This is documentation for an old release of Scikit-learn (version 1.3). Try the latest stable release (version 1.6) or development (unstable) versions.
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. IfFalse
, 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
¶
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Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood