U 3d_ @sdZddlmZddlZddlmZddlmZm Z ddl m Z ddl m Z mZmZed d gd ge d d hgd geeddddgdd dddddZdS)z!Determination of parameter bounds)RealN)LabelBinarizer)check_consistent_length check_array)safe_sparse_dot) StrOptionsIntervalvalidate_paramsz array-likez sparse matrix squared_hingelogbooleanZneither)closed)Xyloss fit_interceptintercept_scalingTg?)rrrcCst|dd}t||tdd|j}ttt||}|r~tj t |df|t |j d}t|tt ||}|dkrtd|d krd |Sd |Sd S) aReturn 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. Zcsc)Z accept_sparse)Z neg_label)dtypegzUIll-posed l1_min_c calculation: l1 will always select zero coefficients for this datar g?g@N)rrrZ fit_transformTnpmaxabsrfullsizearrayrdot ValueError)rrrrrYZdenZbiasr!7/tmp/pip-unpacked-wheel-zrfo1fqw/sklearn/svm/_bounds.pyl1_min_cs$2    r#)__doc__ZnumbersrZnumpyrZ preprocessingrZutils.validationrrZ utils.extmathrZutils._param_validationrr r r#r!r!r!r"s