DtwPythonDist

class DtwPythonDist(dist='euclidean', step_pattern='symmetric2', window_type='none', open_begin=False, open_end=False)[source]

Interface to dynamic time warping distances in the dtw-python package.

Computes the dynamic time warping distance between series, using the dtw-python package.

Parameters:

dist: str, or estimator following sktime BasePairwiseTransformer API: distance to use, a distance on real n-space, default = “euclidean” if str, must be name of one of the functions in scipy.spatial.distance.cdist if estimator, must follow sktime BasePairwiseTransformer API
step_patternstr, optional, default = “symmetric2”,: 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_type: str optional, default = “none”: 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
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)

Attributes:

is_fitted: Whether fit has been called.

Methods

`__call__`(X[, X2])	Compute distance/kernel matrix, call shorthand.
`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`([X, X2])	Fit method for interface compatibility (no logic inside).
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, X2])	Compute distance/kernel matrix.
`transform_diag`(X)	Compute diagonal of distance/kernel matrix.