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Merge branch 'params' of github.com:SheffieldML/GPy into params

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Ricardo 2014-03-17 10:30:55 +00:00
commit 607ed98e51
8 changed files with 65 additions and 32 deletions
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22
GPy/likelihoods/likelihood.py
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@ -142,7 +142,12 @@ class Likelihood(Parameterized):
"""
#conditional_mean: the edpected value of y given some f, under this likelihood
def int_mean(f,m,v):
return self.conditional_mean(f)*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 will overflow
if p < 1e-10:
return 0.
else:
return self.conditional_mean(f)*p
scaled_mean = [quad(int_mean, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
mean = np.array(scaled_mean)[:,None] / np.sqrt(2*np.pi*(variance))
@ -165,7 +170,12 @@ class Likelihood(Parameterized):
# E( V(Y_star|f_star) )
def int_var(f,m,v):
return self.conditional_variance(f)*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_variance will overflow
if p < 1e-10:
return 0.
else:
return self.conditional_variance(f)*p
scaled_exp_variance = [quad(int_var, -np.inf, np.inf,args=(mj,s2j))[0] for mj,s2j in zip(mu,variance)]
exp_var = np.array(scaled_exp_variance)[:,None] / normalizer
@ -178,7 +188,13 @@ 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