pykoop.GridCenters

class GridCenters(n_points_per_feature=2, symmetric_range=False)

Bases: Centers

Centers generated on a uniform grid.

Parameters:

• n_points_per_feature (int) –

• symmetric_range (bool) –

centers_

Centers, shape (n_centers, n_features).

Type:

np.ndarray

n_centers_

Number of centers generated.

Type:

int

n_features_in_

Number of features input.

Type:

int

range_max_

Maximum value of each feature used to generate grid.

Type:

np.ndarray

range_min_

Minimum value of each feature used to generate grid.

Type:

np.ndarray

Examples

Generate centers on a grid

>>> grid = pykoop.GridCenters(n_points_per_feature=4)
>>> grid.fit(X_msd[:, 1:])  # Remove episode feature
GridCenters(n_points_per_feature=4)
>>> grid.centers_
array([...])

__init__(n_points_per_feature=2, symmetric_range=False)

Instantiate GridCenters.

Parameters:

• n_points_per_feature (int) – Number of points in grid for each feature.

• symmetric_range (bool) – If true, the grid range for a given feature is forced to be symmetric about zero (i.e., [-max(abs(x)), max(abs(x))]). Otherwise, the grid range is taken directly on the data (i.e., [min(x), max(x)]). Default is false.

Return type:

None

Methods

__init__([n_points_per_feature, symmetric_range])	Instantiate GridCenters.
fit(X[, y])	Generate centers from data.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.

fit(X, y=None)

Generate centers from data.

Parameters:

• X (np.ndarray) – Data matrix.

• y (Optional[np.ndarray]) – Ignored.

Returns:

Instance of itself.

Return type:

Centers

Raises:

ValueError – If any of the constructor parameters are incorrect.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance