DtwDtaidistMultiv

DtwDtaidistMultiv

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

Multivariate dynamic time warping distance, from dtaidistance.

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

This distance is covers multivariate data. For univariate data and the default euclidean distance, DtwDtaidistUniv is mathematically equivalent but may be more efficient.

To specify the internal distance to be separate from squared euclidean, use AlignerDtwDtai inside a DistFromAligner.

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_ndim.distance_matrix.

• If True, uses dtaidistance.dtw_ndim.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.