def process_result( pomdp, observation, machine_action, Q, param_dict, \
use_confidence_scores=False, \
high_confidence_histogram = None, \
low_confidence_histogram = None, \
confidence_score = 1 ):
action_code_list = \
tbh_pomdp_action_code_manager.get_pomdp_action_code_number_list()
observation_list = \
tbh_action_code_manager.get_action_code_number_list()
if use_confidence_scores:
# determine the observation number in question
observation_number = \
tbh_action_code_manager.get_number_action_code( \
observation_list, observation )
# determine the confidence value
confidence_in_hypothesis = confidence_score
# We now have the machine_action_number,
# the observation_number, and the current belief
# We can do a belief update!
belief_for_policy = \
tbh_pomdp_util.belief_update( \
pomdp, pomdp.belief, \
machine_action_number, \
observation_number, \
confidence_in_hypothesis, \
use_confidence_scores=True, \
high_confidence_model=high_confidence_histogram, \
low_confidence_model=low_confidence_histogram )
pomdp_dict = pomdp_choose_action( pomdp, Q, \
belief_for_policy, action_code_list )
pomdp_dict['confidence_in_hypothesis'] = confidence_in_hypothesis
else:
observation_number = tbh_action_code_manager.get_number_action_code( \
observation_list, observation )
# We now have the machine_action_number, the observation_number
# and the current belief
# We can do a belief update!
belief_for_policy = tbh_pomdp_util.belief_update( pomdp, pomdp.belief, \
machine_action_number, \
observation_number )
pomdp_dict = pomdp_choose_action( pomdp, Q, \
belief_for_policy, action_code_list )
return pomdp_dict
def process_result( pomdp, observation, machine_action, Q, param_dict, use_confidence_scores=False, skype_contacts_dict=None ):
'''
# Take the action_code and translate it into an observation
( observation_code, machine_action_code ) = \
translate_into_pomdp_codes( observation, machine_action )
# Run the belief update
'''
# Determine the desired action from the policy (perhaps solve the QMDP \
# for this particular belief)
# Return data structure
# Sandbox: The POMDP does not do anything
#( new_belief, action_code ) = sandbox_result( belief, observation )
# More sophisticated result
action_code_list = tbh_pomdp_action_code_manager.get_pomdp_action_code_number_list()
observation_list = tbh_action_code_manager.get_action_code_number_list()
if machine_action is None:
machine_action = [ 'null', ['yes_record']]
machine_action_number = tbh_pomdp_action_code_manager.get_number_action_code( \
action_code_list, machine_action )
'''
# Confidence scores using n-best lists
if use_confidence_scores:
# simplified confidence scoring method
# 0.5 for top result
# 0.2 for second result
# 0.1 for third result
observation_number_list = []
probability_list = [ 0.55, 0.25, 0.1 ]
for i in range(0,3):
observation_number = tbh_action_code_manager.get_number_action_code( \
observation_list, observation[i] )
observation_number_list.append( { observation_number : \
probability_list[i] } )
# We now have the machine_action_numbeor, the observation_number, and the current belief
# We can do a belief update!
belief_for_policy = tbh_pomdp_util.belief_update_with_confidence_scores( pomdp, pomdp.belief, \
machine_action_number, \
observation_number_list )
#print pomdp.belief.shape
#print belief_for_policy.shape
pomdp_dict = pomdp_choose_action( pomdp, Q, \
belief_for_policy, action_code_list )
'''
if use_confidence_scores:
# determine the observation number in question
observation_number = tbh_action_code_manager.get_number_action_code( \
observation_list, observation )
# determine the confidence value
confidence_in_hypothesis = determine_confidence_in_hypothesis( \
param_dict['adaboost_score'], LOGISTIC_PARAMETERS )
# using the confidence in the hypothesis
# We now have the machine_action_number,
# the observation_number, and the current belief
# We can do a belief update!
belief_for_policy = \
tbh_pomdp_util.belief_update( \
pomdp, pomdp.belief, \
machine_action_number, \
observation_number, \
confidence_in_hypothesis )
#print pomdp.belief.shape
#print belief_for_policy.shape
pomdp_dict = pomdp_choose_action( pomdp, Q, \
belief_for_policy, action_code_list )
pomdp_dict['confidence_in_hypothesis'] = confidence_in_hypothesis
else:
observation_number = tbh_action_code_manager.get_number_action_code( \
observation_list, observation )
# We now have the machine_action_number, the observation_number
# and the current belief
# We can do a belief update!
belief_for_policy = tbh_pomdp_util.belief_update( pomdp, pomdp.belief, \
machine_action_number, \
observation_number )
pomdp_dict = pomdp_choose_action( pomdp, Q, \
belief_for_policy, action_code_list )
return pomdp_dict
