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Started looking at quadrature code for moments

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This commit is contained in:
Alan Saul 2015-10-22 16:23:08 +01:00
parent e7c8da2cdf
commit 51ccaed020
1 changed files with 6 additions and 10 deletions
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16
GPy/likelihoods/likelihood.py
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@ -176,10 +176,7 @@ class Likelihood(Parameterized):
log_p_ystar = np.array(log_p_ystar).reshape(*y_test.shape)
return log_p_ystar
def quad_limits(self):
return -np.inf, np.inf
def moments_match_ep(self,obs,tau,v):
def moments_match_ep(self,obs,tau,v,Y_metadata_i=None):
"""
Calculation of moments using quadrature
@ -193,23 +190,22 @@ class Likelihood(Parameterized):
sigma2 = 1./tau
#Lets do these for now based on the same idea as Gaussian quadrature
# i.e. multiply anything by close to zero, and its zero.
f_min = mu - 8*np.sqrt(sigma2)
f_max = mu + 8*np.sqrt(sigma2)
f_min = mu - 20*np.sqrt(sigma2)
f_max = mu + 20*np.sqrt(sigma2)
# f_min, f_max = self.quad_limits()
def int_1(f):
return self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))
return self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))
z_scaled, accuracy = quad(int_1, f_min, f_max)
#Compute second integral for first moment
def int_2(f):
return f*self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))
return f*self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))
mean, accuracy = quad(int_2, f_min, f_max)
mean /= z_scaled
#Compute integral for variance
def int_3(f):
return (f**2)*self.pdf(f, obs)*np.exp(-0.5*tau*np.square(mu-f))
return (f**2)*self.pdf(f, obs, Y_metadata=Y_metadata_i)*np.exp(-0.5*tau*np.square(mu-f))
Ef2, accuracy = quad(int_3, f_min, f_max)
Ef2 /= z_scaled
variance = Ef2 - mean**2