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added some clarifying comments with NDL

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James Hensman 2015-03-25 14:22:39 +00:00
parent 534e0df606
commit e74bfd81c6
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GPy/inference/latent_function_inference/expectation_propagation.py
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@ -40,8 +40,11 @@ class EP(LatentFunctionInference):
K = kern.K(X) K = kern.K(X)
if self._ep_approximation is None: if self._ep_approximation is None:
#if we don't yet have the results of runnign EP, run EP and store the computed factors in self._ep_approximation
mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata) mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation = self.expectation_propagation(K, Y, likelihood, Y_metadata)
else: else:
#if we've already run EP, just use the existing approximation stored in self._ep_approximation
mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation mu, Sigma, mu_tilde, tau_tilde, Z_hat = self._ep_approximation
Wi, LW, LWi, W_logdet = pdinv(K + np.diag(1./tau_tilde)) Wi, LW, LWi, W_logdet = pdinv(K + np.diag(1./tau_tilde))