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Stablised exp link_function and quadrature variances

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Alan Saul 2014-03-14 12:42:36 +00:00
parent 1ed7d73219
commit 169fd9b8d4
3 changed files with 15 additions and 6 deletions
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8
GPy/likelihoods/likelihood.py
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@ -178,7 +178,13 @@ class Likelihood(Parameterized):
#E( E(Y_star|f_star)**2 )
def int_pred_mean_sq(f,m,v,predictive_mean_sq):
return self.conditional_mean(f)**2*np.exp(-(0.5/v)*np.square(f - m))
p = np.exp(-(0.5/v)*np.square(f - m))
#If p is zero then conditional_mean**2 will overflow
if p < 1e-10:
return 0.
else:
return self.conditional_mean(f)**2*p
scaled_exp_exp2 = [quad(int_pred_mean_sq, -np.inf, np.inf,args=(mj,s2j,pm2j))[0] for mj,s2j,pm2j in zip(mu,variance,predictive_mean_sq)]
exp_exp2 = np.array(scaled_exp_exp2)[:,None] / normalizer