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Sparse EP

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Ricardo 2013-01-28 00:16:23 +00:00
parent a8738984b3
commit fad0e07624
7 changed files with 399 additions and 8 deletions
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8
GPy/models/GP.py
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@ -29,8 +29,8 @@ class GP(model):
"""
def __init__(self,X,Y=None,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None,likelihood=None,epsilon_ep=1e-3,epsion_em=.1,powerep=[1.,1.]):
#TODO: specify beta parameter explicitely
def __init__(self,X,Y=None,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None,likelihood=None,epsilon_ep=1e-3,epsion_em=.1,power_ep=[1.,1.]):
#TODO: make beta parameter explicit
# parse arguments
self.Xslices = Xslices
@ -87,7 +87,7 @@ class GP(model):
else:
# Y is defined after approximating the likelihood
self.EP = True
self.eta,self.delta = powerep
self.eta,self.delta = power_ep
self.epsilon_ep = epsilon_ep
self.tau_tilde = np.ones([self.N,self.D])
self.v_tilde = np.zeros([self.N,self.D])
@ -116,7 +116,7 @@ class GP(model):
def approximate_likelihood(self):
assert not isinstance(self.likelihood, gaussian), "EP is only available for non-gaussian likelihoods"
self.ep_approx = Full(self.K,self.likelihood,epsilon=self.epsilon_ep,powerep=[self.eta,self.delta])
self.ep_approx = Full(self.K,self.likelihood,epsilon=self.epsilon_ep,power_ep=[self.eta,self.delta])
self.tau_tilde, self.v_tilde, self.Z_hat, self.tau_, self.v_=self.ep_approx.fit_EP()
# Y: EP likelihood is defined as a regression model for mu_tilde
self.Y = self.v_tilde/self.tau_tilde