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[sparse GP] fallback for other inference methods for missing_data

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Max Zwiessele 2014-10-17 17:32:51 +01:00
parent ac224c0dbd
commit b94ebc2aa2
1 changed files with 4 additions and 1 deletions
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3
GPy/core/sparse_gp.py
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@ -110,7 +110,10 @@ class SparseGP(GP):
like them into this dictionary for inner use of the indices inside the like them into this dictionary for inner use of the indices inside the
algorithm. algorithm.
""" """
try:
posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=None) posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata, Lm=Lm, dL_dKmm=None)
except:
posterior, log_marginal_likelihood, grad_dict = self.inference_method.inference(kern, X, Z, likelihood, Y, Y_metadata)
current_values = {} current_values = {}
likelihood.update_gradients(grad_dict['dL_dthetaL']) likelihood.update_gradients(grad_dict['dL_dthetaL'])
current_values['likgrad'] = likelihood.gradient.copy() current_values['likgrad'] = likelihood.gradient.copy()