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

  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            	1           
  1000        	1.38106     	-62.15889   	1.98302     	9.78756            	3           
---> try to load best state
              	1.38106     	-62.15889   	1.98302     	9.78756            	3                eval state : test_on_restore;
              	1.38106     	-62.15889   	1.98302     	9.78756            	3                eval state : test_on_last_epoch;
---> final result              	1.38106     	-62.15889   	1.98302     	9.78756            	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', 'high'], '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           	1.41205     	-81.86714   	2.02161     	2.52811            	0           
  10          	1.42435     	-58.34750   	2.01870     	1.98338            	0           
  100         	1.43184     	-30.14552   	2.03303     	2.16589            	0           
  300         	1.42552     	-41.71379   	2.03252     	2.13441            	2           
  500         	1.42684     	-42.11961   	2.03457     	2.09604            	4           
  1000        	1.43377     	-32.28371   	2.04240     	1.99879            	8           
---> try to load best state
              	1.43377     	-32.28371   	2.04240     	1.99879            	8                eval state : test_on_restore;
              	1.43377     	-32.28371   	2.04240     	1.99879            	8                eval state : test_on_last_epoch;
---> final result
              	1.43377     	-32.28371   	2.04240     	1.99879            	8                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', 'high'], '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           	1.41332     	-70.78203   	2.01795     	4.00254            	0           
  10          	1.41644     	-20.80358   	2.00825     	3.44082            	0           
  100         	1.42121     	-21.71842   	2.01297     	3.26664            	0           
  300         	1.41935     	-17.28876   	2.00928     	3.80736            	2           
  500         	1.41602     	-18.03053   	2.00112     	4.13678            	4           
  1000        	1.41139     	-22.42314   	1.98795     	3.50754            	9           
---> try to load best state
              	1.41139     	-22.42314   	1.98795     	3.50754            	9                eval state : test_on_restore;
              	1.41139     	-22.42314   	1.98795     	3.50754            	9                eval state : test_on_last_epoch;
---> final result
              	1.41139     	-22.42314   	1.98795     	3.50754            	9                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', 'high'], '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           	1.40731     	-84.15215   	2.01278     	8.74204            	0           
  10          	1.41620     	-21.53432   	2.00982     	7.96034            	0           
  100         	1.41890     	-35.50190   	2.01051     	9.04650            	1           
  300         	1.41342     	-116.22144  	2.00925     	10.16325           	3           
  500         	1.41051     	-179.28377  	2.00544     	7.77230            	6           
  1000        	1.40842     	-165.32569  	2.00166     	8.22691            	11          
---> try to load best state
              	1.40842     	-165.32569  	2.00166     	8.22691            	11               eval state : test_on_restore;
              	1.40842     	-165.32569  	2.00166     	8.22691            	11               eval state : test_on_last_epoch;
---> final result
              	1.40842     	-165.32569  	2.00166     	8.22691            	11               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', 'high'], '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           	1.39821     	-41.09210   	2.00888     	10.15382           	0           
  10          	1.40461     	-20.98220   	2.01153     	10.37241           	0           
  100         	1.40756     	-60.20847   	2.00583     	10.32640           	1           
  300         	1.41002     	-131.44097  	2.00969     	10.62757           	3           
  500         	1.41106     	-133.10857  	2.01067     	8.98270            	5           
  1000        	1.40891     	-135.17878  	2.00779     	10.52918           	11          
---> try to load best state
              	1.40891     	-135.17878  	2.00779     	10.52918           	11               eval state : test_on_restore;
              	1.40891     	-135.17878  	2.00779     	10.52918           	11               eval state : test_on_last_epoch;
---> final result
              	1.40891     	-135.17878  	2.00779     	10.52918           	11               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', 'high'], '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           	1.44934     	-28.25312   	2.07363     	2.52861            	0           
  10          	1.45808     	-19.93452   	2.06995     	1.98536            	0           
  100         	1.46528     	-20.37213   	2.07837     	2.16772            	0           
  300         	1.46742     	-20.14245   	2.09543     	2.13818            	2           
  500         	1.47191     	-18.29866   	2.10419     	2.10085            	4           
  1000        	1.48188     	-16.85065   	2.12013     	2.00603            	9           
---> try to load best state
              	1.48188     	-16.85065   	2.12013     	2.00603            	9                eval state : test_on_restore;
              	1.48188     	-16.85065   	2.12013     	2.00603            	9                eval state : test_on_last_epoch;
---> final result
              	1.48188     	-16.85065   	2.12013     	2.00603            	9                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', 'high'], '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           	1.44990     	-23.56960   	2.07070     	4.00320            	0           
  10          	1.45180     	-17.14727   	2.06374     	3.44316            	0           
  100         	1.46753     	-16.54501   	2.08560     	3.26974            	1           
  300         	1.47280     	-16.62677   	2.09937     	3.80817            	2           
  500         	1.46554     	-16.62661   	2.08513     	4.13704            	4           
  1000        	1.45068     	-16.65671   	2.05625     	3.50837            	9           
---> try to load best state
              	1.45068     	-16.65671   	2.05625     	3.50837            	9                eval state : test_on_restore;
              	1.45068     	-16.65671   	2.05625     	3.50837            	9                eval state : test_on_last_epoch;
---> final result
              	1.45068     	-16.65671   	2.05625     	3.50837            	9                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', 'high'], '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           	1.44451     	-22.24274   	2.06685     	8.74204            	0           
  10          	1.45370     	-16.84162   	2.06684     	7.96034            	0           
  100         	1.46723     	-16.59280   	2.08891     	9.04650            	1           
  300         	1.45812     	-18.57025   	2.08067     	10.16325           	3           
  500         	1.45091     	-19.58998   	2.07007     	7.77230            	5           
  1000        	1.44654     	-19.94204   	2.06321     	8.22691            	11          
---> try to load best state
              	1.44654     	-19.94204   	2.06321     	8.22691            	11               eval state : test_on_restore;
              	1.44654     	-19.94204   	2.06321     	8.22691            	11               eval state : test_on_last_epoch;
---> final result
              	1.44654     	-19.94204   	2.06321     	8.22691            	11               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', 'high'], '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           	1.43659     	-22.76580   	2.06393     	10.15382           	0           
  10          	1.44077     	-20.08531   	2.06114     	10.37241           	0           
  100         	1.44713     	-21.94787   	2.06513     	10.32640           	1           
  300         	1.44968     	-21.93473   	2.06908     	10.62757           	3           
  500         	1.45099     	-22.11497   	2.07056     	8.98270            	5           
  1000        	1.44889     	-22.09656   	2.06778     	10.52918           	11          
---> try to load best state
              	1.44889     	-22.09656   	2.06778     	10.52918           	11               eval state : test_on_restore;
              	1.44889     	-22.09656   	2.06778     	10.52918           	11               eval state : test_on_last_epoch;
---> final result
              	1.44889     	-22.09656   	2.06778     	10.52918           	11               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            	2           
  1000        	1.46243     	-255.34819  	2.09642     	8.01738            	4           
---> try to load best state
              	1.46243     	-255.34819  	2.09642     	8.01738            	4                eval state : test_on_restore;
              	1.46243     	-255.34819  	2.09642     	8.01738            	4                eval state : test_on_last_epoch;
---> final result              	1.46243     	-255.34819  	2.09642     	8.01738            	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', 'high'], '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           	1.52671     	-313.08927  	2.16807     	2.53034            	0           
  10          	1.50906     	-173.68718  	2.14029     	1.99115            	0           
  100         	1.52248     	-30.74505   	2.17000     	2.17958            	0           
  300         	1.52073     	-532.31289  	2.16835     	2.14881            	2           
  500         	1.52561     	-780.01934  	2.17227     	2.11184            	4           
  1000        	1.53702     	-177.04913  	2.18324     	2.01906            	9           
---> try to load best state
              	1.53702     	-177.04913  	2.18324     	2.01906            	9                eval state : test_on_restore;
              	1.53702     	-177.04913  	2.18324     	2.01906            	9                eval state : test_on_last_epoch;
---> final result
              	1.53702     	-177.04913  	2.18324     	2.01906            	9                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', 'high'], '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           	1.52673     	-399.69941  	2.16326     	4.00478            	0           
  10          	1.51167     	-683.39327  	2.13856     	3.44832            	0           
  100         	1.51148     	-160.36532  	2.13215     	3.27155            	0           
  300         	1.51638     	-188.13217  	2.13000     	3.80847            	2           
  500         	1.51472     	-142.43777  	2.12687     	4.13713            	4           
  1000        	1.51000     	-158.49372  	2.11845     	3.50893            	9           
---> try to load best state
              	1.51000     	-158.49372  	2.11845     	3.50893            	9                eval state : test_on_restore;
              	1.51000     	-158.49372  	2.11845     	3.50893            	9                eval state : test_on_last_epoch;
---> final result
              	1.51000     	-158.49372  	2.11845     	3.50893            	9                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', 'high'], '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           	1.51905     	-552.99500  	2.15558     	8.74204            	0           
  10          	1.51673     	-1399.41154 	2.14541     	7.96034            	0           
  100         	1.51920     	-141.27645  	2.14269     	9.04650            	1           
  300         	1.51644     	-2735.62793 	2.14560     	10.16325           	3           
  500         	1.51322     	-462.57311  	2.14370     	7.77230            	5           
  1000        	1.51072     	-393.82266  	2.14041     	8.22691            	11          
---> try to load best state
              	1.51072     	-393.82266  	2.14041     	8.22691            	11               eval state : test_on_restore;
              	1.51072     	-393.82266  	2.14041     	8.22691            	11               eval state : test_on_last_epoch;
---> final result
              	1.51072     	-393.82266  	2.14041     	8.22691            	11               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', 'high'], '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           	1.50985     	-246.74105  	2.15050     	10.15382           	0           
  10          	1.50336     	-892.23503  	2.14191     	10.37241           	0           
  100         	1.51143     	-359.48724  	2.14540     	10.32640           	1           
  300         	1.51713     	-263.23722  	2.15086     	10.62757           	3           
  500         	1.51889     	-268.22143  	2.15269     	8.98270            	5           
  1000        	1.51704     	-278.71003  	2.15031     	10.52918           	11          
---> try to load best state
              	1.51704     	-278.71003  	2.15031     	10.52918           	11               eval state : test_on_restore;
              	1.51704     	-278.71003  	2.15031     	10.52918           	11               eval state : test_on_last_epoch;
---> final result
              	1.51704     	-278.71003  	2.15031     	10.52918           	11               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            	4           
---> try to load best state
              	1.44250     	-28.82541   	2.07856     	6.04859            	4                eval state : test_on_restore;
              	1.44250     	-28.82541   	2.07856     	6.04859            	4                eval state : test_on_last_epoch;
---> final result              	1.44250     	-28.82541   	2.07856     	6.04859            	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', 'high'], '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           	1.49085     	-30.38259   	2.13841     	2.52980            	0           
  10          	1.48542     	-33.95416   	2.11938     	1.98937            	0           
  100         	1.49248     	-33.31229   	2.13037     	2.17452            	0           
  300         	1.49765     	-25.35162   	2.14682     	2.14577            	2           
  500         	1.50104     	-23.44361   	2.15302     	2.10906            	4           
  1000        	1.50869     	-22.48166   	2.16544     	2.01620            	9           
---> try to load best state
              	1.50869     	-22.48166   	2.16544     	2.01620            	9                eval state : test_on_restore;
              	1.50869     	-22.48166   	2.16544     	2.01620            	9                eval state : test_on_last_epoch;
---> final result
              	1.50869     	-22.48166   	2.16544     	2.01620            	9                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', 'high'], '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           	1.48973     	-28.74909   	2.13426     	4.00449            	0           
  10          	1.48278     	-27.68008   	2.11576     	3.44736            	0           
  100         	1.48649     	-25.14104   	2.11525     	3.27089            	1           
  300         	1.48507     	-23.18894   	2.11916     	3.80816            	3           
  500         	1.48102     	-23.09994   	2.10963     	4.13700            	4           
  1000        	1.46842     	-23.02317   	2.08657     	3.50866            	9           
---> try to load best state
              	1.46842     	-23.02317   	2.08657     	3.50866            	9                eval state : test_on_restore;
              	1.46842     	-23.02317   	2.08657     	3.50866            	9                eval state : test_on_last_epoch;
---> final result
              	1.46842     	-23.02317   	2.08657     	3.50866            	9                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', 'high'], '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           	1.48404     	-28.84646   	2.12920     	8.74204            	0           
  10          	1.48478     	-27.34050   	2.11976     	7.96034            	0           
  100         	1.48841     	-25.97203   	2.12071     	9.04650            	1           
  300         	1.48170     	-26.58353   	2.12047     	10.16325           	3           
  500         	1.47699     	-27.15664   	2.11497     	7.77230            	5           
  1000        	1.47307     	-27.09901   	2.10977     	8.22691            	11          
---> try to load best state
              	1.47307     	-27.09901   	2.10977     	8.22691            	11               eval state : test_on_restore;
              	1.47307     	-27.09901   	2.10977     	8.22691            	11               eval state : test_on_last_epoch;
---> final result
              	1.47307     	-27.09901   	2.10977     	8.22691            	11               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', 'high'], '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           	1.47875     	-30.87451   	2.12669     	10.15382           	0           
  10          	1.47176     	-42.05217   	2.11411     	10.37241           	0           
  100         	1.47451     	-29.47909   	2.11028     	10.32640           	1           
  300         	1.47805     	-27.26308   	2.11580     	10.62757           	3           
  500         	1.47947     	-27.63332   	2.11729     	8.98270            	5           
  1000        	1.47686     	-27.69191   	2.11411     	10.52918           	11          
---> try to load best state
              	1.47686     	-27.69191   	2.11411     	10.52918           	11               eval state : test_on_restore;
              	1.47686     	-27.69191   	2.11411     	10.52918           	11               eval state : test_on_last_epoch;
---> final result
              	1.47686     	-27.69191   	2.11411     	10.52918           	11               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           	2           
  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', 'high'], '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           	1.47654     	-16.73640   	2.11150     	2.52927            	0           
  10          	1.48563     	-13.89726   	2.10912     	1.98798            	0           
  100         	1.49533     	-14.01074   	2.11687     	2.17156            	0           
  300         	1.49395     	-13.30984   	2.12814     	2.14205            	2           
  500         	1.49423     	-12.10002   	2.13194     	2.10480            	4           
  1000        	1.49769     	-11.01668   	2.14081     	2.01045            	9           
---> try to load best state
              	1.49769     	-11.01668   	2.14081     	2.01045            	9                eval state : test_on_restore;
              	1.49769     	-11.01668   	2.14081     	2.01045            	9                eval state : test_on_last_epoch;
---> final result
              	1.49769     	-11.01668   	2.14081     	2.01045            	9                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', 'high'], '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           	1.47577     	-15.08850   	2.10792     	4.00380            	0           
  10          	1.47686     	-12.30710   	2.09908     	3.44537            	0           
  100         	1.48442     	-11.71616   	2.10844     	3.27026            	1           
  300         	1.47458     	-10.59291   	2.10440     	3.80793            	2           
  500         	1.46939     	-10.35672   	2.09099     	4.13695            	4           
  1000        	1.45736     	-10.23819   	2.06295     	3.50842            	9           
---> try to load best state
              	1.45736     	-10.23819   	2.06295     	3.50842            	9                eval state : test_on_restore;
              	1.45736     	-10.23819   	2.06295     	3.50842            	9                eval state : test_on_last_epoch;
---> final result
              	1.45736     	-10.23819   	2.06295     	3.50842            	9                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', 'high'], '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           	1.47095     	-14.27774   	2.10460     	8.74204            	0           
  10          	1.47721     	-11.58230   	2.10173     	7.96034            	0           
  100         	1.48148     	-11.38269   	2.10728     	9.04650            	1           
  300         	1.47205     	-12.28144   	2.10119     	10.16325           	3           
  500         	1.46695     	-12.73778   	2.09287     	7.77230            	5           
  1000        	1.46338     	-12.87208   	2.08655     	8.22691            	10          
---> try to load best state
              	1.46338     	-12.87208   	2.08655     	8.22691            	10               eval state : test_on_restore;
              	1.46338     	-12.87208   	2.08655     	8.22691            	10               eval state : test_on_last_epoch;
---> final result
              	1.46338     	-12.87208   	2.08655     	8.22691            	10               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', 'high'], '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           	1.46586     	-14.72084   	2.10259     	10.15382           	0           
  10          	1.46386     	-14.01490   	2.09485     	10.37241           	0           
  100         	1.46408     	-13.67215   	2.08642     	10.32640           	1           
  300         	1.46431     	-13.60214   	2.08828     	10.62757           	3           
  500         	1.46522     	-13.72812   	2.08900     	8.98270            	5           
  1000        	1.46256     	-13.70871   	2.08546     	10.52918           	11          
---> try to load best state
              	1.46256     	-13.70871   	2.08546     	10.52918           	11               eval state : test_on_restore;
              	1.46256     	-13.70871   	2.08546     	10.52918           	11               eval state : test_on_last_epoch;
---> final result
              	1.46256     	-13.70871   	2.08546     	10.52918           	11               eval state : final;module_name : GAR;cp_record_file : True;
