class sklearn.covariance.ShrunkCovariance(store_precision=True, assume_centered=False, shrinkage=0.1)
[source]
Covariance estimator with shrinkage
Read more in the User Guide.
Parameters: |
store_precision : boolean, default True Specify if the estimated precision is stored shrinkage : float, 0 <= shrinkage <= 1, default 0.1 Coefficient in the convex combination used for the computation of the shrunk estimate. assume_centered : boolean, default False If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation. |
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Attributes: |
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) `shrinkage` : float, 0 <= shrinkage <= 1 Coefficient in the convex combination used for the computation of the shrunk estimate. |
The regularized covariance is given by
where mu = trace(cov) / n_features
error_norm (comp_cov[, norm, scaling, squared]) | Computes the Mean Squared Error between two covariance estimators. |
fit (X[, y]) | Fits the shrunk covariance model according to the given training data and parameters. |
get_params ([deep]) | Get parameters for this estimator. |
get_precision () | Getter for the precision matrix. |
mahalanobis (observations) | Computes the squared Mahalanobis distances of given observations. |
score (X_test[, y]) | Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. |
set_params (**params) | Set the parameters of this estimator. |
__init__(store_precision=True, assume_centered=False, shrinkage=0.1)
[source]
error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)
[source]
Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
Parameters: |
comp_cov : array-like, 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 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. |
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Returns: |
The Mean Squared Error (in the sense of the Frobenius norm) between : `self` and `comp_cov` covariance estimators. : |
fit(X, y=None)
[source]
Fits the shrunk covariance model according to the given training data and parameters.
Parameters: |
X : array-like, 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. |
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Returns: |
self : object Returns self. |
get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: |
deep: boolean, optional : If True, will return the parameters for this estimator and contained subobjects that are estimators. |
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Returns: |
params : mapping of string to any Parameter names mapped to their values. |
get_precision()
[source]
Getter for the precision matrix.
Returns: |
precision_ : array-like, The precision matrix associated to the current covariance object. |
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mahalanobis(observations)
[source]
Computes the squared Mahalanobis distances of given observations.
Parameters: |
observations : array-like, shape = [n_observations, 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. |
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Returns: |
mahalanobis_distance : array, shape = [n_observations,] Squared Mahalanobis distances of the observations. |
score(X_test, y=None)
[source]
Computes the log-likelihood of a Gaussian data set with self.covariance_
as an estimator of its covariance matrix.
Parameters: |
X_test : array-like, 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. |
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Returns: |
res : float The likelihood of the data set with |
set_params(**params)
[source]
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.
Returns: | self : |
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sklearn.covariance.ShrunkCovariance
© 2007–2016 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.covariance.ShrunkCovariance.html