35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
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"""
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The :mod:`sklearn.covariance` module includes methods and algorithms to
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robustly estimate the covariance of features given a set of points. The
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precision matrix defined as the inverse of the covariance is also estimated.
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Covariance estimation is closely related to the theory of Gaussian Graphical
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Models.
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"""
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from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance, \
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log_likelihood
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from .shrunk_covariance_ import shrunk_covariance, ShrunkCovariance, \
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ledoit_wolf, ledoit_wolf_shrinkage, \
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LedoitWolf, oas, OAS
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from .robust_covariance import fast_mcd, MinCovDet
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from .graph_lasso_ import graph_lasso, GraphLasso, GraphLassoCV
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from .outlier_detection import EllipticEnvelope
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__all__ = ['EllipticEnvelope',
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'EmpiricalCovariance',
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'GraphLasso',
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'GraphLassoCV',
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'LedoitWolf',
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'MinCovDet',
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'OAS',
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'ShrunkCovariance',
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'empirical_covariance',
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'fast_mcd',
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'graph_lasso',
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'ledoit_wolf',
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'ledoit_wolf_shrinkage',
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'log_likelihood',
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'oas',
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'shrunk_covariance']
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