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

  Demo lar cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00303     	0.00866     	0.94061     	0           
  10          	0.00277     	0.00777     	0.95080     	0           
  100         	0.00135     	0.00417     	0.98123     	0           
  300         	0.00122     	0.00357     	0.98436     	1           
  500         	0.00113     	0.00324     	0.98597     	2           
  1000        	0.00099     	0.00275     	0.98827     	4           
---> try to load best state
              	0.00099     	0.00275     	0.98827     	4                eval state : test_on_restore;
              	0.00099     	0.00275     	0.98827     	4                eval state : test_on_last_epoch;
---> final result
              	0.00099     	0.00275     	0.98827     	4                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: {'type_name': 'res_rho'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00294     	0.01102     	0.97219     	0           
  10          	0.00311     	0.01054     	0.97172     	0           
  100         	0.00892     	0.01900     	0.84767     	0           
  300         	0.01199     	0.02540     	0.75740     	0           
  500         	0.01206     	0.02553     	0.75553     	1           
----------------------------------------

  Demo res cigp 
  seed: None 
  interp_data: True 

----------------------------------------
---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low'], 'type': 'x_2_y', 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: None
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00303     	0.00866     	0.94061     	0           
  10          	0.00277     	0.00777     	0.95080     	0           
  100         	0.00135     	0.00417     	0.98123     	0           
  300         	0.00122     	0.00357     	0.98436     	0           
  500         	0.00113     	0.00324     	0.98597     	1           
  1000        	0.00099     	0.00275     	0.98827     	3           
---> try to load best state
              	0.00099     	0.00275     	0.98827     	3                eval state : test_on_restore;
              	0.00099     	0.00275     	0.98827     	3                eval state : test_on_last_epoch;
---> final result
              	0.00099     	0.00275     	0.98827     	3                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'fidelity': ['low', 'high'], 'type': 'x_yl_2_yh', 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'seed': None, 'interp_data': True}
  lr: {'kernel': 0.01, 'optional_param': 0.01, 'noise': 0.01}
  kernel: {'K1': {'SE': {'exp_restrict': False, 'length_scale': 1.0, 'scale': 1.0}}}
  evaluate_method: ['mae', 'rmse', 'r2']
  optimizer: adam
  exp_restrict: False
  input_normalize: True
  output_normalize: True
  noise_init: 10.0
  res_cigp: {'type_name': 'res_standard'}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00294     	0.01102     	0.97200     	0           
  10          	0.00287     	0.01027     	0.97349     	0           
  100         	0.00289     	0.00785     	0.97787     	0           
  300         	0.00334     	0.00857     	0.97782     	0           
  500         	0.00361     	0.00938     	0.97682     	0           
  1000        	0.00422     	0.01146     	0.97410     	1           
---> try to load best state
              	0.00422     	0.01146     	0.97410     	1                eval state : test_on_restore;
              	0.00422     	0.01146     	0.97410     	2                eval state : test_on_last_epoch;
---> final result
              	0.00422     	0.01146     	0.97410     	2                eval state : final;module_name : ResGP;cp_record_file : True;
