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NameLoad ExtSlice Slice Index NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad NameLoad Return NameLoad decorator_list decorator_list
N/A N/A 
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
 __future__ print_function math log N/A sys N/A warnings distutils.version LooseVersion N/A numpy np scipy linalg interpolate scipy.linalg.lapack get_lapack_funcs base LinearModel base RegressorMixin utils arrayfuncs as_float_array check_X_y model_selection check_cv exceptions ConvergenceWarning externals.joblib Parallel delayed externals.six.moves xrange externals.six string_types N/A scipy N/A solve_triangular_args N/A N/A N/A N/A LooseVersion N/A scipy __version__ N/A LooseVersion 0.12 N/A N/A solve_triangular_args N/A check_finite False lars_path N/A N/A X y Xy Gram max_iter alpha_min method copy_X eps copy_Gram verbose return_path return_n_iter positive N/A None None 500 0 lar True N/A N/A N/A np finfo N/A np float eps True 0 True False False N/A N/A Compute Least Angle Regression or Lasso path using LARS algorithm [1]

    The optimization objective for the case method='lasso' is::

    (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

    in the case of method='lars', the objective function is only known in
    the form of an implicit equation (see discussion in [1])

    Read more in the :ref:`User Guide <least_angle_regression>`.

    Parameters
    -----------
    X : array, shape: (n_samples, n_features)
        Input data.

    y : array, shape: (n_samples)
        Input targets.

    positive : boolean (default=False)
        Restrict coefficients to be >= 0.
        When using this option together with method 'lasso' the model
        coefficients will not converge to the ordinary-least-squares solution
        for small values of alpha (neither will they when using method 'lar'
        ..). Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ >
        0.].min()`` when fit_path=True) reached by the stepwise Lars-Lasso
        algorithm are typically in congruence with the solution of the
        coordinate descent lasso_path function.

    max_iter : integer, optional (default=500)
        Maximum number of iterations to perform, set to infinity for no limit.

    Gram : None, 'auto', array, shape: (n_features, n_features), optional
        Precomputed Gram matrix (X' * X), if ``'auto'``, the Gram
        matrix is precomputed from the given X, if there are more samples
        than features.

    alpha_min : float, optional (default=0)
        Minimum correlation along the path. It corresponds to the
        regularization parameter alpha parameter in the Lasso.

    method : {'lar', 'lasso'}, optional (default='lar')
        Specifies the returned model. Select ``'lar'`` for Least Angle
        Regression, ``'lasso'`` for the Lasso.

    eps : float, optional (default=``np.finfo(np.float).eps``)
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems.

    copy_X : bool, optional (default=True)
        If ``False``, ``X`` is overwritten.

    copy_Gram : bool, optional (default=True)
        If ``False``, ``Gram`` is overwritten.

    verbose : int (default=0)
        Controls output verbosity.

    return_path : bool, optional (default=True)
        If ``return_path==True`` returns the entire path, else returns only the
        last point of the path.

    return_n_iter : bool, optional (default=False)
        Whether to return the number of iterations.

    Returns
    --------
    alphas : array, shape: [n_alphas + 1]
        Maximum of covariances (in absolute value) at each iteration.
        ``n_alphas`` is either ``max_iter``, ``n_features`` or the
        number of nodes in the path with ``alpha >= alpha_min``, whichever
        is smaller.

    active : array, shape [n_alphas]
        Indices of active variables at the end of the path.

    coefs : array, shape (n_features, n_alphas + 1)
        Coefficients along the path

    n_iter : int
        Number of iterations run. Returned only if return_n_iter is set
        to True.

    See also
    --------
    lasso_path
    LassoLars
    Lars
    LassoLarsCV
    LarsCV
    sklearn.decomposition.sparse_encode

    References
    ----------
    .. [1] "Least Angle Regression", Effron et al.
           http://statweb.stanford.edu/~tibs/ftp/lars.pdf

    .. [2] `Wikipedia entry on the Least-angle regression
           <https://en.wikipedia.org/wiki/Least-angle_regression>`_

    .. [3] `Wikipedia entry on the Lasso
           <https://en.wikipedia.org/wiki/Lasso_(statistics)#Lasso_method>`_

     N/A n_features N/A N/A X shape N/A 1 N/A n_samples N/A y size N/A max_features N/A min max_iter n_features N/A return_path N/A N/A coefs N/A N/A np zeros N/A N/A max_features 1 n_features N/A alphas N/A N/A np zeros N/A max_features 1 N/A N/A N/A coef prev_coef N/A N/A N/A np zeros n_features N/A N/A np zeros n_features N/A N/A alpha prev_alpha N/A N/A N/A np array N/A 0.0 N/A N/A np array N/A 0.0 N/A N/A n_iter n_active N/A 0 0 N/A N/A active indices N/A N/A list N/A N/A np arange n_features N/A sign_active N/A N/A np empty max_features dtype N/A np int8 N/A drop False N/A L N/A N/A np zeros N/A max_features max_features dtype N/A X dtype N/A N/A swap nrm2 N/A N/A linalg get_blas_funcs N/A swap nrm2 N/A X N/A N/A solve_cholesky N/A get_lapack_funcs N/A potrs N/A X N/A N/A Gram None N/A N/A copy_X N/A N/A X N/A N/A X copy F N/A N/A N/A N/A isinstance Gram string_types N/A Gram auto N/A N/A Gram None N/A N/A N/A N/A X shape N/A 0 N/A N/A X shape N/A 1 N/A N/A Gram N/A N/A np dot N/A X T X N/A N/A copy_Gram N/A N/A Gram N/A N/A Gram copy N/A N/A Xy None N/A N/A Cov N/A N/A np dot N/A X T y N/A N/A Cov N/A N/A Xy copy N/A verbose N/A N/A N/A verbose 1 N/A N/A N/A print Step		Added		Dropped		Active set size		C N/A N/A N/A N/A N/A sys stdout write . N/A N/A N/A N/A sys stdout flush N/A tiny N/A N/A N/A np finfo N/A np float tiny N/A tiny32 N/A N/A N/A np finfo N/A np float32 tiny N/A equality_tolerance N/A N/A N/A np finfo N/A np float32 eps N/A True N/A N/A N/A Cov size N/A N/A positive N/A N/A C_idx N/A N/A np argmax Cov N/A N/A C_idx N/A N/A np argmax N/A N/A np abs Cov N/A C_ N/A Cov N/A C_idx N/A positive N/A N/A C C_ N/A N/A C N/A N/A np fabs C_ N/A N/A C 0.0 N/A return_path N/A N/A alpha N/A alphas N/A N/A n_iter N/A np newaxis N/A coef N/A coefs N/A n_iter N/A prev_alpha N/A alphas N/A N/A N/A n_iter 1 N/A np newaxis N/A prev_coef N/A coefs N/A N/A n_iter 1 N/A N/A alpha N/A 0 N/A C n_samples N/A N/A N/A alpha N/A 0 N/A alpha_min equality_tolerance N/A N/A N/A N/A abs N/A N/A alpha N/A 0 alpha_min equality_tolerance N/A N/A N/A n_iter 0 N/A N/A ss N/A N/A N/A prev_alpha N/A 0 alpha_min N/A N/A prev_alpha N/A 0 N/A alpha N/A 0 N/A N/A coef N/A N/A prev_coef N/A ss N/A coef prev_coef N/A N/A alpha N/A 0 alpha_min N/A return_path N/A N/A N/A coefs N/A n_iter coef N/A N/A N/A N/A n_iter max_iter N/A n_active n_features N/A N/A N/A N/A drop N/A N/A positive N/A N/A N/A sign_active N/A n_active N/A N/A np ones_like C_ N/A N/A N/A sign_active N/A n_active N/A N/A np sign C_ N/A N/A m n N/A n_active N/A C_idx n_active N/A N/A N/A Cov N/A C_idx N/A Cov N/A 0 N/A swap N/A Cov N/A C_idx N/A Cov N/A 0 N/A N/A N/A indices N/A n N/A indices N/A m N/A N/A indices N/A m N/A indices N/A n N/A Cov_not_shortened Cov N/A Cov N/A Cov N/A 1 N/A N/A Gram None N/A N/A N/A N/A N/A X T N/A n N/A N/A X T N/A m N/A swap N/A N/A X T N/A n N/A N/A X T N/A m N/A c N/A N/A nrm2 N/A N/A X T N/A n_active 2 N/A N/A L N/A N/A n_active N/A n_active N/A N/A np dot N/A N/A X T N/A n_active N/A N/A N/A X T N/A n_active T N/A N/A N/A N/A Gram N/A m N/A Gram N/A n N/A swap N/A Gram N/A m N/A Gram N/A n N/A N/A N/A Gram N/A N/A N/A m N/A Gram N/A N/A N/A n N/A swap N/A Gram N/A N/A N/A m N/A Gram N/A N/A N/A n N/A c N/A Gram N/A N/A n_active n_active N/A N/A L N/A N/A n_active N/A n_active N/A Gram N/A N/A n_active N/A n_active N/A n_active N/A N/A N/A N/A linalg solve_triangular N/A L N/A N/A n_active N/A n_active N/A L N/A N/A n_active N/A n_active trans 0 lower 1 overwrite_b True solve_triangular_args N/A v N/A N/A np dot N/A L N/A N/A n_active N/A n_active N/A L N/A N/A n_active N/A n_active N/A diag N/A max N/A N/A np sqrt N/A N/A np abs N/A c v eps N/A N/A L N/A N/A n_active n_active diag N/A N/A diag 1e-07 N/A N/A N/A N/A warnings warn N/A Regressors in active set degenerate. Dropping a regressor, after %i iterations, i.e. alpha=%.3e, with an active set of %i regressors, and the smallest cholesky pivot element being %.3e N/A n_iter alpha n_active diag ConvergenceWarning N/A Cov Cov_not_shortened N/A N/A Cov N/A 0 0 N/A N/A N/A Cov N/A C_idx N/A Cov N/A 0 N/A swap N/A Cov N/A C_idx N/A Cov N/A 0 N/A N/A N/A N/A active append N/A indices N/A n_active N/A n_active 1 N/A N/A verbose 1 N/A N/A N/A print N/A %s		%s		%s		%s		%s N/A n_iter N/A active N/A -1 n_active C N/A N/A N/A method lasso N/A n_iter 0 N/A N/A prev_alpha N/A 0 N/A alpha N/A 0 N/A N/A N/A N/A warnings warn N/A Early stopping the lars path, as the residues are small and the current value of alpha is no longer well controlled. %i iterations, alpha=%.3e, previous alpha=%.3e, with an active set of %i regressors. N/A n_iter alpha prev_alpha n_active ConvergenceWarning N/A N/A N/A least_squares info N/A solve_cholesky N/A L N/A N/A n_active N/A n_active N/A sign_active N/A n_active lower True N/A N/A N/A N/A least_squares size 1 N/A least_squares 0 N/A N/A N/A least_squares N/A 1 N/A AA 1.0 N/A N/A AA N/A 1.0 N/A N/A np sqrt N/A N/A np sum N/A least_squares N/A sign_active N/A n_active N/A N/A N/A N/A np isfinite AA N/A N/A i 0 N/A L_ N/A N/A N/A L N/A N/A n_active N/A n_active copy N/A N/A N/A N/A np isfinite AA N/A N/A N/A N/A L_ flat N/A N/A n_active 1 N/A N/A 2 i eps N/A N/A least_squares info N/A solve_cholesky L_ N/A sign_active N/A n_active lower True N/A tmp N/A max N/A N/A np sum N/A least_squares N/A sign_active N/A n_active eps N/A AA N/A 1.0 N/A N/A np sqrt tmp N/A i 1 N/A least_squares AA N/A N/A Gram None N/A N/A eq_dir N/A N/A np dot N/A N/A N/A X T N/A n_active T least_squares N/A corr_eq_dir N/A N/A np dot N/A N/A X T N/A n_active eq_dir N/A N/A corr_eq_dir N/A N/A np dot N/A N/A Gram N/A N/A n_active N/A n_active T least_squares N/A g1 N/A N/A arrayfuncs min_pos N/A N/A C Cov N/A N/A AA corr_eq_dir tiny N/A positive N/A N/A gamma_ N/A min g1 N/A C AA N/A N/A g2 N/A N/A arrayfuncs min_pos N/A N/A C Cov N/A N/A AA corr_eq_dir tiny N/A gamma_ N/A min g1 g2 N/A C AA N/A drop False N/A z N/A N/A N/A coef N/A active N/A least_squares tiny32 N/A z_pos N/A N/A arrayfuncs min_pos z N/A N/A z_pos gamma_ N/A N/A idx N/A N/A N/A N/A np where N/A z z_pos N/A 0 N/A -1 N/A N/A sign_active N/A idx N/A N/A sign_active N/A idx N/A N/A method lasso N/A N/A gamma_ z_pos N/A drop True N/A n_iter 1 N/A return_path N/A N/A N/A n_iter N/A N/A coefs shape N/A 0 N/A N/A coef alpha prev_alpha prev_coef N/A add_features N/A 2 N/A max 1 N/A max_features n_active N/A coefs N/A N/A np resize coefs N/A N/A n_iter add_features n_features N/A alphas N/A N/A np resize alphas N/A n_iter add_features N/A coef N/A coefs N/A n_iter N/A prev_coef N/A coefs N/A N/A n_iter 1 N/A alpha N/A alphas N/A N/A n_iter N/A np newaxis N/A prev_alpha N/A alphas N/A N/A N/A n_iter 1 N/A np newaxis N/A N/A prev_coef coef N/A N/A prev_alpha N/A 0 N/A alpha N/A 0 N/A coef N/A N/A np zeros_like coef N/A N/A coef N/A active N/A N/A prev_coef N/A active N/A gamma_ least_squares N/A Cov N/A gamma_ corr_eq_dir N/A N/A drop N/A method lasso N/A N/A N/A N/A N/A arrayfuncs cholesky_delete N/A L N/A N/A n_active N/A n_active ii N/A ii idx N/A n_active 1 N/A N/A m n N/A idx n_active N/A drop_idx N/A N/A N/A active pop ii N/A ii idx N/A N/A Gram None N/A N/A ii idx N/A N/A i N/A range ii n_active N/A N/A N/A N/A N/A X T N/A i N/A N/A X T N/A N/A i 1 N/A swap N/A N/A X T N/A i N/A N/A X T N/A N/A i 1 N/A N/A N/A indices N/A i N/A indices N/A N/A i 1 N/A N/A indices N/A N/A i 1 N/A indices N/A i N/A residual N/A y N/A N/A np dot N/A X N/A N/A N/A n_active N/A coef N/A active N/A temp N/A N/A np dot N/A N/A X T N/A n_active residual N/A Cov N/A N/A np r_ N/A N/A temp Cov N/A N/A ii idx N/A N/A i N/A range ii n_active N/A N/A N/A N/A indices N/A i N/A indices N/A N/A i 1 N/A N/A indices N/A N/A i 1 N/A indices N/A i N/A N/A N/A Gram N/A i N/A Gram N/A N/A i 1 N/A swap N/A Gram N/A i N/A Gram N/A N/A i 1 N/A N/A N/A Gram N/A N/A N/A i N/A Gram N/A N/A N/A N/A i 1 N/A swap N/A Gram N/A N/A N/A i N/A Gram N/A N/A N/A N/A i 1 N/A residual N/A y N/A N/A np dot X coef N/A temp N/A N/A np dot N/A N/A X T N/A drop_idx residual N/A Cov N/A N/A np r_ N/A N/A temp Cov N/A sign_active N/A N/A np delete sign_active idx N/A sign_active N/A N/A np append sign_active 0.0 N/A N/A verbose 1 N/A N/A N/A print N/A %s		%s		%s		%s		%s N/A n_iter drop_idx n_active N/A abs temp N/A return_path N/A N/A alphas N/A alphas N/A N/A n_iter 1 N/A coefs N/A coefs N/A N/A n_iter 1 N/A return_n_iter N/A N/A N/A alphas active N/A coefs T n_iter N/A N/A N/A alphas active N/A coefs T N/A N/A return_n_iter N/A N/A N/A alpha active coef n_iter N/A N/A N/A alpha active coef N/A Lars N/A LinearModel RegressorMixin N/A N/A Least Angle Regression model a.k.a. LAR

    Read more in the :ref:`User Guide <least_angle_regression>`.

    Parameters
    ----------
    n_nonzero_coefs : int, optional
        Target number of non-zero coefficients. Use ``np.inf`` for no limit.

    fit_intercept : boolean
        Whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (e.g. data is expected to be already centered).

    positive : boolean (default=False)
        Restrict coefficients to be >= 0. Be aware that you might want to
        remove fit_intercept which is set True by default.

    verbose : boolean or integer, optional
        Sets the verbosity amount

    normalize : boolean, optional, default False
        If True, the regressors X will be normalized before regression.
        This parameter is ignored when `fit_intercept` is set to False.
        When the regressors are normalized, note that this makes the
        hyperparameters learnt more robust and almost independent of the number
        of samples. The same property is not valid for standardized data.
        However, if you wish to standardize, please use
        `preprocessing.StandardScaler` before calling `fit` on an estimator
        with `normalize=False`.

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    copy_X : boolean, optional, default True
        If ``True``, X will be copied; else, it may be overwritten.

    eps : float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems. Unlike the ``tol`` parameter in some iterative
        optimization-based algorithms, this parameter does not control
        the tolerance of the optimization.

    fit_path : boolean
        If True the full path is stored in the ``coef_path_`` attribute.
        If you compute the solution for a large problem or many targets,
        setting ``fit_path`` to ``False`` will lead to a speedup, especially
        with a small alpha.

    Attributes
    ----------
    alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays
        Maximum of covariances (in absolute value) at each iteration.         ``n_alphas`` is either ``n_nonzero_coefs`` or ``n_features``,         whichever is smaller.

    active_ : list, length = n_alphas | list of n_targets such lists
        Indices of active variables at the end of the path.

    coef_path_ : array, shape (n_features, n_alphas + 1)         | list of n_targets such arrays
        The varying values of the coefficients along the path. It is not
        present if the ``fit_path`` parameter is ``False``.

    coef_ : array, shape (n_features,) or (n_targets, n_features)
        Parameter vector (w in the formulation formula).

    intercept_ : float | array, shape (n_targets,)
        Independent term in decision function.

    n_iter_ : array-like or int
        The number of iterations taken by lars_path to find the
        grid of alphas for each target.

    Examples
    --------
    >>> from sklearn import linear_model
    >>> clf = linear_model.Lars(n_nonzero_coefs=1)
    >>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
    ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    Lars(copy_X=True, eps=..., fit_intercept=True, fit_path=True,
       n_nonzero_coefs=1, normalize=True, positive=False, precompute='auto',
       verbose=False)
    >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    [ 0. -1.11...]

    See also
    --------
    lars_path, LarsCV
    sklearn.decomposition.sparse_encode

     __init__ N/A N/A self fit_intercept verbose normalize precompute n_nonzero_coefs eps copy_X fit_path positive N/A True False True auto 500 N/A N/A N/A np finfo N/A np float eps True True False N/A N/A N/A self fit_intercept fit_intercept N/A N/A self verbose verbose N/A N/A self normalize normalize N/A N/A self method lar N/A N/A self precompute precompute N/A N/A self n_nonzero_coefs n_nonzero_coefs N/A N/A self positive positive N/A N/A self eps eps N/A N/A self copy_X copy_X N/A N/A self fit_path fit_path N/A _get_gram N/A N/A self N/A N/A N/A precompute N/A self precompute N/A N/A hasattr precompute __array__ N/A N/A Gram precompute N/A N/A N/A precompute auto N/A N/A Gram auto N/A N/A Gram None N/A Gram N/A fit N/A N/A self X y Xy N/A None N/A N/A Fit the model using X, y as training data.

        parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training data.

        y : array-like, shape (n_samples,) or (n_samples, n_targets)
            Target values.

        Xy : array-like, shape (n_samples,) or (n_samples, n_targets),                 optional
            Xy = np.dot(X.T, y) that can be precomputed. It is useful
            only when the Gram matrix is precomputed.

        returns
        -------
        self : object
            returns an instance of self.
         N/A N/A X y N/A check_X_y X y y_numeric True multi_output True N/A n_features N/A N/A X shape N/A 1 N/A N/A X y X_offset y_offset X_scale N/A N/A self _preprocess_data X y N/A self fit_intercept N/A self normalize N/A self copy_X N/A N/A N/A y ndim 1 N/A N/A y N/A y N/A N/A N/A N/A np newaxis N/A n_targets N/A N/A y shape N/A 1 N/A alpha N/A getattr self alpha 0.0 N/A N/A hasattr self n_nonzero_coefs N/A N/A alpha 0.0 N/A max_iter N/A self n_nonzero_coefs N/A N/A max_iter N/A self max_iter N/A precompute N/A self precompute N/A N/A N/A N/A hasattr precompute __array__ N/A N/A precompute True N/A N/A precompute auto N/A N/A N/A X shape N/A 0 N/A N/A X shape N/A 1 N/A N/A precompute auto N/A N/A N/A y shape N/A 1 1 N/A N/A Gram N/A N/A np dot N/A X T X N/A N/A Gram N/A N/A self _get_gram N/A N/A self alphas_ N/A N/A N/A self n_iter_ N/A N/A N/A self fit_path N/A N/A N/A self coef_ N/A N/A N/A self active_ N/A N/A N/A self coef_path_ N/A N/A k N/A xrange n_targets N/A N/A this_Xy N/A N/A Xy None None N/A Xy N/A N/A N/A k N/A N/A alphas active coef_path n_iter_ N/A lars_path X N/A y N/A N/A N/A k Gram Gram Xy this_Xy copy_X N/A self copy_X copy_Gram True alpha_min alpha method N/A self method verbose N/A max 0 N/A N/A self verbose 1 max_iter max_iter eps N/A self eps return_path True return_n_iter True positive N/A self positive N/A N/A N/A N/A self alphas_ append alphas N/A N/A N/A N/A self active_ append active N/A N/A N/A N/A self n_iter_ append n_iter_ N/A N/A N/A N/A self coef_path_ append coef_path N/A N/A N/A N/A self coef_ append N/A coef_path N/A N/A N/A -1 N/A N/A n_targets 1 N/A N/A N/A N/A self alphas_ N/A self active_ N/A self coef_path_ N/A self coef_ N/A N/A a N/A 0 N/A a N/A N/A self alphas_ N/A self active_ N/A self coef_path_ N/A self coef_ N/A N/A self n_iter_ N/A N/A self n_iter_ N/A 0 N/A N/A N/A self coef_ N/A N/A np empty N/A n_targets n_features N/A k N/A xrange n_targets N/A N/A this_Xy N/A N/A Xy None None N/A Xy N/A N/A N/A k N/A N/A alphas _ N/A N/A self coef_ N/A k n_iter_ N/A lars_path X N/A y N/A N/A N/A k Gram Gram Xy this_Xy copy_X N/A self copy_X copy_Gram True alpha_min alpha method N/A self method verbose N/A max 0 N/A N/A self verbose 1 max_iter max_iter eps N/A self eps return_path False return_n_iter True positive N/A self positive N/A N/A N/A N/A self alphas_ append alphas N/A N/A N/A N/A self n_iter_ append n_iter_ N/A N/A n_targets 1 N/A N/A N/A self alphas_ N/A N/A self alphas_ N/A 0 N/A N/A self n_iter_ N/A N/A self n_iter_ N/A 0 N/A N/A N/A self _set_intercept X_offset y_offset X_scale N/A self N/A N/A LassoLars N/A Lars N/A N/A Lasso model fit with Least Angle Regression a.k.a. Lars

    It is a Linear Model trained with an L1 prior as regularizer.

    The optimization objective for Lasso is::

    (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

    Read more in the :ref:`User Guide <least_angle_regression>`.

    Parameters
    ----------
    alpha : float
        Constant that multiplies the penalty term. Defaults to 1.0.
        ``alpha = 0`` is equivalent to an ordinary least square, solved
        by :class:`LinearRegression`. For numerical reasons, using
        ``alpha = 0`` with the LassoLars object is not advised and you
        should prefer the LinearRegression object.

    fit_intercept : boolean
        whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (e.g. data is expected to be already centered).

    positive : boolean (default=False)
        Restrict coefficients to be >= 0. Be aware that you might want to
        remove fit_intercept which is set True by default.
        Under the positive restriction the model coefficients will not converge
        to the ordinary-least-squares solution for small values of alpha.
        Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ >
        0.].min()`` when fit_path=True) reached by the stepwise Lars-Lasso
        algorithm are typically in congruence with the solution of the
        coordinate descent Lasso estimator.

    verbose : boolean or integer, optional
        Sets the verbosity amount

    normalize : boolean, optional, default False
        If True, the regressors X will be normalized before regression.
        This parameter is ignored when `fit_intercept` is set to False.
        When the regressors are normalized, note that this makes the
        hyperparameters learnt more robust and almost independent of the number
        of samples. The same property is not valid for standardized data.
        However, if you wish to standardize, please use
        `preprocessing.StandardScaler` before calling `fit` on an estimator
        with `normalize=False`.

    copy_X : boolean, optional, default True
        If True, X will be copied; else, it may be overwritten.

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    max_iter : integer, optional
        Maximum number of iterations to perform.

    eps : float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems. Unlike the ``tol`` parameter in some iterative
        optimization-based algorithms, this parameter does not control
        the tolerance of the optimization.

    fit_path : boolean
        If ``True`` the full path is stored in the ``coef_path_`` attribute.
        If you compute the solution for a large problem or many targets,
        setting ``fit_path`` to ``False`` will lead to a speedup, especially
        with a small alpha.

    Attributes
    ----------
    alphas_ : array, shape (n_alphas + 1,) | list of n_targets such arrays
        Maximum of covariances (in absolute value) at each iteration.         ``n_alphas`` is either ``max_iter``, ``n_features``, or the number of         nodes in the path with correlation greater than ``alpha``, whichever         is smaller.

    active_ : list, length = n_alphas | list of n_targets such lists
        Indices of active variables at the end of the path.

    coef_path_ : array, shape (n_features, n_alphas + 1) or list
        If a list is passed it's expected to be one of n_targets such arrays.
        The varying values of the coefficients along the path. It is not
        present if the ``fit_path`` parameter is ``False``.

    coef_ : array, shape (n_features,) or (n_targets, n_features)
        Parameter vector (w in the formulation formula).

    intercept_ : float | array, shape (n_targets,)
        Independent term in decision function.

    n_iter_ : array-like or int.
        The number of iterations taken by lars_path to find the
        grid of alphas for each target.

    Examples
    --------
    >>> from sklearn import linear_model
    >>> clf = linear_model.LassoLars(alpha=0.01)
    >>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1, 0, -1])
    ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    LassoLars(alpha=0.01, copy_X=True, eps=..., fit_intercept=True,
         fit_path=True, max_iter=500, normalize=True, positive=False,
         precompute='auto', verbose=False)
    >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    [ 0.         -0.963257...]

    See also
    --------
    lars_path
    lasso_path
    Lasso
    LassoCV
    LassoLarsCV
    sklearn.decomposition.sparse_encode

     __init__ N/A N/A self alpha fit_intercept verbose normalize precompute max_iter eps copy_X fit_path positive N/A 1.0 True False True auto 500 N/A N/A N/A np finfo N/A np float eps True True False N/A N/A N/A self alpha alpha N/A N/A self fit_intercept fit_intercept N/A N/A self max_iter max_iter N/A N/A self verbose verbose N/A N/A self normalize normalize N/A N/A self method lasso N/A N/A self positive positive N/A N/A self precompute precompute N/A N/A self copy_X copy_X N/A N/A self eps eps N/A N/A self fit_path fit_path N/A N/A _check_copy_and_writeable N/A N/A array copy N/A False N/A N/A N/A copy N/A N/A N/A array flags writeable N/A N/A N/A N/A array copy N/A array N/A _lars_path_residues N/A N/A X_train y_train X_test y_test Gram copy method verbose fit_intercept normalize max_iter eps positive N/A None True lars False True True 500 N/A N/A N/A np finfo N/A np float eps False N/A N/A Compute the residues on left-out data for a full LARS path

    Parameters
    -----------
    X_train : array, shape (n_samples, n_features)
        The data to fit the LARS on

    y_train : array, shape (n_samples)
        The target variable to fit LARS on

    X_test : array, shape (n_samples, n_features)
        The data to compute the residues on

    y_test : array, shape (n_samples)
        The target variable to compute the residues on

    Gram : None, 'auto', array, shape: (n_features, n_features), optional
        Precomputed Gram matrix (X' * X), if ``'auto'``, the Gram
        matrix is precomputed from the given X, if there are more samples
        than features

    copy : boolean, optional
        Whether X_train, X_test, y_train and y_test should be copied;
        if False, they may be overwritten.

    method : 'lar' | 'lasso'
        Specifies the returned model. Select ``'lar'`` for Least Angle
        Regression, ``'lasso'`` for the Lasso.

    verbose : integer, optional
        Sets the amount of verbosity

    fit_intercept : boolean
        whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (e.g. data is expected to be already centered).

    positive : boolean (default=False)
        Restrict coefficients to be >= 0. Be aware that you might want to
        remove fit_intercept which is set True by default.
        See reservations for using this option in combination with method
        'lasso' for expected small values of alpha in the doc of LassoLarsCV
        and LassoLarsIC.

    normalize : boolean, optional, default False
        If True, the regressors X will be normalized before regression.
        This parameter is ignored when `fit_intercept` is set to False.
        When the regressors are normalized, note that this makes the
        hyperparameters learnt more robust and almost independent of the number
        of samples. The same property is not valid for standardized data.
        However, if you wish to standardize, please use
        `preprocessing.StandardScaler` before calling `fit` on an estimator
        with `normalize=False`.

    max_iter : integer, optional
        Maximum number of iterations to perform.

    eps : float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems. Unlike the ``tol`` parameter in some iterative
        optimization-based algorithms, this parameter does not control
        the tolerance of the optimization.


    Returns
    --------
    alphas : array, shape (n_alphas,)
        Maximum of covariances (in absolute value) at each iteration.
        ``n_alphas`` is either ``max_iter`` or ``n_features``, whichever
        is smaller.

    active : list
        Indices of active variables at the end of the path.

    coefs : array, shape (n_features, n_alphas)
        Coefficients along the path

    residues : array, shape (n_alphas, n_samples)
        Residues of the prediction on the test data
     N/A X_train N/A _check_copy_and_writeable X_train copy N/A y_train N/A _check_copy_and_writeable y_train copy N/A X_test N/A _check_copy_and_writeable X_test copy N/A y_test N/A _check_copy_and_writeable y_test copy N/A fit_intercept N/A N/A X_mean N/A N/A X_train mean axis 0 N/A X_train X_mean N/A X_test X_mean N/A y_mean N/A N/A y_train mean axis 0 N/A y_train N/A as_float_array y_train copy False N/A y_train y_mean N/A y_test N/A as_float_array y_test copy False N/A y_test y_mean N/A normalize N/A N/A norms N/A N/A np sqrt N/A N/A np sum N/A X_train 2 axis 0 N/A nonzeros N/A N/A np flatnonzero norms N/A N/A X_train N/A N/A N/A nonzeros N/A norms N/A nonzeros N/A N/A alphas active coefs N/A lars_path X_train y_train Gram Gram copy_X False copy_Gram False method method verbose N/A max 0 N/A verbose 1 max_iter max_iter eps eps positive positive N/A normalize N/A N/A N/A coefs N/A nonzeros N/A N/A norms N/A nonzeros N/A N/A N/A N/A np newaxis N/A residues N/A N/A N/A np dot X_test coefs N/A y_test N/A N/A N/A N/A np newaxis N/A N/A alphas active coefs N/A residues T N/A LarsCV N/A Lars N/A N/A Cross-validated Least Angle Regression model

    Read more in the :ref:`User Guide <least_angle_regression>`.

    Parameters
    ----------
    fit_intercept : boolean
        whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (e.g. data is expected to be already centered).

    positive : boolean (default=False)
        Restrict coefficients to be >= 0. Be aware that you might want to
        remove fit_intercept which is set True by default.

    verbose : boolean or integer, optional
        Sets the verbosity amount

    normalize : boolean, optional, default False
        If True, the regressors X will be normalized before regression.
        This parameter is ignored when `fit_intercept` is set to False.
        When the regressors are normalized, note that this makes the
        hyperparameters learnt more robust and almost independent of the number
        of samples. The same property is not valid for standardized data.
        However, if you wish to standardize, please use
        `preprocessing.StandardScaler` before calling `fit` on an estimator
        with `normalize=False`.

    copy_X : boolean, optional, default True
        If ``True``, X will be copied; else, it may be overwritten.

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    max_iter: integer, optional
        Maximum number of iterations to perform.

    cv : int, cross-validation generator or an iterable, optional
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the default 3-fold cross-validation,
        - integer, to specify the number of folds.
        - An object to be used as a cross-validation generator.
        - An iterable yielding train/test splits.

        For integer/None inputs, :class:`KFold` is used.

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

    max_n_alphas : integer, optional
        The maximum number of points on the path used to compute the
        residuals in the cross-validation

    n_jobs : integer, optional
        Number of CPUs to use during the cross validation. If ``-1``, use
        all the CPUs

    eps : float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems.


    Attributes
    ----------
    coef_ : array, shape (n_features,)
        parameter vector (w in the formulation formula)

    intercept_ : float
        independent term in decision function

    coef_path_ : array, shape (n_features, n_alphas)
        the varying values of the coefficients along the path

    alpha_ : float
        the estimated regularization parameter alpha

    alphas_ : array, shape (n_alphas,)
        the different values of alpha along the path

    cv_alphas_ : array, shape (n_cv_alphas,)
        all the values of alpha along the path for the different folds

    cv_mse_path_ : array, shape (n_folds, n_cv_alphas)
        the mean square error on left-out for each fold along the path
        (alpha values given by ``cv_alphas``)

    n_iter_ : array-like or int
        the number of iterations run by Lars with the optimal alpha.

    See also
    --------
    lars_path, LassoLars, LassoLarsCV
     N/A method lar __init__ N/A N/A self fit_intercept verbose max_iter normalize precompute cv max_n_alphas n_jobs eps copy_X positive N/A True False 500 True auto None 1000 1 N/A N/A N/A np finfo N/A np float eps True False N/A N/A N/A self fit_intercept fit_intercept N/A N/A self positive positive N/A N/A self max_iter max_iter N/A N/A self verbose verbose N/A N/A self normalize normalize N/A N/A self precompute precompute N/A N/A self copy_X copy_X N/A N/A self cv cv N/A N/A self max_n_alphas max_n_alphas N/A N/A self n_jobs n_jobs N/A N/A self eps eps N/A fit N/A N/A self X y N/A N/A N/A Fit the model using X, y as training data.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training data.

        y : array-like, shape (n_samples,)
            Target values.

        Returns
        -------
        self : object
            returns an instance of self.
         N/A N/A self fit_path True N/A N/A X y N/A check_X_y X y y_numeric True N/A X N/A as_float_array X copy N/A self copy_X N/A y N/A as_float_array y copy N/A self copy_X N/A cv N/A check_cv N/A self cv classifier False N/A Gram N/A N/A self precompute auto None N/A cv_paths N/A N/A Parallel n_jobs N/A self n_jobs verbose N/A self verbose N/A N/A N/A delayed _lars_path_residues N/A X N/A train N/A y N/A train N/A X N/A test N/A y N/A test Gram Gram copy False method N/A self method verbose N/A max 0 N/A N/A self verbose 1 normalize N/A self normalize fit_intercept N/A self fit_intercept max_iter N/A self max_iter eps N/A self eps positive N/A self positive N/A N/A train test N/A N/A cv split X y N/A all_alphas N/A N/A np concatenate N/A N/A list N/A zip cv_paths N/A 0 N/A all_alphas N/A N/A np unique all_alphas N/A stride N/A int N/A max 1 N/A int N/A N/A len all_alphas N/A float N/A self max_n_alphas N/A all_alphas N/A all_alphas N/A stride N/A mse_path N/A N/A np empty N/A N/A len all_alphas N/A len cv_paths N/A N/A index N/A alphas active coefs residues N/A enumerate cv_paths N/A N/A alphas N/A alphas N/A -1 N/A residues N/A residues N/A -1 N/A N/A N/A alphas N/A 0 0 N/A N/A alphas N/A N/A np r_ N/A N/A 0 alphas N/A residues N/A N/A np r_ N/A N/A N/A residues N/A N/A 0 N/A np newaxis residues N/A N/A N/A alphas N/A -1 N/A all_alphas N/A -1 N/A N/A alphas N/A N/A np r_ N/A N/A alphas N/A all_alphas N/A -1 N/A residues N/A N/A np r_ N/A N/A residues N/A residues N/A N/A -1 N/A np newaxis N/A this_residues N/A N/A N/A interpolate interp1d alphas residues axis 0 all_alphas N/A this_residues 2 N/A N/A mse_path N/A N/A N/A index N/A N/A np mean this_residues axis -1 N/A mask N/A N/A np all N/A N/A np isfinite mse_path axis -1 N/A all_alphas N/A all_alphas N/A mask N/A mse_path N/A mse_path N/A mask N/A i_best_alpha N/A N/A np argmin N/A N/A mse_path mean axis -1 N/A best_alpha N/A all_alphas N/A i_best_alpha N/A N/A self alpha_ best_alpha N/A N/A self cv_alphas_ all_alphas N/A N/A self cv_mse_path_ mse_path N/A N/A N/A Lars fit self X y N/A self N/A alpha N/A N/A self N/A N/A N/A N/A self alpha_ N/A property N/A LassoLarsCV N/A LarsCV N/A N/A Cross-validated Lasso, using the LARS algorithm

    The optimization objective for Lasso is::

    (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

    Read more in the :ref:`User Guide <least_angle_regression>`.

    Parameters
    ----------
    fit_intercept : boolean
        whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (e.g. data is expected to be already centered).

    positive : boolean (default=False)
        Restrict coefficients to be >= 0. Be aware that you might want to
        remove fit_intercept which is set True by default.
        Under the positive restriction the model coefficients do not converge
        to the ordinary-least-squares solution for small values of alpha.
        Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ >
        0.].min()`` when fit_path=True) reached by the stepwise Lars-Lasso
        algorithm are typically in congruence with the solution of the
        coordinate descent Lasso estimator.
        As a consequence using LassoLarsCV only makes sense for problems where
        a sparse solution is expected and/or reached.

    verbose : boolean or integer, optional
        Sets the verbosity amount

    normalize : boolean, optional, default False
        If True, the regressors X will be normalized before regression.
        This parameter is ignored when `fit_intercept` is set to False.
        When the regressors are normalized, note that this makes the
        hyperparameters learnt more robust and almost independent of the number
        of samples. The same property is not valid for standardized data.
        However, if you wish to standardize, please use
        `preprocessing.StandardScaler` before calling `fit` on an estimator
        with `normalize=False`.

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    max_iter : integer, optional
        Maximum number of iterations to perform.

    cv : int, cross-validation generator or an iterable, optional
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the default 3-fold cross-validation,
        - integer, to specify the number of folds.
        - An object to be used as a cross-validation generator.
        - An iterable yielding train/test splits.

        For integer/None inputs, :class:`KFold` is used.

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

    max_n_alphas : integer, optional
        The maximum number of points on the path used to compute the
        residuals in the cross-validation

    n_jobs : integer, optional
        Number of CPUs to use during the cross validation. If ``-1``, use
        all the CPUs

    eps : float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems.

    copy_X : boolean, optional, default True
        If True, X will be copied; else, it may be overwritten.

    Attributes
    ----------
    coef_ : array, shape (n_features,)
        parameter vector (w in the formulation formula)

    intercept_ : float
        independent term in decision function.

    coef_path_ : array, shape (n_features, n_alphas)
        the varying values of the coefficients along the path

    alpha_ : float
        the estimated regularization parameter alpha

    alphas_ : array, shape (n_alphas,)
        the different values of alpha along the path

    cv_alphas_ : array, shape (n_cv_alphas,)
        all the values of alpha along the path for the different folds

    cv_mse_path_ : array, shape (n_folds, n_cv_alphas)
        the mean square error on left-out for each fold along the path
        (alpha values given by ``cv_alphas``)

    n_iter_ : array-like or int
        the number of iterations run by Lars with the optimal alpha.

    Notes
    -----

    The object solves the same problem as the LassoCV object. However,
    unlike the LassoCV, it find the relevant alphas values by itself.
    In general, because of this property, it will be more stable.
    However, it is more fragile to heavily multicollinear datasets.

    It is more efficient than the LassoCV if only a small number of
    features are selected compared to the total number, for instance if
    there are very few samples compared to the number of features.

    See also
    --------
    lars_path, LassoLars, LarsCV, LassoCV
     N/A method lasso N/A LassoLarsIC N/A LassoLars N/A N/A Lasso model fit with Lars using BIC or AIC for model selection

    The optimization objective for Lasso is::

    (1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

    AIC is the Akaike information criterion and BIC is the Bayes
    Information criterion. Such criteria are useful to select the value
    of the regularization parameter by making a trade-off between the
    goodness of fit and the complexity of the model. A good model should
    explain well the data while being simple.

    Read more in the :ref:`User Guide <least_angle_regression>`.

    Parameters
    ----------
    criterion : 'bic' | 'aic'
        The type of criterion to use.

    fit_intercept : boolean
        whether to calculate the intercept for this model. If set
        to false, no intercept will be used in calculations
        (e.g. data is expected to be already centered).

    positive : boolean (default=False)
        Restrict coefficients to be >= 0. Be aware that you might want to
        remove fit_intercept which is set True by default.
        Under the positive restriction the model coefficients do not converge
        to the ordinary-least-squares solution for small values of alpha.
        Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ >
        0.].min()`` when fit_path=True) reached by the stepwise Lars-Lasso
        algorithm are typically in congruence with the solution of the
        coordinate descent Lasso estimator.
        As a consequence using LassoLarsIC only makes sense for problems where
        a sparse solution is expected and/or reached.

    verbose : boolean or integer, optional
        Sets the verbosity amount

    normalize : boolean, optional, default False
        If True, the regressors X will be normalized before regression.
        This parameter is ignored when `fit_intercept` is set to False.
        When the regressors are normalized, note that this makes the
        hyperparameters learnt more robust and almost independent of the number
        of samples. The same property is not valid for standardized data.
        However, if you wish to standardize, please use
        `preprocessing.StandardScaler` before calling `fit` on an estimator
        with `normalize=False`.

    copy_X : boolean, optional, default True
        If True, X will be copied; else, it may be overwritten.

    precompute : True | False | 'auto' | array-like
        Whether to use a precomputed Gram matrix to speed up
        calculations. If set to ``'auto'`` let us decide. The Gram
        matrix can also be passed as argument.

    max_iter : integer, optional
        Maximum number of iterations to perform. Can be used for
        early stopping.

    eps : float, optional
        The machine-precision regularization in the computation of the
        Cholesky diagonal factors. Increase this for very ill-conditioned
        systems. Unlike the ``tol`` parameter in some iterative
        optimization-based algorithms, this parameter does not control
        the tolerance of the optimization.


    Attributes
    ----------
    coef_ : array, shape (n_features,)
        parameter vector (w in the formulation formula)

    intercept_ : float
        independent term in decision function.

    alpha_ : float
        the alpha parameter chosen by the information criterion

    n_iter_ : int
        number of iterations run by lars_path to find the grid of
        alphas.

    criterion_ : array, shape (n_alphas,)
        The value of the information criteria ('aic', 'bic') across all
        alphas. The alpha which has the smallest information criteria
        is chosen.

    Examples
    --------
    >>> from sklearn import linear_model
    >>> clf = linear_model.LassoLarsIC(criterion='bic')
    >>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
    ... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    LassoLarsIC(copy_X=True, criterion='bic', eps=..., fit_intercept=True,
          max_iter=500, normalize=True, positive=False, precompute='auto',
          verbose=False)
    >>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
    [ 0.  -1.11...]

    Notes
    -----
    The estimation of the number of degrees of freedom is given by:

    "On the degrees of freedom of the lasso"
    Hui Zou, Trevor Hastie, and Robert Tibshirani
    Ann. Statist. Volume 35, Number 5 (2007), 2173-2192.

    https://en.wikipedia.org/wiki/Akaike_information_criterion
    https://en.wikipedia.org/wiki/Bayesian_information_criterion

    See also
    --------
    lars_path, LassoLars, LassoLarsCV
     __init__ N/A N/A self criterion fit_intercept verbose normalize precompute max_iter eps copy_X positive N/A aic True False True auto 500 N/A N/A N/A np finfo N/A np float eps True False N/A N/A N/A self criterion criterion N/A N/A self fit_intercept fit_intercept N/A N/A self positive positive N/A N/A self max_iter max_iter N/A N/A self verbose verbose N/A N/A self normalize normalize N/A N/A self copy_X copy_X N/A N/A self precompute precompute N/A N/A self eps eps N/A fit N/A N/A self X y copy_X N/A True N/A N/A Fit the model using X, y as training data.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            training data.

        y : array-like, shape (n_samples,)
            target values.

        copy_X : boolean, optional, default True
            If ``True``, X will be copied; else, it may be overwritten.

        Returns
        -------
        self : object
            returns an instance of self.
         N/A N/A self fit_path True N/A N/A X y N/A check_X_y X y y_numeric True N/A N/A X y Xmean ymean Xstd N/A N/A LinearModel _preprocess_data X y N/A self fit_intercept N/A self normalize N/A self copy_X N/A max_iter N/A self max_iter N/A Gram N/A N/A self _get_gram N/A N/A alphas_ active_ coef_path_ N/A self n_iter_ N/A lars_path X y Gram Gram copy_X copy_X copy_Gram True alpha_min 0.0 method lasso verbose N/A self verbose max_iter max_iter eps N/A self eps return_n_iter True positive N/A self positive N/A n_samples N/A N/A X shape N/A 0 N/A N/A N/A self criterion aic N/A N/A K 2 N/A N/A N/A N/A self criterion bic N/A N/A K N/A log n_samples N/A N/A N/A ValueError criterion should be either bic or aic N/A R N/A N/A y N/A N/A N/A N/A np newaxis N/A N/A np dot X coef_path_ N/A mean_squared_error N/A N/A np mean N/A R 2 axis 0 N/A df N/A N/A np zeros N/A N/A coef_path_ shape N/A 1 dtype N/A np int N/A N/A k coef N/A enumerate N/A coef_path_ T N/A N/A mask N/A N/A N/A np abs coef N/A N/A N/A np finfo N/A coef dtype eps N/A N/A N/A N/A np any mask N/A N/A N/A N/A df N/A k N/A N/A np sum mask N/A N/A self alphas_ alphas_ N/A N/A N/A np errstate divide ignore N/A N/A N/A self criterion_ N/A N/A n_samples N/A N/A np log mean_squared_error N/A K df N/A n_best N/A N/A np argmin N/A self criterion_ N/A N/A self alpha_ N/A alphas_ N/A n_best N/A N/A self coef_ N/A coef_path_ N/A N/A N/A n_best N/A N/A N/A self _set_intercept Xmean ymean Xstd N/A self N/A N/A