\paragraph{Methods}

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{\hyperref[EmpiricalCovariance.error_norm]{\code{error\_norm}}}(comp\_cov{[}, norm, scaling, squared{]})
 &
Computes the Mean Squared Error between two covariance estimators.
\\\hline

{\hyperref[EmpiricalCovariance.fit]{\code{fit}}}(X)
 &
Fits the Maximum Likelihood Estimator covariance model
\\\hline

{\hyperref[EmpiricalCovariance.get_params]{\code{get\_params}}}({[}deep{]})
 &
Get parameters for the estimator
\\\hline

{\hyperref[EmpiricalCovariance.mahalanobis]{\code{mahalanobis}}}(observations)
 &
Computes the mahalanobis distances of given observations.
\\\hline

{\hyperref[EmpiricalCovariance.score]{\code{score}}}(X\_test{[}, assume\_centered{]})
 &
Computes the log-likelihood of a gaussian data set with \emph{self.covariance\_} as an estimator of its covariance matrix.
\\\hline

{\hyperref[EmpiricalCovariance.set_params]{\code{set\_params}}}(**params)
 &
Set the parameters of the estimator.
\\\hline
\end{longtable}