content: """
  =====================================================
  Gaussian process classification (GPC) on iris dataset
  =====================================================
  
  This example illustrates the predicted probability of GPC for an isotropic
  and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
  The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by
  assigning different length-scales to the two feature dimensions.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn import datasets
  from sklearn.gaussian_process import GaussianProcessClassifier
  from sklearn.gaussian_process.kernels import RBF
  
  # import some data to play with
  iris = datasets.load_iris()
  X = iris.data[:, :2]  # we only take the first two features.
  y = np.array(iris.target, dtype=int)
  
  h = .02  # step size in the mesh
  
  kernel = 1.0 * RBF([1.0])
  gpc_rbf_isotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
  kernel = 1.0 * RBF([1.0, 1.0])
  gpc_rbf_anisotropic = GaussianProcessClassifier(kernel=kernel).fit(X, y)
  
  # create a mesh to plot in
  x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
  y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
  xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                       np.arange(y_min, y_max, h))
  
  titles = ["Isotropic RBF", "Anisotropic RBF"]
  plt.figure(figsize=(10, 5))
  for i, clf in enumerate((gpc_rbf_isotropic, gpc_rbf_anisotropic)):
      # Plot the predicted probabilities. For that, we will assign a color to
      # each point in the mesh [x_min, m_max]x[y_min, y_max].
      plt.subplot(1, 2, i + 1)
  
      Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
  
      # Put the result into a color plot
      Z = Z.reshape((xx.shape[0], xx.shape[1], 3))
      plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), origin="lower")
  
      # Plot also the training points
      plt.scatter(X[:, 0], X[:, 1], c=np.array(["r", "g", "b"])[y])
      plt.xlabel('Sepal length')
      plt.ylabel('Sepal width')
      plt.xlim(xx.min(), xx.max())
      plt.ylim(yy.min(), yy.max())
      plt.xticks(())
      plt.yticks(())
      plt.title("%s, LML: %.3f" %
                (titles[i], clf.log_marginal_likelihood(clf.kernel_.theta)))
  
  plt.tight_layout()
  plt.show()
  