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titanic.drop(columns=['sex'])

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titanic.drop(['pclass'], axis=1)

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isHuman = 0
for images in human_files_short:
    if face_detector(images) == True:
        isHuman +=1

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'''MINIMAX GET MOVES:'''

self.time_left = time_left

        # Initialize the best move so that this function returns something
        # in case the search fails due to timeout
        '''best_move = (1, 1)'''
        legal_moves = game.get_legal_moves()

        if legal_moves == []:

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Traceback (most recent call last):
This script evaluates the performance of the custom_score evaluation
  File "/Users/charliecraine/Documents/GitHub/AIND-Isolation-master/tournament.py", line 157, in <module>
function against a baseline agent using alpha-beta search and iterative
    main()
deepening (ID) called `AB_Improved`. The three `AB_Custom` agents use
ID and alpha-beta search with the custom_score functions defined in
  File "/Users/charliecraine/Documents/GitHub/AIND-Isolation-master/tournament.py", line 153, in main
game_agent.py.
    play_matches(cpu_agents, test_agents, NUM_MATCHES)

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class AlphaBetaPlayer(IsolationPlayer):
    """Game-playing agent that chooses a move using iterative deepening minimax
    search with alpha-beta pruning. You must finish and test this player to
    make sure it returns a good move before the search time limit expires.
    """
    def get_move(self, game, time_left):
        """Search for the best move from the available legal moves and return a
        result before the time limit expires.

        Modify the get_move() method from the MinimaxPlayer class to implement

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class AlphaBetaPlayer(IsolationPlayer):
    """Game-playing agent that chooses a move using iterative deepening minimax
    search with alpha-beta pruning. You must finish and test this player to
    make sure it returns a good move before the search time limit expires.
    """
    def get_move(self, game, time_left):
        """Search for the best move from the available legal moves and return a
        result before the time limit expires.

        Modify the get_move() method from the MinimaxPlayer class to implement

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class AlphaBetaPlayer(IsolationPlayer):
    """Game-playing agent that chooses a move using iterative deepening minimax
    search with alpha-beta pruning. You must finish and test this player to
    make sure it returns a good move before the search time limit expires.
    """

    def get_move(self, game, time_left):
        """Search for the best move from the available legal moves and return a
        result before the time limit expires.

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Failed Test: 7. Test functionality of AlphaBetaPlayer.alphabeta()
----------------------------------------------------------------------
AssertionError: False is not true : Your AlphaBetaAgent.alphabeta function returned a move that was not the optimal move for the given heurisitc. Make sure that you choose the first branch with the max score at the top level; branches searched later that return the same max score may only be returning an upper bound.
Expected Best Move:
(5, 5)
Your Selection: (7, 5)

Test Case Details:
------------------
Heuristic: open_move_score

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class AlphaBetaPlayer(IsolationPlayer):
   
        self.time_left = time_left



    def terminal_test(self, game):
        if self.time_left() < self.TIMER_THRESHOLD:
            raise SearchTimeout()
        return not bool(game.get_legal_moves())