type t = [ `BaseEstimator | `Object | `ShrunkCovariance ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?store_precision:bool ->
?assume_centered:bool ->
?shrinkage:float ->
unit ->
tCovariance estimator with shrinkage
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters ---------- store_precision : bool, default=True Specify if the estimated precision is stored
assume_centered : bool, default=False If True, data will not be centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data will be centered before computation.
shrinkage : float, default=0.1 Coefficient in the convex combination used for the computation of the shrunk estimate. Range is 0, 1.
Attributes ---------- covariance_ : ndarray of shape (n_features, n_features) Estimated covariance matrix
location_ : ndarray of shape (n_features,) Estimated location, i.e. the estimated mean.
precision_ : ndarray of shape (n_features, n_features) Estimated pseudo inverse matrix. (stored only if store_precision is True)
Examples -------- >>> import numpy as np >>> from sklearn.covariance import ShrunkCovariance >>> from sklearn.datasets import make_gaussian_quantiles >>> real_cov = np.array([.8, .3],
... [.3, .4]) >>> rng = np.random.RandomState(0) >>> X = rng.multivariate_normal(mean=0, 0, ... cov=real_cov, ... size=500) >>> cov = ShrunkCovariance().fit(X) >>> cov.covariance_ array([0.7387..., 0.2536...],
[0.2536..., 0.4110...]) >>> cov.location_ array(0.0622..., 0.0193...)
Notes ----- The regularized covariance is given by:
(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)
where mu = trace(cov) / n_features
val error_norm :
?norm:[ `Frobenius | `Spectral ] ->
?scaling:bool ->
?squared:bool ->
comp_cov:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatComputes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
Parameters ---------- comp_cov : array-like of shape (n_features, n_features) The covariance to compare with.
norm : 'frobenius', 'spectral', default='frobenius' The type of norm used to compute the error. Available error types:
scaling : bool, default=True If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.
squared : bool, default=True Whether to compute the squared error norm or the error norm. If True (default), the squared error norm is returned. If False, the error norm is returned.
Returns ------- result : float The Mean Squared Error (in the sense of the Frobenius norm) between `self` and `comp_cov` covariance estimators.
val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> tFit the shrunk covariance model according to the given training data and parameters.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training data, where n_samples is the number of samples and n_features is the number of features.
y: Ignored not used, present for API consistence purpose.
Returns ------- self : object
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 : mapping of string to any Parameter names mapped to their values.
Getter for the precision matrix.
Returns ------- precision_ : array-like of shape (n_features, n_features) The precision matrix associated to the current covariance object.
Computes the squared Mahalanobis distances of given observations.
Parameters ---------- X : array-like of shape (n_samples, n_features) The observations, the Mahalanobis distances of the which we compute. Observations are assumed to be drawn from the same distribution than the data used in fit.
Returns ------- dist : ndarray of shape (n_samples,) Squared Mahalanobis distances of the observations.
val score :
?y:Py.Object.t ->
x_test:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatComputes the log-likelihood of a Gaussian data set with `self.covariance_` as an estimator of its covariance matrix.
Parameters ---------- X_test : array-like of shape (n_samples, n_features) Test data of which we compute the likelihood, where n_samples is the number of samples and n_features is the number of features. X_test is assumed to be drawn from the same distribution than the data used in fit (including centering).
y : Ignored Not used, present for API consistence purpose.
Returns ------- res : float The likelihood of the data set with `self.covariance_` as an estimator of its covariance matrix.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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 : object Estimator instance.
Attribute covariance_: get value or raise Not_found if None.
Attribute covariance_: get value as an option.
Attribute location_: get value or raise Not_found if None.
Attribute precision_: get value or raise Not_found if None.
Attribute precision_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.