package sklearn

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sklearn
- Sklearn

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type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?store_precision:bool ->
  ?assume_centered:bool ->
  ?shrinkage:[ `F of float | `T0_shrinkage_1 of Py.Object.t ] ->
  unit ->
  t

Covariance estimator with shrinkage

Read more in the :ref:`User Guide <shrunk_covariance>`.

Parameters ---------- store_precision : boolean, default True Specify if the estimated precision is stored

assume_centered : boolean, 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, 0 <= shrinkage <= 1, default 0.1 Coefficient in the convex combination used for the computation of the shrunk estimate.

Attributes ---------- location_ : array-like, shape (n_features,) Estimated location, i.e. the estimated mean.

covariance_ : array-like, shape (n_features, n_features) Estimated covariance matrix

precision_ : array-like, 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:string ->
  ?scaling:bool ->
  ?squared:bool ->
  comp_cov:Arr.t ->
  t ->
  Py.Object.t

Computes 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 : str The type of norm used to compute the error. Available error types:

'frobenius' (default): sqrt(tr(A^t.A))
'spectral': sqrt(max(eigenvalues(A^t.A)) where A is the error ``(comp_cov - self.covariance_)``.

scaling : bool If True (default), the squared error norm is divided by n_features. If False, the squared error norm is not rescaled.

squared : bool 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 ------- 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:Arr.t -> t -> t

Fits 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 not used, present for API consistence purpose.

Returns ------- self : object

val get_params : ?deep:bool -> t -> Dict.t

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.

val get_precision : t -> Arr.t

Getter for the precision matrix.

Returns ------- precision_ : array-like The precision matrix associated to the current covariance object.

val mahalanobis : x:Arr.t -> t -> Arr.t

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 : array, shape = n_samples, Squared Mahalanobis distances of the observations.

val score : ?y:Py.Object.t -> x_test:Arr.t -> t -> float

Computes 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 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 -> t -> t

Set 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.

val location_ : t -> Arr.t

Attribute location_: get value or raise Not_found if None.

val location_opt : t -> Arr.t option

Attribute location_: get value as an option.

val covariance_ : t -> Arr.t

Attribute covariance_: get value or raise Not_found if None.

val covariance_opt : t -> Arr.t option

Attribute covariance_: get value as an option.

val precision_ : t -> Arr.t

Attribute precision_: get value or raise Not_found if None.

val precision_opt : t -> Arr.t option

Attribute precision_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.