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alphaBeta.py

+from Board import Board
+import numpy as np
+
+
+class AlphaBeta:
+    def __init__(self, size):
+        actualBoard = np.zeros(size * size)
+        self.boardObject = Board(size, actualBoard)
+        self.test_list = []
+        self.the_very_best = []
+
+    def start_pruning(self, boardSize, color, moves):
+        if boardSize ==7:
+            horizon = 3
+        else:
+            horizon = 2
+            
+        if color == "w":
+            is_max = True
+        else:
+            is_max = False
+        best_move = ()
+        best_score, best_move = self.alpha_beta_recursive(moves, horizon, -10000, 10000, is_max, None)
+        
+        return best_move
+
+
+    def alpha_beta_recursive(self, moves, depth, alpha, beta, is_max, made_move):
+        if depth == 0 or len(moves) == 0:
+            evaluated = self.boardObject.eval_func(self.boardObject.board)
+
+            return evaluated, made_move
+
+        if is_max:
+            best_move = ()
+            value = alpha
+            for move in moves:
+                self.boardObject.add_taijitu(move)
+                new_moves = self.boardObject.generate_moves()
+
+                former_value, former_move = self.alpha_beta_recursive(new_moves, depth - 1, value, beta, False, move)
+                new_value = max(value, former_value)
+                if new_value != value:
+                    best_move = move
+                    value = new_value
+                self.boardObject.remove_taijitu(move)
+                if new_value >= beta:
+                    break
+            return value, best_move
+        else:
+            best_move = ()
+            value = beta
+            for move in moves:
+                self.boardObject.add_taijitu(move)
+                new_moves = self.boardObject.generate_moves()
+
+                former_value, former_move = self.alpha_beta_recursive(new_moves, depth - 1, alpha, value, True, move)
+                self.the_very_best.append(former_move)
+                new_value = min(value, former_value)
+                if new_value != value:
+                    best_move = move
+                    value = new_value
+                self.boardObject.remove_taijitu(move)
+                if new_value <= alpha:
+                    break
+            return value, best_move