric_gene(key = param_key, value = altered_param)
 
         altered_objective.reset_state() # Использовать ли reset_right_part
         return altered_objective

epde/optimizers/moeadd/moeadd.py

@@ -157,18 +157,18 @@ def delete_point(self, point):
         for level in self.levels:
             temp = []
             for element in level:
-                if element != point or element in history:
+                if not np.array_equal(element.obj_fun, point.obj_fun) or any(np.array_equal(element.obj_fun, h) for h in history):
                     temp.append(element)
-                history.append(element)
+                history.append(element.obj_fun)
             if not len(temp) == 0:
                 new_levels.append(temp)
 
         population_cleared = []
         history = []
         for elem in self.population:
-            if elem != point or elem in history:
+            if not np.array_equal(elem.obj_fun, point.obj_fun) or any(np.array_equal(elem.obj_fun, h) for h in history):
                 population_cleared.append(elem)
-            history.append(elem)
+            history.append(elem.obj_fun)
 
         if len(population_cleared) != sum([len(level) for level in new_levels]):
             print(len(population_cleared), len(self.population), sum([len(level) for level in new_levels]))