`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`¶

sklearn.covariance.empirical_covariance¶

Examples using sklearn.covariance.empirical_covariance¶

`sklearn.covariance`.empirical_covariance¶

Examples using `sklearn.covariance.empirical_covariance`¶