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Integrate Gael's minor comments + Magnify examples + 1D data case.
Review of the variables names suggested by Gael. Better illustation on examples (boxplot, optimized ranges). One-dimensional data are now handled in a way that is consistent with the rest of sklearn: arrays of shape (x, ) are considered as a sample with only one observation described by x features and a warning suggests to the user that he should reshape his array. This fix an issue opened in the issues list.
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- examples/covariance/plot_mahalanobis_distances.py 28 additions, 26 deletionsexamples/covariance/plot_mahalanobis_distances.py
- examples/covariance/plot_robust_vs_empirical_covariance.py 5 additions, 2 deletionsexamples/covariance/plot_robust_vs_empirical_covariance.py
- sklearn/covariance/empirical_covariance_.py 4 additions, 1 deletionsklearn/covariance/empirical_covariance_.py
- sklearn/covariance/robust_covariance.py 189 additions, 176 deletionssklearn/covariance/robust_covariance.py
- sklearn/covariance/shrunk_covariance_.py 21 additions, 7 deletionssklearn/covariance/shrunk_covariance_.py
- sklearn/covariance/tests/test_covariance.py 7 additions, 2 deletionssklearn/covariance/tests/test_covariance.py
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