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This commit is contained in:
Nicolo Fusi
commit
49d2e4e4f6
62 changed files with 2469 additions and 1230 deletions
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@ -107,6 +107,20 @@ class sparse_GP_regression(GP_regression):
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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) - np.dot(self.C, self.psi1VVpsi1), self.Kmmi) + 0.5*self.E # dD
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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.beta = p[self.M*self.Q]
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self.kern._set_params(p[self.Z.size + 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(),self.beta,self.kern._get_params_transformed()])
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def _get_param_names(self):
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return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + ['noise_precision']+self.kern._get_param_names_transformed()
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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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@ -116,18 +130,9 @@ class sparse_GP_regression(GP_regression):
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D = +0.5*np.sum(self.psi1VVpsi1 * self.C)
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return A+B+C+D
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def set_param(self, p):
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self.Z = p[:self.M*self.Q].reshape(self.M, self.Q)
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self.beta = p[self.M*self.Q]
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self.kern.set_param(p[self.Z.size + 1:])
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self._computations()
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def get_param(self):
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return np.hstack([self.Z.flatten(),self.beta,self.kern.extract_param()])
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def get_param_names(self):
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return sum([['iip_%i_%i'%(i,j) for i in range(self.Z.shape[0])] for j in range(self.Z.shape[1])],[]) + ['noise_precision']+self.kern.extract_param_names()
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def _log_likelihood_gradients(self):
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return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
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def dL_dbeta(self):
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"""
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Compute the gradient of the log likelihood wrt beta.
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@ -172,9 +177,6 @@ class sparse_GP_regression(GP_regression):
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dL_dZ += self.kern.dK_dX(dL_dpsi1,self.Z,self.X)
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return dL_dZ
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def log_likelihood_gradients(self):
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return np.hstack([self.dL_dZ().flatten(), self.dL_dbeta(), self.dL_dtheta()])
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def _raw_predict(self, Xnew, slices, full_cov=False):
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"""Internal helper function for making predictions, does not account for normalisation"""
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@ -201,94 +203,3 @@ class sparse_GP_regression(GP_regression):
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pb.errorbar(self.X[:,0], pb.ylim()[0]+np.zeros(self.N), xerr=2*np.sqrt(self.X_uncertainty.flatten()))
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if self.Q==2:
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pb.plot(self.Z[:,0],self.Z[:,1],'wo')
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class sgp_debugB(sparse_GP_regression):
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def _computations(self):
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sparse_GP_regression._computations(self)
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# Compute dL_dpsi
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self.dL_dpsi0 = - 0.5 * self.D * self.beta * np.ones(self.N)
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self.dL_dpsi1 = np.zeros_like(self.psi1)
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self.dL_dpsi2 = - 0.5 * self.beta * (self.D*( - self.Kmmi))
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# Compute dL_dKmm
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self.dL_dKmm = -0.5 * self.D * mdot(self.Lmi.T, self.A, self.Lmi) # dB
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def log_likelihood(self):
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A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta))
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B = -0.5*self.beta*self.D*self.trace_K
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C = -0.5*self.D * self.B_logdet
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D = -0.5*self.beta*self.trYYT
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E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv)
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return B
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def dL_dbeta(self):
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dA_dbeta = 0.5 * self.N*self.D/self.beta
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dB_dbeta = - 0.5 * self.D * self.trace_K
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dC_dbeta = - 0.5 * self.D * np.sum(self.Bi*self.A)/self.beta
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dD_dbeta = - 0.5 * self.trYYT
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tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V)
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dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta
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return np.squeeze(dB_dbeta)
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class sgp_debugC(sparse_GP_regression):
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def _computations(self):
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sparse_GP_regression._computations(self)
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# Compute dL_dpsi
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self.dL_dpsi0 = np.zeros(self.N)
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self.dL_dpsi1 = np.zeros_like(self.psi1)
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self.dL_dpsi2 = - 0.5 * self.beta * (self.D*(self.LBL_inv))
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# Compute dL_dKmm
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self.dL_dKmm = -0.5 * self.D * (- self.LBL_inv - 2.*self.beta*mdot(self.LBL_inv, self.psi2, self.Kmmi) + self.Kmmi) # dC
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def log_likelihood(self):
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A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta))
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B = -0.5*self.beta*self.D*self.trace_K
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C = -0.5*self.D * self.B_logdet
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D = -0.5*self.beta*self.trYYT
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E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv)
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return C
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def dL_dbeta(self):
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dA_dbeta = 0.5 * self.N*self.D/self.beta
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dB_dbeta = - 0.5 * self.D * self.trace_K
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dC_dbeta = - 0.5 * self.D * np.sum(self.Bi*self.A)/self.beta
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dD_dbeta = - 0.5 * self.trYYT
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tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V)
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dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta
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return np.squeeze(dC_dbeta)
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class sgp_debugE(sparse_GP_regression):
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def _computations(self):
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sparse_GP_regression._computations(self)
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# Compute dL_dpsi
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self.dL_dpsi0 = np.zeros(self.N)
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self.dL_dpsi1 = np.zeros_like(self.psi1)
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self.dL_dpsi2 = - 0.5 * self.beta * (self.G)
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# Compute dL_dKmm
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tmp = mdot(self.beta*self.psi2, self.Kmmi, self.psi1VVpsi1)
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self.dL_dKmm = -0.5*mdot(self.Kmmi,tmp + tmp.T + self.psi1VVpsi1,self.Kmmi)
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#self.dL_dKmm = np.dot(np.dot(self.G,self.beta*self.psi2) - np.dot(self.LBL_inv, self.psi1VVpsi1), self.Kmmi) + 0.5*self.G # dE
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def log_likelihood(self):
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A = -0.5*self.N*self.D*(np.log(2.*np.pi) - np.log(self.beta))
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B = -0.5*self.beta*self.D*self.trace_K
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C = -0.5*self.D * self.B_logdet
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D = -0.5*self.beta*self.trYYT
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E = +0.5*np.sum(self.psi1VVpsi1 * self.LBL_inv)
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return E
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def dL_dbeta(self):
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dA_dbeta = 0.5 * self.N*self.D/self.beta
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dB_dbeta = - 0.5 * self.D * self.trace_K
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dC_dbeta = - 0.5 * self.D * np.sum(self.Bi*self.A)/self.beta
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dD_dbeta = - 0.5 * self.trYYT
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tmp = mdot(self.LBi.T, self.LLambdai, self.psi1V)
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dE_dbeta = (np.sum(np.square(self.C)) - 0.5 * np.sum(self.A * np.dot(tmp, tmp.T)))/self.beta
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return np.squeeze(dE_dbeta)
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