def payoff_vector(self, opponents_actions):
        """
        Return an array of payoff values, one for each own action, given
        a profile of the opponents' actions.

        Parameters
        ----------
        opponents_actions : see `best_response`.

        Returns
        -------
        payoff_vector : ndarray(float, ndim=1)
            An array representing the player's payoff vector given the
            profile of the opponents' actions.

        """
        def reduce_last_player(payoff_array, action):
            """
            Given `payoff_array` with ndim=M, return the payoff array
            with ndim=M-1 fixing the last player's action to be `action`.

            """
            if isinstance(action, numbers.Integral):  # pure action
                return payoff_array.take(action, axis=-1)
            else:  # mixed action
                return payoff_array.dot(action)

        if self.num_opponents == 1:
            payoff_vector = \
                reduce_last_player(self.payoff_array, opponents_actions)
        elif self.num_opponents >= 2:
            payoff_vector = self.payoff_array
            for i in reversed(range(self.num_opponents)):
                payoff_vector = \
                    reduce_last_player(payoff_vector, opponents_actions[i])
        else:  # Trivial case with self.num_opponents == 0
            payoff_vector = self.payoff_array

        return payoff_vector
dataset/ETHPy150Open QuantEcon/QuantEcon.py/quantecon/game_theory/normal_form_game.py/Player.payoff_vector/original