import numbers

import warnings

import numpy as np

# from scipy import optimize, sparse

# from scipy.special import expit

# from .base import LinearClassifierMixin, SparseCoefMixin, BaseEstimator

# from .sag import sag_solver

# from sklearn.preprocessing import LabelEncoder, LabelBinarizer

from sklearn.svm.base import _fit_liblinear

# from sklearn.utils import check_array, check_consistent_length, compute_class_weight

# from sklearn.utils import check_random_state

# from sklearn.utils.extmath import (log_logistic, safe_sparse_dot, softmax,

# squared_norm)

from sklearn.utils.extmath import softmax

from sklearn.utils.extmath import row_norms

# from sklearn.utils.fixes import logsumexp

# from sklearn.utils.optimize import newton_cg

from sklearn.utils.validation import check_X_y

# from sklearn.exceptions import (ConvergenceWarning,ChangedBehaviorWarning)

from sklearn.exceptions import NotFittedError

from sklearn.utils.multiclass import check_classification_targets

from sklearn.externals.joblib import Parallel, delayed, effective_n_jobs

from sklearn.utils.fixes import _joblib_parallel_args

# from sklearn.model_selection import check_cv

# from sklearn.externals import six

# from sklearn.metrics import get_scorer

from sklearn.linear_model.logistic import _check_solver,_check_multi_class,logistic_regression_path

from sklearn.linear_model import LogisticRegression

from sklearn.metrics.pairwise import pairwise_kernels

class KernelLogisticRegression(LogisticRegression):

return self.classes_[indices]