Aligner

AlignerDTW

DistanceDistance-matrixUnequal length

Aligner interface for dtw-python.

Behaviour: computes the full alignment between X[0] and X[1]: assumes pairwise alignment (only two series) and univariate if multivariate series are passed: alignment is computed on univariate series with variable_to_align; if this is not set, defaults to the first variable of X[0] raises an error if variable_to_align is not present in X[0] or X[1]

Schnellstart

python

from sktime.alignment.dtw_python import AlignerDTW

estimator = AlignerDTW(dist_method='euclidean', step_pattern='symmetric2', window_type='none', window_size=None, open_begin=False, open_end=False, variable_to_align=None)

Parameter(7)

dist_methodstr, optional, default = “euclidean”: distance function to use, a distance on real n-space one of the functions in scipy.spatial.distance.cdist
step_patternstr, optional, or dtw_python stepPattern object, optional: step pattern to use in time warping one of: ‘symmetric1’, ‘symmetric2’ (default), ‘asymmetric’, and dozens of other more non-standard step patterns; list can be displayed by calling help(stepPattern) in dtw
window_typestring, the chosen windowing function: “none”, “itakura”, “sakoechiba”, or “slantedband”
“none” (default) - no windowing
“sakoechiba” - a band around main diagonal
“slantedband” - a band around slanted diagonal
“itakura” - Itakura parallelogram
window_size: int, optional, default=None: size of the window if a windowing function is used if None and window_type=”sakoechiba”, defaults to 10% of series length
open_beginboolean, optional, default=False
open_end: boolean, optional, default=False: whether to perform open-ended alignments open_begin = whether alignment open ended at start (low index) open_end = whether alignment open ended at end (high index)
variable_to_alignstring, default = first variable in X[0] as passed to fit: which variable to use for univariate alignment

Beispiele

Basic usage example:
>>> import numpy as np
>>> import pandas as pd
>>> from sktime.alignment.dtw_python import AlignerDTW
>>> X = [
... pd. DataFrame ({ 'col1': np. random. randn (100)}),
... pd. DataFrame ({ 'col1': np. random. randn (100)})
... ]
>>> aligner = AlignerDTW (dist_method = 'euclidean', step_pattern = 'symmetric2')
>>> aligner. fit (X) AlignerDTW(
... )
>>> alignment_df = aligner. get_alignment () Advanced usage example with open-ended alignment:
>>> aligner_advanced = AlignerDTW (
... dist_method = 'cityblock',
... window_type = 'sakoechiba',
... window_size = 10,
... step_pattern = 'asymmetric',
... open_begin = True,
... open_end = True,
... )
>>> X_advanced = [
... pd. DataFrame ({ 'col1': np. random. randn (150)}),
... pd. DataFrame ({ 'col1': np. random. randn (150)})
... ]
>>> aligner_advanced. fit (X_advanced) AlignerDTW(
... )
>>> alignment_df_advanced = aligner_advanced. get_alignment ()