def transform(self, X):
    'add per-sample timestamp information to the data matrix\n\n        Args:\n            X (float): the data to decorrelate\n            nsamp(int): number of samples to interpolate\n\n        Returns:\n            np.ndarray: the decorrelated data\n        '
    if (not hasattr(self, 'W_')):
        self.fit(X)
    (X, self.W_) = temporally_decorrelate(X, W=self.W_, reg=self.reg, eta=self.eta, axis=self.axis)
    return X
add per-sample timestamp information to the data matrix

Args:
    X (float): the data to decorrelate
    nsamp(int): number of samples to interpolate

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            nsamp(int): number of samples to interpolate\n\n        Returns:\n            np.ndarray: the decorrelated data\n        '
    if (not hasattr(self, 'W_')):
        self.fit(X)
    (X, self.W_) = temporally_decorrelate(X, W=self.W_, reg=self.reg, eta=self.eta, axis=self.axis)
    return X