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svgp tests are passing with re-ordered chols

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This commit is contained in:
James Hensman 2015-05-05 14:29:07 +01:00
parent 5d1875ec44
commit 0450e228a8
1 changed files with 2 additions and 4 deletions
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6
GPy/inference/latent_function_inference/svgp.py
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@ -100,10 +100,8 @@ class SVGP(LatentFunctionInference):
#sum (gradients of) expected likelihood and KL part
log_marginal = F.sum()
dL_dm, dL_dS, dL_dKmm, dL_dKmn = dF_dm - dKL_dm*0, dF_dS- dKL_dS*0, dF_dKmm- dKL_dKmm*0, dF_dKmn
#log_marginal = F.sum() - KL
#dL_dm, dL_dS, dL_dKmm, dL_dKmn = dF_dm - dKL_dm, dF_dS- dKL_dS, dF_dKmm- dKL_dKmm, dF_dKmn
log_marginal = F.sum() - KL
dL_dm, dL_dS, dL_dKmm, dL_dKmn = dF_dm - dKL_dm, dF_dS- dKL_dS, dF_dKmm- dKL_dKmm, dF_dKmn
dL_dchol = 2.*np.array([np.dot(a,b) for a, b in zip(dL_dS, L) ])
dL_dchol = choleskies.triang_to_flat(dL_dchol)