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[inference] changed gaussian variance to precision (which it really is)

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Max Zwiessele 2015-09-11 16:59:55 +01:00
parent 8132084de6
commit 69f6cfa6f7
5 changed files with 45 additions and 45 deletions
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4
GPy/inference/latent_function_inference/expectation_propagation.py
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@ -35,7 +35,7 @@ class EP(ExactGaussianInference):
# TODO: update approximation in the end as well? Maybe even with a switch?
pass
def inference(self, kern, X, likelihood, Y, mean_function=None, Y_metadata=None, gaussian_variance=None, K=None):
def inference(self, kern, X, likelihood, Y, mean_function=None, Y_metadata=None, precision=None, K=None):
num_data, output_dim = Y.shape
assert output_dim ==1, "ep in 1D only (for now!)"
@ -49,7 +49,7 @@ class EP(ExactGaussianInference):
#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
return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, gaussian_variance=1./tau_tilde, K=K)
return super(EP, self).inference(kern, X, likelihood, mu_tilde[:,None], mean_function=mean_function, Y_metadata=Y_metadata, precision=1./tau_tilde, K=K)
def expectation_propagation(self, K, Y, likelihood, Y_metadata):