KernelFromDist

class KernelFromDist(dist, dist_diag=None)[source]

Kernel function obtained from a distance function.

Formal details (for real valued objects, mixed typed rows in analogy): Let \(d: \mathbb{R}^D \times \mathbb{R}^D\rightarrow \mathbb{R}\) be the pairwise function in dist, when applied to D-vectors. If dist_diag=None, then KernelFromDist(dist) corresponds to the kernel function \(k(x, y) := d(x, x)^2 + d(y, y)^2 - 0.5 \cdot d(x, y)^2\). If dist_diag is provided, and corresponds to a function \(f:\mathbb{R}^D \rightarrow \mathbb{R}\), then KernelFromDist(dist) corresponds to the kernel function \(k(x, y) := f(x, x)^2 + f(y, y)^2 - 0.5 \cdot d(x, y)^2\).

It should be noted that \(k\) is, in general, not positive semi-definite.

Parameters:

distpairwise transformer of BasePairwiseTransformer scitype, or: callable np.ndarray (n_samples, nd) x (n_samples, nd) -> (n_samples x n_samples)
dist_diagpairwise transformer of BasePairwiseTransformer scitype, or: series-to-panel transformer of Basetransformer scitype, or callable np.ndarray (n_samples, nd) -> (n_samples, )

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.