pysindy.feature_library.FourierLibrary

class pysindy.feature_library.FourierLibrary(n_frequencies=1, include_sin=True, include_cos=True)[source]

Generate a library with trigonometric functions.

Parameters:

  • n_frequencies (int, optional (default 1)) – Number of frequencies to include in the library. The library will include functions \(\sin(x), \sin(2x), \dots \sin(n_{frequencies}x)\) for each input feature \(x\) (depending on which of sine and/or cosine features are included).

  • include_sin (boolean, optional (default True)) – If True, include sine terms in the library.

  • include_cos (boolean, optional (default True)) – If True, include cosine terms in the library.

Attributes:

  • n_features_in_ (int) – The total number of input features.

  • n_output_features_ (int) – The total number of output features. The number of output features is 2 * n_input_features_ * n_frequencies if both sines and cosines are included. Otherwise it is n_input_features * n_frequencies.

Examples

>>> import numpy as np
>>> from pysindy.feature_library import FourierLibrary
>>> x = np.array([[0.],[1.],[2.]])
>>> lib = FourierLibrary(n_frequencies=2).fit(x)
>>> lib.transform(x)
array([[ 0.        ,  1.        ,  0.        ,  1.        ],
       [ 0.84147098,  0.54030231,  0.90929743, -0.41614684],
       [ 0.90929743, -0.41614684, -0.7568025 , -0.65364362]])
>>> lib.get_feature_names()
['sin(1 x0)', 'cos(1 x0)', 'sin(2 x0)', 'cos(2 x0)']

Methods

fit

Compute number of output features.

get_feature_names

Return feature names for output features

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.

transform

Transform data to Fourier features

Attributes

n_features_in_

n_output_features_

fit(x_full, y=None)[source]

Compute number of output features.

Parameters:

x (array-like, shape (n_samples, n_features)) – The data.

Returns:

self

Return type:

instance

get_feature_names(input_features=None)[source]

Return feature names for output features

Parameters:

input_features (list of string, length n_features, optional) – String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.

Returns:

output_feature_names

Return type:

list of string, length n_output_features

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

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 = '$UNCHANGED$') → FourierLibrary

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

transform(x_full)[source]

Transform data to Fourier features

Parameters:

x (array-like, shape (n_samples, n_features)) – The data to transform, row by row.

Returns:

xp – The matrix of features, where n_output_features is the number of Fourier features generated from the inputs.

Return type:

np.ndarray, shape (n_samples, n_output_features)