IndepDist

class IndepDist(dist, aggfun=None)[source]

Variable-wise aggregate of multivariate kernel or distance function.

A common baseline method to turn a univariate time series distance or kernel into a multivariate time series distance or kernel.

Also sometimes known as “independent distance” in the special case where aggfun is the sum or mean and the pairwise transformer is a time series distance.

Formal details (for real valued objects, mixed typed rows in analogy): Let \(d: \mathbb{R}^n \times \mathbb{R}^n\rightarrow \mathbb{R}\) be the pairwise function in dist, when applied to univariate series of length \(n\). This class represents the pairwise function \(d_g: \mathbb{R}^{n\times D} \times \mathbb{R}^{n\times D}\rightarrow \mathbb{R}\) defined as \(d_g(x, y) := g(d(x_1, y_1), \dots, d(x_D, y_D))\), where \(x_i\), \(y_i\) denote the \(i\)-th column, and \(x\), :math:``y` are interpreted as multivariate time series with :math:`D` variables, and where :math:`g` is a function :math:`g: \mathbb{R}^D\times \mathbb{R}^D \rightarrow \mathbb{R}`, representing the input ``aggfun.

In particular, if aggfun="sum" (or default), then \(g(x) = \sum_{i=1}^D x_i\), and \(d_g(x, y) := \sum_{i=1}^D d(x_i, y_i)\), which corresponds to the usual terminology “independent distance”.

Parameters:

distpairwise transformer of BasePairwiseTransformer scitype, or: callable np.ndarray (n_samples, nd) x (n_samples, nd) -> (n_samples x n_samples)
aggfunoptional, str or callable np.ndarray (m, nd, nd) -> (nd, nd): aggregation function over the variables, \(g\) above “sum” = np.sum = default “mean” = np.mean “median” = np.median “max” = np.max “min” = np.min when starting with a function (m) -> scalar, use np.apply_along_axis to create a function (m, nd, nd) -> (nd, nd) and pass that as aggfun

Attributes:

is_fitted: Whether fit has been called.

Examples

>>> from sktime.dists_kernels.indep import IndepDist
>>> from sktime.dists_kernels.dtw import DtwDist
>>>
>>> dist = IndepDist(DtwDist())

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.