| """Methods and algorithms to robustly estimate covariance. | |
| They estimate the covariance of features at given sets of points, as well as the | |
| precision matrix defined as the inverse of the covariance. Covariance estimation is | |
| closely related to the theory of Gaussian graphical models. | |
| """ | |
| # Authors: The scikit-learn developers | |
| # SPDX-License-Identifier: BSD-3-Clause | |
| from ._elliptic_envelope import EllipticEnvelope | |
| from ._empirical_covariance import ( | |
| EmpiricalCovariance, | |
| empirical_covariance, | |
| log_likelihood, | |
| ) | |
| from ._graph_lasso import GraphicalLasso, GraphicalLassoCV, graphical_lasso | |
| from ._robust_covariance import MinCovDet, fast_mcd | |
| from ._shrunk_covariance import ( | |
| OAS, | |
| LedoitWolf, | |
| ShrunkCovariance, | |
| ledoit_wolf, | |
| ledoit_wolf_shrinkage, | |
| oas, | |
| shrunk_covariance, | |
| ) | |
| __all__ = [ | |
| "EllipticEnvelope", | |
| "EmpiricalCovariance", | |
| "GraphicalLasso", | |
| "GraphicalLassoCV", | |
| "LedoitWolf", | |
| "MinCovDet", | |
| "OAS", | |
| "ShrunkCovariance", | |
| "empirical_covariance", | |
| "fast_mcd", | |
| "graphical_lasso", | |
| "ledoit_wolf", | |
| "ledoit_wolf_shrinkage", | |
| "log_likelihood", | |
| "oas", | |
| "shrunk_covariance", | |
| ] | |