class sklearn.covariance.EmpiricalCovariance(store_precision=True, assume_centered=False)
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Maximum likelihood covariance estimator
Read more in the User Guide.
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>>> import numpy as np >>> from sklearn.covariance import EmpiricalCovariance >>> from sklearn.datasets import make_gaussian_quantiles >>> real_cov = np.array([[.8, .3], ... [.3, .4]]) >>> np.random.seed(0) >>> X = np.random.multivariate_normal(mean=[0, 0], ... cov=real_cov, ... size=500) >>> cov = EmpiricalCovariance().fit(X) >>> cov.covariance_ array([[0.7569..., 0.2818...], [0.2818..., 0.3928...]]) >>> cov.location_ array([0.0622..., 0.0193...])
error_norm (comp_cov[, norm, scaling, squared]) | Computes the Mean Squared Error between two covariance estimators. |
fit (X[, y]) | Fits the Maximum Likelihood Estimator covariance model according to the given training data and parameters. |
get_params ([deep]) | Get parameters for this estimator. |
get_precision () | Getter for the precision matrix. |
mahalanobis (X) | Computes the squared Mahalanobis distances of given observations. |
score (X_test[, y]) | Computes the log-likelihood of a Gaussian data set with self.covariance_ as an estimator of its covariance matrix. |
set_params (**params) | Set the parameters of this estimator. |
__init__(store_precision=True, assume_centered=False)
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error_norm(comp_cov, norm=’frobenius’, scaling=True, squared=True)
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Computes the Mean Squared Error between two covariance estimators. (In the sense of the Frobenius norm).
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fit(X, y=None)
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Fits the Maximum Likelihood Estimator covariance model according to the given training data and parameters.
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get_params(deep=True)
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Get parameters for this estimator.
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get_precision()
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Getter for the precision matrix.
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mahalanobis(X)
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Computes the squared Mahalanobis distances of given observations.
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score(X_test, y=None)
[source]
Computes the log-likelihood of a Gaussian data set with self.covariance_
as an estimator of its covariance matrix.
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set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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sklearn.covariance.EmpiricalCovariance
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.covariance.EmpiricalCovariance.html