pykoop.GaussianMixtureRandomCenters
- class GaussianMixtureRandomCenters(n_centers=100, estimator=None)
Bases:
Centers
Centers generated from sampling a Gaussian mixture model.
- Parameters:
n_centers (int) –
estimator (BaseEstimator) –
- centers_
Centers, shape (n_centers, n_features).
- Type:
np.ndarray
- estimator_
Fit Gaussian mixture model.
Examples
Generate centers by sampling a Gaussian mixture model
>>> gmm = pykoop.GaussianMixtureRandomCenters(n_centers=100, ... estimator=sklearn.mixture.GaussianMixture(n_components=3)) >>> gmm.fit(X_msd[:, 1:]) # Remove episode feature GaussianMixtureRandomCenters(estimator=GaussianMixture(n_components=3)) >>> gmm.centers_ array([...])
- __init__(n_centers=100, estimator=None)
Instantiate
GaussianMixtureRandomCenters
.- Parameters:
n_centers (int) – Number of centers to generate.
estimator (sklearn.base.BaseEstimator) –
Gaussian mixture model. Possible algorithms include
If a random seed is desired, it must be set in the chosen estimator. Defaults to
sklearn.mixture.GaussianMixture(n_components=2)
.
- Return type:
None
Methods
__init__
([n_centers, estimator])Instantiate
GaussianMixtureRandomCenters
.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