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

import numpy as np

from sklearn.preprocessing import StandardScaler

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

class ZNorm(StandardScaler):
    """ZNorm feature scaler
    This scaler works just like :any:`sklearn.preprocessing.StandardScaler` but
    only takes the zero effort impostors into account when estimating the mean
    and standard deviation. You should not use this scaler when PAD scores are

[docs] def __init__(self, copy=True, **kwargs): """Initialize self. See help(type(self)) for accurate signature.""" super(ZNorm, 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 negative samples if y is not None: X = np.asarray(X)[~y, ...] return super(ZNorm, self).fit(X)