def __add_generator(self, cases, config):
# select one case to take care of
index = random.randrange(len(cases))
feature_generator = cases[index][0]
bond = cases[index][1]
print 'selected feature generator', cases[index][0].get_name()
print 'path', cases[index][0].get_features()
print 'bond value', cases[index][1].get_value()
# Get labels by bond value/modality
#labels = self.__ontology.get_equivalent_labels(g.get_name());
labels = self.__ontology.get_labels_per_modality(bond.get_value())
print 'labels', labels
# Select label from the list of possible labels
index = random.randrange(len(labels))
label = labels[index]
print 'selected label', label
cost = 0
time = feature_generator.get_time()
level = self.__ontology.get_level(label)
modalities = self.__ontology.get_modalities(label)
bond_structure = self.__ontology.get_bond_structure(label)
# name,level,modality,type,cost,time
new_generator = MyGenerator(label, level, modalities, 'ontological',
cost, time)
# set feature location associated to the new candidate
new_generator.set_rlocation(feature_generator.get_rlocation())
# define the bond structure of the generator here
new_generator.set_bond_structure(bond_structure)
config.add_generator(new_generator)
# make changes to the connection of the new configuration given the newly added generator
self.__place_support_bonds(feature_generator, new_generator, config)
self.__place_ontological_bonds(new_generator, config)
return config
def propose(self,
config,
with_temporal_bonds=False,
selection_type='level',
feature_thresh=0.5):
ontological_generators = config.get_ontological_generators()
if not ontological_generators: return config
# First, select an ontological generator to be replaced in the configuration
g_r = None
level = 4
while level == 4:
g_r_index = self.sample('uniform', ontological_generators)
g_r = ontological_generators[g_r_index]
level = g_r.get_level()
g_r_inbonds = g_r.get_inbonds()
unconnected_feature_generators = []
# Second, find the feature generators that explain such ontological generator
cost = 0
time = g_r.get_time()
for inbond in g_r_inbonds:
print 'inbond connector: ', inbond.get_value(
), inbond.get_connector()
if inbond.get_connector() != None:
g = config.get_generator_by(inbond.get_connector(), 'id')
if g.get_type() == 'feature':
unconnected_feature_generators.append(g)
print 'unconnected features: ', repr(unconnected_feature_generators)
# print configuration information
# config.print_info()
# for when feature generator close their inbonds with outbonds of ontological generators
if not unconnected_feature_generators:
g_r_outbonds = g_r.get_outbonds()
for outbond in g_r_outbonds:
if outbond.get_connector() != None:
g = config.get_generator_by(outbond.get_connector())
if g.get_type() == 'feature':
unconnected_feature_generators.append(g)
print 'REPLACE ', g_r.get_name()
labels = None
if selection_type == 'level':
labels = self.__ontology.get_same_level_labels(g_r.get_name())
else: # if by modality
labels = self.__ontology.get_equivalent_labels(g_r.get_name())
# print 'candidates for replacement: ',labels
# select candidates uniformly random
k = self.__k if self.__k <= len(labels) else max(self.__k, len(labels))
candidates = random.sample(labels, k)
candidate_configs = []
candidate_generators = []
for i in range(len(candidates)):
# collect all data to create a generator
label_name = candidates[i]
level = self.__ontology.get_level(label_name)
modalities = self.__ontology.get_modalities(label_name)
bond_structure = self.__ontology.get_bond_structure(label_name)
# name,level,modality,type,cost,time
new_generator = MyGenerator(label_name, level, modalities,
'ontological', cost, time)
# set feature location associated to the new candidate
new_generator.set_rlocation(g_r.get_rlocation())
# define the bond structure of the generator here
new_generator.set_bond_structure(bond_structure)
# create a new configuration in which the new candidate replaces the one to be remove
#print 'removing generator ',g_r.get_id()
new_config = config.replace(g_r, new_generator)
# make changes to the connection of the new configuration given the newly added generator
for feature_generator in unconnected_feature_generators:
self.__place_support_bonds(feature_generator, new_generator,
new_config, feature_thresh)
self.__place_ontological_bonds(new_generator, new_config)
# record newly proposed configs with
candidate_configs.append(new_config)
candidate_generators.append(new_generator)
#self.__update_bonds_after_replacement(new_config,g_f,new_generator)
if with_temporal_bonds:
#print 'LOCAL PROPOSAL: TRYING TEMPORAL BONDS...'
time.sort()
# account for temporal bonds linking to the past
for i in range(len(candidate_generators)):
self.__place_past_ontological_bonds(candidate_generators[i],
candidate_configs[i],
time[0])
# account for temporal connections linking to the future
for i in range(len(candidate_generators)):
self.__place_future_ontological_bonds(candidate_generators[i],
candidate_configs[i],
time[-1])
# compute local energy contributions by new candidates configuration
candidate_local_energies = []
for i in range(len(candidate_generators)):
# get local energy that is being contributed by new
#candidate_configs[i].print_info()
local_energy = candidate_configs[i].get_local_energy(
candidate_generators[i], self.__bond_weights)
#print 'i#', i, ' local energy: ', local_energy
candidate_local_energies.append(local_energy)
# os.system("read -p 'pause'")
# select a candidate according to probability computed based on their locally contributed energy
candidate_index = self.__probabilistic_candidate_selection(
candidate_local_energies)
# update the current configuration to the new one
#self.__discard_configuration(config) # check if the upper code is making a copy of this configuration for other purposes
new_proposal = candidate_configs[candidate_index]
# discard temporary configurations, and others...
del candidate_configs[candidate_index]
for i in range(len(candidate_configs)):
self.__discard_configuration(candidate_configs[0])
del labels
return new_proposal
def __construct_new_configuration(self, config, candidates,
feature_generator):
# follow similar idea of function below...
candidate_configs = []
candidate_generators = []
feature_generator.get_time().sort()
current_time = feature_generator.get_time()[-1]
for i in range(len(candidates)):
# collect all data to create a generator
label_name = candidates[i]
level = self.__ontology.get_level(label_name)
modalities = self.__ontology.get_modalities(label_name)
bond_structure = self.__ontology.get_bond_structure(label_name)
# name,level,modality,type,cost,time
ontological_generator = MyGenerator(label_name, level, modalities,
'ontological', 0,
[current_time])
# set feature location associated to the new candidate
ontological_generator.set_rlocation(
feature_generator.get_rlocation())
# define the bond structure of the generator here
ontological_generator.set_bond_structure(bond_structure)
# create a new configuration in which the new candidate replaces the one to be remove
new_config = config.get_copy()
new_config.add_generator(ontological_generator)
candidate_generators.append(ontological_generator)
# make changes to the connection of the new configuration given the newly added generator
#self.__update_bonds_after_replacement(new_config, g_f, g_c)
self.__place_support_bonds(feature_generator,
ontological_generator, new_config)
self.__place_ontological_bonds(ontological_generator, new_config)
# record newly proposed configs with
candidate_configs.append(new_config)
# account for temporal bonds linking to the past
for i in range(len(candidate_generators)):
self.__place_past_ontological_bonds(candidate_generators[i],
candidate_configs[i],
current_time)
# account for temporal connections linking to the future
#for i in range(len(candidate_generators)):
# self.__place_future_ontological_bonds(candidate_generator[i],candidate_configs[i],current_time)
# compute local energy contributions by new candidates configuration
candidate_local_energies = []
for i in range(len(candidate_generators)):
# get local energy that is being contributed by new
local_energy = candidate_configs[i].get_local_energy(
candidate_generators[i], self.__bond_weights)
candidate_local_energies.append(local_energy)
# select a candidate according to probability computed based on their locally contributed energy
candidate_index = self.__probabilistic_candidate_selection(
candidate_local_energies)
# update the current configuration to the new one
#self.__discard_configuration(config) # check if the upper code is making a copy of this configuration for other purposes
new_config = candidate_configs[candidate_index]
# discard temporary configurations, and others...
del candidate_configs[candidate_index]
for i in range(len(candidate_configs)):
self.__discard_configuration(candidate_configs[0])
return new_config
def time_based_propose(self, config, current_time):
# probability of maintaining the same explanation
change_explanation = True
same_state_prob = 0.3
if random.uniform(0, 1) < same_state_prob:
change_explanation = False
feature_generators = config.get_feature_generators()
newly_added_feature_generators = []
past_feature_generators = []
past_feature_generator_connectors = {}
past_inbond_labels = []
for feature_generator in feature_generators:
if self.__intersect(feature_generator.get_time(), [current_time]):
newly_added_feature_generators.append(feature_generator)
elif self.__intersect(feature_generator.get_time(),
[current_time - self.__time_unit]):
past_feature_generators.append(feature_generator)
# used to know to which ontological generator to connect
# (case when aiming at keeping the same explanation for the new feat generators)
past_feature_generator_connectors[
feature_generator.get_id()] = []
for bond in feature_generator.get_outbonds():
# check to which one it is
if bond.get_connector() != None:
#past_feature_generator_connectors[feature_generator.get_id()].append(bond)
if bond.get_value(
) not in past_feature_generator_connectors:
past_feature_generator_connectors[
bond.get_value()] = []
past_feature_generator_connectors[
bond.get_value()].append((feature_generator, bond))
for bond in feature_generator.get_inbonds():
#coordinate = bond.get_connector_coordinate()
ontological_generator = config.get_generator_by(
bond.get_connector())
past_inbond_labels.append(ontological_generator.get_name())
#outbond = ontological_generator.get_outbond_by_coordinate(coordinate)
#past_labels.append()
#print len(newly_added_feature_generators),' newly added features: ',repr(newly_added_feature_generators)
#print len(past_feature_generators),' past feature generators: ',repr(past_feature_generators)
#print len(past_inbond_labels),' past inbond labels: ',repr(past_inbond_labels)
#any_feature = newly_added_feature_generators[random.randrange(len(newly_added_feature_generators))]
if not past_feature_generators and not past_inbond_labels and not len(
config.get_ontological_generators()):
for feature_generator in newly_added_feature_generators:
top_labels = feature_generator.get_top_labels()
index = random.randrange(len(top_labels))
label = top_labels[index]
#print 'feature: ' + g_f.get_name()
#print ' top labels: '
#print 'selected label: ',label.name
#print 'level: ',self.__ontology.get_level(label.name)
ontological_generator = MyGenerator(
label.name, self.__ontology.get_level(label.name),
self.__ontology.get_modalities(label.name), 'ontological',
label.cost, [current_time])
#print 'feature time: ', feature.time
ontological_generator.set_rlocation(
feature_generator.get_rlocation())
bond_structure = self.__ontology.get_bond_structure(label.name)
ontological_generator.set_bond_structure(bond_structure)
config.add_generator(ontological_generator)
self.__place_support_bonds(feature_generator,
ontological_generator, config)
#print 'ontological generators ',len(config.get_ontological_generators())
self.__place_ontological_bonds(ontological_generator, config)
return config
if not change_explanation:
new_ontological_generators = []
for feature_generator in newly_added_feature_generators:
#print 'feature id: ',feature_generator.get_id(),' feature name: ',feature_generator.get_name()
# for when feature generators's outbonds connect to ontological generators's inbonds
for bond in feature_generator.get_outbonds():
#print 'out of outbond connections...'
# if current generator can connect to existing concepts already explaining past features
if bond.get_value(
) in past_feature_generator_connectors: # escolher a mesma configuracao ou uma nova dada certa probabilidade
# find out what the concept is
past_bond = past_feature_generator_connectors[
bond.get_value()][1]
if bond.get_connector() != past_bond.get_connector():
if bond.get_connector() != None:
config.remove_generator(bond.get_connector())
ontological_generator = config.get_generator_by(
past_bond.get_connector())
ontological_generator.get_time().append(
current_time)
self.__place_support_bonds(feature_generator,
ontological_generator,
config)
# for when ontological generators's outbonds connect to feature generators's inbonds
context_labels = []
for label in past_inbond_labels:
bond_structure = self.__ontology.get_bond_structure(label)
for bond in bond_structure:
bond_value = bond[0]
if self.__intersect([bond_value],
feature_generator.get_modality()):
context_labels.append(label)
if context_labels:
#print 'out of inbond connections...'
label = context_labels[random.randrange(
len(context_labels))]
level = self.__ontology.get_level(label)
modalities = self.__ontology.get_modalities(label)
ontological_generator = MyGenerator(
label, level, modalities, 'ontological', 0,
[current_time])
ontological_generator.set_rlocation(
feature_generator.get_rlocation())
bond_structure = self.__ontology.get_bond_structure(label)
ontological_generator.set_bond_structure(bond_structure)
# remove old explanation if it exists before proposing a new one
#print 'before - number of inbonds: ',len(feature_generator.get_inbonds())
#config.print_info()
connectors = []
for inbond in feature_generator.get_inbonds():
connectors.append(inbond.get_connector())
#print 'remove connector ',inbond.get_connector()
for generator_id in connectors:
config.remove_generator(generator_id)
del connectors
#print 'after - number of inbonds: ',len(feature_generator.get_inbonds())
config.add_generator(ontological_generator)
new_ontological_generators.append(ontological_generator)
self.__place_support_bonds(feature_generator,
ontological_generator, config)
#print 'after connecting new generator: ',len(feature_generator.get_inbonds())
#config.print_info()
self.__place_ontological_bonds(ontological_generator,
config)
# move to explain the next feature generator
#past_inbond_labels.remove(label)
#break
#for generator in new_ontological_generators:
return config
# print 'NEW EXPLANATIONS BASED ON THE PAST'
# if new feature are to receive new explanations based on the past
new_ontological_generators = []
for feature_generator in newly_added_feature_generators:
# for when feature generators's outbonds connect to ontological generators's inbonds
for bond in feature_generator.get_outbonds():
# if current generator can connect to existing concepts already explaining past features
if bond.get_value() in past_feature_generator_connectors:
# find out what the concept is
past_bond = past_feature_generator_connectors[
bond.get_value()][1]
generator_id = past_bond.get_connector()
g_k = config.get_generator_by(generator_id)
# find out the exact interaction
# this can be later resolved by having a composite generator that has a name that combines one or more
# generators (that will be easier to implement and faster -- only thought of this now)
label = g_k.get_name(
) #self.__get_interaction_name(g_k,config)
# choose probabilistically whether to propose new explanations for the newly added features
# or to make them explain the current configuration state
# conditional_cooccurrences = self.__ontology.conditional_cooccurrence('interactions_'+bond.get_value(),label,True,order=1)
conditional_cooccurrences = self.__ontology.conditional_cooccurrence(
bond.get_value() + '_' + bond.get_value(),
label,
True,
order=1)
labels = conditional_cooccurrences.keys()
label_counts = conditional_cooccurrences.values()
label_index = self.__probabilistic_candidate_selection(
label_counts)
candidates = [labels[label_index]]
else:
# get all candidate labels
labels = self.__ontology.get_equivalent_labels(
bond.get_value())
# select candidates uniformly random
candidates = random.sample(labels,
max(self.__k, len(labels)))
del labels
self.__construct_new_configuration(config, candidates,
feature_generator)
# for when ontological generator's outbonds connect to feature generators's inbonds
#connect = False
context_labels = []
for label in past_inbond_labels:
bond_structure = self.__ontology.get_bond_structure(label)
for bond in bond_structure:
bond_value = bond[0]
if self.__intersect([bond_value],
feature_generator.get_modality()):
context_labels.append(label)
#connect = True
if context_labels:
label = context_labels[random.randrange(len(context_labels))]
level = self.__ontology.get_level(label)
#print 'level: ',repr(level)
modality = ''
if level == 3:
modality = 'actions'
elif level == 2:
modality = 'objects'
#print 'modality: ',modality
conditional_cooccurrences = self.__ontology.conditional_occurrence(
modality + '_' + modality, label, True, order=1)
labels = conditional_cooccurrences.keys()
#print 'labels: ',repr(labels)
label_counts = conditional_cooccurrences.values()
index = self.__probabilistic_candidate_selection(label_counts)
next_label = labels[index]
level = self.__ontology.get_level(next_label)
modalities = self.__ontology.get_modalities(next_label)
bond_structure = self.__ontology.get_bond_structure(next_label)
ontological_generator = MyGenerator(next_label, level,
modalities, 'ontological',
0, [current_time])
ontological_generator.set_rlocation(
feature_generator.get_rlocation())
ontological_generator.set_bond_structure(bond_structure)
config.add_generator(ontological_generator)
new_ontological_generators.append(ontological_generator)
# remove old explanation if it exists before proposing a new one
#print 'before - number of inbonds: ',len(feature_generator.get_inbonds())
#config.print_info()
connectors = []
for inbond in feature_generator.get_inbonds():
connectors.append(inbond.get_connector())
#print 'remove connector ',inbond.get_connector()
for generator_id in connectors:
config.remove_generator(generator_id)
del connectors
#print 'after - number of inbonds: ',len(feature_generator.get_inbonds())
self.__place_support_bonds(feature_generator,
ontological_generator, config)
#config.print_info()
#print 'after connecting new generator: ',len(feature_generator.get_inbonds())
self.__place_ontological_bonds(ontological_generator, config)
# move to explain the next feature generator
#past_inbond_labels.remove(label)
#break
# account for temporal bonds linking to the past
for generator in new_ontological_generators:
self.__place_past_ontological_bonds(generator, config,
current_time)
return config