def run_trial(network, start, tally, correct):
# what kind of trial is will determine what kind of objects to make for child and experimenter (experimenter only needs two).
# select reps for child objects.
# select reps for experimenter objects.
# try to map child and experimenter objects.
# make some objects for child, two objects are the same in a dimension.
obj_analog, network.memory = add_objects(network.memory,
['obj1', 'obj2', 'obj3'])
# select a dimension.
dimension = random.sample(['x_ext', 'y_ext', 'total_ext'], 1)
dimension.append(random.sample(['same'], 1)[0])
# get a prop.
prop = sample_LTM_rep(network.memory, dimension, start, threshold=0.95)
# select the objects.
objs = select_objs(obj_analog)
# apply prop to objects.
obj_analog, network.memory = apply_preds(obj_analog, objs, prop,
dimension[0], network.memory)
# sample a prop from LTM to enter recipient; call it sampled_analog.
sampled_rep = sample_LTM_rep(network.memory,
dimension,
start,
threshold=0.95)
sampled_analog = sampled_rep[0].myanalog
# put obj_analog in the driver and sampled_analog in the recipient.
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(obj_analog), 'analog',
'driver')
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(sampled_analog), 'analog',
'recipient')
# clear and update the contents of driver and recipient.
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# do mapping and print results.
network.do_map()
basicRunDORA.DORA_GUI.term_map_display(network.memory) # show mappings.
# clear mapping result.
network.memory = basicRunDORA.reset_inferences(network.memory)
network.memory = basicRunDORA.reset_maker_made_units(network.memory)
network.memory = basicRunDORA.reset_mappings(network.memory)
network.memory = basicRunDORA.update_Names_nil(network.memory)
network.memory = basicRunDORA.indexMemory(network.memory)
network.memory = basicRunDORA.initialize_memorySet(network.memory)
#returns.
return tally, correct
def clear_mem():
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
# to load a sym file.
symstring = load_sym('rule_transfer_symFile.py')
exec symstring
# initialise memory.
memory = buildNetwork.initializeMemorySet()
# interpret the sym file.
mysym = buildNetwork.interpretSymfile(symProps)
# build the network object with memory.
memory = basicRunDORA.dataTypes.memorySet()
memory = buildNetwork.buildTheNetwork(mysym[0], memory)
#memory = buildNetwork.buildTheNetwork(symProps, memory)
network = basicRunDORA.runDORA(memory, parameters)
# make sure the driver and recipient are all set up.
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# other useful ops.
basicRunDORA.swap_driverRecipient(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# DORA operations run operatons.
network.do_retrieval()
network.do_map()
network.do_entropy_ops_between()
network.do_entropy_ops_within(pred_only=True)
network.do_compression()
network.do_unpacking()
pred_ok = basicRunDORA.predication_requirements(network.memory)
def run_trial(network, start, tally, correct, trial_type):
# create reps; the kind of trial will determine what kind of objects to make (types are 'whole', 'feature', 'relation').
obj_analogs, network.memory = add_objects(network.memory, trial_type)
# child_analog is the last analog of obj_analogs.
child_analog = obj_analogs[2]
# if the trial type is whole, put child analog in driver and one of experimenter analogs in recipient, and try to map.
if trial_type == 'whole':
# place child objects in driver, and one experimenter object in recipient, and map.
exp_analog = random.choice(obj_analogs[:2])
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(child_analog),
'analog', 'driver')
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(exp_analog), 'analog',
'recipient')
# clear and update the contents of driver and recipient.
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# do mapping and print results.
network.do_map()
basicRunDORA.DORA_GUI.term_map_display(
network.memory) # show mappings.
# clear mapping result.
network.memory = basicRunDORA.reset_inferences(network.memory)
network.memory = basicRunDORA.reset_maker_made_units(network.memory)
network.memory = basicRunDORA.reset_mappings(network.memory)
network.memory = basicRunDORA.update_Names_nil(network.memory)
network.memory = basicRunDORA.indexMemory(network.memory)
network.memory = basicRunDORA.initialize_memorySet(network.memory)
else: # for 'feature' and 'relation' trials.
# find the dimension experimenter objects match on by comparing experimenter sets.
dimension = None
Exp1 = obj_analogs[0]
Exp2 = obj_analogs[1]
for link in Exp1.myPOs[0].mySemantics:
found_flag = False
if link.mySemantic.ont_status == 'value':
dim = link.mySemantic.dimension
val = link.mySemantic.amount
for link2 in Exp1.myPOs[1].mySemantics:
if link2.mySemantic.ont_status == 'value' and link2.mySemantic.dimension == dim and link2.mySemantic.amount == val:
found_flag = True
dimension = dim
break
if found_flag:
break
# sample experimenter objects to use for mapping.
exp_analog = random.choice(obj_analogs[:2])
# sample two props from LTM that match dimension.
propExp = sample_LTM_rep(network.memory,
dimension,
start,
threshold=0.95)
propChild = sample_LTM_rep(network.memory,
dimension,
start,
threshold=0.95)
# apply propExp to experimenter objects.
objsExp = select_objs(exp_analog)
exp_analog, network.memory = apply_preds(exp_analog, objsExp, propExp,
dimension, network.memory)
# apply propChild to child objects.
# first, find any child objects that match on the dimension and put them in objsChild.
val0 = [
x.mySemantic.amount for x in child_analog.myPOs[0].mySemantics
if x.mySemantic.dimension == dimension
and x.mySemantic.ont_status == 'value'
]
val1 = [
x.mySemantic.amount for x in child_analog.myPOs[1].mySemantics
if x.mySemantic.dimension == dimension
and x.mySemantic.ont_status == 'value'
]
val2 = [
x.mySemantic.amount for x in child_analog.myPOs[2].mySemantics
if x.mySemantic.dimension == dimension
and x.mySemantic.ont_status == 'value'
]
if val0 == val1:
objsChild = [child_analog.myPOs[0], child_analog.myPOs[1]]
elif val1 == val2:
objsChild = [child_analog.myPOs[1], child_analog.myPOs[2]]
elif val0 == val2:
objsChild = [child_analog.myPOs[0], child_analog.myPOs[2]]
# apply propChild to child objects.
child_analog, network.memory = apply_preds(child_analog, objsChild,
propChild, dimension,
network.memory)
# child objects in driver, experimenter in recipient, and map.
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(child_analog),
'analog', 'driver')
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(exp_analog), 'analog',
'recipient')
# clear and update the contents of driver and recipient.
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# do mapping and print results.
network.do_map()
basicRunDORA.DORA_GUI.term_map_display(
network.memory) # show mappings.
# clear mapping result.
network.memory = basicRunDORA.reset_inferences(network.memory)
network.memory = basicRunDORA.reset_maker_made_units(network.memory)
network.memory = basicRunDORA.reset_mappings(network.memory)
network.memory = basicRunDORA.update_Names_nil(network.memory)
network.memory = basicRunDORA.indexMemory(network.memory)
network.memory = basicRunDORA.initialize_memorySet(network.memory)
#returns.
return tally, correct
def run_NBtrial(network, start, tally, correct):
# make some objects.
obj_analog, network.memory = add_objects(network.memory,
['tree1', 'tree2'])
# select a dimension.
dimension = random.sample(['x_ext', 'y_ext', 'total_ext'], 1)
dimension.append(random.sample(['more', 'less'], 1)[0])
# get a prop.
prop = sample_LTM_rep(network.memory, dimension, start, threshold=0.95)
correct = 0
if len(prop) > 0:
if prop[0].my_type == 'P':
tally['P'] += 1
for myRB in prop[0].myRBs:
my_semantics = [
link.mySemantic.name for link in myRB.myPred[0].mySemantics
]
if dimension[0] in my_semantics and dimension[
1] in my_semantics:
correct = 1
print('P', correct)
if correct == 0:
pdb.set_trace()
elif prop[0].my_type == 'RB':
tally['RB'] += 1
my_semantics = [
link.mySemantic.name for link in prop[0].myPred[0].mySemantics
]
if dimension[0] in my_semantics and dimension[1] in my_semantics:
correct = 1
print('RB', correct)
else:
tally['PO'] += 1
print('guess')
if correct == 0:
rand_num = random.random()
if rand_num > .5:
correct = 1
else:
tally['PO'] += 1
rand_num = random.random()
if rand_num > .5:
correct = 1
else:
correct = 0
# select the objects.
objs = select_objs(obj_analog)
# apply prop to objects.
obj_analog, network.memory = apply_preds(obj_analog, objs, prop,
dimension[0], network.memory)
# sample a prop from LTM to enter recipient; call it sampled_analog.
sampled_rep = sample_LTM_rep(network.memory,
dimension,
start,
threshold=0.95)
sampled_analog = sampled_rep[0].myanalog
# put obj_analog in the driver and sampled_analog in the recipient.
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(obj_analog), 'analog',
'driver')
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(sampled_analog), 'analog',
'recipient')
# clear and update the contents of driver and recipient.
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# do mapping and print results.
network.do_map()
basicRunDORA.DORA_GUI.term_map_display(network.memory) # show mappings.
# clear mapping result.
network.memory = basicRunDORA.reset_inferences(network.memory)
network.memory = basicRunDORA.reset_maker_made_units(network.memory)
network.memory = basicRunDORA.reset_mappings(network.memory)
network.memory = basicRunDORA.update_Names_nil(network.memory)
network.memory = basicRunDORA.indexMemory(network.memory)
network.memory = basicRunDORA.initialize_memorySet(network.memory)
#returns.
return tally, correct
def run_dora(parameters):
network, memory, mapping_adj = init_network(parameters)
precisions, recalls, fscores = [], [], []
for cycle in range(parameters['run_cyles']):
print('\n\n************************* Cycle: ' + str(cycle + 1))
# pdb.set_trace()
# clear driver and recipient
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
# choose random analog from memory
analog = random.choice(
network.memory.analogs) # currently only one analog
# choose random propositions from memory
# put the analog in the driver
analog_num = network.memory.analogs.index(analog)
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, analog_num, 'analog', 'driver')
# retrieve analogs similar to one in the driver from LTM
network.memory = basicRunDORA.findDriverRecipient(network.memory)
network.do_retrieval()
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# print('In driver : ' + network.memory.driver.Ps[0].name[1])
# if len(network.memory.recipient.Ps) > 0:
# print('In recipient : ' + network.memory.recipient.Ps[0].name[1])
# if network.memory.recipient.Ps[0].name[1] == '3':
# network.do_map()
# print('\n\nMappings:\n')
# basicRunDORA.DORA_GUI.term_map_display(network.memory, only_Ps=True, only_max=False)
if len(network.memory.recipient.Ps) > 0:
print
'retrieval successful'
# map the analogs in driver and recipient
network.do_map()
# display ALL mappings
# display mappings
print('\n\nMappings:\n')
basicRunDORA.DORA_GUI.term_map_display(network.memory,
only_Ps=False,
only_max=False)
# store all mappings :
for myP in network.memory.driver.Ps:
for mapping in myP.mappingConnections:
map_weight = mapping.weight
map_unit = mapping.recipientToken
mapping_adj[network.memory.Ps.index(myP),
network.memory.Ps.index(map_unit)] += map_weight
# append rmse to list to keep track of RMSE evolution
temp_mapping_adj = mapping_adj
for ind in range(temp_mapping_adj.shape[0]):
if sum(temp_mapping_adj[ind, :]) > 0:
temp_mapping_adj[ind, :] = temp_mapping_adj[ind, :] / sum(
temp_mapping_adj[ind, :])
prec, rec, fsc = get_metrics(temp_mapping_adj)
precisions.append(prec)
recalls.append(rec)
fscores.append(fsc)
# normalise mappings
for ind in range(mapping_adj.shape[0]):
if sum(mapping_adj[ind, :]) > 0:
mapping_adj[ind, :] = mapping_adj[ind, :] / sum(
mapping_adj[ind, :])
return precisions, recalls, fscores, mapping_adj
def run_RGtrial(network, start, tally, correct):
# make some objects for child.
obj_analog1, network.memory = add_objects(network.memory,
['obj1', 'obj2', 'obj3'])
# make some objects for experimenter.
obj_analog2, network.memory = add_objects(network.memory,
['obj1', 'obj2', 'obj3'])
# select a dimension.
dimension = random.sample(['x_ext', 'y_ext', 'total_ext'], 1)
dimension.append(random.sample(['more', 'less'], 1)[0])
# get two props.
prop1 = sample_LTM_rep(network.memory, dimension, start, threshold=0.95)
prop2 = sample_LTM_rep(network.memory, dimension, start, threshold=0.95)
prop3 = sample_LTM_rep(network.memory, dimension, start, threshold=0.95)
prop4 = sample_LTM_rep(network.memory, dimension, start, threshold=0.95)
# apply prop to objects.
obj_analog1, network.memory = apply_preds(
obj_analog1, [obj_analog1.myPOs[0], obj_analog1.myPOs[1]], prop1,
dimension[0], network.memory)
obj_analog1, network.memory = apply_preds(
obj_analog1, [obj_analog1.myPOs[1], obj_analog1.myPOs[2]], prop2,
dimension[0], network.memory)
obj_analog2, network.memory = apply_preds(
obj_analog2, [obj_analog2.myPOs[0], obj_analog2.myPOs[1]], prop3,
dimension[0], network.memory)
obj_analog2, network.memory = apply_preds(
obj_analog2, [obj_analog2.myPOs[1], obj_analog2.myPOs[2]], prop4,
dimension[0], network.memory)
# put obj_analog1 in the driver and obj_analog2 in the recipient.
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(obj_analog1), 'analog',
'driver')
network.memory = basicRunDORA.add_tokens_to_set(
network.memory, network.memory.analogs.index(obj_analog2), 'analog',
'recipient')
# clear and update the contents of driver and recipient.
network.memory = basicRunDORA.clearDriverSet(network.memory)
network.memory = basicRunDORA.clearRecipientSet(network.memory)
network.memory = basicRunDORA.findDriverRecipient(network.memory)
# do mapping and print results.
network.do_map()
basicRunDORA.DORA_GUI.term_map_display(network.memory) # show mappings.
# order the recipient and driver objects.
driver_objs = []
for myPO in network.memory.driver.POs:
if myPO.predOrObj == 0:
driver_objs.append(myPO)
recipient_objs = []
for myPO in network.memory.recipient.POs:
if myPO.predOrObj == 0:
recipient_objs.append(myPO)
# select an item from recipient as the sticker.
sticker_index = random.sample([0, 1, 2], 1)
sticker_index = sticker_index[0]
# find recipient sticker max map unit.
max_map = 0.0
max_map_unit = None
for mapping in recipient_objs[sticker_index].mappingConnections:
if mapping.weight > max_map:
max_map = mapping.weight
max_map_unit = mapping.driverToken
# check if maps to appropriate driver object and update correct variable.
if max_map_unit is driver_objs[sticker_index]:
correct = 1
elif max_map_unit == None:
rand_num = random.random()
if rand_num < .34:
correct = 1
else:
correct = 0
else:
correct = 0
# clear mapping result.
network.memory = basicRunDORA.reset_inferences(network.memory)
network.memory = basicRunDORA.reset_maker_made_units(network.memory)
network.memory = basicRunDORA.reset_mappings(network.memory)
network.memory = basicRunDORA.update_Names_nil(network.memory)
network.memory = basicRunDORA.indexMemory(network.memory)
network.memory = basicRunDORA.initialize_memorySet(network.memory)
#returns.
return correct