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sktime

KinematicFeatures

KinematicFeatures

class KinematicFeatures(features=None)[source]

Kinematic feature transformer - velocity, acceleration, curvature.

Takes a discrete N-dimensional space curve, N>=1, and computes a selection of kinematic features.

For noisy time series, is strongly recommended to pipeline this with KalmanFilterTransformerPK or KalmanFilterTransformerFP (prior), or other smoothing or trajectory fitting transformers, as this transformer does not carry out its own smoothing.

For min/max/quantiles of velocity etc, pipeline with SummaryTransformer (post).

For a time series input \(x(t)\), observed at discrete times, this transformer computes (when selected) discretized versions of:

  • "v" - vector of velocity: \(\vec{v}(t) := \Delta x(t)\)

  • "v_abs" - absolute velocity: \(v(t) := \left| \Delta x(t) \right|\)

  • "a" - vector of velocity: \(\vec{a}(t) := \Delta \Delta x(t)\)

  • "a_abs" - absolute velocity: \(a(t) := \left| \Delta \Delta x(t) \right|\)

  • "curv" - curvature: \(c(t) := \frac{\sqrt{v(t)^2 a(t)^2 - \left\langle \vec{v}(t), \vec{a}(t)\right\rangle^2}}{v(t)^3}\)

where \(\Delta\) denotes first finite differences, that is, \(\Delta z(t) = z(t) - z(t-1)\) for any discrete time series \(z(t)\).

Note: this estimator currently ignores non-equidistant location index, and considers only the integer location index.

Parameters:
featuresstr or list of str, optional, default=[“v_abs”, “a_abs”, “c_abs”]

list of features to compute, possible features:

  • “v” - vector of velocity

  • “v_abs” - absolute velocity

  • “a” - vector of acceleration

  • “a_abs” - absolute acceleration

  • “curv” - curvature

Attributes:
is_fitted

Whether fit has been called.

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from sktime.transformations.kinematic import KinematicFeatures

>>> traj3d = pd.DataFrame(columns=["x", "y", "z"])
>>> traj3d["x"] = pd.Series(np.sin(np.arange(200)/100))
>>> traj3d["y"] = pd.Series(np.cos(np.arange(200)/100))
>>> traj3d["z"] = pd.Series(np.arange(200)/100)

>>> t = KinematicFeatures()
>>> Xt = t.fit_transform(traj3d)

Methods

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[, y])

Fit transformer to X, optionally to y.

fit_transform(X[, y])

Fit to data, then transform it.

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.

inverse_transform(X[, y])

Inverse transform X and return an inverse transformed version.

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[, y])

Transform X and return a transformed version.

update(X[, y, update_params])

Update transformer with X, optionally y.