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[sparsegp] check for missing data
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@ -42,6 +42,9 @@ class SparseGP(GP):
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def __init__(self, X, Y, Z, kernel, likelihood, mean_function=None, X_variance=None, inference_method=None,
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name='sparse gp', Y_metadata=None, normalizer=False):
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self.missing_data = np.isnan(Y).any()
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#pick a sensible inference method
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if inference_method is None:
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if isinstance(likelihood, likelihoods.Gaussian):
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