def update_feedback_processing_object(self, X_obs):
if self.FP is None:
self.FP = FeedbackProcessing(self.D, self.m, self.original_bounds,
self.alpha_grid_distribution,
self.TGN_speed)
self.FP.initialize_data(X_obs)
else:
self.FP.update_data(X_obs)
def __init__(self, PPBO_settings):
"""
Basic settings
"""
self.D = PPBO_settings.D #Problem dimension
self.bounds = PPBO_settings.original_bounds #Boundaries of each variables as a sequence of tuplets
self.alpha_grid_distribution = PPBO_settings.alpha_grid_distribution
self.user_feedback_grid_size = PPBO_settings.user_feedback_grid_size #How many possible points in the user feedback grid?
self.n_irfs_periods = 20 #This must be same as in a model.mod file
self.FP = FeedbackProcessing(self.D, self.user_feedback_grid_size,
self.bounds, self.alpha_grid_distribution,
PPBO_settings.TGN_speed)
self.current_xi = None
self.current_x = None
self.current_xi_grid = None
self.dsge_results = None
self.user_feedback = None #variable to store user feedback
self.user_feedback_was_given = False
self.popup_slider_has_been_called = False
self.results = pd.DataFrame(
columns=(['alpha_xi_x' + str(i) for i in range(1, self.D + 1)] +
['xi' + str(i)
for i in range(1, self.D + 1)] + ['alpha_star']),
dtype=np.float64
) #The output of the session is a dataframe containing user feedback
self.engine = 'OCTAVE'
''' Prepare model files to temp '''
self.path_to_dsge = os.getcwd() + '/dsge' #CHECK THAT THIS IS CORRECT
if os.path.exists(self.path_to_dsge + '/temp'):
shutil.rmtree(self.path_to_dsge + '/temp')
os.mkdir(self.path_to_dsge + '/temp')
for i in range(self.user_feedback_grid_size):
shutil.copy2('dsge/US_FU19_rep.mod',
'dsge/temp/US_FU19_rep_' + str(i) + '.mod')
shutil.copy2('dsge/prior_function_US_FU19.m',
'dsge/temp/prior_function_US_FU19.m')
def __init__(self,PPBO_settings):
"""
Basic settings
"""
self.D = PPBO_settings.D #Problem dimension
self.bounds = PPBO_settings.original_bounds #Boundaries of each variables as a sequence of tuplets
self.alpha_grid_distribution = PPBO_settings.alpha_grid_distribution
self.user_feedback_grid_size = 100 #How many possible points in the user feedback grid?
self.FP = FeedbackProcessing(self.D, self.user_feedback_grid_size,self.bounds,self.alpha_grid_distribution,PPBO_settings.TGN_speed)
self.current_xi = None
self.current_x = None
self.current_xi_grid = None
self.user_feedback = None #variable to store user feedback
self.user_feedback_was_given = False
self.popup_configuration_movie_has_been_called = False
self.results = pd.DataFrame(columns=(['alpha_xi_x' + str(i) for i in range(1,6+1)]
+ ['xi' + str(i) for i in range(1,6+1)]
+ ['alpha_star']),dtype=np.float64) #The output of the session is a dataframe containing user feedback