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gd	geddhgdgeeddddgdddddddZdS )z!Determination of parameter bounds    )RealN   )LabelBinarizer)check_consistent_lengthcheck_array)safe_sparse_dot)
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| S d| S dS )a  Return the lowest bound for C.

    The lower bound for C is computed such that for C in (l1_min_C, infinity)
    the model is guaranteed not to be empty. This applies to l1 penalized
    classifiers, such as LinearSVC with penalty='l1' and
    linear_model.LogisticRegression with penalty='l1'.

    This value is valid if class_weight parameter in fit() is not set.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Training vector, where `n_samples` is the number of samples and
        `n_features` is the number of features.

    y : array-like of shape (n_samples,)
        Target vector relative to X.

    loss : {'squared_hinge', 'log'}, default='squared_hinge'
        Specifies the loss function.
        With 'squared_hinge' it is the squared hinge loss (a.k.a. L2 loss).
        With 'log' it is the loss of logistic regression models.

    fit_intercept : bool, default=True
        Specifies if the intercept should be fitted by the model.
        It must match the fit() method parameter.

    intercept_scaling : float, default=1.0
        When fit_intercept is True, instance vector x becomes
        [x, intercept_scaling],
        i.e. a "synthetic" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        It must match the fit() method parameter.

    Returns
    -------
    l1_min_c : float
        Minimum value for C.
    Zcsc)Zaccept_sparse)Z	neg_label   )dtypeg        zUIll-posed l1_min_c calculation: l1 will always select zero coefficients for this datar   g      ?g       @N)r   r   r   Zfit_transformTnpmaxabsr   fullsizearrayr   dot
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r#   )__doc__Znumbersr   Znumpyr   Zpreprocessingr   Zutils.validationr   r   Zutils.extmathr   Zutils._param_validationr   r	   r
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