import time

    import numpy as np
    import matplotlib
    import matplotlib.pyplot as plt

    from sklearn import svm
    from sklearn.datasets import make_moons, make_blobs
    from sklearn.covariance import EllipticEnvelope
    from sklearn.ensemble import IsolationForest
    from sklearn.neighbors import LocalOutlierFactor


    matplotlib.rcParams['contour.negative_linestyle'] = 'solid'

    # Example settings
    n_samples = 300
    outliers_fraction = 0.15
    n_outliers = int(outliers_fraction * n_samples)
    n_inliers = n_samples - n_outliers

    # define outlier/anomaly detection methods to be compared
    anomaly_algorithms = [
        ("Robust covariance", EllipticEnvelope(contamination=outliers_fraction)),
        ("One-Class SVM", svm.OneClassSVM(nu=outliers_fraction, kernel="rbf",
                                          gamma=0.1)),
        ("Isolation Forest", IsolationForest( 
                                             contamination=outliers_fraction )),
        ("Local Outlier Factor", LocalOutlierFactor(
            n_neighbors=35, contamination=outliers_fraction))]

    # Define datasets
    blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)
    datasets = [
        make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5,
                   **blobs_params)[0],
        make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5],
                   **blobs_params)[0],
        make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3],
                   **blobs_params)[0],
        4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0)[0] -
              np.array([0.5, 0.25])),
        14. * (np.random.RandomState(42).rand(n_samples, 2) - 0.5)]

    # Compare given classifiers under given settings
    xx, yy = np.meshgrid(np.linspace(-7, 7, 150),
                         np.linspace(-7, 7, 150))

    plt.figure(figsize=(len(anomaly_algorithms) * 2 + 3, 12.5))
    plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
                        hspace=.01)

    plot_num = 1
    rng = np.random.RandomState(42)

    for i_dataset, X in enumerate(datasets):
        # Add outliers
        X = np.concatenate([X, rng.uniform(low=-6, high=6,
                           size=(n_outliers, 2))], axis=0)

        for name, algorithm in anomaly_algorithms:
            t0 = time.time()
            algorithm.fit(X)
            t1 = time.time()
            plt.subplot(len(datasets), len(anomaly_algorithms), plot_num)
            if i_dataset == 0:
                plt.title(name, size=18)

            # fit the data and tag outliers
            if name == "Local Outlier Factor":
                y_pred = algorithm.fit_predict(X)
            else:
                y_pred = algorithm.fit(X).predict(X)

            # plot the levels lines and the points
            if name != "Local Outlier Factor":  # LOF does not implement predict
                Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])
                Z = Z.reshape(xx.shape)
                plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='black')

            colors = np.array(['#377eb8', '#ff7f00'])
            plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[(y_pred + 1) // 2])

            plt.xlim(-7, 7)
            plt.ylim(-7, 7)
            plt.xticks(())
            plt.yticks(())
            plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
                     transform=plt.gca().transAxes, size=15,
                     horizontalalignment='right')
            plot_num += 1

    plt.show()