from .OutlierNormalizer import OutlierNormalizer
from .StandardNormalizer import StandardNormalizer
from .WinsorizationNormalizer import WinsorizationNormalizer
from weakref import ReferenceType
from numpy import ndarray


class MatrixNormalizerAdapter:

    def __init__(self, matrix_reference: ReferenceType):
        self.matrix_reference = matrix_reference

    def standard(self, positive_required=False, verbose=False):
        normalizer = StandardNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

    def winsorization(self, positive_required=False, limits=0.05, verbose=False
        ):
        normalizer = WinsorizationNormalizer(positive_required, limits=limits)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

    def outlier(self, positive_required=False, verbose=False):
        normalizer = OutlierNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

b4fb6d5c64e07741394631dfca7563c1a867001e
<mask token>


class MatrixNormalizerAdapter:

    def __init__(self, matrix_reference: ReferenceType):
        self.matrix_reference = matrix_reference

    def standard(self, positive_required=False, verbose=False):
        normalizer = StandardNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)
    <mask token>
    <mask token>

<mask token>


class MatrixNormalizerAdapter:

    def __init__(self, matrix_reference: ReferenceType):
        self.matrix_reference = matrix_reference

    def standard(self, positive_required=False, verbose=False):
        normalizer = StandardNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)
    <mask token>

    def outlier(self, positive_required=False, verbose=False):
        normalizer = OutlierNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

<mask token>


class MatrixNormalizerAdapter:

    def __init__(self, matrix_reference: ReferenceType):
        self.matrix_reference = matrix_reference

    def standard(self, positive_required=False, verbose=False):
        normalizer = StandardNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

    def winsorization(self, positive_required=False, limits=0.05, verbose=False
        ):
        normalizer = WinsorizationNormalizer(positive_required, limits=limits)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

    def outlier(self, positive_required=False, verbose=False):
        normalizer = OutlierNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

from .OutlierNormalizer import OutlierNormalizer
from .StandardNormalizer import StandardNormalizer
from .WinsorizationNormalizer import WinsorizationNormalizer
from weakref import ReferenceType
from numpy import ndarray


class MatrixNormalizerAdapter:

    def __init__(self, matrix_reference: ReferenceType):
        self.matrix_reference = matrix_reference

    def standard(self, positive_required=False, verbose=False):
        normalizer = StandardNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

    def winsorization(self, positive_required=False, limits=0.05, verbose=False
        ):
        normalizer = WinsorizationNormalizer(positive_required, limits=limits)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)

    def outlier(self, positive_required=False, verbose=False):
        normalizer = OutlierNormalizer(positive_required)
        self.matrix_reference().array = normalizer.normalize(self.
            matrix_reference(), verbose=verbose)
