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

  Demo GAR 
  seed: None 
  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': None, '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: 0.0001
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.50454     	-852102.19406	3.24533     	8.15607            	0           
  10          	1.76966     	-243.62594  	2.34967     	6.18536            	0           
  100         	1.53861     	-58.70414   	2.14404     	5.88762            	0           
----------------------------------------

  Demo GAR 
  seed: None 
  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': None, '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: 0.0001
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	2.50454     	-852102.19406	3.24533     	8.15607            	0           
  10          	1.76966     	-243.62594  	2.34967     	6.18536            	0           
  100         	1.53861     	-58.70414   	2.14404     	5.88762            	0           
  300         	1.23874     	-38.89169   	1.85821     	6.11666            	1           
  500         	1.12207     	-11.75843   	1.74289     	6.57725            	1           
  1000        	1.03877     	-3.22936    	1.64268     	5.41079            	3           
---> try to load best state
              	1.03877     	-3.22936    	1.64268     	5.41079            	3                eval state : test_on_restore;
              	1.03877     	-3.22936    	1.64268     	5.41079            	3                eval state : test_on_last_epoch;
---> final result              	1.03877     	-3.22936    	1.64268     	5.41079            	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': None, '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: 0.0001
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.05441     	-3.17065    	1.66723     	2.39747            	0           
  10          	1.05490     	-3.37322    	1.67505     	1.75923            	0           
  100         	1.05111     	-3.37818    	1.67738     	1.58209            	0           
  300         	1.05130     	-3.35444    	1.67692     	1.43537            	2           
  500         	1.05189     	-3.35601    	1.67706     	1.33915            	4           
  1000        	1.05153     	-3.31933    	1.67710     	1.22050            	7           
---> try to load best state
              	1.05153     	-3.31933    	1.67710     	1.22050            	7                eval state : test_on_restore;
              	1.05153     	-3.31933    	1.67710     	1.22050            	7                eval state : test_on_last_epoch;
---> final result
              	1.05153     	-3.31933    	1.67710     	1.22050            	7                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': None, '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: 0.0001
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.05621     	-3.04882    	1.66871     	2.64537            	0           
  10          	1.04786     	-3.07570    	1.66529     	2.00737            	0           
  100         	1.05411     	-3.35898    	1.67703     	4.72258            	0           
  300         	1.05450     	-3.36845    	1.67747     	3.73948            	2           
  500         	1.05275     	-3.37058    	1.67663     	2.38428            	4           
  1000        	1.05425     	-3.37362    	1.67827     	4.02589            	8           
---> try to load best state
              	1.05425     	-3.37362    	1.67827     	4.02589            	8                eval state : test_on_restore;
              	1.05425     	-3.37362    	1.67827     	4.02589            	8                eval state : test_on_last_epoch;
---> final result
              	1.05425     	-3.37362    	1.67827     	4.02589            	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': None, '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: 0.0001
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.06415     	-3.07983    	1.67829     	4.77827            	0           
  10          	1.05030     	-2.95581    	1.67024     	5.18822            	0           
  100         	1.05959     	-3.21989    	1.68135     	3.91768            	1           
  300         	1.06051     	-3.29743    	1.68516     	4.05507            	3           
  500         	1.05963     	-3.33618    	1.68550     	5.93465            	5           
  1000        	1.05714     	-3.30997    	1.67733     	3.92895            	10          
---> try to load best state
              	1.05714     	-3.30997    	1.67733     	3.92895            	10               eval state : test_on_restore;
              	1.05714     	-3.30997    	1.67733     	3.92895            	10               eval state : test_on_last_epoch;
---> final result
              	1.05714     	-3.30997    	1.67733     	3.92895            	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': None, '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: 0.0001
  grid_config: {'grid_size': [-1, -1], 'type': 'fixed', 'dimension_map': 'identity'}
---> training record
  epoch       	mae         	r2          	rmse        	gaussian_loss      	time        
  1           	1.05199     	-3.05072    	1.67101     	5.81445            	0           
  10          	1.04734     	-2.94483    	1.66861     	8.15042            	0           
  100         	1.05228     	-3.13212    	1.67769     	5.25286            	1           
  300         	1.05016     	-3.09619    	1.67490     	7.58039            	3           
  500         	1.04981     	-3.02482    	1.67179     	5.82423            	5           
  1000        	1.05455     	-3.09831    	1.68296     	4.34523            	11          
---> try to load best state
              	1.05455     	-3.09831    	1.68296     	4.34523            	11               eval state : test_on_restore;
              	1.05455     	-3.09831    	1.68296     	4.34523            	11               eval state : test_on_last_epoch;
---> final result
              	1.05455     	-3.09831    	1.68296     	4.34523            	11               eval state : final;module_name : GAR;cp_record_file : True;
