`sklearn.covariance`.empirical_covariance#

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

Compute the Maximum likelihood covariance estimator.

Parameters:

Xndarray of shape (n_samples, n_features): Data from which to compute the covariance estimate.
assume_centeredbool, default=False: If True, data will not be centered before computation. Useful when working with data whose mean is almost, but not exactly zero. If False, data will be centered before computation.

Returns:

covariancendarray of shape (n_features, n_features): Empirical covariance (Maximum Likelihood Estimator).

Examples

>>> from sklearn.covariance import empirical_covariance
>>> X = [[1,1,1],[1,1,1],[1,1,1],
...      [0,0,0],[0,0,0],[0,0,0]]
>>> empirical_covariance(X)
array([[0.25, 0.25, 0.25],
       [0.25, 0.25, 0.25],
       [0.25, 0.25, 0.25]])

Examples using `sklearn.covariance.empirical_covariance`#

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

sklearn.covariance.empirical_covariance#

Examples using sklearn.covariance.empirical_covariance#

`sklearn.covariance`.empirical_covariance#

Examples using `sklearn.covariance.empirical_covariance`#