sklearn.covariance.empirical_covariance

sklearn.covariance.empirical_covariance(X, *, assume_centered=False)

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

Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood