----------------------------------------

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05957     	0.22893     	0.85146     	0           
  10          	0.05872     	0.22776     	0.85305     	0           
  100         	0.04965     	0.21620     	0.86844     	0           
  300         	0.04105     	0.19867     	0.89130     	0           
  500         	0.04096     	0.19303     	0.89869     	0           
  1000        	0.04043     	0.18886     	0.90464     	1           
---> try to load best state
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_restore;
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_last_epoch;
---> final result
              	0.04043     	0.18886     	0.90464     	1                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04390     	0.15629     	0.39973     	0           
  10          	0.04372     	0.15655     	0.39341     	0           
----------------------------------------

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05957     	0.22893     	0.85146     	0           
  10          	0.05872     	0.22776     	0.85305     	0           
  100         	0.04965     	0.21620     	0.86844     	0           
  300         	0.04105     	0.19867     	0.89130     	0           
  500         	0.04096     	0.19303     	0.89869     	0           
  1000        	0.04043     	0.18886     	0.90464     	1           
---> try to load best state
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_restore;
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_last_epoch;
---> final result
              	0.04043     	0.18886     	0.90464     	1                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05300     	0.17175     	0.29686     	0           
  10          	0.05307     	0.17117     	0.29836     	0           
----------------------------------------

  Demo lar cigp 
  seed: 4 
  interp_data: False 

----------------------------------------
---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.001, 'optional_param': 0.001, 'noise': 0.001}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.05957     	0.22893     	0.85146     	0           
  10          	0.05872     	0.22776     	0.85305     	0           
  100         	0.04965     	0.21620     	0.86844     	0           
  300         	0.04105     	0.19867     	0.89130     	0           
  500         	0.04096     	0.19303     	0.89869     	0           
  1000        	0.04043     	0.18886     	0.90464     	1           
---> try to load best state
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_restore;
              	0.04043     	0.18886     	0.90464     	1                eval state : test_on_last_epoch;
---> final result
              	0.04043     	0.18886     	0.90464     	1                eval state : final;
----------> finish x-yl training


---------->
lar cigp for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.06507     	0.17474     	0.45807     	0           
  10          	0.06152     	0.16934     	0.46908     	0           
  100         	0.04286     	0.15592     	0.42628     	0           
---> try to load best state
              	0.04286     	0.15592     	0.42628     	0                eval state : test_on_restore;
              	0.04286     	0.15592     	0.42628     	0                eval state : test_on_last_epoch;
---> final result
              	0.04286     	0.15592     	0.42628     	0                eval state : final;
              	0.04286     	0.15592     	0.42628     	0                eval state : final;module_name : LarGP;cp_record_file : True;
---> end


---------->
lar cigp for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'plasmonic2_MF', 'interp_data': False, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[-1]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 100.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.04892     	0.13947     	0.83043     	0           
  10          	0.05073     	0.13789     	0.84438     	0           
  100         	0.04050     	0.13449     	0.91495     	0           
---> try to load best state
              	0.04050     	0.13449     	0.91495     	0                eval state : test_on_restore;
              	0.04050     	0.13449     	0.91495     	0                eval state : test_on_last_epoch;
---> final result
              	0.04050     	0.13449     	0.91495     	0                eval state : final;
              	0.04050     	0.13449     	0.91495     	0                eval state : final;module_name : LarGP;cp_record_file : True;
