class_docstr: 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 :math:`x(t)`, observed at discrete times,
  this transformer computes (when selected) discretized versions of:
  
  * `"v"` - vector of velocity: :math:`\vec{v}(t) := \Delta x(t)`
  * `"v_abs"` - absolute velocity: :math:`v(t) := \left| \Delta x(t) \right|`
  * `"a"` - vector of velocity: :math:`\vec{a}(t) := \Delta \Delta x(t)`
  * `"a_abs"` - absolute velocity: :math:`a(t) := \left| \Delta \Delta x(t) \right|`
  * `"curv"` - curvature: :math:`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 :math:`\Delta` denotes first finite differences, that is,
  :math:`\Delta z(t) = z(t) - z(t-1)` for any discrete time series :math:`z(t)`.
  
  Note: this estimator currently ignores non-equidistant location index,
  and considers only the integer location index.
  
  Parameters
  ----------
  features : str 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
  
  Example
  -------
  >>> import numpy as np
  >>> import pandas as pd
  >>> from sktime.transformations.series.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)
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for transformers.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for transformers.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      params1 = {}
      params2 = {"features": ["v", "a"]}
      return [params1, params2]
  
imports: import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the KinematicFeatures obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list