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

  Demo DC 
  seed: None 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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           	0.00157     	0.00419     	0.98065     	0           
  10          	0.00131     	0.00361     	0.98410     	0           
  100         	0.00043     	0.00136     	0.99588     	0           
---> try to load best state
              	0.00043     	0.00136     	0.99588     	0                eval state : test_on_restore;
              	0.00043     	0.00136     	0.99588     	0                eval state : test_on_last_epoch;
---> final result
              	0.00043     	0.00136     	0.99588     	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': False, '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[-1]'], '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           	0.01585     	0.04858     	-0.75201    	0           
  10          	0.01179     	0.03985     	0.03379     	0           
  100         	0.00692     	0.03084     	0.70461     	0           
---> try to load best state
              	0.00692     	0.03084     	0.70461     	0                eval state : test_on_restore;
              	0.00692     	0.03084     	0.70461     	0                eval state : test_on_last_epoch;
---> final result
              	0.00692     	0.03084     	0.70461     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01053     	0.04642     	-1.74693    	0           
  10          	0.00831     	0.04298     	-1.26432    	0           
  100         	0.00699     	0.03982     	-0.55594    	0           
---> try to load best state
              	0.00699     	0.03982     	-0.55594    	0                eval state : test_on_restore;
              	0.00699     	0.03982     	-0.55594    	0                eval state : test_on_last_epoch;
---> final result
              	0.00699     	0.03982     	-0.55594    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.00881     	0.04716     	-3.06424    	0           
  10          	0.00726     	0.04294     	-1.84261    	0           
  100         	0.00577     	0.03830     	-0.69719    	0           
---> try to load best state
              	0.00577     	0.03830     	-0.69719    	0                eval state : test_on_restore;
              	0.00577     	0.03830     	-0.69719    	0                eval state : test_on_last_epoch;
---> final result
              	0.00577     	0.03830     	-0.69719    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.00807     	0.04714     	-3.53992    	0           
  10          	0.00673     	0.04241     	-1.75425    	0           
  100         	0.00245     	0.01933     	0.81085     	0           
---> try to load best state
              	0.00245     	0.01933     	0.81085     	0                eval state : test_on_restore;
              	0.00245     	0.01933     	0.81085     	0                eval state : test_on_last_epoch;
---> final result
              	0.00245     	0.01933     	0.81085     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 0 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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           	0.00182     	0.00485     	0.97439     	0           
  10          	0.00155     	0.00437     	0.97714     	0           
  100         	0.00102     	0.00390     	0.98215     	0           
---> try to load best state
              	0.00102     	0.00390     	0.98215     	0                eval state : test_on_restore;
              	0.00102     	0.00390     	0.98215     	0                eval state : test_on_last_epoch;
---> final result
              	0.00102     	0.00390     	0.98215     	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': False, '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[-1]'], '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           	0.02020     	0.07725     	-1202.04075 	0           
  10          	0.02020     	0.07725     	-1192.89208 	0           
  100         	0.02019     	0.07725     	-1185.33145 	0           
---> try to load best state
              	0.02019     	0.07725     	-1185.33145 	0                eval state : test_on_restore;
              	0.02019     	0.07725     	-1185.33145 	0                eval state : test_on_last_epoch;
---> final result
              	0.02019     	0.07725     	-1185.33145 	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01855     	0.07182     	-271.29213  	0           
  10          	0.01764     	0.07120     	-160.43396  	0           
  100         	0.01645     	0.07034     	-102.88006  	0           
---> try to load best state
              	0.01645     	0.07034     	-102.88006  	0                eval state : test_on_restore;
              	0.01645     	0.07034     	-102.88006  	0                eval state : test_on_last_epoch;
---> final result
              	0.01645     	0.07034     	-102.88006  	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01026     	0.04894     	-1.32889    	0           
  10          	0.00818     	0.04420     	-0.10765    	0           
  100         	0.00797     	0.04360     	-0.03349    	0           
---> try to load best state
              	0.00797     	0.04360     	-0.03349    	0                eval state : test_on_restore;
              	0.00797     	0.04360     	-0.03349    	0                eval state : test_on_last_epoch;
---> final result
              	0.00797     	0.04360     	-0.03349    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.00957     	0.04846     	-2.17031    	0           
  10          	0.00747     	0.04350     	-0.30176    	0           
  100         	0.00626     	0.03983     	0.22617     	0           
---> try to load best state
              	0.00626     	0.03983     	0.22617     	0                eval state : test_on_restore;
              	0.00626     	0.03983     	0.22617     	0                eval state : test_on_last_epoch;
---> final result
              	0.00626     	0.03983     	0.22617     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 1 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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           	0.00172     	0.00496     	0.97211     	0           
  10          	0.00152     	0.00439     	0.97671     	0           
  100         	0.00082     	0.00291     	0.98780     	0           
---> try to load best state
              	0.00082     	0.00291     	0.98780     	0                eval state : test_on_restore;
              	0.00082     	0.00291     	0.98780     	0                eval state : test_on_last_epoch;
---> final result
              	0.00082     	0.00291     	0.98780     	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': False, '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[-1]'], '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           	0.02009     	0.07573     	-1455.33690 	0           
  10          	0.01962     	0.07534     	-847.62059  	0           
  100         	0.01930     	0.07509     	-625.90982  	0           
---> try to load best state
              	0.01930     	0.07509     	-625.90982  	0                eval state : test_on_restore;
              	0.01930     	0.07509     	-625.90982  	0                eval state : test_on_last_epoch;
---> final result
              	0.01930     	0.07509     	-625.90982  	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01839     	0.07011     	-136.79287  	0           
  10          	0.01683     	0.06843     	-61.19843   	0           
  100         	0.01596     	0.06738     	-42.80720   	0           
---> try to load best state
              	0.01596     	0.06738     	-42.80720   	0                eval state : test_on_restore;
              	0.01596     	0.06738     	-42.80720   	0                eval state : test_on_last_epoch;
---> final result
              	0.01596     	0.06738     	-42.80720   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01183     	0.04675     	-1.19710    	0           
  10          	0.00743     	0.03913     	0.09600     	0           
  100         	0.00556     	0.03478     	0.46216     	0           
---> try to load best state
              	0.00556     	0.03478     	0.46216     	0                eval state : test_on_restore;
              	0.00556     	0.03478     	0.46216     	0                eval state : test_on_last_epoch;
---> final result
              	0.00556     	0.03478     	0.46216     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.00933     	0.04911     	-1.76885    	0           
  10          	0.00725     	0.04217     	-0.29131    	0           
  100         	0.00553     	0.03743     	0.35898     	0           
---> try to load best state
              	0.00553     	0.03743     	0.35898     	0                eval state : test_on_restore;
              	0.00553     	0.03743     	0.35898     	0                eval state : test_on_last_epoch;
---> final result
              	0.00553     	0.03743     	0.35898     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 2 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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           	0.00183     	0.00579     	0.95671     	0           
  10          	0.00148     	0.00504     	0.96431     	0           
  100         	0.00051     	0.00202     	0.99213     	0           
---> try to load best state
              	0.00051     	0.00202     	0.99213     	0                eval state : test_on_restore;
              	0.00051     	0.00202     	0.99213     	0                eval state : test_on_last_epoch;
---> final result
              	0.00051     	0.00202     	0.99213     	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': False, '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[-1]'], '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           	0.02229     	0.06264     	-17.96870   	0           
  10          	0.02004     	0.05899     	-11.02093   	0           
  100         	0.01233     	0.04811     	-4.43914    	0           
---> try to load best state
              	0.01233     	0.04811     	-4.43914    	0                eval state : test_on_restore;
              	0.01233     	0.04811     	-4.43914    	0                eval state : test_on_last_epoch;
---> final result
              	0.01233     	0.04811     	-4.43914    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01705     	0.05840     	-16.93217   	0           
  10          	0.01314     	0.05379     	-8.76357    	0           
  100         	0.00917     	0.05034     	-4.28079    	0           
---> try to load best state
              	0.00917     	0.05034     	-4.28079    	0                eval state : test_on_restore;
              	0.00917     	0.05034     	-4.28079    	0                eval state : test_on_last_epoch;
---> final result
              	0.00917     	0.05034     	-4.28079    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01228     	0.05012     	-6.00328    	0           
  10          	0.00855     	0.04631     	-3.31294    	0           
  100         	0.00662     	0.04283     	-1.88406    	0           
---> try to load best state
              	0.00662     	0.04283     	-1.88406    	0                eval state : test_on_restore;
              	0.00662     	0.04283     	-1.88406    	0                eval state : test_on_last_epoch;
---> final result
              	0.00662     	0.04283     	-1.88406    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.00645     	0.03369     	0.08876     	0           
  10          	0.00343     	0.02203     	0.74249     	0           
  100         	0.00148     	0.01036     	0.95418     	0           
---> try to load best state
              	0.00148     	0.01036     	0.95418     	0                eval state : test_on_restore;
              	0.00148     	0.01036     	0.95418     	0                eval state : test_on_last_epoch;
---> final result
              	0.00148     	0.01036     	0.95418     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 3 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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           	0.00162     	0.00486     	0.97011     	0           
  10          	0.00131     	0.00425     	0.97541     	0           
  100         	0.00054     	0.00212     	0.99180     	0           
---> try to load best state
              	0.00054     	0.00212     	0.99180     	0                eval state : test_on_restore;
              	0.00054     	0.00212     	0.99180     	0                eval state : test_on_last_epoch;
---> final result
              	0.00054     	0.00212     	0.99180     	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': False, '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[-1]'], '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           	0.02304     	0.06282     	-18.29212   	0           
  10          	0.02118     	0.06018     	-10.98765   	0           
  100         	0.01442     	0.04962     	-5.01191    	0           
---> try to load best state
              	0.01442     	0.04962     	-5.01191    	0                eval state : test_on_restore;
              	0.01442     	0.04962     	-5.01191    	0                eval state : test_on_last_epoch;
---> final result
              	0.01442     	0.04962     	-5.01191    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01553     	0.05884     	-12.96374   	0           
  10          	0.01343     	0.05665     	-8.54787    	0           
  100         	0.00934     	0.05164     	-4.90207    	0           
---> try to load best state
              	0.00934     	0.05164     	-4.90207    	0                eval state : test_on_restore;
              	0.00934     	0.05164     	-4.90207    	0                eval state : test_on_last_epoch;
---> final result
              	0.00934     	0.05164     	-4.90207    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01079     	0.04197     	-0.12795    	0           
  10          	0.00735     	0.03668     	0.25301     	0           
  100         	0.00489     	0.03334     	0.51195     	0           
---> try to load best state
              	0.00489     	0.03334     	0.51195     	0                eval state : test_on_restore;
              	0.00489     	0.03334     	0.51195     	0                eval state : test_on_last_epoch;
---> final result
              	0.00489     	0.03334     	0.51195     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.00571     	0.03412     	0.20787     	0           
  10          	0.00423     	0.02996     	0.55338     	0           
  100         	0.00251     	0.01933     	0.84947     	0           
---> try to load best state
              	0.00251     	0.01933     	0.84947     	0                eval state : test_on_restore;
              	0.00251     	0.01933     	0.84947     	0                eval state : test_on_last_epoch;
---> final result
              	0.00251     	0.01933     	0.84947     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end

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

  Demo DC 
  seed: 4 
  interp_data: False 

----------------------------------------
---> Training x -> yl part
---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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           	0.00143     	0.00616     	0.94417     	0           
  10          	0.00121     	0.00545     	0.95403     	0           
  100         	0.00087     	0.00360     	0.97717     	0           
---> try to load best state
              	0.00087     	0.00360     	0.97717     	0                eval state : test_on_restore;
              	0.00087     	0.00360     	0.97717     	0                eval state : test_on_last_epoch;
---> final result
              	0.00087     	0.00360     	0.97717     	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': False, '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[-1]'], '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           	0.03370     	0.08273     	-75.14570   	0           
  10          	0.03423     	0.08326     	-105.48433  	0           
  100         	0.03410     	0.08313     	-96.22933   	0           
---> try to load best state
              	0.03410     	0.08313     	-96.22933   	0                eval state : test_on_restore;
              	0.03410     	0.08313     	-96.22933   	0                eval state : test_on_last_epoch;
---> final result
              	0.03410     	0.08313     	-96.22933   	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01709     	0.05705     	-14.46179   	0           
  10          	0.01310     	0.05193     	-8.87708    	0           
  100         	0.00866     	0.04489     	-2.36149    	0           
---> try to load best state
              	0.00866     	0.04489     	-2.36149    	0                eval state : test_on_restore;
              	0.00866     	0.04489     	-2.36149    	0                eval state : test_on_last_epoch;
---> final result
              	0.00866     	0.04489     	-2.36149    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01438     	0.05784     	-17.16608   	0           
  10          	0.01238     	0.05487     	-12.72981   	0           
  100         	0.00853     	0.04495     	-2.21625    	0           
---> try to load best state
              	0.00853     	0.04495     	-2.21625    	0                eval state : test_on_restore;
              	0.00853     	0.04495     	-2.21625    	0                eval state : test_on_last_epoch;
---> final result
              	0.00853     	0.04495     	-2.21625    	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
---> end


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

---> module config
  dataset: {'name': 'burger_v4_02', 'interp_data': False, '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[-1]'], '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           	0.01117     	0.05236     	-7.17202    	0           
  10          	0.00875     	0.04699     	-2.41259    	0           
  100         	0.00330     	0.01767     	0.78822     	0           
---> try to load best state
              	0.00330     	0.01767     	0.78822     	0                eval state : test_on_restore;
              	0.00330     	0.01767     	0.78822     	0                eval state : test_on_last_epoch;
---> final result
              	0.00330     	0.01767     	0.78822     	0                eval state : final;module_name : DC_cigp;cp_record_file : True;
