from .base import BaseLibLinear, BaseSVC, BaseLibSVM
from ..base import RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin
from ..feature_selection.from_model import _LearntSelectorMixin


class LinearSVC(BaseLibLinear, 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)
        The seed of the pseudo random number generator to use when
        shuffling the data.


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

        `coef_` is 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.

    **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. Furthermore
        SGDClassifier is scalable to large number of samples as it uses
        a Stochastic Gradient Descent optimizer.

        Finally SGDClassifier can fit both dense and sparse data without
        memory copy if the input is C-contiguous or CSR.

    """

    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):
        super(LinearSVC, self).__init__(
            penalty=penalty, loss=loss, dual=dual, tol=tol, C=C,
            multi_class=multi_class, fit_intercept=fit_intercept,
            intercept_scaling=intercept_scaling,
            class_weight=class_weight, verbose=verbose,
            random_state=random_state)


class SVC(BaseSVC):
    """C-Support Vector Classification.

    The implementations is a based on libsvm. The fit time complexity
    is more than quadratic with the number of samples which makes it hard
    to scale to dataset with more than a couple of 10000 samples.

    The multiclass support is handled according to a one-vs-one scheme.

    For details on the precise mathematical formulation of the provided
    kernel functions and how `gamma`, `coef0` and `degree` affect each,
    see the corresponding section in the narrative documentation:
    :ref:`svm_kernels`.

    .. The narrative documentation is available at http://scikit-learn.org/

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

    kernel : string, optional (default='rbf')
         Specifies the kernel type to be used in the algorithm.
         It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
         a callable.
         If none is given, 'rbf' will be used. If a callable is given it is
         used to precompute the kernel matrix.

    degree : int, optional (default=3)
        Degree of the polynomial kernel function ('poly').
        Ignored by all other kernels.