content: """
  ==========================================
  One-class SVM with non-linear kernel (RBF)
  ==========================================
  
  An example using a one-class SVM for novelty detection.
  
  :ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
  algorithm that learns a decision function for novelty detection:
  classifying new data as similar or different to the training set.
  """
  print(__doc__)
  
  import numpy as np
  import matplotlib.pyplot as plt
  import matplotlib.font_manager
  from sklearn import svm
  
  xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
  # Generate train data
  X = 0.3 * np.random.randn(100, 2)
  X_train = np.r_[X + 2, X - 2]
  # Generate some regular novel observations
  X = 0.3 * np.random.randn(20, 2)
  X_test = np.r_[X + 2, X - 2]
  # Generate some abnormal novel observations
  X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
  
  # fit the model
  clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
  clf.fit(X_train)
  y_pred_train = clf.predict(X_train)
  y_pred_test = clf.predict(X_test)
  y_pred_outliers = clf.predict(X_outliers)
  n_error_train = y_pred_train[y_pred_train == -1].size
  n_error_test = y_pred_test[y_pred_test == -1].size
  n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
  
  # plot the line, the points, and the nearest vectors to the plane
  Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
  Z = Z.reshape(xx.shape)
  
  plt.title("Novelty Detection")
  plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.Blues_r)
  a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='red')
  plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='orange')
  
  b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')
  b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')
  c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')
  plt.axis('tight')
  plt.xlim((-5, 5))
  plt.ylim((-5, 5))
  plt.legend([a.collections[0], b1, b2, c],
             ["learned frontier", "training observations",
              "new regular observations", "new abnormal observations"],
             loc="upper left",
             prop=matplotlib.font_manager.FontProperties(size=11))
  plt.xlabel(
      "error train: %d/200 ; errors novel regular: %d/40 ; "
      "errors novel abnormal: %d/40"
      % (n_error_train, n_error_test, n_error_outliers))
  plt.show()
  