Duvenaud (2014). "The Kernel Cookbook:
    Advice on Covariance functions".
    <https://www.cs.toronto.edu/~duvenaud/cookbook/>`_

Examples
--------
>>> from sklearn.datasets import load_iris
>>> from sklearn.gaussian_process import GaussianProcessClassifier
>>> from sklearn.gaussian_process.kernels import RationalQuadratic
>>> X, y = load_iris(return_X_y=True)
>>> kernel = RationalQuadratic(length_scale=1.0, alpha=1.5)
>>> gpc = GaussianProcessClassifier(kernel=kernel,
...         random_state=0).fit(X, y)
>>> gpc.score(X, y)
0.9733...
>>> gpc.predict_proba(X[:2,:])
array([[0.8881..., 0.0566..., 0.05518...],
        [0.8678..., 0.0707... , 0.0614...]])
c