Fixed bugs with uncalculated points (#202)

Author: LebedevIlyaG

iOpt/method/index_method.py

@@ -121,11 +121,11 @@ def update_optimum(self, point: SearchDataItem) -> None:
 
         :param point: the point of a new trial.
         """
-
-        if self.best is None or self.best.get_index() < point.get_index() or (
-                self.best.get_index() == point.get_index() and point.get_z() < self.best.get_z()):
-            self.best = point
-            self.recalcR = True
-            self.Z[point.get_index()] = point.get_z()
-        # self.UpdateZ(point)
-        self.search_data.solution.best_trials[0] = self.best
+        if point.get_index() >= 0:
+            if self.best is None or self.best.get_index() < point.get_index() or (
+                    self.best.get_index() == point.get_index() and point.get_z() < self.best.get_z()):
+                self.best = point
+                self.recalcR = True
+                self.Z[point.get_index()] = point.get_z()
+            # self.UpdateZ(point)
+            self.search_data.solution.best_trials[0] = self.best

iOpt/method/mixed_integer_method.py

@@ -101,7 +101,7 @@ def first_iteration(self) -> list[SearchDataItem]:
                 else:
                     for i in range(number_of_points_in_one_interval):
                         x = id_comb + h * (i + 1)
-                        if not is_init_image_x:
+                        if not is_init_image_x or len(image_x) >= i:
                             image_x.append(self.evolvent.get_image(x))
 
                         y = Point(copy.copy(image_x[i]), self.discreteParameters[id_comb])

iOpt/solution.py

@@ -9,7 +9,7 @@ class Solution:
     """
     def __init__(self,
                  problem: Problem,
-                 best_trials: np.ndarray(shape=(1), dtype=Trial) = [Trial([], [])],
+                 best_trials: np.ndarray(shape=(1), dtype=Trial) =  np.array([Trial([], [])]),
                  number_of_global_trials: int = 1,
                  number_of_local_trials: int = 0,
                  solving_time: np.double = 0.0,