def mclennan_tourky(g, init=None, epsilon=1e-3, max_iter=200,
                    full_output=False):
    r"""
    Find one mixed-action epsilon-Nash equilibrium of an N-player normal
    form game by the fixed point computation algorithm by McLennan and
    Tourky [1]_.

    Parameters
    ----------
    g : NormalFormGame
        NormalFormGame instance.

    init : array_like(int or array_like(float, ndim=1)), optional
        Initial action profile, an array of N objects, where each object
        must be an iteger (pure action) or an array of floats (mixed
        action). If None, default to an array of zeros (the zero-th
        action for each player).

    epsilon : scalar(float), optional(default=1e-3)
        Value of epsilon-optimality.

    max_iter : scalar(int), optional(default=100)
        Maximum number of iterations.

    full_output : bool, optional(default=False)
        If False, only the computed Nash equilibrium is returned. If
        True, the return value is `(NE, res)`, where `NE` is the Nash
        equilibrium and `res` is a `NashResult` object.

    Returns
    -------
    NE : tuple(ndarray(float, ndim=1))
        Tuple of computed Nash equilibrium mixed actions.

    res : NashResult
        Object containing information about the computation. Returned
        only when `full_output` is True. See `NashResult` for details.

    Examples
    --------
    Consider the following version of 3-player "anti-coordination" game,
    where action 0 is a safe action which yields payoff 1, while action
    1 yields payoff :math:`v` if no other player plays 1 and payoff 0
    otherwise:

    >>> N = 3
    >>> v = 2
    >>> payoff_array = np.empty((2,)*n)
    >>> payoff_array[0, :] = 1
    >>> payoff_array[1, :] = 0
    >>> payoff_array[1].flat[0] = v
    >>> g = NormalFormGame((Player(payoff_array),)*N)
    >>> print(g)
    3-player NormalFormGame with payoff profile array:
    [[[[ 1.,  1.,  1.],   [ 1.,  1.,  2.]],
      [[ 1.,  2.,  1.],   [ 1.,  0.,  0.]]],
     [[[ 2.,  1.,  1.],   [ 0.,  1.,  0.]],
      [[ 0.,  0.,  1.],   [ 0.,  0.,  0.]]]]

    This game has a unique symmetric Nash equilibrium, where the
    equilibrium action is given by :math:`(p^*, 1-p^*)` with :math:`p^*
    = 1/v^{1/(N-1)}`:

    >>> p_star = 1/(v**(1/(N-1)))
    >>> [p_star, 1 - p_star]
    [0.7071067811865475, 0.29289321881345254]

    Obtain an approximate Nash equilibrium of this game by
    `mclennan_tourky`:

    >>> epsilon = 1e-5  # Value of epsilon-optimality
    >>> NE = mclennan_tourky(g, epsilon=epsilon)
    >>> print(NE[0], NE[1], NE[2], sep='\n')
    [ 0.70710754  0.29289246]
    [ 0.70710754  0.29289246]
    [ 0.70710754  0.29289246]
    >>> g.is_nash(NE, tol=epsilon)
    True

    Additional information is returned if `full_output` is set True:

    >>> NE, res = mclennan_tourky(g, epsilon=epsilon, full_output=True)
    >>> res.converged
    True
    >>> res.num_iter
    18

    References
    ----------
    .. [1] A. McLennan and R. Tourky, "From Imitation Games to
       Kakutani," 2006.

    """
    try:
        N = g.N
    except:
        raise TypeError('g must be a NormalFormGame')
    if N < 2:
        raise NotImplementedError('Not implemented for 1-player games')

    if init is None:
        init = (0,) * N
    try:
        l = len(init)
    except TypeError:
        raise TypeError('init must be array_like')
    if l != N:
        raise ValueError(
            'init must be of length {N}'.format(N=N)
        )

    indptr = np.empty(N+1, dtype=int)
    indptr[0] = 0
    indptr[1:] = np.cumsum(g.nums_actions)
    x_init = _flatten_action_profile(init, indptr)

    is_approx_fp = lambda x: _is_epsilon_nash(x, g, epsilon, indptr)
    x_star, converged, num_iter = \
        _compute_fixed_point_ig(_best_response_selection, x_init, max_iter,
                                verbose=0, print_skip=1,
                                is_approx_fp=is_approx_fp,
                                g=g, indptr=indptr)
    NE = _get_action_profile(x_star, indptr)

    if not full_output:
        return NE

    res = NashResult(NE=NE,
                     converged=converged,
                     num_iter=num_iter,
                     max_iter=max_iter,
                     init=init,
                     epsilon=epsilon)

    return NE, res