content: """
  A tutorial exercise for using different SVM kernels.
  
  Adapted from:
  https://scikit-learn.org/stable/auto_examples/exercises/plot_iris_exercise.html
  """
  
  import numpy as np
  
  import matplotlib
  matplotlib.use('Agg')
  
  import matplotlib.pyplot as plt
  from sklearn import datasets, svm
  
  kernel = 'linear'  # choice of linear, rbf, poly
  test_split = 0.1
  random_seed = 0
  degree = 3
  gamma = 10
  
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  
  X = X[y != 0, :2]
  y = y[y != 0]
  
  n_sample = len(X)
  
  np.random.seed(random_seed)
  order = np.random.permutation(n_sample)
  X = X[order]
  y = y[order].astype(np.float)
  
  split_pos = int((1 - test_split) * n_sample)
  X_train = X[:split_pos]
  y_train = y[:split_pos]
  X_test = X[split_pos:]
  y_test = y[split_pos:]
  
  # fit the model
  clf = svm.SVC(kernel=kernel, degree=degree, gamma=gamma)
  clf.fit(X_train, y_train)
  
  print("Train accuracy: %s" % clf.score(X_train, y_train))
  print("Test accuracy: %f" % clf.score(X_test, y_test))
  
  plt.figure()
  plt.clf()
  plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired,
              edgecolor='k', s=20)
  
  # Circle out the test data
  plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none',
              zorder=10, edgecolor='k')
  
  plt.axis('tight')
  x_min = X[:, 0].min()
  x_max = X[:, 0].max()
  y_min = X[:, 1].min()
  y_max = X[:, 1].max()
  
  XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
  Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
  
  # Put the result into a color plot
  Z = Z.reshape(XX.shape)
  plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
  plt.contour(XX, YY, Z, colors=['k', 'k', 'k'],
              linestyles=['--', '-', '--'], levels=[-.5, 0, .5])
  
  plt.title(kernel)
  plt.savefig("plot.png")
  