FlatDist

class FlatDist(transformer)[source]

Panel distance or kernel from applying tabular trafo to flattened time series.

Applies the wrapped tabular distance or kernel to flattened series. Flattening is done to a 2D numpy array of shape (n_instances, (n_vars, n_timepts))

Formal details (for real valued objects, mixed typed rows in analogy): Let \(d:\mathbb{R}^k \times \mathbb{R}^{k}\rightarrow \mathbb{R}\) be the pairwise function in transformer, when applied to k-vectors (here, \(d\) could be a distance function or a kernel function). Let \(x_1, \dots, x_N\in \mathbb{R}^{n \times \ell}\), \(y_1, \dots y_M \in \mathbb{R}^{n \times \ell}\) be collections of matrices, representing time series panel valued inputs X and X2, as follows: \(x_i\) is the i-th instance in X, and \(x_{i, j\ell}\) is the j-th time point, \ell-th variable of X. Analogous for \(y\) and X2. Let \(f:\mathbb{R}^{n \times \ell} \rightarrow \mathbb{R}^{n \cdot \ell}\) be the mapping that flattens matrices by column-first lexicographical ordering, and assume \(k = n \cdot \ell\).

Then, transform(X, X2) returns the \((N \times M)\) matrix with \((i, j)\)-th entry \(d\left(f(x_i), f(y_j)\right)\).

Parameters:

transformer: pairwise transformer of BasePairwiseTransformer scitype, or: callable np.ndarray (n_samples, d) x (n_samples, d) -> (n_samples x n_samples)

Attributes:

is_fitted: Whether fit has been called.

Examples

Euclidean distance between time series of equal length, considered as vectors

>>> from sktime.dists_kernels import FlatDist, ScipyDist
>>> euc_tsdist = FlatDist(ScipyDist())

Gaussian kernel between time series of equal length, considered as vectors

>>> from sklearn.gaussian_process.kernels import RBF
>>> flat_gaussian_tskernel = FlatDist(RBF())

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])	Test parameters for FlatDist.
`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.