DtwDtaidistUniv

DtwDtaidistUniv

class DtwDtaidistUniv(use_c=False, window=None, max_dist=None, max_step=None, max_length_diff=None, penalty=None, psi=None, use_pruning=False)[source]

Univariate dynamic time warping distance, from dtaidistance.

Direct interface to dtaidistance.dtw.distance_matrix and dtaidistance.dtw.distance_matrix_fast.

This distance is specifically for univariate time series. While mathematically equivalent for using the multivariate DtwDtaidistMultiv for univariate data, this class uses a more efficient implementation and a different internal API.

To specify an inner scalar distance, use DtwDtaidistMultiv with the inner_dist parameter set to the desired scalar distance.

Parameters:

use_c: bool, optional, default=False

Whether to use the faster C variant: True for C, False for Python. True requires a C compiled installation of dtaidistance.

• If False, uses dtaidistance.dtw.distance_matrix.

• If True, uses dtaidistance.dtw.distance_matrix_fast.

windowinteger, optional, default=infinite

Sakoe Chiba window width, from diagonal to boundary. Only allow for maximal shifts from the two diagonals smaller than this number. The maximally allowed warping, thus difference between indices i in series 1 and j in series 2, is thus |i-j| < 2*window + |len(s1) - len(s2)|. It includes the diagonal, meaning that Euclidean distance is obtained by setting window=1. If the two series are of equal length, this means that the band appearing on the cumulative cost matrix is of width 2*window-1. In other definitions of DTW this number may be referred to as the window instead.

max_dist: float, optional, default=infinite

Stop if the returned values will be larger than this value.

max_step: float, optional, default=infinite

Do not allow steps larger than this value. If the difference between two values in the two series is larger than this, thus if |s1[i]-s2[j]| > max_step, replace that value with infinity.

max_length_diff: int, optional, default=infinite

Return infinity if difference of length of two series is larger than this value.

penalty: float, optional, default=0

Penalty to add if compression or expansion is applied

psi: integer or 4-tuple of integers or none, optional, default=none

Psi relaxation parameter (ignore start and end of matching). If psi is a single integer, it is used for both start and end relaxations for both series in a pair of series. If psi is a 4-tuple, it is used as the psi-relaxation for (begin series1, end series1, begin series2, end series2). Useful for cyclical series.

use_pruning: bool, optional, default=False

Prune values based on Euclidean distance.

Attributes:

is_fitted

Whether fit has been called.

References

[1]

H. Sakoe, S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 26(1), pp. 43–49, 1978.

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.