"""
Base class for numerical differentiation methods
"""
import abc
from sklearn.base import BaseEstimator
[docs]class BaseDifferentiation(BaseEstimator):
"""
Base class for differentiation methods.
Simply forces differentiation methods to implement a
``_differentiate`` function.
"""
def __init__(self):
pass
# Force subclasses to implement this
@abc.abstractmethod
def _differentiate(self, x, t=1):
"""
Numerically differentiate data.
Parameters
----------
x: array-like, shape (n_samples, n_input_features)
Data to be differentiated. Rows of x should correspond to the same
point in time.
t: float or numpy array of shape (n_samples,)
If t is a float, it is interpreted as the timestep between
samples in x.
If t is a numpy array, it specifies the times corresponding
to the rows of x. That is, t[i] should be the time at which
the measurements x[i, :] were taken.
The points in t are assumed to be increasing.
Returns
-------
x_dot: array-like, shape (n_samples, n_input_features)
Numerical time derivative of x. Entries where derivatives were
not computed will have the value np.nan.
"""
raise NotImplementedError
def __call__(self, x, t=1):
return self._differentiate(x, t)