import numpy as np

from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
    LinearModel
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_X_y


class LinearSVC(BaseEstimator, LinearClassifierMixin,
                _LearntSelectorMixin, SparseCoefMixin):
    """Linear Support Vector Classification.

    Similar to SVC with parameter kernel='linear', but implemented in terms of
    liblinear rather than libsvm, so it has more flexibility in the choice of
    penalties and loss functions and should scale better (to large numbers of
    samples).

    This class supports both dense and sparse input and the multiclass support
    is handled according to a one-vs-the-rest scheme.

    Parameters
    ----------
    C : float, optional (default=1.0)
        Penalty parameter C of the error term.

    loss : string, 'l1' or 'l2' (default='l2')
        Specifies the loss function. 'l1' is the hinge loss (standard SVM)
        while 'l2' is the squared hinge loss.

    penalty : string, 'l1' or 'l2' (default='l2')
        Specifies the norm used in the penalization. The 'l2'
        penalty is the standard used in SVC. The 'l1' leads to `coef_`
        vectors that are sparse.

    dual : bool, (default=True)
        Select the algorithm to either solve the dual or primal
        optimization problem. Prefer dual=False when n_samples > n_features.

    tol : float, optional (default=1e-4)
        Tolerance for stopping criteria

    multi_class: string, 'ovr' or 'crammer_singer' (default='ovr')
        Determines the multi-class strategy if `y` contains more than
        two classes.
        `ovr` trains n_classes one-vs-rest classifiers, while `crammer_singer`
        optimizes a joint objective over all classes.
        While `crammer_singer` is interesting from an theoretical perspective
        as it is consistent it is seldom used in practice and rarely leads to
        better accuracy and is more expensive to compute.
        If `crammer_singer` is chosen, the options loss, penalty and dual will
        be ignored.

    fit_intercept : boolean, optional (default=True)
        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).

    intercept_scaling : float, optional (default=1)
        when self.fit_intercept is True, instance vector x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        The intercept becomes intercept_scaling * synthetic feature weight
        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased

    class_weight : {dict, 'auto'}, optional
        Set the parameter C of class i to class_weight[i]*C for
        SVC. If not given, all classes are supposed to have
        weight one. The 'auto' mode uses the values of y to
        automatically adjust weights inversely proportional to
        class frequencies.

    verbose : int, (default=0)
        Enable verbose output. Note that this setting takes advantage of a
        per-process runtime setting in liblinear that, if enabled, may not work
        properly in a multithreaded context.

    random_state : int seed, RandomState instance, or None (default=None)
        The seed of the pseudo random number generator to use when
        shuffling the data.

    max_iter : int, (default=1000)
        The maximum number of iterations to be run.

    Attributes
    ----------
    coef_ : array, shape = [n_features] if n_classes == 2 \
            else [n_classes, n_features]
        Weights assigned to the features (coefficients in the primal
        problem). This is only available in the case of linear kernel.

        `coef_` is a readonly property derived from `raw_coef_` that \
        follows the internal memory layout of liblinear.

    intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
        Constants in decision function.

    Notes
    -----
    The underlying C implementation uses a random number generator to
    select features when fitting the model. It is thus not uncommon,
    to have slightly different results for the same input data. If
    that happens, try with a smaller tol parameter.

    The underlying implementation (liblinear) uses a sparse internal
    representation for the data that will incur a memory copy.

    Predict output may not match that of standalone liblinear in certain
    cases. See :ref:`differences from liblinear <liblinear_differences>`
    in the narrative documentation.

    **References:**
    `LIBLINEAR: A Library for Large Linear Classification
    <http://www.csie.ntu.edu.tw/~cjlin/liblinear/>`__

    See also
    --------
    SVC
        Implementation of Support Vector Machine classifier using libsvm:
        the kernel can be non-linear but its SMO algorithm does not
        scale to large number of samples as LinearSVC does.

        Furthermore SVC multi-class mode is implemented using one
        vs one scheme while LinearSVC uses one vs the rest. It is
        possible to implement one vs the rest with SVC by using the
        :class:`sklearn.multiclass.OneVsRestClassifier` wrapper.

        Finally SVC can fit dense data without memory copy if the input
        is C-contiguous. Sparse data will still incur memory copy though.

    sklearn.linear_model.SGDClassifier
        SGDClassifier can optimize the same cost function as LinearSVC
        by adjusting the penalty and loss parameters. In addition it requires
        less memory, allows incremental (online) learning, and implements
        various loss functions and regularization regimes.

    """

    def __init__(self, penalty='l2', loss='l2', dual=True, tol=1e-4, C=1.0,
                 multi_class='ovr', fit_intercept=True, intercept_scaling=1,
                 class_weight=None, verbose=0, random_state=None, max_iter=1000):
        self.penalty = penalty
        self.loss = loss
        self.dual = dual
        self.tol = tol
        self.C = C
        self.multi_class = multi_class
        self.fit_intercept = fit_intercept
        self.intercept_scaling = intercept_scaling
        self.class_weight = class_weight
        self.verbose = verbose
        self.random_state = random_state
        self.max_iter = max_iter

    def fit(self, X, y):
        """Fit the model according to the given training data.

        Parameters
        ----------
        X : {array-like, 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-like, shape = [n_samples]
            Target vector relative to X

        Returns
        -------
        self : object
            Returns self.
        """
        if self.C < 0:
            raise ValueError("Penalty term must be positive; got (C=%r)"
                             % self.C)

        X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64, order="C")
        self.classes_ = np.unique(y)
        self.coef_, self.intercept_, self.n_iter_ = _fit_liblinear(
            X, y, self.C, self.fit_intercept, self.intercept_scaling,
            self.class_weight, self.penalty, self.dual, self.verbose,
            self.max_iter, self.tol, self.random_state, self.multi_class,
            self.loss
            )

        if self.multi_class == "crammer_singer" and len(self.classes_) == 2:
            self.coef_ = (self.coef_[1] - self.coef_[0]).reshape(1, -1)
            if self.fit_intercept:
                intercept = self.intercept_[1] - self.intercept_[0]
                self.intercept_ = np.array([intercept])

        return self


class LinearSVR(LinearModel, RegressorMixin):
    """Linear Support Vector Regression.

    Similar to SVR with parameter kernel='linear', but implemented in terms of
    liblinear rather than libsvm, so it has more flexibility in the choice of
    penalties and loss functions and should scale better (to large numbers of
    samples).

    This class supports both dense and sparse input.

    Parameters
    ----------
    C : float, optional (default=1.0)
        Penalty parameter C of the error term. The penalty is a squared
        l2 penalty. The bigger this parameter, the less regularization is used.

    loss : string, 'l1' or 'l2' (default='l2')
        Specifies the loss function. 'l1' is the epsilon-insensitive loss
        (standard SVR) while 'l2' is the squared epsilon-insensitive loss.

    epsilon : float, optional (default=0.1)
        Epsilon parameter in the epsilon-insensitive loss function. Note
        that the value of this parameter depends on the scale of the target
        variable y. If unsure, set epsilon=0.

    dual : bool, (default=True)
        Select the algorithm to either solve the dual or primal
        optimization problem. Prefer dual=False when n_samples > n_features.

    tol : float, optional (default=1e-4)
        Tolerance for stopping criteria

    fit_intercept : boolean, optional (default=True)
        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).

    intercept_scaling : float, optional (default=1)
        when self.fit_intercept is True, instance vector x becomes
        [x, self.intercept_scaling],
        i.e. a "synthetic" feature with constant value equals to
        intercept_scaling is appended to the instance vector.
        The intercept becomes intercept_scaling * synthetic feature weight
        Note! the synthetic feature weight is subject to l1/l2 regularization
        as all other features.
        To lessen the effect of regularization on synthetic feature weight
        (and therefore on the intercept) intercept_scaling has to be increased