def toy_heaviside(seed=default_seed, max_iters=100, optimize=True, plot=True):
    """
    Simple 1D classification example using a heavy side gp transformation

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

    """

    try:import pods
    except ImportError:print('pods unavailable, see https://github.com/sods/ods for example datasets')
    data = pods.datasets.toy_linear_1d_classification(seed=seed)
    Y = data['Y'][:, 0:1]
    Y[Y.flatten() == -1] = 0

    # Model definition
    kernel = GPy.kern.RBF(1)
    likelihood = GPy.likelihoods.Bernoulli(gp_link=GPy.likelihoods.link_functions.Heaviside())
    ep = GPy.inference.latent_function_inference.expectation_propagation.EP()
    m = GPy.core.GP(X=data['X'], Y=Y, kernel=kernel, likelihood=likelihood, inference_method=ep, name='gp_classification_heaviside')
    #m = GPy.models.GPClassification(data['X'], likelihood=likelihood)

    # Optimize
    if optimize:
        # Parameters optimization:
        for _ in range(5):
            m.optimize(max_iters=int(max_iters/5))
        print(m)

    # 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
dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/classification.py/toy_heaviside/original