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gradietns check :)

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Ricardo 2013-05-13 15:09:20 +01:00
parent fda9cae3c3
commit b84f70b205
1 changed files with 19 additions and 13 deletions
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32
GPy/models/FITC.py
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@ -17,6 +17,8 @@ def backsub_both_sides(L,X):
class FITC(sparse_GP):
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
#self.fixed_beta_star = np.random.rand(X.shape[0],1) #TODO erase
sparse_GP.__init__(self, X, likelihood, kernel=kernel, Z=Z, X_variance=None, normalize_X=False)
def _set_params(self, p):
@ -55,9 +57,10 @@ class FITC(sparse_GP):
self.beta_star = self.likelihood.precision/(1. + self.likelihood.precision*self.Diag0[:,None]) #Includes Diag0 in the precision
self.V_star = self.beta_star * self.likelihood.Y
#self.true_V_star = self.V_star.copy() #TODO eraseme
#self.true_beta_star = self.beta_star.copy() #TODO eraseme
#self.beta_star = self.fixed_beta_star.copy() #TODO eraseme
#self.V_star = self.beta_star * self.likelihood.Y #TODO eraseme
# The rather complex computations of self.A
@ -245,7 +248,7 @@ class FITC(sparse_GP):
raise NotImplementedError, "heteroscedatic derivates not implemented"
else:
# likelihood is not heterscedatic
dbstar_dnoise = self.likelihood.precision * (self.beta_star**2 * self.Diag0[:,None] - self.beta_star) #check
dbstar_dnoise = self.likelihood.precision * (self.beta_star**2 * self.Diag0[:,None] - self.beta_star)
#dbstar_dnoise = self.likelihood.precision * (self.true_beta_star**2 * self.Diag0[:,None] - self.true_beta_star) #TODO erase
Lmi_psi1 = mdot(self.Lmi,self.psi1)
LBiLmipsi1 = np.dot(self.LBi,Lmi_psi1)
@ -253,27 +256,29 @@ class FITC(sparse_GP):
aux_1 = self.likelihood.Y.T * np.dot(self._LBi_Lmi_psi1V.T,LBiLmipsi1)
aux_2 = np.dot(LBiLmipsi1.T,self._LBi_Lmi_psi1V)
dA_dnoise = 0.5 * self.D * (dbstar_dnoise/self.beta_star).sum() - 0.5 * self.D * np.sum(self.likelihood.Y**2 * dbstar_dnoise) # check
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T) #check
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T) #check
dD_dnoise_1 = mdot(self.V_star*LBiLmipsi1.T,LBiLmipsi1*dbstar_dnoise.T*self.likelihood.Y.T) #check
#dD_dnoise_1 = mdot(self.true_V_star*LBiLmipsi1.T,LBiLmipsi1*dbstar_dnoise.T*self.likelihood.Y.T) #TODO eraseme
dA_dnoise = 0.5 * self.D * (dbstar_dnoise/self.beta_star).sum() - 0.5 * self.D * np.sum(self.likelihood.Y**2 * dbstar_dnoise)
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T)
dC_dnoise = -0.5 * np.sum(mdot(self.LBi.T,self.LBi,Lmi_psi1) * Lmi_psi1 * dbstar_dnoise.T)
dD_dnoise_1 = mdot(self.V_star*LBiLmipsi1.T,LBiLmipsi1*dbstar_dnoise.T*self.likelihood.Y.T)
alpha = mdot(LBiLmipsi1,self.V_star)
alpha_ = mdot(LBiLmipsi1.T,alpha)
dD_dnoise_2 = -0.5 * self.D * np.sum(alpha_**2 * dbstar_dnoise ) #check
dD_dnoise_2 = -0.5 * self.D * np.sum(alpha_**2 * dbstar_dnoise )
dD_dnoise_1 = mdot(self.V_star.T,self.psi1.T,self.Lmi.T,self.LBi.T,self.LBi,self.Lmi,self.psi1,dbstar_dnoise*self.likelihood.Y)
dD_dnoise_2 = 0.5*mdot(self.V_star.T,self.psi1.T,Hi,self.psi1,dbstar_dnoise*self.psi1.T,Hi,self.psi1,self.V_star)
dD_dnoise = dD_dnoise_1 + dD_dnoise_2
self.partial_for_likelihood = dA_dnoise + dC_dnoise + dD_dnoise
self.partial_for_likelihood = dD_dnoise
def log_likelihood(self):
""" Compute the (lower bound on the) log marginal likelihood """
A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)
C = -self.D * (np.sum(np.log(np.diag(self.LB))))
D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))
return A + C + D
#return A + C + D
return D
def _log_likelihood_gradients(self):
pass
@ -283,8 +288,8 @@ class FITC(sparse_GP):
if self.has_uncertain_inputs:
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
#dL_dtheta = self.dlogbeta_dtheta + self.dyby_dtheta #+ self.dlogB_dtheta + self.dD_dtheta
dL_dtheta = self.dA_dtheta + self.dlogB_dtheta + self.dD_dtheta
dL_dtheta = self.dD_dtheta #TODO eraseme
return dL_dtheta
def dL_dZ(self):
@ -292,6 +297,7 @@ class FITC(sparse_GP):
raise NotImplementedError, "FITC approximation not implemented for uncertain inputs"
else:
dL_dZ = self.dA_dX + self.dlogB_dX + self.dD_dX
dL_dZ = self.dD_dX #TODO eraseme
return dL_dZ
def _raw_predict(self, Xnew, which_parts, full_cov=False):