pykoop.GaussianRandomCenters
- class GaussianRandomCenters(n_centers=100, random_state=None)
Bases:
Centers
Centers sampled from a Gaussian distribution.
Inspired by center generation approach used in [CHH19].
- Parameters:
n_centers (int)
random_state (int | RandomState | None)
- centers_
Centers, shape (n_centers, n_features).
- Type:
np.ndarray
- mean_
Mean feature.
- Type:
np.ndarray
- cov_
Covariance matrix.
- Type:
np.ndarray
Examples
Generate centers from a Gaussian distribution
>>> rand = pykoop.GaussianRandomCenters(n_centers=10) >>> rand.fit(X_msd[:, 1:]) # Remove episode feature GaussianRandomCenters(n_centers=10) >>> rand.centers_ array([...])
- __init__(n_centers=100, random_state=None)
Instantiate
GaussianRandomCenters
.
Methods
__init__
([n_centers, random_state])Instantiate
GaussianRandomCenters
.fit
(X[, y])Generate centers from data.
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:
- 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.
- 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