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

Computes the Maximum likelihood covariance estimator

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.

covariancendarray of shape (n_features, n_features)

Empirical covariance (Maximum Likelihood Estimator).


>>> 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