 n_classes * (n_classes-1) / 2)
    Returns the decision function of the sample for each class
    in the model.
    If decision_function_shape='ovr', the shape is (n_samples,
    n_classes).

Notes
-----
If decision_function_shape='ovo', the function values are proportional
to the distance of the samples X to the separating hyperplane. If the
exact distances are required, divide the function values by the norm of
the weight vector (``coef_``). See also `this question
<https://stats.stackexchange.com/questions/14876/
interpreting-distance-from-hyperplane-in-svm>`_ for further details.
If decision_function_shape='ovr', the decision function is a monotonic
transformation of ovo decision function.
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