Source code for bob.fusion.base.preprocessor.Tanh

import numpy as np

from sklearn.preprocessing import StandardScaler

# to fix the sphinx docs
StandardScaler.__module__ = "sklearn.preprocessing"

class Tanh(StandardScaler):
    """A tanh feature scaler:

    .. math::

        0.5 \\left( \\tanh\\left( 0.01 \\cdot \\frac{X - \\mu}{\\sigma}\\right) + 1 \\right)

    This scaler is both efficient and is robust to outliers.

    The original implementation in ``Hampel, Frank R., et al. "Robust
    statistics: the approach based on influence functions." (1986).`` uses an
    influence function but this is not used here.

[docs] def __init__(self, copy=True, **kwargs): """Initialize self. See help(type(self)) for accurate signature.""" super(Tanh, self).__init__( copy=copy, with_mean=True, with_std=True, **kwargs )
[docs] def fit(self, X, y=None): """Estimates the mean and standard deviation of samples. Only positive samples are used in estimation. """ # the fitting is done only on positive samples if y is not None: X = np.asarray(X)[y, ...] return super(Tanh, self).fit(X)
[docs] def transform(self, X, copy=None): """Perform scaling.""" X = super(Tanh, self).transform(X, copy=copy) X *= 0.01 X = np.tanh(X, out=X) X += 1 X *= 0.5 return X
[docs] def inverse_transform(self, X, copy=None): """Perform inverse scaling.""" copy = copy if copy is not None else self.copy if copy: X = X.copy() X *= 2 X -= 1 X = np.arctanh(X, out=X) X *= 100 return super(Tanh, self).inverse_transform(X, copy=copy)