def mm_top1(<EOL>n_items, data, initial_params=None, alpha=<NUM_LIT:0.0>,<EOL>max_iter=<NUM_LIT>, tol=<NUM_LIT>):
return _mm(n_items, data, initial_params, alpha, max_iter, tol, _mm_top1)<EOL>
Compute the ML estimate of model parameters using the MM algorithm.

    This function computes the maximum-likelihood (ML) estimate of model
    parameters given top-1 data (see :ref:`data-top1`), using the
    minorization-maximization (MM) algorithm [Hun04]_, [CD12]_.

    If ``alpha > 0``, the function returns the maximum a-posteriori (MAP)
    estimate under a (peaked) Dirichlet prior. See :ref:`regularization` for
    details.

    Parameters
    ----------
    n_items : int
        Number of distinct items.
    data : list of lists
        Top-1 data.
    initial_params : array_like, optional
        Parameters used to initialize the iterative procedure.
    alpha : float, optional
        Regularization parameter.
    max_iter : int, optional
        Maximum number of iterations allowed.
    tol : float, optional
        Maximum L1-norm of the difference between successive iterates to
        declare convergence.

    Returns
    -------
    params : numpy.ndarray
        The ML estimate of model parameters.