46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
|
"""
|
||
|
|
The :mod:`sklearn.covariance` module includes methods and algorithms to
|
||
|
|
robustly estimate the covariance of features given a set of points. The
|
||
|
|
precision matrix defined as the inverse of the covariance is also estimated.
|
||
|
|
Covariance estimation is closely related to the theory of Gaussian Graphical
|
||
|
|
Models.
|
||
|
|
"""
|
||
|
|
|
||
|
|
from ._empirical_covariance import (
|
||
|
|
empirical_covariance,
|
||
|
|
EmpiricalCovariance,
|
||
|
|
log_likelihood,
|
||
|
|
)
|
||
|
|
from ._shrunk_covariance import (
|
||
|
|
shrunk_covariance,
|
||
|
|
ShrunkCovariance,
|
||
|
|
ledoit_wolf,
|
||
|
|
ledoit_wolf_shrinkage,
|
||
|
|
LedoitWolf,
|
||
|
|
oas,
|
||
|
|
OAS,
|
||
|
|
)
|
||
|
|
from ._robust_covariance import fast_mcd, MinCovDet
|
||
|
|
from ._graph_lasso import graphical_lasso, GraphicalLasso, GraphicalLassoCV
|
||
|
|
from ._elliptic_envelope import EllipticEnvelope
|
||
|
|
|
||
|
|
|
||
|
|
__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",
|
||
|
|
]
|