Stablised exp link_function and quadrature variances

GPy/likelihoods/likelihood.py

@@ -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
 

GPy/likelihoods/link_functions.py

@@ -6,6 +6,9 @@ from scipy import stats
 import scipy as sp
 from GPy.util.univariate_Gaussian import std_norm_pdf,std_norm_cdf,inv_std_norm_cdf
 
+_exp_lim_val = np.finfo(np.float64).max
+_lim_val = np.log(_exp_lim_val)
+
 class GPTransformation(object):
     """
     Link function class for doing non-Gaussian likelihoods approximation
@@ -92,16 +95,16 @@ class Log(GPTransformation):
 
     """
     def transf(self,f):
-        return np.exp(f)
+        return np.exp(np.clip(f, -_lim_val, _lim_val))
 
     def dtransf_df(self,f):
-        return np.exp(f)
+        return np.exp(np.clip(f, -_lim_val, _lim_val))
 
     def d2transf_df2(self,f):
-        return np.exp(f)
+        return np.exp(np.clip(f, -_lim_val, _lim_val))
 
     def d3transf_df3(self,f):
-        return np.exp(f)
+        return np.exp(np.clip(f, -_lim_val, _lim_val))
 
 class Log_ex_1(GPTransformation):
     """

GPy/likelihoods/poisson.py

@@ -21,7 +21,7 @@ class Poisson(Likelihood):
     """
     def __init__(self, gp_link=None):
         if gp_link is None:
-            gp_link = link_functions.Log_ex_1()
+            gp_link = link_functions.Log()
 
         super(Poisson, self).__init__(gp_link, name='Poisson')