def sparse_toy_linear_1d_classification_uncertain_input(num_inducing=10, seed=default_seed, optimize=True, plot=True):
    """
    Sparse 1D classification example

    :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')
    import numpy as np
    data = pods.datasets.toy_linear_1d_classification(seed=seed)
    Y = data['Y'][:, 0:1]
    Y[Y.flatten() == -1] = 0
    X = data['X']
    X_var = np.random.uniform(0.3,0.5,X.shape)

    # Model definition
    m = GPy.models.SparseGPClassificationUncertainInput(X, X_var, Y, num_inducing=num_inducing)
    m['.*len'] = 4.

    # Optimize
    if optimize:
        m.optimize()

    # 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
Sparse 1D classification example

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