mirror of
https://github.com/SheffieldML/GPy.git
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LinearCF Psi Stat not working yet, strange bug in psi computations
This commit is contained in:
Max Zwiessele
parent
c502b66ea3
commit
42474f0044
8 changed files with 353 additions and 244 deletions
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@ -308,6 +308,7 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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Slatentgrads = ax3.quiver(xlatent, S, Ulatent, Sg, color=colors,
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units=quiver_units, scale_units=quiver_scale_units,
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scale=quiver_scale)
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ax3.set_ylim(0, 1.)
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xZ = np.tile(np.arange(0, Z.shape[0])[:, None], Z.shape[1])
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UZ = np.zeros_like(Z)
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@ -427,11 +428,11 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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cbarkmmdl.update_normal(imkmmdl)
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ax2.relim()
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ax3.relim()
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# ax3.relim()
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ax4.relim()
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ax5.relim()
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ax2.autoscale()
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ax3.autoscale()
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# ax3.autoscale()
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ax4.autoscale()
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ax5.autoscale()
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@ -30,22 +30,22 @@ class sparse_GP(GP):
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"""
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def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
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self.scale_factor = 100.0 # a scaling factor to help keep the algorithm stable
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self.scale_factor = 100.0# a scaling factor to help keep the algorithm stable
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self.auto_scale_factor = False
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self.Z = Z
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self.M = Z.shape[0]
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self.likelihood = likelihood
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if X_variance is None:
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self.has_uncertain_inputs = False
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self.has_uncertain_inputs=False
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else:
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assert X_variance.shape == X.shape
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self.has_uncertain_inputs = True
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assert X_variance.shape==X.shape
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self.has_uncertain_inputs=True
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self.X_variance = X_variance
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GP.__init__(self, X, likelihood, kernel=kernel, normalize_X=normalize_X)
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# normalize X uncertainty also
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#normalize X uncertainty also
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if self.has_uncertain_inputs:
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self.X_variance /= np.square(self._Xstd)
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@ -54,155 +54,156 @@ class sparse_GP(GP):
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# kernel computations, using BGPLVM notation
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self.Kmm = self.kern.K(self.Z)
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if self.has_uncertain_inputs:
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self.psi0 = self.kern.psi0(self.Z, self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z, self.X, self.X_variance).T
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self.psi2 = self.kern.psi2(self.Z, self.X, self.X_variance)
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self.psi0 = self.kern.psi0(self.Z,self.X, self.X_variance)
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self.psi1 = self.kern.psi1(self.Z,self.X, self.X_variance).T
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self.psi2 = self.kern.psi2(self.Z,self.X, self.X_variance)
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else:
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self.psi0 = self.kern.Kdiag(self.X)
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self.psi1 = self.kern.K(self.Z, self.X)
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self.psi1 = self.kern.K(self.Z,self.X)
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self.psi2 = None
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def _computations(self):
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# TODO: find routine to multiply triangular matrices
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#TODO: find routine to multiply triangular matrices
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sf = self.scale_factor
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sf2 = sf ** 2
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sf2 = sf**2
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# The rather complex computations of psi2_beta_scaled
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#The rather complex computations of psi2_beta_scaled
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if self.likelihood.is_heteroscedastic:
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assert self.likelihood.D == 1 # TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
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assert self.likelihood.D == 1 #TODO: what if the likelihood is heterscedatic and there are multiple independent outputs?
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if self.has_uncertain_inputs:
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self.psi2_beta_scaled = (self.psi2 * (self.likelihood.precision.flatten().reshape(self.N, 1, 1) / sf2)).sum(0)
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self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision.flatten().reshape(self.N,1,1)/sf2)).sum(0)
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else:
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision.flatten().reshape(1, self.N)) / sf)
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# self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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tmp = self.psi1*(np.sqrt(self.likelihood.precision.flatten().reshape(1,self.N))/sf)
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#self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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self.psi2_beta_scaled = tdot(tmp)
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else:
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if self.has_uncertain_inputs:
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self.psi2_beta_scaled = (self.psi2 * (self.likelihood.precision / sf2)).sum(0)
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self.psi2_beta_scaled = (self.psi2*(self.likelihood.precision/sf2)).sum(0)
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else:
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tmp = self.psi1 * (np.sqrt(self.likelihood.precision) / sf)
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# self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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tmp = self.psi1*(np.sqrt(self.likelihood.precision)/sf)
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#self.psi2_beta_scaled = np.dot(tmp,tmp.T)
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self.psi2_beta_scaled = tdot(tmp)
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self.Kmmi, self.Lm, self.Lmi, self.Kmm_logdet = pdinv(self.Kmm)
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self.V = (self.likelihood.precision / self.scale_factor) * self.likelihood.Y
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self.V = (self.likelihood.precision/self.scale_factor)*self.likelihood.Y
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# Compute A = L^-1 psi2 beta L^-T
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# self. A = mdot(self.Lmi,self.psi2_beta_scaled,self.Lmi.T)
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm, self.psi2_beta_scaled.T, lower=1)[0]
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self.A = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1)[0]
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#Compute A = L^-1 psi2 beta L^-T
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#self. A = mdot(self.Lmi,self.psi2_beta_scaled,self.Lmi.T)
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm,self.psi2_beta_scaled.T,lower=1)[0]
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self.A = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1)[0]
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self.B = np.eye(self.M) / sf2 + self.A
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self.B = np.eye(self.M)/sf2 + self.A
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self.Bi, self.LB, self.LBi, self.B_logdet = pdinv(self.B)
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self.psi1V = np.dot(self.psi1, self.V)
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.Bi), lower=1, trans=1)[0]
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self.C = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1, trans=1)[0]
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.Bi),lower=1,trans=1)[0]
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self.C = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1,trans=1)[0]
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# self.Cpsi1V = np.dot(self.C,self.psi1V)
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# back substitute C into psi1V
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tmp, _ = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.psi1V), lower=1, trans=0)
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tmp, _ = linalg.lapack.flapack.dpotrs(self.LB, tmp, lower=1)
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self.Cpsi1V, _ = linalg.lapack.flapack.dtrtrs(self.Lm, tmp, lower=1, trans=1)
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#back substutue C into psi1V
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tmp,info1 = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.psi1V),lower=1,trans=0)
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tmp,info2 = linalg.lapack.flapack.dpotrs(self.LB,tmp,lower=1)
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self.Cpsi1V,info3 = linalg.lapack.flapack.dtrtrs(self.Lm,tmp,lower=1,trans=1)
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#self.Cpsi1V = np.dot(self.C,self.psi1V)
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self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V, self.psi1V.T) # TODO: stabilize?
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self.E = tdot(self.Cpsi1V / sf)
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self.Cpsi1VVpsi1 = np.dot(self.Cpsi1V,self.psi1V.T)
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self.E = tdot(self.Cpsi1V/sf)
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# Compute dL_dpsi # FIXME: this is untested for the heterscedastic + uncertin inputs case
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self.dL_dpsi0 = -0.5 * self.D * (self.likelihood.precision * np.ones([self.N, 1])).flatten()
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self.dL_dpsi1 = np.dot(self.Cpsi1V, self.V.T)
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self.dL_dpsi0 = - 0.5 * self.D * (self.likelihood.precision * np.ones([self.N,1])).flatten()
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self.dL_dpsi1 = np.dot(self.Cpsi1V,self.V.T)
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if self.likelihood.is_heteroscedastic:
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if self.has_uncertain_inputs:
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# self.dL_dpsi2 = 0.5 * self.likelihood.precision[:,None,None] * self.D * self.Kmmi[None,:,:] # dB
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]/sf2 * self.D * self.C[None,:,:] # dC
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]* self.E[None,:,:] # dD
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self.dL_dpsi2 = 0.5 * self.likelihood.precision[:, None, None] * (self.D * (self.Kmmi - self.C / sf2) - self.E)[None, :, :]
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#self.dL_dpsi2 = 0.5 * self.likelihood.precision[:,None,None] * self.D * self.Kmmi[None,:,:] # dB
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]/sf2 * self.D * self.C[None,:,:] # dC
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision[:,None,None]* self.E[None,:,:] # dD
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self.dL_dpsi2 = 0.5*self.likelihood.precision[:,None,None]*(self.D*(self.Kmmi - self.C/sf2) -self.E)[None,:,:]
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else:
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# self.dL_dpsi1 += mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
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# self.dL_dpsi1 += -mdot(self.C,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)/sf2) #dC
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# self.dL_dpsi1 += -mdot(self.E,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dD
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self.dL_dpsi1 += np.dot(self.Kmmi - self.C / sf2 - self.E, self.psi1 * self.likelihood.precision.reshape(1, self.N))
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#self.dL_dpsi1 += mdot(self.Kmmi,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dB
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#self.dL_dpsi1 += -mdot(self.C,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)/sf2) #dC
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#self.dL_dpsi1 += -mdot(self.E,self.psi1*self.likelihood.precision.flatten().reshape(1,self.N)) #dD
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self.dL_dpsi1 += np.dot(self.Kmmi - self.C/sf2 -self.E,self.psi1*self.likelihood.precision.reshape(1,self.N))
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self.dL_dpsi2 = None
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else:
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# self.dL_dpsi2 = 0.5 * self.likelihood.precision * self.D * self.Kmmi # dB
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision/sf2 * self.D * self.C # dC
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# self.dL_dpsi2 += - 0.5 * self.likelihood.precision * self.E # dD
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self.dL_dpsi2 = 0.5 * self.likelihood.precision * (self.D * (self.Kmmi - self.C / sf2) - self.E)
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#self.dL_dpsi2 = 0.5 * self.likelihood.precision * self.D * self.Kmmi # dB
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision/sf2 * self.D * self.C # dC
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#self.dL_dpsi2 += - 0.5 * self.likelihood.precision * self.E # dD
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self.dL_dpsi2 = 0.5*self.likelihood.precision*(self.D*(self.Kmmi - self.C/sf2) -self.E)
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if self.has_uncertain_inputs:
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# repeat for each of the N psi_2 matrices
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self.dL_dpsi2 = np.repeat(self.dL_dpsi2[None, :, :], self.N, axis=0)
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#repeat for each of the N psi_2 matrices
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self.dL_dpsi2 = np.repeat(self.dL_dpsi2[None,:,:],self.N,axis=0)
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else:
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self.dL_dpsi1 += 2.*np.dot(self.dL_dpsi2, self.psi1)
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self.dL_dpsi1 += 2.*np.dot(self.dL_dpsi2,self.psi1)
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self.dL_dpsi2 = None
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# Compute dL_dKmm
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# self.dL_dKmm_old = -0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi)*sf2 # dB
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# self.dL_dKmm += -0.5 * self.D * (- self.C/sf2 - 2.*mdot(self.C, self.psi2_beta_scaled, self.Kmmi) + self.Kmmi) # dC
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# self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - self.Cpsi1VVpsi1, self.Kmmi) + 0.5*self.E # dD
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(self.B), lower=1, trans=1)[0]
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self.dL_dKmm = -0.5 * self.D * sf2 * linalg.lapack.flapack.dtrtrs(self.Lm, np.asfortranarray(tmp.T), lower=1, trans=1)[0] # dA
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tmp = np.dot(self.D * self.C + self.E * sf2, self.psi2_beta_scaled) - self.Cpsi1VVpsi1
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tmp = linalg.lapack.flapack.dpotrs(self.Lm, np.asfortranarray(tmp.T), lower=1)[0].T
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self.dL_dKmm += 0.5 * (self.D * self.C / sf2 + self.E) + tmp # d(C+D)
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#self.dL_dKmm_old = -0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi)*sf2 # dB
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#self.dL_dKmm += -0.5 * self.D * (- self.C/sf2 - 2.*mdot(self.C, self.psi2_beta_scaled, self.Kmmi) + self.Kmmi) # dC
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#self.dL_dKmm += np.dot(np.dot(self.E*sf2, self.psi2_beta_scaled) - self.Cpsi1VVpsi1, self.Kmmi) + 0.5*self.E # dD
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tmp = linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(self.B),lower=1,trans=1)[0]
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self.dL_dKmm = -0.5*self.D*sf2*linalg.lapack.flapack.dtrtrs(self.Lm,np.asfortranarray(tmp.T),lower=1,trans=1)[0] #dA
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tmp = np.dot(self.D*self.C + self.E*sf2,self.psi2_beta_scaled) - self.Cpsi1VVpsi1
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tmp = linalg.lapack.flapack.dpotrs(self.Lm,np.asfortranarray(tmp.T),lower=1)[0].T
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self.dL_dKmm += 0.5*(self.D*self.C/sf2 + self.E) +tmp # d(C+D)
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# the partial derivative vector for the likelihood
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if self.likelihood.Nparams == 0:
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# save computation here.
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#the partial derivative vector for the likelihood
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if self.likelihood.Nparams ==0:
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#save computation here.
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self.partial_for_likelihood = None
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elif self.likelihood.is_heteroscedastic:
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raise NotImplementedError, "heteroscedatic derivates not implemented"
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# self.partial_for_likelihood = - 0.5 * self.D*self.likelihood.precision + 0.5 * (self.likelihood.Y**2).sum(1)*self.likelihood.precision**2 #dA
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# self.partial_for_likelihood += 0.5 * self.D * (self.psi0*self.likelihood.precision**2 - (self.psi2*self.Kmmi[None,:,:]*self.likelihood.precision[:,None,None]**2).sum(1).sum(1)/sf2) #dB
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# self.partial_for_likelihood += 0.5 * self.D * np.sum(self.Bi*self.A)*self.likelihood.precision #dC
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# self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD
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#self.partial_for_likelihood = - 0.5 * self.D*self.likelihood.precision + 0.5 * (self.likelihood.Y**2).sum(1)*self.likelihood.precision**2 #dA
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#self.partial_for_likelihood += 0.5 * self.D * (self.psi0*self.likelihood.precision**2 - (self.psi2*self.Kmmi[None,:,:]*self.likelihood.precision[:,None,None]**2).sum(1).sum(1)/sf2) #dB
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#self.partial_for_likelihood += 0.5 * self.D * np.sum(self.Bi*self.A)*self.likelihood.precision #dC
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#self.partial_for_likelihood += -np.diag(np.dot((self.C - 0.5 * mdot(self.C,self.psi2_beta_scaled,self.C) ) , self.psi1VVpsi1 ))*self.likelihood.precision #dD
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else:
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# likelihood is not heterscedatic
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self.partial_for_likelihood = -0.5 * self.N * self.D * self.likelihood.precision + 0.5 * self.likelihood.trYYT * self.likelihood.precision ** 2
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self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum() * self.likelihood.precision ** 2 - np.trace(self.A) * self.likelihood.precision * sf2)
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self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi, self.A) * self.likelihood.precision
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self.partial_for_likelihood += self.likelihood.precision * (0.5 * trace_dot(self.psi2_beta_scaled, self.E * sf2) - np.trace(self.Cpsi1VVpsi1))
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#likelihood is not heterscedatic
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self.partial_for_likelihood = - 0.5 * self.N*self.D*self.likelihood.precision + 0.5 * self.likelihood.trYYT*self.likelihood.precision**2
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self.partial_for_likelihood += 0.5 * self.D * (self.psi0.sum()*self.likelihood.precision**2 - np.trace(self.A)*self.likelihood.precision*sf2)
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self.partial_for_likelihood += 0.5 * self.D * trace_dot(self.Bi,self.A)*self.likelihood.precision
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self.partial_for_likelihood += self.likelihood.precision*(0.5*trace_dot(self.psi2_beta_scaled,self.E*sf2) - np.trace(self.Cpsi1VVpsi1))
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def log_likelihood(self):
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""" Compute the (lower bound on the) log marginal likelihood """
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sf2 = self.scale_factor ** 2
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sf2 = self.scale_factor**2
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if self.likelihood.is_heteroscedastic:
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A = -0.5 * self.N * self.D * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.V * self.likelihood.Y)
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B = -0.5 * self.D * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self.A) * sf2)
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A = -0.5*self.N*self.D*np.log(2.*np.pi) +0.5*np.sum(np.log(self.likelihood.precision)) -0.5*np.sum(self.V*self.likelihood.Y)
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B = -0.5*self.D*(np.sum(self.likelihood.precision.flatten()*self.psi0) - np.trace(self.A)*sf2)
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else:
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A = -0.5 * self.N * self.D * (np.log(2.*np.pi) + np.log(self.likelihood._variance)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT
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B = -0.5 * self.D * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self.A) * sf2)
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C = -0.5 * self.D * (self.B_logdet + self.M * np.log(sf2))
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D = 0.5 * np.trace(self.Cpsi1VVpsi1)
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return A + B + C + D
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A = -0.5*self.N*self.D*(np.log(2.*np.pi) + np.log(self.likelihood._variance)) -0.5*self.likelihood.precision*self.likelihood.trYYT
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B = -0.5*self.D*(np.sum(self.likelihood.precision*self.psi0) - np.trace(self.A)*sf2)
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C = -0.5*self.D * (self.B_logdet + self.M*np.log(sf2))
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D = 0.5*np.trace(self.Cpsi1VVpsi1)
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return A+B+C+D
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def _set_params(self, p):
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self.Z = p[:self.M * self.Q].reshape(self.M, self.Q)
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self.kern._set_params(p[self.Z.size:self.Z.size + self.kern.Nparam])
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self.likelihood._set_params(p[self.Z.size + self.kern.Nparam:])
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self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
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self.kern._set_params(p[self.Z.size:self.Z.size+self.kern.Nparam])
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self.likelihood._set_params(p[self.Z.size+self.kern.Nparam:])
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self._compute_kernel_matrices()
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if self.auto_scale_factor:
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self.scale_factor = np.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision)
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# if self.auto_scale_factor:
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self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
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#if self.auto_scale_factor:
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# if self.likelihood.is_heteroscedastic:
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# self.scale_factor = max(1,np.sqrt(self.psi2_beta_scaled.sum(0).mean()))
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# else:
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# self.scale_factor = np.sqrt(self.psi2.sum(0).mean()*self.likelihood.precision)
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# self.scale_factor = 1.
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#self.scale_factor = 1.
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self._computations()
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def _get_params(self):
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return np.hstack([self.Z.flatten(), GP._get_params(self)])
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return np.hstack([self.Z.flatten(),GP._get_params(self)])
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def _get_param_names(self):
|
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return sum([['iip_%i_%i' % (i, j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])], []) + GP._get_param_names(self)
|
||||
return sum([['iip_%i_%i'%(i,j) for j in range(self.Z.shape[1])] for i in range(self.Z.shape[0])],[]) + GP._get_param_names(self)
|
||||
|
||||
def update_likelihood_approximation(self):
|
||||
"""
|
||||
|
|
@ -214,9 +215,9 @@ class sparse_GP(GP):
|
|||
if self.has_uncertain_inputs:
|
||||
raise NotImplementedError, "EP approximation not implemented for uncertain inputs"
|
||||
else:
|
||||
self.likelihood.fit_DTC(self.Kmm, self.psi1)
|
||||
# self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
self.likelihood.fit_DTC(self.Kmm,self.psi1)
|
||||
#self.likelihood.fit_FITC(self.Kmm,self.psi1,self.psi0)
|
||||
self._set_params(self._get_params()) # update the GP
|
||||
|
||||
|
||||
def _log_likelihood_gradients(self):
|
||||
|
|
@ -226,13 +227,13 @@ class sparse_GP(GP):
|
|||
"""
|
||||
Compute and return the derivative of the log marginal likelihood wrt the parameters of the kernel
|
||||
"""
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm, self.Z)
|
||||
dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
|
||||
if self.has_uncertain_inputs:
|
||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z, self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1.T,self.Z,self.X, self.X_variance)
|
||||
dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2, self.Z,self.X, self.X_variance)
|
||||
else:
|
||||
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1, self.Z, self.X)
|
||||
dL_dtheta += self.kern.dK_dtheta(self.dL_dpsi1,self.Z,self.X)
|
||||
dL_dtheta += self.kern.dKdiag_dtheta(self.dL_dpsi0, self.X)
|
||||
|
||||
return dL_dtheta
|
||||
|
|
@ -243,22 +244,22 @@ class sparse_GP(GP):
|
|||
"""
|
||||
dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm, self.Z) # factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
|
||||
if self.has_uncertain_inputs:
|
||||
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1, self.Z, self.X, self.X_variance)
|
||||
dL_dZ += self.kern.dpsi1_dZ(self.dL_dpsi1,self.Z,self.X, self.X_variance)
|
||||
dL_dZ += self.kern.dpsi2_dZ(self.dL_dpsi2, self.Z, self.X, self.X_variance)
|
||||
else:
|
||||
dL_dZ += self.kern.dK_dX(self.dL_dpsi1, self.Z, self.X)
|
||||
dL_dZ += self.kern.dK_dX(self.dL_dpsi1,self.Z,self.X)
|
||||
return dL_dZ
|
||||
|
||||
def _raw_predict(self, Xnew, which_parts='all', full_cov=False):
|
||||
"""Internal helper function for making predictions, does not account for normalization"""
|
||||
|
||||
Kx = self.kern.K(self.Z, Xnew)
|
||||
mu = mdot(Kx.T, self.C / self.scale_factor, self.psi1V)
|
||||
mu = mdot(Kx.T, self.C/self.scale_factor, self.psi1V)
|
||||
if full_cov:
|
||||
Kxx = self.kern.K(Xnew, which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C / self.scale_factor ** 2), Kx) # NOTE this won't work for plotting
|
||||
Kxx = self.kern.K(Xnew,which_parts=which_parts)
|
||||
var = Kxx - mdot(Kx.T, (self.Kmmi - self.C/self.scale_factor**2), Kx) #NOTE this won't work for plotting
|
||||
else:
|
||||
Kxx = self.kern.Kdiag(Xnew, which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx * np.dot(self.Kmmi - self.C / self.scale_factor ** 2, Kx), 0)
|
||||
Kxx = self.kern.Kdiag(Xnew,which_parts=which_parts)
|
||||
var = Kxx - np.sum(Kx*np.dot(self.Kmmi - self.C/self.scale_factor**2, Kx),0)
|
||||
|
||||
return mu, var[:, None]
|
||||
return mu,var[:,None]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue