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Fixed two small lbugs
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8377d95fbe
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2 changed files with 2 additions and 2 deletions
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@ -175,7 +175,7 @@ class EP(EPBase, ExactGaussianInference):
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if self.ep_mode=="nested":
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if self.ep_mode=="nested":
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#Force EP at each step of the optimization
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#Force EP at each step of the optimization
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self._ep_approximation = None
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self._ep_approximation = None
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post_params, ga_approx, log_Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
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post_params, ga_approx, cav_params, log_Z_tilde = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
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elif self.ep_mode=="alternated":
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elif self.ep_mode=="alternated":
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if getattr(self, '_ep_approximation', None) is None:
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if getattr(self, '_ep_approximation', None) is None:
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#if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
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#if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
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@ -60,7 +60,7 @@ class Gaussian(Likelihood):
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def ep_gradients(self, Y, cav_tau, cav_v, dL_dKdiag,Y_metadata=None):
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def ep_gradients(self, Y, cav_tau, cav_v, dL_dKdiag,Y_metadata=None):
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return self.exact_inference_gradients(dL_dKdiag)
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return self.exact_inference_gradients(dL_dKdiag)
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def exact_inference_gradients(self, dL_dKdiag):
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def exact_inference_gradients(self, dL_dKdiag, Y_metadata=None):
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return dL_dKdiag.sum()
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return dL_dKdiag.sum()
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def _preprocess_values(self, Y):
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def _preprocess_values(self, Y):
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