PELT

Schnellstart

from sktime.detection.pelt import PELT

estimator = PELT(cost=None, penalty=None, min_segment_length=1, step_size=1, split_cost=0.0, prune=True, pruning_margin=0.0)

Parameter(7)

costBaseIntervalScorer, optional, default=L2Cost()

Cost scorer used for changepoint detection.

penaltyfloat or None, default=None

Penalty for adding a changepoint. None uses a BIC penalty computed from the data at prediction time.

min_segment_lengthint, default=1

Minimum number of observations in a segment.

step_sizeint, default=1

Only multiples of step_size are considered as changepoints (JumpPELT).

split_costfloat, default=0.0

Additive cost of splitting a segment.

prunebool, default=True

If False, disables pruning (optimal partitioning).

pruning_marginfloat, default=0.0

Margin added to the pruning criterion.

Beispiele

>>> from sktime.detection.pelt import PELT
>>> from sktime.detection.costs._l2_cost import L2Cost
>>> import numpy as np
>>> X = np. concatenate ([np. zeros (50), 10 * np. ones (50)])
>>> detector = PELT (cost = L2Cost (), penalty = 15)
>>> detector. fit (X). predict (X)

Referenzen