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

  Demo DC 
  seed: None 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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: 100.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00167     	0.00425     	0.97896     	0           
  10          	0.00139     	0.00366     	0.98273     	0           
  100         	0.00038     	0.00120     	0.99614     	0           
---> try to load best state
              	0.00038     	0.00120     	0.99614     	0                eval state : test_on_restore;
              	0.00038     	0.00120     	0.99614     	0                eval state : test_on_last_epoch;
---> final result
              	0.00038     	0.00120     	0.99614     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03707     	0.07603     	-12.95451   	0           
  10          	0.03706     	0.07601     	-12.84307   	0           
  100         	0.03704     	0.07600     	-12.75090   	0           
---> try to load best state
              	0.03704     	0.07600     	-12.75090   	0                eval state : test_on_restore;
              	0.03704     	0.07600     	-12.75090   	0                eval state : test_on_last_epoch;
---> final result
              	0.03704     	0.07600     	-12.75090   	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02665     	0.06494     	-9.84520    	0           
  10          	0.02663     	0.06493     	-9.75924    	0           
  100         	0.02662     	0.06493     	-9.68816    	0           
---> try to load best state
              	0.02662     	0.06493     	-9.68816    	0                eval state : test_on_restore;
              	0.02662     	0.06493     	-9.68816    	0                eval state : test_on_last_epoch;
---> final result
              	0.02662     	0.06493     	-9.68816    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01930     	0.05764     	-9.46432    	0           
  10          	0.01362     	0.05194     	-5.36810    	0           
  100         	0.00835     	0.04632     	-2.78145    	0           
---> try to load best state
              	0.00835     	0.04632     	-2.78145    	0                eval state : test_on_restore;
              	0.00835     	0.04632     	-2.78145    	0                eval state : test_on_last_epoch;
---> final result
              	0.00835     	0.04632     	-2.78145    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': None, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01365     	0.05377     	-7.79303    	0           
  10          	0.01067     	0.05158     	-5.68291    	0           
  100         	0.00849     	0.04805     	-4.08387    	0           
---> try to load best state
              	0.00849     	0.04805     	-4.08387    	0                eval state : test_on_restore;
              	0.00849     	0.04805     	-4.08387    	0                eval state : test_on_last_epoch;
---> final result
              	0.00849     	0.04805     	-4.08387    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 0 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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: 100.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00193     	0.00491     	0.97230     	0           
  10          	0.00164     	0.00443     	0.97534     	0           
  100         	0.00111     	0.00408     	0.98026     	0           
---> try to load best state
              	0.00111     	0.00408     	0.98026     	0                eval state : test_on_restore;
              	0.00111     	0.00408     	0.98026     	0                eval state : test_on_last_epoch;
---> final result
              	0.00111     	0.00408     	0.98026     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02105     	0.07689     	-129147.91543	0           
  10          	0.02105     	0.07689     	-128163.07771	0           
  100         	0.02105     	0.07689     	-127348.98476	0           
---> try to load best state
              	0.02105     	0.07689     	-127348.98476	0                eval state : test_on_restore;
              	0.02105     	0.07689     	-127348.98476	0                eval state : test_on_last_epoch;
---> final result
              	0.02105     	0.07689     	-127348.98476	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02017     	0.07215     	-1625.62234 	0           
  10          	0.02017     	0.07214     	-1613.21612 	0           
  100         	0.02017     	0.07214     	-1602.95211 	0           
---> try to load best state
              	0.02017     	0.07214     	-1602.95211 	0                eval state : test_on_restore;
              	0.02017     	0.07214     	-1602.95211 	0                eval state : test_on_last_epoch;
---> final result
              	0.02017     	0.07214     	-1602.95211 	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01929     	0.06122     	-16.49093   	0           
  10          	0.01677     	0.05881     	-11.34650   	0           
  100         	0.01119     	0.05036     	-2.22601    	0           
---> try to load best state
              	0.01119     	0.05036     	-2.22601    	0                eval state : test_on_restore;
              	0.01119     	0.05036     	-2.22601    	0                eval state : test_on_last_epoch;
---> final result
              	0.01119     	0.05036     	-2.22601    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 0, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01654     	0.05944     	-16.21306   	0           
  10          	0.01410     	0.05730     	-11.52644   	0           
  100         	0.01029     	0.04964     	-3.21661    	0           
---> try to load best state
              	0.01029     	0.04964     	-3.21661    	0                eval state : test_on_restore;
              	0.01029     	0.04964     	-3.21661    	0                eval state : test_on_last_epoch;
---> final result
              	0.01029     	0.04964     	-3.21661    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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: 100.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00183     	0.00503     	0.96967     	0           
  10          	0.00162     	0.00445     	0.97472     	0           
  100         	0.00083     	0.00302     	0.98676     	0           
---> try to load best state
              	0.00083     	0.00302     	0.98676     	0                eval state : test_on_restore;
              	0.00083     	0.00302     	0.98676     	0                eval state : test_on_last_epoch;
---> final result
              	0.00083     	0.00302     	0.98676     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02061     	0.07523     	-6921.30091 	0           
  10          	0.02053     	0.07519     	-3264.39037 	0           
  100         	0.02024     	0.07499     	-1649.07537 	0           
---> try to load best state
              	0.02024     	0.07499     	-1649.07537 	0                eval state : test_on_restore;
              	0.02024     	0.07499     	-1649.07537 	0                eval state : test_on_last_epoch;
---> final result
              	0.02024     	0.07499     	-1649.07537 	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02011     	0.07067     	-407.58907  	0           
  10          	0.01978     	0.07049     	-253.41799  	0           
  100         	0.01884     	0.06985     	-173.15615  	0           
---> try to load best state
              	0.01884     	0.06985     	-173.15615  	0                eval state : test_on_restore;
              	0.01884     	0.06985     	-173.15615  	0                eval state : test_on_last_epoch;
---> final result
              	0.01884     	0.06985     	-173.15615  	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02114     	0.06130     	-12.24534   	0           
  10          	0.01852     	0.05711     	-6.28050    	0           
  100         	0.01172     	0.04789     	-1.47298    	0           
---> try to load best state
              	0.01172     	0.04789     	-1.47298    	0                eval state : test_on_restore;
              	0.01172     	0.04789     	-1.47298    	0                eval state : test_on_last_epoch;
---> final result
              	0.01172     	0.04789     	-1.47298    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 1, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01708     	0.06061     	-17.24614   	0           
  10          	0.01428     	0.05643     	-8.29832    	0           
  100         	0.00876     	0.04814     	-1.37248    	0           
---> try to load best state
              	0.00876     	0.04814     	-1.37248    	0                eval state : test_on_restore;
              	0.00876     	0.04814     	-1.37248    	0                eval state : test_on_last_epoch;
---> final result
              	0.00876     	0.04814     	-1.37248    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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: 100.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00196     	0.00588     	0.95321     	0           
  10          	0.00157     	0.00513     	0.96155     	0           
  100         	0.00050     	0.00189     	0.99260     	0           
---> try to load best state
              	0.00050     	0.00189     	0.99260     	0                eval state : test_on_restore;
              	0.00050     	0.00189     	0.99260     	0                eval state : test_on_last_epoch;
---> final result
              	0.00050     	0.00189     	0.99260     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02532     	0.06634     	-66.99715   	0           
  10          	0.02531     	0.06633     	-66.47567   	0           
  100         	0.02531     	0.06633     	-66.05432   	0           
---> try to load best state
              	0.02531     	0.06633     	-66.05432   	0                eval state : test_on_restore;
              	0.02531     	0.06633     	-66.05432   	0                eval state : test_on_last_epoch;
---> final result
              	0.02531     	0.06633     	-66.05432   	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02192     	0.06486     	-70.75042   	0           
  10          	0.02110     	0.06370     	-49.56653   	0           
  100         	0.01928     	0.06139     	-28.40506   	0           
---> try to load best state
              	0.01928     	0.06139     	-28.40506   	0                eval state : test_on_restore;
              	0.01928     	0.06139     	-28.40506   	0                eval state : test_on_last_epoch;
---> final result
              	0.01928     	0.06139     	-28.40506   	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02252     	0.06309     	-26.14845   	0           
  10          	0.02015     	0.06037     	-17.57813   	0           
  100         	0.01043     	0.04850     	-5.44802    	0           
---> try to load best state
              	0.01043     	0.04850     	-5.44802    	0                eval state : test_on_restore;
              	0.01043     	0.04850     	-5.44802    	0                eval state : test_on_last_epoch;
---> final result
              	0.01043     	0.04850     	-5.44802    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 2, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02044     	0.05898     	-7.57582    	0           
  10          	0.01672     	0.05375     	-5.65370    	0           
  100         	0.00764     	0.03921     	-1.10152    	0           
---> try to load best state
              	0.00764     	0.03921     	-1.10152    	0                eval state : test_on_restore;
              	0.00764     	0.03921     	-1.10152    	0                eval state : test_on_last_epoch;
---> final result
              	0.00764     	0.03921     	-1.10152    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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: 100.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00173     	0.00494     	0.96765     	0           
  10          	0.00140     	0.00432     	0.97342     	0           
  100         	0.00050     	0.00200     	0.99223     	0           
---> try to load best state
              	0.00050     	0.00200     	0.99223     	0                eval state : test_on_restore;
              	0.00050     	0.00200     	0.99223     	0                eval state : test_on_last_epoch;
---> final result
              	0.00050     	0.00200     	0.99223     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02663     	0.06753     	-115.72349  	0           
  10          	0.02662     	0.06752     	-114.82844  	0           
  100         	0.02662     	0.06752     	-114.08862  	0           
---> try to load best state
              	0.02662     	0.06752     	-114.08862  	0                eval state : test_on_restore;
              	0.02662     	0.06752     	-114.08862  	0                eval state : test_on_last_epoch;
---> final result
              	0.02662     	0.06752     	-114.08862  	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02071     	0.06433     	-83.47385   	0           
  10          	0.01921     	0.06263     	-34.84112   	0           
  100         	0.01387     	0.05685     	-9.56919    	0           
---> try to load best state
              	0.01387     	0.05685     	-9.56919    	0                eval state : test_on_restore;
              	0.01387     	0.05685     	-9.56919    	0                eval state : test_on_last_epoch;
---> final result
              	0.01387     	0.05685     	-9.56919    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02124     	0.05674     	-5.50629    	0           
  10          	0.01759     	0.05035     	-1.36678    	0           
  100         	0.01064     	0.04160     	0.04685     	0           
---> try to load best state
              	0.01064     	0.04160     	0.04685     	0                eval state : test_on_restore;
              	0.01064     	0.04160     	0.04685     	0                eval state : test_on_last_epoch;
---> final result
              	0.01064     	0.04160     	0.04685     	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 3, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01782     	0.05593     	-9.92685    	0           
  10          	0.01321     	0.04741     	-2.45903    	0           
  100         	0.00537     	0.03363     	0.35609     	0           
---> try to load best state
              	0.00537     	0.03363     	0.35609     	0                eval state : test_on_restore;
              	0.00537     	0.03363     	0.35609     	0                eval state : test_on_last_epoch;
---> final result
              	0.00537     	0.03363     	0.35609     	0                eval state : final;module_name : DC;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: True 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]'], 'outputs_format': ['y[0]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  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: 100.0
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.00151     	0.00627     	0.93993     	0           
  10          	0.00128     	0.00555     	0.95060     	0           
  100         	0.00090     	0.00369     	0.97620     	0           
---> try to load best state
              	0.00090     	0.00369     	0.97620     	0                eval state : test_on_restore;
              	0.00090     	0.00369     	0.97620     	0                eval state : test_on_last_epoch;
---> final result
              	0.00090     	0.00369     	0.97620     	0                eval state : final;
----------> finish x-yl training


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 4, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.03551     	0.08309     	-335.78541  	0           
  10          	0.03538     	0.08299     	-284.66351  	0           
  100         	0.03501     	0.08268     	-176.33500  	0           
---> try to load best state
              	0.03501     	0.08268     	-176.33500  	0                eval state : test_on_restore;
              	0.03501     	0.08268     	-176.33500  	0                eval state : test_on_last_epoch;
---> final result
              	0.03501     	0.08268     	-176.33500  	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 8, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02388     	0.06463     	-52.36904   	0           
  10          	0.02222     	0.06259     	-26.75624   	0           
  100         	0.01281     	0.05205     	-8.60272    	0           
---> try to load best state
              	0.01281     	0.05205     	-8.60272    	0                eval state : test_on_restore;
              	0.01281     	0.05205     	-8.60272    	0                eval state : test_on_last_epoch;
---> final result
              	0.01281     	0.05205     	-8.60272    	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 16, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.02172     	0.06371     	-52.78381   	0           
  10          	0.01935     	0.06129     	-30.09391   	0           
  100         	0.01255     	0.05528     	-15.45608   	0           
---> try to load best state
              	0.01255     	0.05528     	-15.45608   	0                eval state : test_on_restore;
              	0.01255     	0.05528     	-15.45608   	0                eval state : test_on_last_epoch;
---> final result
              	0.01255     	0.05528     	-15.45608   	0                eval state : final;module_name : DC;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': True, 'seed': 4, 'train_start_index': 0, 'train_sample': 32, 'eval_start_index': 0, 'eval_sample': 128, 'inputs_format': ['x[0]', 'y[0]'], 'outputs_format': ['y[2]'], 'force_2d': True, 'x_sample_to_last_dim': False, 'y_sample_to_last_dim': False, 'slice_param': [0.6, 0.4]}
  lr: {'kernel': 0.1, 'optional_param': 0.1, 'noise': 0.1}
  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: 100.0
  pca: {'type': 'listPCA', 'r': 0.99}
---> training record
  epoch       	mae         	rmse        	r2          	time        
  1           	0.01881     	0.06113     	-17.18102   	0           
  10          	0.01640     	0.05866     	-14.99202   	0           
  100         	0.01140     	0.05338     	-9.02233    	0           
---> try to load best state
              	0.01140     	0.05338     	-9.02233    	0                eval state : test_on_restore;
              	0.01140     	0.05338     	-9.02233    	0                eval state : test_on_last_epoch;
---> final result
              	0.01140     	0.05338     	-9.02233    	0                eval state : final;module_name : DC;cp_record_file : True;
