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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.50956     	-68018595.98209	3.24143     	13.78282           	0           
  10          	1.74542     	-128.67666  	2.31972     	8.66994            	0           
  100         	1.57780     	-71.40429   	2.16567     	10.70518           	0           
  300         	1.46014     	-45.44831   	2.04735     	10.33763           	1           
  500         	1.42424     	-223.56396  	2.01904     	8.39878            	2           
  1000        	1.38106     	-62.15889   	1.98302     	9.78756            	5           
---> try to load best state
              	1.38106     	-62.15889   	1.98302     	9.78756            	5                eval state : test_on_restore;
              	1.38106     	-62.15889   	1.98302     	9.78756            	5                eval state : test_on_last_epoch;
---> final result              	1.38106     	-62.15889   	1.98302     	9.78756            	5                eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.40440     	-104.52731  	1.98734     	2.52768            	0           
  10          	1.40751     	-101.76757  	1.98131     	1.98169            	0           
  100         	1.41540     	-40.05363   	2.00209     	2.16308            	0           
  300         	1.41288     	-32.66684   	2.00600     	2.13180            	1           
  500         	1.41504     	-32.91509   	2.00977     	2.09341            	2           
  1000        	1.42734     	-22.47134   	2.02520     	1.99660            	5           
---> try to load best state
              	1.42734     	-22.47134   	2.02520     	1.99660            	5                eval state : test_on_restore;
              	1.42734     	-22.47134   	2.02520     	1.99660            	5                eval state : test_on_last_epoch;
---> final result
              	1.42734     	-22.47134   	2.02520     	1.99660            	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.40802     	-67.92669   	1.98547     	3.97802            	0           
  10          	1.40463     	-19.25510   	1.98220     	3.44010            	0           
  100         	1.41330     	-21.65729   	1.99429     	5.06732            	0           
  300         	1.40949     	-15.40847   	1.98964     	3.97856            	1           
  500         	1.40316     	-16.95733   	1.97704     	5.86961            	2           
  1000        	1.39612     	-21.14706   	1.96002     	5.56456            	4           
---> try to load best state
              	1.39612     	-21.14706   	1.96002     	5.56456            	4                eval state : test_on_restore;
              	1.39612     	-21.14706   	1.96002     	5.56456            	4                eval state : test_on_last_epoch;
---> final result
              	1.39612     	-21.14706   	1.96002     	5.56456            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.40055     	-70.92367   	1.97884     	9.70860            	0           
  10          	1.40291     	-20.04566   	1.98038     	7.51797            	0           
  100         	1.41056     	-32.72175   	1.98860     	12.86867           	0           
  300         	1.40242     	-94.61948   	1.98387     	8.83305            	1           
  500         	1.39274     	-106.92393  	1.97149     	6.95374            	2           
  1000        	1.39553     	-115.72653  	1.97536     	7.39978            	4           
---> try to load best state
              	1.39553     	-115.72653  	1.97536     	7.39978            	4                eval state : test_on_restore;
              	1.39553     	-115.72653  	1.97536     	7.39978            	4                eval state : test_on_last_epoch;
---> final result
              	1.39553     	-115.72653  	1.97536     	7.39978            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 0, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.38625     	-50.97405   	1.97419     	10.07784           	0           
  10          	1.39280     	-20.62898   	1.98179     	11.18753           	0           
  100         	1.39586     	-70.34880   	1.97915     	8.04789            	0           
  300         	1.39793     	-95.64808   	1.98295     	8.55294            	1           
  500         	1.39394     	-89.61881   	1.97716     	8.59615            	2           
  1000        	1.40042     	-94.19271   	1.98460     	8.59943            	4           
---> try to load best state
              	1.40042     	-94.19271   	1.98460     	8.59943            	4                eval state : test_on_restore;
              	1.40042     	-94.19271   	1.98460     	8.59943            	4                eval state : test_on_last_epoch;
---> final result
              	1.40042     	-94.19271   	1.98460     	8.59943            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.52335     	-83351904.00790	3.24225     	15.61585           	0           
  10          	1.74653     	-93.48183   	2.32141     	8.53047            	0           
  100         	1.58442     	-230.80195  	2.17663     	8.24347            	0           
  300         	1.48504     	-180.88756  	2.08384     	11.50755           	1           
  500         	1.45205     	-132.66915  	2.05979     	8.61968            	2           
  1000        	1.41187     	-41.89260   	2.02992     	9.22444            	4           
---> try to load best state
              	1.41187     	-41.89260   	2.02992     	9.22444            	4                eval state : test_on_restore;
              	1.41187     	-41.89260   	2.02992     	9.22444            	4                eval state : test_on_last_epoch;
---> final result              	1.41187     	-41.89260   	2.02992     	9.22444            	4                eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.44134     	-24.33427   	2.04194     	2.52823            	0           
  10          	1.44112     	-20.35174   	2.03185     	1.98353            	0           
  100         	1.45053     	-23.14706   	2.05332     	2.16547            	0           
  300         	1.45496     	-20.41041   	2.07233     	2.13587            	1           
  500         	1.45968     	-17.94440   	2.08302     	2.09855            	2           
  1000        	1.47526     	-16.62046   	2.10572     	2.00420            	4           
---> try to load best state
              	1.47526     	-16.62046   	2.10572     	2.00420            	4                eval state : test_on_restore;
              	1.47526     	-16.62046   	2.10572     	2.00420            	4                eval state : test_on_last_epoch;
---> final result
              	1.47526     	-16.62046   	2.10572     	2.00420            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.44404     	-21.98810   	2.04082     	3.97860            	0           
  10          	1.44535     	-16.56872   	2.04584     	3.44302            	0           
  100         	1.46945     	-16.85918   	2.08645     	5.06741            	0           
  300         	1.47180     	-18.00605   	2.09556     	3.97904            	1           
  500         	1.45896     	-17.39852   	2.07210     	5.86964            	2           
  1000        	1.44023     	-17.07960   	2.03584     	5.56458            	4           
---> try to load best state
              	1.44023     	-17.07960   	2.03584     	5.56458            	4                eval state : test_on_restore;
              	1.44023     	-17.07960   	2.03584     	5.56458            	4                eval state : test_on_last_epoch;
---> final result
              	1.44023     	-17.07960   	2.03584     	5.56458            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.43756     	-20.11820   	2.03574     	9.70860            	0           
  10          	1.44607     	-16.18613   	2.04765     	7.51797            	0           
  100         	1.46400     	-16.39677   	2.07570     	12.86867           	0           
  300         	1.44939     	-18.98659   	2.05834     	8.83305            	1           
  500         	1.43627     	-19.40295   	2.04118     	6.95374            	2           
  1000        	1.43797     	-19.94478   	2.04280     	7.39978            	4           
---> try to load best state
              	1.43797     	-19.94478   	2.04280     	7.39978            	4                eval state : test_on_restore;
              	1.43797     	-19.94478   	2.04280     	7.39978            	4                eval state : test_on_last_epoch;
---> final result
              	1.43797     	-19.94478   	2.04280     	7.39978            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 1, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.42480     	-21.22992   	2.03274     	10.07784           	0           
  10          	1.43214     	-19.01880   	2.03876     	11.18753           	0           
  100         	1.43630     	-23.03337   	2.04090     	8.04789            	0           
  300         	1.43969     	-22.82318   	2.04740     	8.55294            	1           
  500         	1.43597     	-22.58373   	2.04220     	8.59615            	2           
  1000        	1.44255     	-23.10131   	2.04950     	8.59943            	4           
---> try to load best state
              	1.44255     	-23.10131   	2.04950     	8.59943            	4                eval state : test_on_restore;
              	1.44255     	-23.10131   	2.04950     	8.59943            	4                eval state : test_on_last_epoch;
---> final result
              	1.44255     	-23.10131   	2.04950     	8.59943            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.51367     	-237797447.73177	3.24155     	11.07231           	0           
  10          	1.81004     	-187.71945  	2.41125     	9.54110            	0           
  100         	1.64445     	-102.10195  	2.27181     	6.76697            	0           
  300         	1.52824     	-97.76163   	2.14994     	5.35425            	1           
  500         	1.49497     	-125.50232  	2.12124     	5.91066            	1           
  1000        	1.46243     	-255.34819  	2.09642     	8.01738            	3           
---> try to load best state
              	1.46243     	-255.34819  	2.09642     	8.01738            	3                eval state : test_on_restore;
              	1.46243     	-255.34819  	2.09642     	8.01738            	3                eval state : test_on_last_epoch;
---> final result              	1.46243     	-255.34819  	2.09642     	8.01738            	3                eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.50550     	-398.40643  	2.12568     	2.52972            	0           
  10          	1.49317     	-324.83923  	2.10740     	1.98950            	0           
  100         	1.50956     	-48.39416   	2.14616     	2.17690            	0           
  300         	1.50693     	-554.65006  	2.14461     	2.14605            	1           
  500         	1.51175     	-345.65241  	2.14996     	2.10898            	2           
  1000        	1.52859     	-145.48059  	2.16833     	2.01659            	4           
---> try to load best state
              	1.52859     	-145.48059  	2.16833     	2.01659            	4                eval state : test_on_restore;
              	1.52859     	-145.48059  	2.16833     	2.01659            	4                eval state : test_on_last_epoch;
---> final result
              	1.52859     	-145.48059  	2.16833     	2.01659            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.50916     	-592.87065  	2.12332     	3.97997            	0           
  10          	1.49774     	-641.88814  	2.10957     	3.44771            	0           
  100         	1.50164     	-189.16149  	2.11129     	5.06740            	0           
  300         	1.50533     	-115.49211  	2.11479     	3.97902            	1           
  500         	1.50243     	-117.83821  	2.10896     	5.86965            	2           
  1000        	1.49599     	-151.74451  	2.09682     	5.56458            	4           
---> try to load best state
              	1.49599     	-151.74451  	2.09682     	5.56458            	4                eval state : test_on_restore;
              	1.49599     	-151.74451  	2.09682     	5.56458            	4                eval state : test_on_last_epoch;
---> final result
              	1.49599     	-151.74451  	2.09682     	5.56458            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.50039     	-654.77603  	2.11479     	9.70860            	0           
  10          	1.50050     	-662.05948  	2.11304     	7.51797            	0           
  100         	1.50885     	-103.93657  	2.12217     	12.86867           	0           
  300         	1.50484     	-716.21612  	2.12412     	8.83305            	1           
  500         	1.49486     	-932.12006  	2.11354     	6.95374            	2           
  1000        	1.49811     	-621.56919  	2.11732     	7.39978            	4           
---> try to load best state
              	1.49811     	-621.56919  	2.11732     	7.39978            	4                eval state : test_on_restore;
              	1.49811     	-621.56919  	2.11732     	7.39978            	4                eval state : test_on_last_epoch;
---> final result
              	1.49811     	-621.56919  	2.11732     	7.39978            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 2, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.48761     	-347.61411  	2.11120     	10.07784           	0           
  10          	1.48989     	-1098.74239 	2.11332     	11.18753           	0           
  100         	1.49981     	-309.90575  	2.12251     	8.04789            	0           
  300         	1.50486     	-246.08035  	2.12824     	8.55294            	1           
  500         	1.50193     	-261.67868  	2.12361     	8.59615            	2           
  1000        	1.50796     	-262.30247  	2.13076     	8.59943            	5           
---> try to load best state
              	1.50796     	-262.30247  	2.13076     	8.59943            	5                eval state : test_on_restore;
              	1.50796     	-262.30247  	2.13076     	8.59943            	5                eval state : test_on_last_epoch;
---> final result
              	1.50796     	-262.30247  	2.13076     	8.59943            	5                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.49234     	-125715593.29664	3.24605     	11.62396           	0           
  10          	1.80343     	-43.06547   	2.42038     	7.04846            	0           
  100         	1.66232     	-155.55180  	2.31008     	6.81480            	0           
  300         	1.54505     	-62.95521   	2.17396     	8.63682            	1           
  500         	1.49954     	-41.58066   	2.12739     	4.83601            	1           
  1000        	1.44250     	-28.82541   	2.07856     	6.04859            	3           
---> try to load best state
              	1.44250     	-28.82541   	2.07856     	6.04859            	3                eval state : test_on_restore;
              	1.44250     	-28.82541   	2.07856     	6.04859            	3                eval state : test_on_last_epoch;
---> final result              	1.44250     	-28.82541   	2.07856     	6.04859            	3                eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.47793     	-28.73011   	2.10068     	2.52929            	0           
  10          	1.46878     	-33.55933   	2.07891     	1.98729            	0           
  100         	1.48319     	-29.47456   	2.10985     	2.17249            	0           
  300         	1.48607     	-24.86285   	2.12400     	2.14333            	1           
  500         	1.48879     	-22.70727   	2.13109     	2.10649            	2           
  1000        	1.50046     	-21.93875   	2.14959     	2.01391            	4           
---> try to load best state
              	1.50046     	-21.93875   	2.14959     	2.01391            	4                eval state : test_on_restore;
              	1.50046     	-21.93875   	2.14959     	2.01391            	4                eval state : test_on_last_epoch;
---> final result
              	1.50046     	-21.93875   	2.14959     	2.01391            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.47684     	-28.17027   	2.09680     	3.97971            	0           
  10          	1.47234     	-26.49225   	2.08949     	3.44709            	0           
  100         	1.48174     	-23.57354   	2.10646     	5.06735            	0           
  300         	1.47969     	-25.61339   	2.10939     	3.97916            	1           
  500         	1.47286     	-24.40246   	2.09428     	5.86963            	2           
  1000        	1.45656     	-23.60583   	2.06569     	5.56457            	4           
---> try to load best state
              	1.45656     	-23.60583   	2.06569     	5.56457            	4                eval state : test_on_restore;
              	1.45656     	-23.60583   	2.06569     	5.56457            	4                eval state : test_on_last_epoch;
---> final result
              	1.45656     	-23.60583   	2.06569     	5.56457            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.47040     	-26.99968   	2.09144     	9.70860            	0           
  10          	1.47317     	-25.23007   	2.09161     	7.51797            	0           
  100         	1.47974     	-25.69106   	2.10266     	12.86867           	0           
  300         	1.47161     	-27.45851   	2.09772     	8.83305            	1           
  500         	1.45890     	-27.56069   	2.08246     	6.95374            	2           
  1000        	1.46255     	-27.64659   	2.08698     	7.39978            	4           
---> try to load best state
              	1.46255     	-27.64659   	2.08698     	7.39978            	4                eval state : test_on_restore;
              	1.46255     	-27.64659   	2.08698     	7.39978            	4                eval state : test_on_last_epoch;
---> final result
              	1.46255     	-27.64659   	2.08698     	7.39978            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 3, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.46092     	-27.81305   	2.08939     	10.07784           	0           
  10          	1.46040     	-35.50868   	2.08521     	11.18753           	0           
  100         	1.46229     	-28.94229   	2.08583     	8.04789            	0           
  300         	1.46619     	-27.93178   	2.09173     	8.55294            	1           
  500         	1.46140     	-27.96561   	2.08476     	8.59615            	2           
  1000        	1.46867     	-28.48603   	2.09372     	8.59943            	4           
---> try to load best state
              	1.46867     	-28.48603   	2.09372     	8.59943            	4                eval state : test_on_restore;
              	1.46867     	-28.48603   	2.09372     	8.59943            	4                eval state : test_on_last_epoch;
---> final result
              	1.46867     	-28.48603   	2.09372     	8.59943            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end

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

  Demo GAR 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalzie: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.48543     	-71615639.24380	3.25116     	16.51141           	0           
  10          	1.79052     	-28.96707   	2.41025     	11.74173           	0           
  100         	1.72176     	-166.03770  	2.36581     	9.62157            	0           
  300         	1.57607     	-240.27643  	2.19183     	12.48707           	1           
  500         	1.51734     	-121.79365  	2.13064     	10.51570           	1           
  1000        	1.44255     	-18.24220   	2.06781     	9.14806            	4           
---> try to load best state
              	1.44255     	-18.24220   	2.06781     	9.14806            	4                eval state : test_on_restore;
              	1.44255     	-18.24220   	2.06781     	9.14806            	4                eval state : test_on_last_epoch;
---> final result              	1.44255     	-18.24220   	2.06781     	9.14806            	4                eval state : final;
----------> finish x-yl training


---------->
SGAR for 4 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.47049     	-15.14623   	2.07788     	2.52884            	0           
  10          	1.46840     	-13.46966   	2.06599     	1.98582            	0           
  100         	1.48451     	-14.99761   	2.09032     	2.16921            	0           
  300         	1.48392     	-13.40454   	2.10361     	2.13962            	1           
  500         	1.48369     	-11.83489   	2.10843     	2.10228            	2           
  1000        	1.49112     	-10.75870   	2.12319     	2.00822            	4           
---> try to load best state
              	1.49112     	-10.75870   	2.12319     	2.00822            	4                eval state : test_on_restore;
              	1.49112     	-10.75870   	2.12319     	2.00822            	4                eval state : test_on_last_epoch;
---> final result
              	1.49112     	-10.75870   	2.12319     	2.00822            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 8 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.46943     	-14.37279   	2.07418     	3.97916            	0           
  10          	1.46943     	-11.78101   	2.07674     	3.44512            	0           
  100         	1.48159     	-11.88717   	2.10194     	5.06736            	0           
  300         	1.47122     	-11.15178   	2.09409     	3.97906            	1           
  500         	1.46310     	-10.54250   	2.07381     	5.86962            	2           
  1000        	1.44722     	-10.27060   	2.03996     	5.56456            	4           
---> try to load best state
              	1.44722     	-10.27060   	2.03996     	5.56456            	4                eval state : test_on_restore;
              	1.44722     	-10.27060   	2.03996     	5.56456            	4                eval state : test_on_last_epoch;
---> final result
              	1.44722     	-10.27060   	2.03996     	5.56456            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 16 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.46405     	-13.25349   	2.07041     	9.70860            	0           
  10          	1.46916     	-11.13450   	2.07777     	7.51797            	0           
  100         	1.47686     	-11.31090   	2.09092     	12.86867           	0           
  300         	1.46465     	-12.47224   	2.07776     	8.83305            	1           
  500         	1.45164     	-12.58907   	2.06024     	6.95374            	2           
  1000        	1.45532     	-12.89176   	2.06400     	7.39978            	4           
---> try to load best state
              	1.45532     	-12.89176   	2.06400     	7.39978            	4                eval state : test_on_restore;
              	1.45532     	-12.89176   	2.06400     	7.39978            	4                eval state : test_on_last_epoch;
---> final result
              	1.45532     	-12.89176   	2.06400     	7.39978            	4                eval state : final;module_name : GAR;cp_record_file : True;
---> end


---------->
SGAR for 32 samples
---> Training x,yl -> yh part

---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['low', 'medium'], 'type': 'x_yl_2_yh', 'connection_method': 'res_mapping', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': 4, 'interp_data': False}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K2': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}, 'K3': {'SE': {'exp_restrict': True, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2', 'gaussian_loss']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 1e-05
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.45270     	-13.91558   	2.06716     	10.07784           	0           
  10          	1.45495     	-13.13056   	2.06738     	11.18753           	0           
  100         	1.45317     	-14.03391   	2.05969     	8.04789            	0           
  300         	1.45425     	-13.96884   	2.06277     	8.55294            	1           
  500         	1.44910     	-13.80096   	2.05519     	8.59615            	2           
  1000        	1.45642     	-14.11667   	2.06418     	8.59943            	4           
---> try to load best state
              	1.45642     	-14.11667   	2.06418     	8.59943            	4                eval state : test_on_restore;
              	1.45642     	-14.11667   	2.06418     	8.59943            	4                eval state : test_on_last_epoch;
---> final result
              	1.45642     	-14.11667   	2.06418     	8.59943            	4                eval state : final;module_name : GAR;cp_record_file : True;
