8.2.1. sklearn.covariance.EmpiricalCovariance¶
- class sklearn.covariance.EmpiricalCovariance(store_precision=True, assume_centered=False)¶
Maximum likelihood covariance estimator
Parameters : store_precision : bool
Specifies if the estimated precision is stored
Attributes
covariance_ 2D ndarray, shape (n_features, n_features) Estimated covariance matrix precision_ 2D ndarray, shape (n_features, n_features) Estimated pseudo-inverse matrix. (stored only if store_precision is True) Methods
error_norm(comp_cov[, norm, scaling, squared]) Computes the Mean Squared Error between two covariance estimators. fit(X) Fits the Maximum Likelihood Estimator covariance model mahalanobis(observations) Computes the mahalanobis distances of given observations. score(X_test[, assume_centered]) 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 the estimator. - __init__(store_precision=True, assume_centered=False)¶
Parameters : store_precision: bool :
Specify if the estimated precision is stored
assume_centered: Boolean :
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.
- error_norm(comp_cov, norm='frobenius', scaling=True, squared=True)¶
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 (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. :
- fit(X)¶
Fits the Maximum Likelihood Estimator 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.
Returns : self : object
Returns self.
- mahalanobis(observations)¶
Computes the mahalanobis distances of given observations.
The provided observations are assumed to be centered. One may want to center them using a location estimate first.
Parameters : observations: array-like, shape = [n_observations, n_features] :
The observations, the Mahalanobis distances of the which we compute.
Returns : mahalanobis_distance: array, shape = [n_observations,] :
Mahalanobis distances of the observations.
- score(X_test, assume_centered=False)¶
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.
Returns : res : float
The likelihood of the data set with self.covariance_ as an estimator of its covariance matrix.
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :