r"""
.. _sec-normal:
Gaussian process change
====================================================================================================
Description
----------------------------------------------------------------------------------------------------
This cost function detects changes in the mean and scale of a Gaussian time series.
Formally, for a signal :math:`\{y_t\}_t` on an interval :math:`I`,
.. math:: c(y_{I}) = |I| \log\det\widehat{\Sigma}_I
where :math:`\widehat{\Sigma}_I` is the empirical covariance matrix of the sub-signal :math:`\{y_t\}_{t\in I}`.
Usage
----------------------------------------------------------------------------------------------------
Start with the usual imports and create a signal.
.. code-block:: python
import numpy as np
import matplotlib.pylab as plt
import ruptures as rpt
# creation of data
n, dim = 500, 3 # number of samples, dimension
n_bkps, sigma = 3, 5 # number of change points, noise standart deviation
signal, bkps = rpt.pw_constant(n, dim, n_bkps, noise_std=sigma)
Then create a :class:`CostNormal` instance and print the cost of the sub-signal :code:`signal[50:150]`.
.. code-block:: python
c = rpt.costs.CostNormal().fit(signal)
print(c.error(50, 150))
You can also compute the sum of costs for a given list of change points.
.. code-block:: python
print(c.sum_of_costs(bkps))
print(c.sum_of_costs([10, 100, 200, 250, n]))
In order to use this cost class in a change point detection algorithm (inheriting from :class:`BaseEstimator`), either pass a :class:`CostNormal` instance (through the argument ``'custom_cost'``) or set :code:`model="normal"`.
.. code-block:: python
c = rpt.costs.CostNormal(); algo = rpt.Dynp(custom_cost=c)
# is equivalent to
algo = rpt.Dynp(model="normal")
Code explanation
----------------------------------------------------------------------------------------------------
.. autoclass:: ruptures.costs.CostNormal
:members:
:special-members: __init__
"""
import numpy as np
from numpy.linalg import slogdet
from ruptures.base import BaseCost
from ruptures.costs import NotEnoughPoints
[docs]class CostNormal(BaseCost):
"""Maximum Gaussian likelihood."""
model = "normal"
[docs] def __init__(self):
self.signal = None
self.min_size = 2
[docs] def fit(self, signal):
"""Set parameters of the instance.
Args:
signal (array): signal. Shape (n_samples,) or (n_samples, n_features)
Returns:
self
"""
if signal.ndim == 1:
self.signal = signal.reshape(-1, 1)
else:
self.signal = signal
return self
[docs] def error(self, start, end):
"""Return the approximation cost on the segment [start:end].
Args:
start (int): start of the segment
end (int): end of the segment
Returns:
float: segment cost
Raises:
NotEnoughPoints: when the segment is too short (less than ``'min_size'`` samples).
"""
if end - start < self.min_size:
raise NotEnoughPoints
sub = self.signal[start:end]
if self.signal.shape[1] > 1:
cov = np.cov(sub.T)
else:
cov = np.array([[sub.var()]])
_, val = slogdet(cov)
return val * (end - start)