def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
    """
    Simple 1D classification example using EP approximation

    :param seed: seed value for data generation (default is 4).
    :type seed: int

    """
    try:import pods
    except ImportError:raise ImportWarning('Need pods for example datasets. See https://github.com/sods/ods, or pip install pods.')
    data = pods.datasets.toy_linear_1d_classification(seed=seed)
    Y = data['Y'][:, 0:1]
    Y[Y.flatten() == -1] = 0

    # Model definition
    m = GPy.models.GPClassification(data['X'], Y)

    # Optimize
    if optimize:
        #m.update_likelihood_approximation()
        # Parameters optimization:
        m.optimize()
        #m.update_likelihood_approximation()
        #m.pseudo_EM()

    # Plot
    if plot:
        from matplotlib import pyplot as plt
        fig, axes = plt.subplots(2, 1)
        m.plot_f(ax=axes[0])
        m.plot(ax=axes[1])

    print(m)
    return m