sklearn.covariance.empirical_covariance()

sklearn.covariance.empirical_covariance

sklearn.covariance.empirical_covariance(X, assume_centered=False) [source]

Computes the Maximum likelihood covariance estimator

Parameters:	X : ndarray, shape (n_samples, n_features) Data from which to compute the covariance estimate assume_centered : Boolean If True, data are not centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data are centered before computation.
Returns:	covariance : 2D ndarray, shape (n_features, n_features) Empirical covariance (Maximum Likelihood Estimator).

Examples using sklearn.covariance.empirical_covariance

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood