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beginning of adding variational GH quadrature to the likelihood class

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This commit is contained in:
James Hensman 2014-10-16 12:14:56 +01:00
parent f9a16059e4
commit 84c87f7886
4 changed files with 39 additions and 7 deletions
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4
GPy/inference/latent_function_inference/laplace.py
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@ -82,7 +82,7 @@ class Laplace(LatentFunctionInference):
#define the objective function (to be maximised)
def obj(Ki_f, f):
return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + likelihood.logpdf(f, Y, Y_metadata=Y_metadata)
return -0.5*np.dot(Ki_f.flatten(), f.flatten()) + np.sum(likelihood.logpdf(f, Y, Y_metadata=Y_metadata))
difference = np.inf
iteration = 0
@ -152,7 +152,7 @@ class Laplace(LatentFunctionInference):
Ki_W_i = K - C.T.dot(C)
#compute the log marginal
log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, Y_metadata=Y_metadata) - np.sum(np.log(np.diag(L)))
log_marginal = -0.5*np.dot(Ki_f.flatten(), f_hat.flatten()) + likelihood.logpdf(f_hat, Y, Y_metadata=Y_metadata).sum() - np.sum(np.log(np.diag(L)))
# Compute matrices for derivatives
dW_df = -likelihood.d3logpdf_df3(f_hat, Y, Y_metadata=Y_metadata) # -d3lik_d3fhat