def transform(self, X):
    'add per-sample timestamp information to the data matrix\n\n        Args:\n            X (float): the data to decorrelate\n\n        Returns:\n            np.ndarray: the decorrelated data\n        '
    if (not hasattr(self, 'sigma2_')):
        self.fit(X)
    (X, self.W_) = standardize_channel_power(X, sigma2=self.sigma2_, reg=self.reg, axis=self.axis)
    return X
add per-sample timestamp information to the data matrix

Args:
    X (float): the data to decorrelate

Returns:
    np.ndarray: the decorrelated data
def transform(self, X):
    'add per-sample timestamp information to the data matrix\n\n        Args:\n            X (float): the data to decorrelate\n\n        Returns:\n            np.ndarray: the decorrelated data\n        '
    if (not hasattr(self, 'sigma2_')):
        self.fit(X)
    (X, self.W_) = standardize_channel_power(X, sigma2=self.sigma2_, reg=self.reg, axis=self.axis)
    return X