def minimax_a_b_p(self,
boardObj,
maximizingPlayer,
alpha=-math.inf,
beta=math.inf,
depth=0):
""" recursion, finds the best move based on the heuristic of the board """
if depth == 0 or boardObj.Game_Finish():
return boardObj # .Heuristic(self.player,self.enemy)
if maximizingPlayer:
maxEval = Othello()
maxEval.setHeuristic(-math.inf)
test = self.posibleMoves(boardObj, self.player)
if test == []:
return boardObj
for child in test:
evalBoard = self.minimax_a_b_p(child,
False,
alpha=alpha,
beta=beta,
depth=depth - 1)
if maxEval.Heuristic(self.player,
self.enemy) < evalBoard.Heuristic(
self.player, self.enemy):
maxEval = evalBoard
alpha = max(alpha, evalBoard.Heuristic(self.player,
self.enemy))
if beta <= alpha:
break
return maxEval
else:
minEval = Othello()
minEval.setHeuristic(math.inf)
test = self.posibleMoves(boardObj, self.enemy)
if test == []:
return boardObj
for child in test:
evalBoard = self.minimax_a_b_p(child,
True,
alpha=alpha,
beta=beta,
depth=depth - 1)
if minEval.Heuristic(self.player,
self.enemy) > evalBoard.Heuristic(
self.player, self.enemy):
minEval = evalBoard
beta = min(beta, evalBoard.Heuristic(self.player, self.enemy))
if beta <= alpha:
break
return minEval
