Transformer

EAgglo

Hierarchical agglomerative estimation of multiple change points.

Quickstart

python

from sktime.detection.eagglo import EAgglo

estimator = EAgglo(member=None, alpha=1.0, penalty=None)

Parameters(3)

memberarray_like (default=None): Assigns points to the initial cluster membership, therefore the first dimension should be the same as for data. If None it will be initialized to dummy vector where each point is assigned to separate cluster.
alphafloat (default=1.0): Fixed constant alpha in (0, 2] used in the divergence measure, as the alpha-th absolute moment, see equation (4) in [1].
penaltystr or callable or None (default=None): Function that defines a penalization of the sequence of goodness-of-fit statistic, when overfitting is a concern. If None not penalty is applied. Could also be an existing penalty name, either len_penalty or mean_diff_penalty.

Examples

>>> from sktime.detection.datagen import piecewise_normal_multivariate
>>> X = piecewise_normal_multivariate (means = [[1, 3 ], [4, 5 ]], lengths = [3, 4 ],
... random_state = 10)
>>> from sktime.detection.eagglo import EAgglo
>>> model = EAgglo ()
>>> model. fit_transform (X) array([0, 0, 0, 1, 1, 1, 1])

References

[1 ] (1, 2) Matteson, David S., and Nicholas A. James. “A nonparametric approach for multiple change point analysis of multivariate data.” Journal of the American Statistical Association 109.505 (2014): 334-345. [2 ] James, Nicholas A., and David S. Matteson. “ecp: An R package for nonparametric multiple change point analysis of multivariate data.” arXiv preprint arXiv:1309.3295 (2013).