51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
|
"""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 sklearn.covariance._elliptic_envelope import EllipticEnvelope
|
||
|
|
from sklearn.covariance._empirical_covariance import (
|
||
|
|
EmpiricalCovariance,
|
||
|
|
empirical_covariance,
|
||
|
|
log_likelihood,
|
||
|
|
)
|
||
|
|
from sklearn.covariance._graph_lasso import (
|
||
|
|
GraphicalLasso,
|
||
|
|
GraphicalLassoCV,
|
||
|
|
graphical_lasso,
|
||
|
|
)
|
||
|
|
from sklearn.covariance._robust_covariance import MinCovDet, fast_mcd
|
||
|
|
from sklearn.covariance._shrunk_covariance import (
|
||
|
|
OAS,
|
||
|
|
LedoitWolf,
|
||
|
|
ShrunkCovariance,
|
||
|
|
ledoit_wolf,
|
||
|
|
ledoit_wolf_shrinkage,
|
||
|
|
oas,
|
||
|
|
shrunk_covariance,
|
||
|
|
)
|
||
|
|
|
||
|
|
__all__ = [
|
||
|
|
"OAS",
|
||
|
|
"EllipticEnvelope",
|
||
|
|
"EmpiricalCovariance",
|
||
|
|
"GraphicalLasso",
|
||
|
|
"GraphicalLassoCV",
|
||
|
|
"LedoitWolf",
|
||
|
|
"MinCovDet",
|
||
|
|
"ShrunkCovariance",
|
||
|
|
"empirical_covariance",
|
||
|
|
"fast_mcd",
|
||
|
|
"graphical_lasso",
|
||
|
|
"ledoit_wolf",
|
||
|
|
"ledoit_wolf_shrinkage",
|
||
|
|
"log_likelihood",
|
||
|
|
"oas",
|
||
|
|
"shrunk_covariance",
|
||
|
|
]
|