36 lines
1.3 KiB
Python
36 lines
1.3 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,
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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 graphical_lasso, GraphicalLasso, GraphicalLassoCV
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from ._elliptic_envelope import EllipticEnvelope
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__all__ = ['EllipticEnvelope',
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'EmpiricalCovariance',
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'GraphicalLasso',
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'GraphicalLassoCV',
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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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'graphical_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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