<commit_before>#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 
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. <commit_msg>Updated training_type options in gaussian_process_complete_config.txt.<commit_after>#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 
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, differential_evolution, 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. 
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