[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/minimax.py

@@ -1,5 +1,6 @@
 import math
 
+
 def minimax(depth, node_index, is_maximizing_player, scores, height):
     """
     Minimax algorithm to determine the optimal move for a player in a two-player zero-sum game.
@@ -25,7 +26,9 @@ def minimax(depth, node_index, is_maximizing_player, scores, height):
         for i in range(2):
             # Recursive call for the next level of depth
             val = minimax(depth + 1, node_index * 2 + i, False, scores, height)
-            best_score = max(best_score, val)  # Maximizer chooses the highest score available
+            best_score = max(
+                best_score, val
+            )  # Maximizer chooses the highest score available
         return best_score
 
     # Minimizing player's move
@@ -34,17 +37,23 @@ def minimax(depth, node_index, is_maximizing_player, scores, height):
         for i in range(2):
             # Recursive call for the next level of depth
             val = minimax(depth + 1, node_index * 2 + i, True, scores, height)
-            best_score = min(best_score, val)  # Minimizer chooses the lowest score available
+            best_score = min(
+                best_score, val
+            )  # Minimizer chooses the lowest score available
         return best_score
 
+
 def main():
     # Scores array representing the leaf nodes of a binary tree (depth = 3)
     scores = [3, 5, 2, 9, 12, 5, 23, 23]
     # Calculate the height of the binary tree based on the number of leaf nodes
     height = math.ceil(math.log2(len(scores)))
 
     # Print the optimal outcome for the maximizing player
-    print("Optimal value for the maximizing player:", minimax(0, 0, True, scores, height))
+    print(
+        "Optimal value for the maximizing player:", minimax(0, 0, True, scores, height)
+    )
+
 
 if __name__ == "__main__":
     main()

machine_learning/minimax_alpha_beta_pruning.py

@@ -1,9 +1,12 @@
 import math
 
-def minimax_with_pruning(depth, node_index, is_maximizing_player, scores, height, alpha, beta):
+
+def minimax_with_pruning(
+    depth, node_index, is_maximizing_player, scores, height, alpha, beta
+):
     """
     Minimax algorithm with alpha-beta pruning to determine the optimal move with improved efficiency.
-    
+
     Parameters:
     - depth (int): Current depth in the game tree, used to track recursion level.
     - node_index (int): Index of the current node in the scores array, representing leaf nodes.
@@ -24,7 +27,9 @@ def minimax_with_pruning(depth, node_index, is_maximizing_player, scores, height
     if is_maximizing_player:
         best_score = -math.inf  # Start with the worst possible score for maximizer
         for i in range(2):  # Two branches at each level in binary tree
-            val = minimax_with_pruning(depth + 1, node_index * 2 + i, False, scores, height, alpha, beta)
+            val = minimax_with_pruning(
+                depth + 1, node_index * 2 + i, False, scores, height, alpha, beta
+            )
             best_score = max(best_score, val)  # Maximizer selects the maximum value
             alpha = max(alpha, best_score)  # Update alpha (best option for maximizer)
             if beta <= alpha:
@@ -35,21 +40,28 @@ def minimax_with_pruning(depth, node_index, is_maximizing_player, scores, height
     else:
         best_score = math.inf  # Start with the worst possible score for minimizer
         for i in range(2):
-            val = minimax_with_pruning(depth + 1, node_index * 2 + i, True, scores, height, alpha, beta)
+            val = minimax_with_pruning(
+                depth + 1, node_index * 2 + i, True, scores, height, alpha, beta
+            )
             best_score = min(best_score, val)  # Minimizer selects the minimum value
             beta = min(beta, best_score)  # Update beta (best option for minimizer)
             if beta <= alpha:
                 break  # Alpha cut-off
         return best_score
 
+
 def main():
     # Scores array representing the leaf nodes of a binary tree (depth = 3)
     scores = [3, 5, 2, 9, 12, 5, 23, 23]
     # Calculate the height of the binary tree based on the number of leaf nodes
     height = math.ceil(math.log2(len(scores)))
 
     # Print the optimal outcome for the maximizing player with alpha-beta pruning
-    print("Optimal value for the maximizing player with pruning:", minimax_with_pruning(0, 0, True, scores, height, -math.inf, math.inf))
+    print(
+        "Optimal value for the maximizing player with pruning:",
+        minimax_with_pruning(0, 0, True, scores, height, -math.inf, math.inf),
+    )
+
 
 if __name__ == "__main__":
     main()