def toy_linear_1d_classification(seed=default_seed, optimize=True, plot=True):
try:import pods<EOL>except ImportError:raise ImportWarning('<STR_LIT>')<EOL>data = pods.datasets.toy_linear_1d_classification(seed=seed)<EOL>Y = data['<STR_LIT:Y>'][:, <NUM_LIT:0>:<NUM_LIT:1>]<EOL>Y[Y.flatten() == -<NUM_LIT:1>] = <NUM_LIT:0><EOL>m = GPy.models.GPClassification(data['<STR_LIT:X>'], Y)<EOL>if optimize:<EOL><INDENT>m.optimize()<EOL><DEDENT>if plot:<EOL><INDENT>from matplotlib import pyplot as plt<EOL>fig, axes = plt.subplots(<NUM_LIT:2>, <NUM_LIT:1>)<EOL>m.plot_f(ax=axes[<NUM_LIT:0>])<EOL>m.plot(ax=axes[<NUM_LIT:1>])<EOL><DEDENT>print(m)<EOL>return m<EOL>
Simple 1D classification example using EP approximation

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