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Examples working
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@ -129,7 +129,8 @@ def sparse_toy_linear_1d_classification(seed=default_seed):
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Z = np.random.uniform(data['X'].min(),data['X'].max(),(10,1))
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# Model definition
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m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z)
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m = GPy.models.sparse_GP(data['X'],likelihood=likelihood,kernel=kernel,Z=Z,normalize_X=True)
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m.set('len',.5)
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m.ensure_default_constraints()
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# Optimize
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