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

  Demo GAR 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'DoublePendu_mfGent_v01', 'fidelity': ['medium'], '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           	2.51590     	-66010604.72042	3.25273     	13.78275           	0           
  10          	1.74093     	-122.11218  	2.30334     	10.42152           	0           
  100         	1.54111     	-103.33626  	2.12251     	8.50587            	0           
  300         	1.31934     	-15.00755   	1.90049     	9.36598            	1           
  500         	1.23471     	-9.91603    	1.81800     	9.56387            	1           
  1000        	1.16469     	-5.54922    	1.74270     	8.55981            	3           
---> try to load best state
              	1.16469     	-5.54922    	1.74270     	8.55981            	3                eval state : test_on_restore;
              	1.16469     	-5.54922    	1.74270     	8.55981            	3                eval state : test_on_last_epoch;
---> final result              	1.16469     	-5.54922    	1.74270     	8.55981            	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': ['medium', '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           	1.17824     	-5.42793    	1.77927     	2.52453            	0           
  10          	1.18602     	-5.72390    	1.78201     	1.97084            	0           
  100         	1.17377     	-5.74637    	1.77335     	2.14527            	0           
  300         	1.16756     	-5.64343    	1.76713     	2.11319            	2           
  500         	1.17067     	-5.67976    	1.77106     	2.07294            	4           
  1000        	1.17162     	-5.67602    	1.77165     	1.96845            	8           
---> try to load best state
              	1.17162     	-5.67602    	1.77165     	1.96845            	8                eval state : test_on_restore;
              	1.17162     	-5.67602    	1.77165     	1.96845            	8                eval state : test_on_last_epoch;
---> final result
              	1.17162     	-5.67602    	1.77165     	1.96845            	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': ['medium', '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           	1.17905     	-5.74655    	1.77717     	3.98198            	0           
  10          	1.17663     	-5.54930    	1.77243     	3.38994            	0           
  100         	1.17441     	-5.46586    	1.77141     	3.89539            	0           
  300         	1.16577     	-5.29291    	1.76290     	3.70023            	2           
  500         	1.17416     	-5.38908    	1.77099     	3.73537            	4           
  1000        	1.17215     	-5.36612    	1.76986     	4.74770            	9           
---> try to load best state
              	1.17215     	-5.36612    	1.76986     	4.74770            	9                eval state : test_on_restore;
              	1.17215     	-5.36612    	1.76986     	4.74770            	9                eval state : test_on_last_epoch;
---> final result
              	1.17215     	-5.36612    	1.76986     	4.74770            	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': ['medium', '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           	1.17634     	-5.79107    	1.77537     	8.19600            	0           
  10          	1.17831     	-5.71573    	1.77440     	8.82385            	0           
  100         	1.17302     	-5.51033    	1.76938     	8.68960            	1           
  300         	1.17539     	-5.49256    	1.77209     	9.17372            	3           
  500         	1.17665     	-5.46442    	1.77208     	7.73440            	5           
  1000        	1.17532     	-5.47173    	1.77246     	7.49078            	10          
---> try to load best state
              	1.17532     	-5.47173    	1.77246     	7.49078            	10               eval state : test_on_restore;
              	1.17532     	-5.47173    	1.77246     	7.49078            	10               eval state : test_on_last_epoch;
---> final result
              	1.17532     	-5.47173    	1.77246     	7.49078            	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': ['medium', '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           	1.17244     	-5.60681    	1.77182     	9.51210            	0           
  10          	1.17491     	-5.49820    	1.77114     	9.13108            	0           
  100         	1.17378     	-5.65520    	1.77100     	8.77332            	1           
  300         	1.17473     	-5.65136    	1.77209     	10.88578           	3           
  500         	1.17529     	-5.63533    	1.77286     	8.77827            	5           
  1000        	1.17502     	-5.57180    	1.77226     	9.03932            	11          
---> try to load best state
              	1.17502     	-5.57180    	1.77226     	9.03932            	11               eval state : test_on_restore;
              	1.17502     	-5.57180    	1.77226     	9.03932            	11               eval state : test_on_last_epoch;
---> final result
              	1.17502     	-5.57180    	1.77226     	9.03932            	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': ['medium'], '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           	2.53387     	-76102593.18517	3.25511     	15.61930           	0           
  10          	1.73551     	-132.39865  	2.29964     	8.84374            	0           
  100         	1.54437     	-87.33385   	2.13347     	8.34654            	0           
  300         	1.35218     	-26.16958   	1.95554     	11.53110           	1           
  500         	1.27099     	-15.29355   	1.88524     	10.21581           	2           
  1000        	1.20065     	-7.93214    	1.81680     	10.87703           	4           
---> try to load best state
              	1.20065     	-7.93214    	1.81680     	10.87703           	4                eval state : test_on_restore;
              	1.20065     	-7.93214    	1.81680     	10.87703           	4                eval state : test_on_last_epoch;
---> final result              	1.20065     	-7.93214    	1.81680     	10.87703           	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': ['medium', '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           	1.20908     	-7.60348    	1.85136     	2.52530            	0           
  10          	1.21648     	-7.76314    	1.85291     	1.97385            	0           
  100         	1.20592     	-8.15396    	1.84293     	2.14917            	0           
  300         	1.19935     	-7.94062    	1.83598     	2.11731            	2           
  500         	1.20208     	-8.09314    	1.84002     	2.07748            	4           
  1000        	1.20293     	-8.18024    	1.84080     	1.97427            	9           
---> try to load best state
              	1.20293     	-8.18024    	1.84080     	1.97427            	9                eval state : test_on_restore;
              	1.20293     	-8.18024    	1.84080     	1.97427            	9                eval state : test_on_last_epoch;
---> final result
              	1.20293     	-8.18024    	1.84080     	1.97427            	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': ['medium', '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           	1.20834     	-7.98563    	1.84762     	3.98285            	0           
  10          	1.20711     	-7.62642    	1.84279     	3.39349            	0           
  100         	1.20659     	-7.81595    	1.84258     	3.89587            	0           
  300         	1.19846     	-7.40583    	1.83491     	3.70142            	2           
  500         	1.20660     	-7.56861    	1.84313     	3.73619            	4           
  1000        	1.20435     	-7.48836    	1.84168     	4.74774            	8           
---> try to load best state
              	1.20435     	-7.48836    	1.84168     	4.74774            	8                eval state : test_on_restore;
              	1.20435     	-7.48836    	1.84168     	4.74774            	8                eval state : test_on_last_epoch;
---> final result
              	1.20435     	-7.48836    	1.84168     	4.74774            	8                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': ['medium', '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           	1.20651     	-8.09597    	1.84700     	8.19600            	0           
  10          	1.20966     	-7.94028    	1.84569     	8.82385            	0           
  100         	1.20526     	-7.67605    	1.84182     	8.68960            	1           
  300         	1.20781     	-7.71238    	1.84496     	9.17372            	3           
  500         	1.20930     	-7.67215    	1.84499     	7.73440            	4           
  1000        	1.20776     	-7.69404    	1.84526     	7.49078            	9           
---> try to load best state
              	1.20776     	-7.69404    	1.84526     	7.49078            	9                eval state : test_on_restore;
              	1.20776     	-7.69404    	1.84526     	7.49078            	9                eval state : test_on_last_epoch;
---> final result
              	1.20776     	-7.69404    	1.84526     	7.49078            	9                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': ['medium', '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           	1.20354     	-7.74131    	1.84382     	9.51210            	0           
  10          	1.20595     	-7.46274    	1.84160     	9.13108            	0           
  100         	1.20633     	-7.74505    	1.84193     	8.77332            	1           
  300         	1.20739     	-7.78071    	1.84321     	10.88578           	3           
  500         	1.20822     	-7.77143    	1.84450     	8.77827            	5           
  1000        	1.20864     	-7.68732    	1.84495     	9.03932            	10          
---> try to load best state
              	1.20864     	-7.68732    	1.84495     	9.03932            	10               eval state : test_on_restore;
              	1.20864     	-7.68732    	1.84495     	9.03932            	10               eval state : test_on_last_epoch;
---> final result
              	1.20864     	-7.68732    	1.84495     	9.03932            	10               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': ['medium'], '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           	2.52771     	-162792679.73363	3.25403     	11.10623           	0           
  10          	1.81369     	-82.94383   	2.40659     	7.23929            	0           
  100         	1.61332     	-65.96936   	2.23896     	6.80591            	0           
  300         	1.41307     	-38.83951   	2.04379     	5.52541            	1           
  500         	1.34291     	-51.79079   	1.98142     	5.45047            	1           
  1000        	1.28529     	-23.70805   	1.93261     	5.54847            	4           
---> try to load best state
              	1.28529     	-23.70805   	1.93261     	5.54847            	4                eval state : test_on_restore;
              	1.28529     	-23.70805   	1.93261     	5.54847            	4                eval state : test_on_last_epoch;
---> final result              	1.28529     	-23.70805   	1.93261     	5.54847            	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': ['medium', '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           	1.30429     	-21.33969   	1.97436     	2.52735            	0           
  10          	1.30351     	-26.14429   	1.97192     	1.98212            	0           
  100         	1.29276     	-22.72603   	1.95884     	2.16202            	0           
  300         	1.28686     	-20.76664   	1.95314     	2.13131            	2           
  500         	1.28980     	-20.36663   	1.95724     	2.09289            	4           
  1000        	1.29076     	-19.51213   	1.95802     	1.99402            	8           
---> try to load best state
              	1.29076     	-19.51213   	1.95802     	1.99402            	8                eval state : test_on_restore;
              	1.29076     	-19.51213   	1.95802     	1.99402            	8                eval state : test_on_last_epoch;
---> final result
              	1.29076     	-19.51213   	1.95802     	1.99402            	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': ['medium', '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           	1.30682     	-21.44047   	1.97769     	3.98483            	0           
  10          	1.29741     	-22.06230   	1.96252     	3.40054            	0           
  100         	1.29348     	-22.62223   	1.95906     	3.89647            	0           
  300         	1.28515     	-21.90767   	1.95202     	3.70241            	2           
  500         	1.29312     	-22.23086   	1.95904     	3.73690            	4           
  1000        	1.29102     	-22.49872   	1.95776     	4.74778            	8           
---> try to load best state
              	1.29102     	-22.49872   	1.95776     	4.74778            	8                eval state : test_on_restore;
              	1.29102     	-22.49872   	1.95776     	4.74778            	8                eval state : test_on_last_epoch;
---> final result
              	1.29102     	-22.49872   	1.95776     	4.74778            	8                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': ['medium', '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           	1.30376     	-21.17403   	1.97588     	8.19600            	0           
  10          	1.30163     	-21.16089   	1.96781     	8.82385            	0           
  100         	1.29308     	-22.08368   	1.96020     	8.68960            	1           
  300         	1.29503     	-22.14827   	1.96164     	9.17372            	3           
  500         	1.29614     	-22.34775   	1.96150     	7.73440            	5           
  1000        	1.29493     	-22.77887   	1.96156     	7.49078            	10          
---> try to load best state
              	1.29493     	-22.77887   	1.96156     	7.49078            	10               eval state : test_on_restore;
              	1.29493     	-22.77887   	1.96156     	7.49078            	10               eval state : test_on_last_epoch;
---> final result
              	1.29493     	-22.77887   	1.96156     	7.49078            	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': ['medium', '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           	1.29812     	-21.12135   	1.96605     	9.51210            	0           
  10          	1.29582     	-21.22866   	1.96089     	9.13108            	0           
  100         	1.29339     	-22.97285   	1.95838     	8.77332            	1           
  300         	1.29405     	-22.50455   	1.95859     	10.88578           	3           
  500         	1.29444     	-22.38451   	1.95893     	8.77827            	5           
  1000        	1.29430     	-22.14568   	1.95813     	9.03932            	10          
---> try to load best state
              	1.29430     	-22.14568   	1.95813     	9.03932            	10               eval state : test_on_restore;
              	1.29430     	-22.14568   	1.95813     	9.03932            	10               eval state : test_on_last_epoch;
---> final result
              	1.29430     	-22.14568   	1.95813     	9.03932            	10               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': ['medium'], '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           	2.50068     	-107490198.10277	3.25473     	11.62053           	0           
  10          	1.79167     	-48.67214   	2.39495     	6.98114            	0           
  100         	1.60472     	-86.39080   	2.24469     	6.18000            	0           
  300         	1.40136     	-41.50173   	2.03337     	5.68913            	1           
  500         	1.31537     	-32.99172   	1.94977     	7.62018            	1           
  1000        	1.23273     	-27.48438   	1.87243     	5.17580            	3           
---> try to load best state
              	1.23273     	-27.48438   	1.87243     	5.17580            	3                eval state : test_on_restore;
              	1.23273     	-27.48438   	1.87243     	5.17580            	3                eval state : test_on_last_epoch;
---> final result              	1.23273     	-27.48438   	1.87243     	5.17580            	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': ['medium', '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           	1.24491     	-30.56732   	1.91070     	2.52624            	0           
  10          	1.24834     	-36.72593   	1.91147     	1.97781            	0           
  100         	1.24041     	-29.62646   	1.90007     	2.15554            	0           
  300         	1.23466     	-27.31348   	1.89299     	2.12411            	2           
  500         	1.23769     	-26.51006   	1.89782     	2.08509            	4           
  1000        	1.23853     	-25.79451   	1.89840     	1.98404            	8           
---> try to load best state
              	1.23853     	-25.79451   	1.89840     	1.98404            	8                eval state : test_on_restore;
              	1.23853     	-25.79451   	1.89840     	1.98404            	8                eval state : test_on_last_epoch;
---> final result
              	1.23853     	-25.79451   	1.89840     	1.98404            	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': ['medium', '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           	1.24486     	-27.54140   	1.90915     	3.98411            	0           
  10          	1.24062     	-32.70128   	1.89983     	3.39790            	0           
  100         	1.23896     	-36.00252   	1.89898     	3.89592            	0           
  300         	1.22961     	-40.16465   	1.88939     	3.70128            	2           
  500         	1.23876     	-40.54678   	1.89903     	3.73608            	4           
  1000        	1.23633     	-40.44346   	1.89736     	4.74774            	8           
---> try to load best state
              	1.23633     	-40.44346   	1.89736     	4.74774            	8                eval state : test_on_restore;
              	1.23633     	-40.44346   	1.89736     	4.74774            	8                eval state : test_on_last_epoch;
---> final result
              	1.23633     	-40.44346   	1.89736     	4.74774            	8                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': ['medium', '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           	1.24273     	-27.62665   	1.90750     	8.19600            	0           
  10          	1.24457     	-28.24665   	1.90379     	8.82385            	0           
  100         	1.23765     	-29.10081   	1.89735     	8.68960            	1           
  300         	1.24024     	-28.68328   	1.90030     	9.17372            	3           
  500         	1.24168     	-28.70721   	1.90021     	7.73440            	4           
  1000        	1.24035     	-29.39288   	1.90078     	7.49078            	9           
---> try to load best state
              	1.24035     	-29.39288   	1.90078     	7.49078            	9                eval state : test_on_restore;
              	1.24035     	-29.39288   	1.90078     	7.49078            	9                eval state : test_on_last_epoch;
---> final result
              	1.24035     	-29.39288   	1.90078     	7.49078            	9                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': ['medium', '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           	1.23893     	-27.58123   	1.90158     	9.51210            	0           
  10          	1.23988     	-26.57675   	1.89856     	9.13108            	0           
  100         	1.23873     	-31.89636   	1.89791     	8.77332            	1           
  300         	1.23961     	-30.91014   	1.89864     	10.88578           	3           
  500         	1.24025     	-31.01789   	1.89941     	8.77827            	5           
  1000        	1.24008     	-31.86894   	1.89846     	9.03932            	10          
---> try to load best state
              	1.24008     	-31.86894   	1.89846     	9.03932            	10               eval state : test_on_restore;
              	1.24008     	-31.86894   	1.89846     	9.03932            	10               eval state : test_on_last_epoch;
---> final result
              	1.24008     	-31.86894   	1.89846     	9.03932            	10               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': ['medium'], '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           	2.49085     	-66100846.02609	3.25971     	16.51154           	0           
  10          	1.78133     	-38.46726   	2.38203     	9.76526            	0           
  100         	1.68052     	-253.95073  	2.31085     	10.62402           	0           
  300         	1.44244     	-48.60531   	2.05099     	10.85094           	1           
  500         	1.33137     	-15.05644   	1.93955     	10.52518           	1           
  1000        	1.23076     	-5.81855    	1.84852     	10.52625           	3           
---> try to load best state
              	1.23076     	-5.81855    	1.84852     	10.52625           	3                eval state : test_on_restore;
              	1.23076     	-5.81855    	1.84852     	10.52625           	3                eval state : test_on_last_epoch;
---> final result              	1.23076     	-5.81855    	1.84852     	10.52625           	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': ['medium', '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           	1.23904     	-5.99339    	1.88315     	2.52568            	0           
  10          	1.24657     	-6.08782    	1.88560     	1.97539            	0           
  100         	1.23704     	-6.16290    	1.87522     	2.15156            	0           
  300         	1.23114     	-6.10550    	1.86765     	2.11976            	2           
  500         	1.23400     	-6.21537    	1.87209     	2.08027            	4           
  1000        	1.23431     	-6.23814    	1.87229     	1.97780            	8           
---> try to load best state
              	1.23431     	-6.23814    	1.87229     	1.97780            	8                eval state : test_on_restore;
              	1.23431     	-6.23814    	1.87229     	1.97780            	8                eval state : test_on_last_epoch;
---> final result
              	1.23431     	-6.23814    	1.87229     	1.97780            	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': ['medium', '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           	1.23844     	-6.11118    	1.87929     	3.98332            	0           
  10          	1.23764     	-5.69740    	1.87461     	3.39494            	0           
  100         	1.23600     	-5.77430    	1.87367     	3.89566            	0           
  300         	1.22696     	-5.53723    	1.86393     	3.70072            	2           
  500         	1.23605     	-5.67010    	1.87383     	3.73575            	3           
  1000        	1.23388     	-5.66650    	1.87236     	4.74773            	7           
---> try to load best state
              	1.23388     	-5.66650    	1.87236     	4.74773            	7                eval state : test_on_restore;
              	1.23388     	-5.66650    	1.87236     	4.74773            	7                eval state : test_on_last_epoch;
---> final result
              	1.23388     	-5.66650    	1.87236     	4.74773            	7                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': ['medium', '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           	1.23611     	-6.15954    	1.87746     	8.19600            	0           
  10          	1.23913     	-5.95246    	1.87665     	8.82385            	0           
  100         	1.23406     	-5.75627    	1.87182     	8.68960            	1           
  300         	1.23666     	-5.79080    	1.87515     	9.17372            	3           
  500         	1.23803     	-5.74960    	1.87510     	7.73440            	5           
  1000        	1.23664     	-5.77583    	1.87563     	7.49078            	10          
---> try to load best state
              	1.23664     	-5.77583    	1.87563     	7.49078            	10               eval state : test_on_restore;
              	1.23664     	-5.77583    	1.87563     	7.49078            	10               eval state : test_on_last_epoch;
---> final result
              	1.23664     	-5.77583    	1.87563     	7.49078            	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': ['medium', '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           	1.23366     	-5.88383    	1.87467     	9.51210            	0           
  10          	1.23726     	-5.80121    	1.87389     	9.13108            	0           
  100         	1.23677     	-5.87164    	1.87436     	8.77332            	1           
  300         	1.23749     	-5.90119    	1.87526     	10.88578           	3           
  500         	1.23803     	-5.89039    	1.87620     	8.77827            	5           
  1000        	1.23767     	-5.81736    	1.87556     	9.03932            	10          
---> try to load best state
              	1.23767     	-5.81736    	1.87556     	9.03932            	10               eval state : test_on_restore;
              	1.23767     	-5.81736    	1.87556     	9.03932            	10               eval state : test_on_last_epoch;
---> final result
              	1.23767     	-5.81736    	1.87556     	9.03932            	10               eval state : final;module_name : GAR;cp_record_file : True;
