minimax_my.py

  def minimax(self, last_moves, d, min_val, max_val, only_closest = True, children = None): #recursive minimax with alpha-beta that performs evaluation in every node, not only in leafs (thus evaluation should be very cheap)
        if d == 0:
            cur_evalutaion = self.evaluate(last_moves)
            return cur_evalutaion, d
        last_move = last_moves[-1]
        turn = (self.board[last_move] + 1) % 2
        if children == None:
            children = set(self.getChildren(only_closest))
        d1 = d
        if turn == 0: #we are in max node - user move
            v = min_val
            for coord in children:
                if self.board[coord] != -1:
                    continue
                self.board[coord] = turn
                children = children.difference([coord])
                buf = self.get_all_neigh_possible_moves(coord)
                children = children.union(buf)
                last_moves.append(coord)
                v1, d1 = self.minimax(last_moves, d-1, v, max_val, only_closest, children)
                last_moves.pop()
                children = children.difference(buf)
                children = children.union([coord])
                self.board[coord] = -1
                if v1 > v:
                    v = v1
                if v >= max_val:
                    return max_val, d1    
            return v, d1
        if turn == 1: #we are in min node - AI move
            v = max_val
            for coord in children:
                if self.board[coord] != -1:
                    continue
                self.board[coord] = turn
                children = children.difference([coord])
                buf = self.get_all_neigh_possible_moves(coord)
                children = children.union(buf)
                last_moves.append(coord)
                v1, d1 = self.minimax(last_moves, d-1, min_val, v, only_closest, children)
                last_moves.pop()
                children = children.difference(buf)
                children = children.union([coord])
                self.board[coord] = -1
                if v1 < v:
                    v = v1
                if v <= min_val:
                    return min_val, d1         
            return v, d1