pysindy.feature_library.ParameterizedLibrary

class pysindy.feature_library.ParameterizedLibrary(parameter_library: BaseFeatureLibrary = PolynomialLibrary(degree=1), feature_library: BaseFeatureLibrary = PolynomialLibrary(), num_parameters: int = 3, num_features: int = 3)[source]

A tensor product of two libraries with different inputs. Typically, this is a feature library of the input data and a parameter library of input control, making the SINDyCP method. If the input libraries are weak, the temporal derivatives are automatically rescaled by the appropriate domain volumes.

Parameters:

  • parameter_library (BaseFeatureLibrary, optional (default PolynomialLibrary).)

  • features. (Specifies the library function to apply to the input data)

  • feature_library (BaseFeatureLibrary, optional (default PolynomialLibrary).)

  • features.

  • num_parameters (int, optional (default 3))

  • control. (Specifies the number of features in the input)

  • num_features (int, optional (default 3))

  • data. (Specifies the number of features in the input)

Attributes:

see GeneralizedLibrary

Examples

>>> import numpy as np
>>> from pysindy.feature_library import ParameterizedLibrary,PolynomialLibrary
>>> from pysindy import AxesArray
>>> xs=[np.random.random((5,3)) for n in range(3)]
>>> us=[np.random.random((5,3)) for n in range(3)]
>>> feature_lib=PolynomialLibrary(degree=3)
>>> parameter_lib=PolynomialLibrary(degree=1)
>>> lib=ParameterizedLibrary(feature_library=feature_lib,
>>>     parameter_library=parameter_lib,num_features=3,num_parameters=3)
>>> xus=[AxesArray(np.concatenate([xs[i],us[i]],axis=-1)) for i in range(3)]
>>> lib.fit(xus)
>>> lib.transform(xus)

Methods

calc_trajectory

set_fit_request

Configure whether metadata should be requested to be passed to the fit method.

set_transform_request

Configure whether metadata should be requested to be passed to the transform method.

Attributes

n_features_in_

n_output_features_

set_fit_request(*, x_full: bool | None | str = '$UNCHANGED$') → ParameterizedLibrary

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

x_full (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x_full parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_transform_request(*, x_full: bool | None | str = '$UNCHANGED$') → ParameterizedLibrary

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

x_full (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for x_full parameter in transform.

Returns:

self – The updated object.

Return type:

object