 the kernel becomes equivalent to the RBF kernel and for
    nu=0.5 to the absolute exponential kernel. Important intermediate
    values are nu=1.5 (once differentiable functions) and nu=2.5
    (twice differentiable functions). Note that values of nu not in
    [0.5, 1.5, 2.5, inf] incur a considerably higher computational cost
    (appr. 10 times higher) since they require to evaluate the modified
    Bessel function. Furthermore, in contrast to l, nu is kept fixed to
    its initial value and not optimized.

References
----------
.. [1] `Carl Edward Rasmussen, Christopher K. I. Williams (2006).
    "Gaussian Processes for Machine Learning". The MIT Press.
    <http://www.gaussianprocess.org/gpml/>`_

Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn.gaussian_process import GaussianProcessClassifier
>>> from sklearn.gaussian_process.kernels import Matern
>>> X, y = load_iris(return_X_y=True)
>>> kernel = 1.0 * Matern(length_scale=1.0, nu=1.5)
>>> gpc = GaussianProcessClassifier(kernel=kernel,
...         random_state=0).fit(X, y)
>>> gpc.score(X, y)
0.9866...
>>> gpc.predict_proba(X[:2,:])
array([[0.8513..., 0.0368..., 0.1117...],
        [0.8086..., 0.0693..., 0.1220...]])
c