def initialize_modelM(self): nn = Ort_NN(dims=[self.Xprobit.shape[1], 20, self.proj_dim], N=0, proj_dim=0, name=None) k_list, gp_nnjoint = bloc_diag_initialize_models( x=np.copy(self.Xprobit), y=np.copy(self.Ynorm), input_dim=self.proj_dim, model='diagonal_joint', kernel='Matern52', ARD=True, nn=nn, decomp=self.decomposition) # last kernel is the Y kernel # k2_list, gp_nnjoint_test = initialize_m_models(x=np.copy(self.Xprobit), y=np.copy(self.Ynorm), # input_dim=self.proj_dim, # model='joint', # kernel='Matern52', # ARD=True, # nn=nn, # decomp=self.decomposition) # last kernel is the Y kernel gp_nnjoint.likelihood.variance = 1e-06 # 0.001 # gp_nnjoint_test.likelihood.variance = 1e-06 return k_list, gp_nnjoint, nn
def initialize_modelM(self): nn = Ort_NN(dims=[self.Xprobit.shape[1], 20, self.proj_dim], N=0, proj_dim=0, name=None) # nn = NN(dims=[self.Xprobit.shape[1], 20, self.proj_dim], N=0, proj_dim=0, # name=None) k_list, gp_nnjoint = bloc_diag_initialize_models( x=np.copy(self.Xprobit), y=np.copy(self.Ynorm), input_dim=self.proj_dim, model='joint', kernel='Matern52', ARD=True, nn=nn, decomp=self.decomposition) # last kernel is the Y kernel # k_list, gp_nnjoint = initialize_m_models(x=np.copy(self.Xprobit), y=np.copy(self.Ynorm), # input_dim=self.proj_dim, # model='joint', # kernel='Matern52', # ARD=True, # nn=nn, # decomp=self.decomposition) # last kernel is the Y kernel # kern_joint = Kstack(k_list) # gp_nnjoint = NN_MoGPR(X=np.copy(self.Xprobit), Y=np.copy(self.Ynorm), kern=kern_joint, nn=nn, Mo_dim=self.Mo_dim) gp_nnjoint.likelihood.variance = 1e-06 # 0.001 return k_list, gp_nnjoint, nn
def initialize_modelM(self): nn = Ort_NN(dims=[self.data_x.shape[1], 20, self.proj_dim], N=0, proj_dim=0, name=None) k_list, gp_nnjoint = initialize_m_models(x=np.copy(self.Xnorm), y=np.copy(self.Ynorm), # k_list, gp_nnjoint = initialize_m_models(x=np.copy(self.X_inf), y=np.copy(self.Ynorm), input_dim=self.proj_dim, model='BLR', kernel='Matern52', ARD=True, nn=nn) # last kernel is the Manifold GP kernel gp_nnjoint.likelihood.variance = 1e-06 # 0.001 return k_list, gp_nnjoint, nn
def initialize_modelM(self): nn = Ort_NN(dims=[self.Xnorm.shape[1], 20, self.proj_dim], N=0, proj_dim=0, name=None) kernm, gpm = initialize_m_models(x=np.copy(self.Xnorm), y=np.copy(self.Ynorm), input_dim=self.proj_dim, model='encoder', kernel='Matern52', ARD=True, nn=nn, decomp=None) gpm.likelihood.variance = 0.01 return kernm, gpm
def initialize_modelM(self): nn = Ort_NN(dims=[self.Xnorm.shape[1], 20, self.proj_dim], N=0, proj_dim=0, name=None) # nn = NN(dims=[self.Xnorm.shape[1], 20, self.proj_dim], N=0, proj_dim=0, # name=None) kernm, gpm = initialize_m_models(x=np.copy(self.Xnorm), y=np.copy(self.Ynorm), input_dim=self.proj_dim, model='encoder', kernel='Matern52', ARD=True, nn=nn, decomp=None) gpm.likelihood.variance = 0.01 # k1 = gpflow.kernels.RBF(input_dim=self.proj_dim, ARD=True, active_dims=list(range(self.proj_dim))) # coreg = gpflow.kernels.Coregion(input_dim=1, output_dim=self.Xnorm.shape[1] + 1, # rank=self.Xnorm.shape[1] + 1, active_dims=[self.proj_dim]) # coreg.W = np.random.randn(self.Xnorm.shape[1] + 1, self.Xnorm.shape[1] + 1) # values = np.random.normal(loc=0., scale=1., size=[len(self.binary_tree_list)]) # tree_coregion = TreeCoregion(input_dim=1, output_dim=self.Xnorm.shape[1] + 1, # indices_tree=self.binary_tree_list, values=values, # active_dims=[self.proj_dim]) # kc = k1 * coreg # kc = k1 * tree_coregion # nn = NN(dims=[self.Xnorm.shape[1], 20, self.proj_dim], N=0, proj_dim=0, # name=None) # otherwise re-initialized at each BO iteration # nn = NN(dims=[self.Xnorm.shape[1], 6, self.proj_dim], N=0, proj_dim=0, # name=None) # otherwise re-initialized at each BO iteration # nn_mogp = NN_MOGPR(X=np.copy(self.Xnorm), Y=np.copy(self.Ynorm), kern=kc, nn=nn) # nn_mogp.likelihood.variance = 0.0001 return kernm, gpm, nn