def make_affordance(things, effects, keys, thing_index, effect_indices):
#normalize each thing vector for use as affordance component
v = things.vectors[:,thing_index].copy()
for i in range(v.shape[1]):
v[:,i] = hrr.normalize(v[:,i])
# thing_sum = np.sum(things.vectors[:,thing_index], axis=1)
thing_sum = np.sum(v, axis=1)
effect_sum = np.sum(effects.vectors[:,effect_indices], axis=1)
vector = hrr.bind(keys.get('THING'), thing_sum, do_normalize=False) + hrr.bind(keys.get('EFFECT'), effect_sum, do_normalize=False)
return vector
def encode_bayesian_property_addition(chosen_properties, subj, action, obj,
context):
generating_fillers = {}
generating_fillers['subject'] = subj
generating_fillers['action'] = action
generating_fillers['object'] = obj
subject_vector = subj['identity']
action_vector = action['identity']
object_vector = obj['identity']
subject_props = {}
object_props = {}
chosen_props = {}
# arrays of property strings
chosen_props['subject'] = []
chosen_props['object'] = []
# probabilistically choose properties of subject, limited to properties that the subject actually possesses
for prop in subj['properties'].keys():
subject_props[prop] = context.distribution_params[action_from_vector(
context, action)]['subject'][prop][0] / (
context.distribution_params[action_from_vector(
context, action)]['subject'][prop][0] +
context.distribution_params[action_from_vector(
context, action)]['subject'][prop][1])
total_subject = sum(subject_props.values())
for prop in subject_props.keys():
if random.random() < min(subject_props[prop], subject_props[prop] /
(total_subject / 1.)):
subject_vector += context.properties[prop][int(
subj['properties'][prop])]
chosen_props['subject'].append(prop)
# probabilistically choose properties of object, limited to properties that the object actually possesses
for prop in obj['properties'].keys():
object_props[prop] = context.distribution_params[action_from_vector(
context, action)]['object'][prop][0] / (
context.distribution_params[action_from_vector(
context, action)]['object'][prop][0] +
context.distribution_params[action_from_vector(
context, action)]['object'][prop][1])
total_object = sum(object_props.values())
for prop in object_props.keys():
if random.random() < min(object_props[prop], object_props[prop] /
(total_object / 1.)):
object_vector += context.properties[prop][int(
obj['properties'][prop])]
chosen_props['object'].append(prop)
chosen_properties.append(chosen_props)
vector = normalize(
encode(context.roles['subject'], subject_vector) +
encode(context.roles['action'], action_vector) +
encode(context.roles['object'], object_vector))
return vector, generating_fillers
def make_affordance(things, effects, keys, thing_index, effect_indices):
#normalize each thing vector for use as affordance component
v = things.vectors[:, thing_index].copy()
for i in range(v.shape[1]):
v[:, i] = hrr.normalize(v[:, i])
# thing_sum = np.sum(things.vectors[:,thing_index], axis=1)
thing_sum = np.sum(v, axis=1)
effect_sum = np.sum(effects.vectors[:, effect_indices], axis=1)
vector = hrr.bind(keys.get('THING'), thing_sum,
do_normalize=False) + hrr.bind(
keys.get('EFFECT'), effect_sum, do_normalize=False)
return vector
def encode_baseline(subj, action, obj, context):
generating_fillers = {}
generating_fillers['subject'] = subj
generating_fillers['action'] = action
generating_fillers['object'] = obj
subject_vector = subj['identity']
action_vector = action['identity']
object_vector = obj['identity']
vector = normalize(
encode(context.roles['subject'], subject_vector) +
encode(context.roles['action'], action_vector) +
encode(context.roles['object'], object_vector))
return vector, generating_fillers
def encode_all_property_addition(subj, action, obj, context):
generating_fillers = {}
generating_fillers['subject'] = subj
generating_fillers['action'] = action
generating_fillers['object'] = obj
subject_vector = subj['identity']
action_vector = action['identity']
object_vector = obj['identity']
for p in subj['properties'].keys():
subject_vector += context.properties[p][int(subj['properties'][p])]
for p in action['properties'].keys():
action_vector += context.properties[p][int(action['properties'][p])]
for p in obj['properties'].keys():
object_vector += context.properties[p][int(obj['properties'][p])]
vector = normalize(
encode(context.roles['subject'], subject_vector) +
encode(context.roles['action'], action_vector) +
encode(context.roles['object'], object_vector))
return vector, generating_fillers
def encode_selective_property_addition(subj,
action,
obj,
context,
subject_property=None,
action_property=None,
object_property=None):
generating_fillers = {}
generating_fillers['subject'] = subj
generating_fillers['action'] = action
generating_fillers['object'] = obj
subject_vector = subj['identity']
action_vector = action['identity']
object_vector = obj['identity']
if subject_property != None and subject_property in subj[
'properties'].keys():
subject_vector += context.properties[subject_property][int(
subj['properties'][subject_property])]
generating_fillers['subject_property'] = subject_property
else:
generating_fillers['subject_property'] = None
if action_property != None and action_property in action[
'properties'].keys():
action_vector += context.properties[action_property][int(
action['properties'][action_property])]
generating_fillers['action_property'] = action_property
else:
generating_fillers['action_property'] = None
if object_property != None and object_property in obj['properties'].keys():
object_vector += context.properties[object_property][int(
obj['properties'][object_property])]
generating_fillers['object_property'] = object_property
else:
generating_fillers['object_property'] = None
vector = normalize(
encode(context.roles['subject'], subject_vector) +
encode(context.roles['action'], action_vector) +
encode(context.roles['object'], object_vector))
return vector, generating_fillers
def fillers_to_baseline(fillers, context):
vector = normalize(
encode(context.roles['subject'], fillers['subject']['identity']) +
encode(context.roles['action'], fillers['action']['identity']) +
encode(context.roles['object'], fillers['object']['identity']))
return vector