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| #Gaussian Process Complete Options | |
| #--------------------------------- | |
| #General options | |
| max_num_runs = 100 #number of planned runs | |
| target_cost = 0.1 #cost to beat | |
| #Gaussian process options | |
| controller_type = 'gaussian_process' | |
| num_params = 2 #number of parameters | |
| min_boundary = [-10.,-10.] #minimum boundary | |
| max_boundary = [10.,10.] #maximum boundary | |
| length_scale = [1.0] #initial lengths scales for GP | |
| cost_has_noise = True #whether cost function has noise | |
| noise_level = 0.1 #initial noise level | |
| update_hyperparameters = True #whether noise level and lengths scales are updated | |
| trust_region = [5,5] #maximum move distance from best params | |
| default_bad_cost = 10 #default cost for bad run | |
| default_bad_uncertainty = 1 #default uncertainty for bad run | |
| learner_archive_filename = 'a_word' #filename of gp archive | |
| learner_archive_file_type = 'mat' #file type of archive | |
| predict_global_minima_at_end = True #find predicted global minima at end | |
| predict_local_minima_at_end = True #find all local minima of landscape at end | |
| no_delay = True #whether to wait for the GP to make predictions or not. Default True (do not wait) | |
| #Training source options | |
| training_type = 'random' #training type can be random or nelder_mead | |
| first_params = [1.9,-1.0] #first parameters to try in initial training | |
| gp_training_filename = None #filename for training from previous experiment | |
| gp_training_file_type = 'pkl' #training data file type | |
| #if you use nelder_mead for the initial training source see the CompleteNelderMeadConfig.txt for options. |