ó ‡ˆ\c@s^dZddlZddlmZddlmZmZddlm Z de dd „Z dS( s!Determination of parameter boundsiÿÿÿÿNi(tLabelBinarizer(tcheck_consistent_lengtht check_array(tsafe_sparse_dott squared_hingegð?cCs|dkrtdƒ‚nt|ddƒ}t||ƒtddƒj|ƒj}tjtjt ||ƒƒƒ}|rÙtj tj |ƒdf|d tj |ƒj ƒ}t|ttj||ƒƒjƒƒ}n|d krôtd ƒ‚n|dkrd |Sd |SdS(s‚ Return the lowest bound for C 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 or sparse matrix, shape = [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y : array, 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 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 Rtlogs)loss type not in ("squared_hinge", "log")t accept_sparsetcsct neg_labeliÿÿÿÿitdtypegsUIll-posed l1_min_c calculation: l1 will always select zero coefficients for this datagà?g@N(RR(t ValueErrorRRRt fit_transformtTtnptmaxtabsRtfulltsizetarrayR tdot(tXtytlosst fit_intercepttintercept_scalingtYtdentbias((s1lib/python2.7/site-packages/sklearn/svm/bounds.pytl1_min_c s(  !*  ( t__doc__tnumpyR t preprocessingRtutils.validationRRt utils.extmathRtTrueR(((s1lib/python2.7/site-packages/sklearn/svm/bounds.pyts