Repository: scikit-learn/scikit-learn
File: sklearn/covariance/_graph_lasso.py (Python)

Code before:
    Read more in the :ref:`User Guide <sparse_inverse_covariance>`.

    .. versionchanged:: v0.20
        GraphLassoCV has been renamed to GraphicalLassoCV

    Parameters
    ----------
    alphas : int or array-like of shape (n_alphas,), dtype=float, default=4
        If an integer is given, it fixes the number of points on the
        grids of alpha to be used. If a list is given, it gives the
        grid to be used. See the notes in the class docstring for
        more details. Range is [1, inf) for an integer.
        Range is (0, inf] for an array-like of floats.

    n_refinements : int, default=4
        The number of times the grid is refined. Not used if explicit
        values of alphas are passed. Range is [1, inf).

    cv : int, cross-validation generator or iterable, default=None
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the default 5-fold cross-validation,
        - integer, to specify the number of folds,
        - :term:`CV splitter`,
        - An iterable yielding (train, test) splits as arrays of indices.

        For integer/None inputs :class:`~sklearn.model_selection.KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

        .. versionchanged:: 0.20
            ``cv`` default value if None changed from 3-fold to 5-fold.

    tol : float, default=1e-4
        The tolerance to declare convergence: if the dual gap goes below
        this value, iterations are stopped. Range is (0, inf].

    enet_tol : float, default=1e-4
        The tolerance for the elastic net solver used to calculate the descent
        direction. This parameter controls the accuracy of the search direction
        for a given column update, not of the overall parameter estimate. Only
        used for mode='cd'. Range is (0, inf].

    max_iter : int, default=100
        Maximum number of iterations.

    mode : {'cd', 'lars'}, default='cd'
        The Lasso solver to use: coordinate descent or LARS. Use LARS for
        very sparse underlying graphs, where number of features is greater

Review comment (suggestion):
If we're being picky, this (and all other occurrences below) also has to start with a lowercase `an`

Code after:
    Read more in the :ref:`User Guide <sparse_inverse_covariance>`.

    .. versionchanged:: v0.20
        GraphLassoCV has been renamed to GraphicalLassoCV

    Parameters
    ----------
    alphas : int or array-like of shape (n_alphas,), dtype=float, default=4
        If an integer is given, it fixes the number of points on the
        grids of alpha to be used. If a list is given, it gives the
        grid to be used. See the notes in the class docstring for
        more details. Range is [1, inf) for an integer.
        Range is (0, inf] for an array-like of floats.

    n_refinements : int, default=4
        The number of times the grid is refined. Not used if explicit
        values of alphas are passed. Range is [1, inf).

    cv : int, cross-validation generator or iterable, default=None
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the default 5-fold cross-validation,
        - integer, to specify the number of folds,
        - :term:`CV splitter`,
        - an iterable yielding (train, test) splits as arrays of indices.

        For integer/None inputs :class:`~sklearn.model_selection.KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

        .. versionchanged:: 0.20
            ``cv`` default value if None changed from 3-fold to 5-fold.

    tol : float, default=1e-4
        The tolerance to declare convergence: if the dual gap goes below
        this value, iterations are stopped. Range is (0, inf].

    enet_tol : float, default=1e-4
        The tolerance for the elastic net solver used to calculate the descent
        direction. This parameter controls the accuracy of the search direction
        for a given column update, not of the overall parameter estimate. Only
        used for mode='cd'. Range is (0, inf].

    max_iter : int, default=100
        Maximum number of iterations.

    mode : {'cd', 'lars'}, default='cd'
        The Lasso solver to use: coordinate descent or LARS. Use LARS for
        very sparse underlying graphs, where number of features is greater