Fix review issue

Author: MaximSmolskiy

data_structures/heap/max_heap.py

@@ -38,7 +38,9 @@ def insert(self, value: int) -> None:
     def __swap_down(self, i: int) -> None:
         """Swap the element down"""
         while self.__size >= 2 * i:
-            if 2 * i + 1 > self.__size or self.__heap[2 * i] > self.__heap[2 * i + 1]:
+            if 2 * i + 1 > self.__size:  # noqa: SIM114
+                bigger_child = 2 * i
+            elif self.__heap[2 * i] > self.__heap[2 * i + 1]:
                 bigger_child = 2 * i
             else:
                 bigger_child = 2 * i + 1

graphs/minimum_spanning_tree_prims.py

@@ -16,7 +16,9 @@ def top_to_bottom(self, heap, start, size, positions):
         if start > size // 2 - 1:
             return
         else:
-            if 2 * start + 2 >= size or heap[2 * start + 1] < heap[2 * start + 2]:
+            if 2 * start + 2 >= size:  # noqa: SIM114
+                smallest_child = 2 * start + 1
+            elif heap[2 * start + 1] < heap[2 * start + 2]:
                 smallest_child = 2 * start + 1
             else:
                 smallest_child = 2 * start + 2

machine_learning/decision_tree.py

@@ -105,7 +105,9 @@ def train(self, x, y):
         the predictor
         """
         for i in range(len(x)):
-            if len(x[:i]) < self.min_leaf_size or len(x[i:]) < self.min_leaf_size:
+            if len(x[:i]) < self.min_leaf_size:  # noqa: SIM114
+                continue
+            elif len(x[i:]) < self.min_leaf_size:
                 continue
             else:
                 error_left = self.mean_squared_error(x[:i], np.mean(y[:i]))

matrix/sherman_morrison.py

@@ -65,9 +65,9 @@ def validate_indices(self, loc: tuple[int, int]) -> bool:
         >>> a.validate_indices((0, 0))
         True
         """
-        if not (isinstance(loc, (list, tuple)) and len(loc) == 2) or not (
-            0 <= loc[0] < self.row and 0 <= loc[1] < self.column
-        ):
+        if not (isinstance(loc, (list, tuple)) and len(loc) == 2):  # noqa: SIM114
+            return False
+        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
         else:
             return True