def create(self, otherchunk, actrvariables=None):
"""
Create (aka set) a chunk for manual control. The chunk is returned (and could be used by device or external environment).
"""
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {
x[0]: utilities.check_bound_vars(actrvariables, x[1])
for x in otherchunk.removeunused()
} #creates dict of attr-val pairs according to otherchunk
except ACTRError as arg:
raise ACTRError(
"Setting the chunk '%s' in the manual buffer is impossible; %s"
% (otherchunk, arg))
new_chunk = chunks.Chunk(self._MANUAL,
**mod_attr_val) #creates new chunk
if new_chunk.cmd.values not in utilities.CMDMANUAL:
raise ACTRError(
"Motor module received an invalid command: '%s'. The valid commands are: '%s'"
% (new_chunk.cmd.values, utilities.CMDMANUAL))
if new_chunk.cmd.values == utilities.CMDPRESSKEY:
pressed_key = new_chunk.key.values.upper(
) #change key into upper case
mod_attr_val["key"] = pressed_key
new_chunk = chunks.Chunk(self._MANUAL,
**mod_attr_val) #creates new chunk
if pressed_key not in self.LEFT_HAND and new_chunk.key.values not in self.RIGHT_HAND:
raise ACTRError("Motor module received an invalid key: %s" %
pressed_key)
return new_chunk
def create(self, otherchunk, harvest=None, actrvariables=None):
"""
Create (aka set) a chunk in goal buffer.
"""
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()} #creates dict of attr-val pairs according to otherchunk
except utilities.ACTRError as arg:
raise utilities.ACTRError("Setting the buffer using the chunk '%s' is impossible; %s" % (otherchunk, arg))
new_chunk = chunks.Chunk(otherchunk.typename, **mod_attr_val) #creates new chunk
self.add(new_chunk, 0, harvest) #put chunk using add
def modify(self, otherchunk, actrvariables=None):
"""
Modify the chunk in Buffer according to the info in otherchunk.
"""
if actrvariables == None:
actrvariables = {}
elem = self._data.pop()
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()} #creates dict of attr-val pairs according to otherchunk
except utilities.ACTRError as arg:
raise utilities.ACTRError("The modification by the chunk '%s is impossible; %s" % (otherchunk, arg))
elem_attr_val = {x[0]: x[1] for x in elem}
elem_attr_val.update(mod_attr_val) #updates original chunk with attr-val from otherchunk
mod_chunk = chunks.Chunk(otherchunk.typename, **elem_attr_val) #creates new chunk
self._data.add(mod_chunk) #put chunk directly into buffer
def modify(self, otherchunk, actrvariables=None):
"""
Modify the chunk in Buffer according to the info in otherchunk.
"""
if actrvariables == None:
actrvariables = {}
elem = self._data.pop()
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()} #creates dict of attr-val pairs according to otherchunk
except utilities.ACTRError as arg:
raise utilities.ACTRError("The chunk '%s' is not defined correctly; %s" % (otherchunk, arg))
elem_attr_val = {x[0]: x[1] for x in elem}
elem_attr_val.update(mod_attr_val) #updates original chunk with attr-val from otherchunk
mod_chunk = chunks.Chunk(otherchunk.typename, **elem_attr_val) #creates new chunk
self._data.add(mod_chunk) #put chunk directly into buffer
def retrieveorset(self, name, updated, otherchunk, temp_actrvariables, time):
"""
Find out whether a buffer should be set (for buffers that are not attached to any dm, i.e., Goal or Motor or Vision) or should trigger retrieval.
"""
updated.state = updated._BUSY
if isinstance(updated, goals.Goal):
yield from self.clear(name, updated, otherchunk, temp_actrvariables, time, freeing=False)
extra_time = utilities.calculate_setting_time(updated)
time += extra_time
yield Event(roundtime(time), name, self._UNKNOWN)
if self.model_parameters['production_compilation']:
RHSdict = otherchunk._asdict()
RHSdict = {item[0]: item[1] for item in RHSdict.items()}
self.current_slotvals[name] = RHSdict
updated.create(otherchunk, list(self.dm.values())[0], temp_actrvariables)
created_elem = list(updated)[0]
updated.state = updated._FREE
yield Event(roundtime(time), name, "CREATED A CHUNK: %s" % str(created_elem))
elif isinstance(updated, vision.VisualLocation):
extra_time = utilities.calculate_setting_time(updated)
time += extra_time #0 ms to create chunk in location (pop-up effect)
yield Event(roundtime(time), name, self._UNKNOWN)
chunk, stim = updated.find(otherchunk, actrvariables=temp_actrvariables, extra_tests=self.extra_tests.get(name, {})) #extra_time currently ignored
if chunk:
yield from self.clear(name, updated, None, temp_actrvariables, time, freeing=False)
updated.add(chunk, stim, time)
updated.state = updated._FREE
else:
updated.state = updated._ERROR
yield Event(roundtime(time), name, "ENCODED LOCATION:'%s'" % str(chunk))
elif isinstance(updated, vision.Visual):
mod_attr_val = {x[0]: utilities.check_bound_vars(temp_actrvariables, x[1]) for x in otherchunk.removeunused()}
if (not mod_attr_val['cmd'].values) or mod_attr_val['cmd'].values not in utilities.CMDVISUAL:
raise ACTRError("Visual module received no command or an invalid command: '%s'. The valid commands are: '%s'" % (mod_attr_val['cmd'].values, utilities.CMDVISUAL))
if mod_attr_val['cmd'].values == utilities.CMDMOVEATTENTION:
ret = yield from self.visualshift(name, updated, otherchunk, temp_actrvariables, time)
return ret #visual action returns value, namely, its continuation method
elif mod_attr_val['cmd'].values == utilities.CMDCLEAR:
updated.stop_automatic_buffering()
updated.state = updated._FREE
yield Event(roundtime(time), name, "VISUAL STOPPED FROM AUTOMATIC BUFFERING AT ITS CURRENT FOCUS")
elif isinstance(updated, motor.Motor):
ret = yield from self.motorset(name, updated, otherchunk, temp_actrvariables, time)
return ret #motor action returns value, namely, its continuation method
else:
yield from self.retrieve(name, updated, otherchunk, temp_actrvariables, time)
def create(self, otherchunk, harvest=None, actrvariables=None):
"""
Create (aka set) a chunk in goal buffer.
"""
try:
mod_attr_val = {
x[0]: utilities.check_bound_vars(actrvariables, x[1])
for x in otherchunk.removeunused()
} #creates dict of attr-val pairs according to otherchunk
except utilities.ACTRError as arg:
raise utilities.ACTRError(
"Setting the buffer using the chunk '%s' is impossible; %s" %
(otherchunk, arg))
new_chunk = chunks.Chunk(otherchunk.typename,
**mod_attr_val) #creates new chunk
self.add(new_chunk, 0, harvest) #put chunk using add
def shift(self, otherchunk, harvest=None, actrvariables=None):
"""
Return a chunk, time needed to attend and shift eye focus to the chunk, and the landing site of eye mvt.
"""
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()} #creates dict of attr-val pairs according to otherchunk
except ACTRError as arg:
raise ACTRError("The chunk '%s' is not defined correctly; %s" % (otherchunk, arg))
vis_delay = None
for each in self.environment.stimulus:
try:
if self.environment.stimulus[each]['position'] == (float(mod_attr_val['screen_pos'].screen_x), float(mod_attr_val['screen_pos'].screen_y)):
mod_attr_val['value'] = self.environment.stimulus[each]['text']
vis_delay = self.environment.stimulus[each].get('vis_delay')
except (AttributeError, KeyError):
raise ACTRError("The chunk in the visual buffer is not defined correctly. It is not possible to move attention.")
new_chunk = chunks.Chunk(self._VISUAL, **mod_attr_val) #creates new chunk
if new_chunk.cmd not in utilities.CMDVISUAL:
raise ACTRError("Visual module received an invalid command: '%s'. The valid commands are: '%s'" % (new_chunk.cmd, utilities.CMDVISUAL))
if new_chunk.cmd == utilities.CMDMOVEATTENTION and self.model_parameters['emma']:
angle_distance = utilities.calculate_visual_angle(self.current_focus, [float(new_chunk.screen_pos.screen_x), float(new_chunk.screen_pos.screen_y)], self.environment.size, self.environment.simulated_screen_size, self.environment.viewing_distance)
encoding_time = utilities.calculate_delay_visual_attention(angle_distance=angle_distance, K=self.model_parameters["eye_mvt_scaling_parameter"], k=self.model_parameters['eye_mvt_angle_parameter'], emma_noise=self.model_parameters['emma_noise'], vis_delay=vis_delay)
preparation_time = utilities.calculate_preparation_time(emma_noise=self.model_parameters['emma_noise'])
execution_time = utilities.calculate_execution_time(angle_distance, emma_noise=self.model_parameters['emma_noise'])
landing_site = utilities.calculate_landing_site([float(new_chunk.screen_pos.screen_x), float(new_chunk.screen_pos.screen_y)], angle_distance, emma_landing_site_noise=self.model_parameters['emma_landing_site_noise'])
elif new_chunk.cmd == utilities.CMDMOVEATTENTION and not self.model_parameters['emma']:
encoding_time = 0.085
preparation_time = 0
execution_time = 0.085
landing_site = (float(new_chunk.screen_pos.screen_x), float(new_chunk.screen_pos.screen_y))
else:
raise ACTRError("Visual module received an invalid command: '%s'. The only valid command currently is: %s" % (new_chunk.cmd, utilities.CMDMOVEATTENTION))
return new_chunk, (encoding_time, preparation_time, execution_time), landing_site
def shift(self, otherchunk, harvest=None, actrvariables=None, model_parameters=None):
"""
Return a chunk, time needed to attend and shift eye focus to the chunk, and the landing site of eye mvt.
"""
if model_parameters == None:
model_parameters = {}
model_parameters = model_parameters.copy()
model_parameters.update(self.model_parameters)
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()}
except ACTRError as arg:
raise ACTRError("Shifting towards the chunk '%s' is impossible; %s" % (otherchunk, arg))
vis_delay = None
for each in self.environment.stimulus:
try:
if self.environment.stimulus[each]['position'] == (float(mod_attr_val['screen_pos'].values.screen_x.values), float(mod_attr_val['screen_pos'].values.screen_y.values)):
mod_attr_val['value'] = self.environment.stimulus[each]['text']
vis_delay = self.environment.stimulus[each].get('vis_delay')
except (AttributeError, KeyError):
raise ACTRError("The chunk in the visual buffer is not defined correctly. It is not possible to move attention.")
new_chunk = chunks.Chunk(self._VISUAL, **mod_attr_val) #creates new chunk
if model_parameters['emma']:
angle_distance = utilities.calculate_visual_angle(self.environment.current_focus, [float(new_chunk.screen_pos.values.screen_x.values), float(new_chunk.screen_pos.values.screen_y.values)], self.environment.size, self.environment.simulated_screen_size, self.environment.viewing_distance)
encoding_time = utilities.calculate_delay_visual_attention(angle_distance=angle_distance, K=model_parameters["eye_mvt_scaling_parameter"], k=model_parameters['eye_mvt_angle_parameter'], emma_noise=model_parameters['emma_noise'], vis_delay=vis_delay)
preparation_time = utilities.calculate_preparation_time(emma_noise=model_parameters['emma_noise'], emma_preparation_time=model_parameters['emma_preparation_time'])
execution_time = utilities.calculate_execution_time(angle_distance, emma_noise=model_parameters['emma_noise'])
landing_site = utilities.calculate_landing_site([float(new_chunk.screen_pos.values.screen_x.values), float(new_chunk.screen_pos.values.screen_y.values)], angle_distance, emma_landing_site_noise=model_parameters['emma_landing_site_noise'])
elif not model_parameters['emma']:
encoding_time = 0.085
preparation_time = 0
execution_time = 0.085
landing_site = (float(new_chunk.screen_pos.values.screen_x.values), float(new_chunk.screen_pos.values.screen_y.values))
return new_chunk, (encoding_time, preparation_time, execution_time), landing_site
def shift(self, otherchunk, harvest=None, actrvariables=None, model_parameters=None):
"""
Return a chunk, time needed to attend and shift eye focus to the chunk, and the landing site of eye mvt.
"""
if model_parameters == None:
model_parameters = {}
model_parameters = model_parameters.copy()
model_parameters.update(self.model_parameters)
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()}
except ACTRError as arg:
raise ACTRError("Shifting towards the chunk '%s' is impossible; %s" % (otherchunk, arg))
vis_delay = None
for each in self.environment.stimulus:
try:
if self.environment.stimulus[each]['position'] == (float(mod_attr_val['screen_pos'].values.screen_x.values), float(mod_attr_val['screen_pos'].values.screen_y.values)):
mod_attr_val['value'] = self.environment.stimulus[each]['text']
vis_delay = self.environment.stimulus[each].get('vis_delay')
except (AttributeError, KeyError):
raise ACTRError("The chunk in the visual buffer is not defined correctly. It is not possible to move attention.")
new_chunk = chunks.Chunk(self._VISUAL, **mod_attr_val) #creates new chunk
if model_parameters['emma']:
angle_distance = utilities.calculate_visual_angle(self.environment.current_focus, [float(new_chunk.screen_pos.values.screen_x.values), float(new_chunk.screen_pos.values.screen_y.values)], self.environment.size, self.environment.simulated_screen_size, self.environment.viewing_distance)
encoding_time = utilities.calculate_delay_visual_attention(angle_distance=angle_distance, K=model_parameters["eye_mvt_scaling_parameter"], k=model_parameters['eye_mvt_angle_parameter'], emma_noise=model_parameters['emma_noise'], vis_delay=vis_delay)
preparation_time = utilities.calculate_preparation_time(emma_noise=model_parameters['emma_noise'])
execution_time = utilities.calculate_execution_time(angle_distance, emma_noise=model_parameters['emma_noise'])
landing_site = utilities.calculate_landing_site([float(new_chunk.screen_pos.values.screen_x.values), float(new_chunk.screen_pos.values.screen_y.values)], angle_distance, emma_landing_site_noise=model_parameters['emma_landing_site_noise'])
elif not model_parameters['emma']:
encoding_time = 0.085
preparation_time = 0
execution_time = 0.085
landing_site = (float(new_chunk.screen_pos.values.screen_x.values), float(new_chunk.screen_pos.values.screen_y.values))
return new_chunk, (encoding_time, preparation_time, execution_time), landing_site
def create(self, otherchunk, actrvariables=None):
"""
Create (aka set) a chunk for manual control. The chunk is returned (and could be used by device or external environment).
"""
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()} #creates dict of attr-val pairs according to otherchunk
except ACTRError as arg:
raise ACTRError("Setting the chunk '%s' in the manual buffer is impossible; %s" % (otherchunk, arg))
new_chunk = chunks.Chunk(self._MANUAL, **mod_attr_val) #creates new chunk
if new_chunk.cmd.values not in utilities.CMDMANUAL:
raise ACTRError("Motor module received an invalid command: '%s'. The valid commands are: '%s'" % (new_chunk.cmd.values, utilities.CMDMANUAL))
if new_chunk.cmd.values == utilities.CMDPRESSKEY:
pressed_key = new_chunk.key.values.upper() #change key into upper case
mod_attr_val["key"] = pressed_key
new_chunk = chunks.Chunk(self._MANUAL, **mod_attr_val) #creates new chunk
if pressed_key not in self.LEFT_HAND and new_chunk.key.values not in self.RIGHT_HAND:
raise ACTRError("Motor module received an invalid key: %s" % pressed_key)
return new_chunk
def retrieve(self, time, otherchunk, actrvariables, buffers, extra_tests, model_parameters):
"""
Retrieve a chunk from declarative memory that matches otherchunk.
"""
model_parameters = model_parameters.copy()
model_parameters.update(self.model_parameters)
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1], negative_impossible=False) for x in otherchunk.removeunused()}
except utilities.ACTRError as arg:
raise utilities.ACTRError("Retrieving the chunk '%s' is impossible; %s" % (otherchunk, arg))
chunk_tobe_matched = chunks.Chunk(otherchunk.typename, **mod_attr_val)
max_A = float("-inf")
#collect the subset of dm that is useful (only chunks that match the searched chunk will be used
if len(chunk_tobe_matched.removeunused()) == 0 or ( model_parameters["subsymbolic"] and model_parameters["partial_matching"]):
used_dm = self.dm
else:
used_dm = {}
for x in chunk_tobe_matched.removeunused():
temp_chunk = chunks.Chunk(typename=getattr(chunk_tobe_matched, "typename"), **{x[0]: x[1]})
temp_data = {}
for x in self.dm._data:
if temp_chunk <= x:
temp_data.update(self.dm._data[x])
#update used_dm with found chunks (either by creating it, if it is empty, or by intersecting with already present chunks)
if not used_dm:
used_dm = temp_data
elif len(used_dm) <= len(temp_data):
temp_data2 = {}
for i in used_dm:
if i in temp_data:
temp_data2[i] = temp_data[i]
used_dm = temp_data2
elif len(temp_data) < len(used_dm):
temp_data2 = {}
for i in temp_data:
if i in used_dm:
temp_data2[i] = used_dm[i]
used_dm = temp_data2
retrieved = None
#loop through this subset and check activation
for chunk in used_dm:
try:
if extra_tests["recently_retrieved"] == False or extra_tests["recently_retrieved"] == 'False':
if self.__finst and chunk in self.recent:
continue
else:
if self.__finst and chunk not in self.recent:
continue
except KeyError:
pass
if model_parameters["subsymbolic"]: #if subsymbolic, check activation
A_pm = 0
if model_parameters["partial_matching"]:
A_pm = chunk_tobe_matched.match(chunk, partialmatching=True, mismatch_penalty=model_parameters["mismatch_penalty"])
else:
if not chunk_tobe_matched <= chunk:
continue
try:
A_bll = utilities.baselevel_learning(time, self.dm[chunk], model_parameters["baselevel_learning"], model_parameters["decay"], self.dm.activations.get(chunk), optimized_learning=model_parameters["optimized_learning"]) #bll
except UnboundLocalError:
continue
if math.isnan(A_bll):
raise utilities.ACTRError("The following chunk cannot receive base activation: %s. The reason is that one of its traces did not appear in a past moment." % chunk)
try:
A_sa = utilities.spreading_activation(chunk, buffers, self.dm, model_parameters["buffer_spreading_activation"], model_parameters["strength_of_association"], model_parameters["spreading_activation_restricted"], model_parameters["association_only_from_chunks"])
except IndexError:
A_sa = float(0)
inst_noise = utilities.calculate_instantaneous_noise(model_parameters["instantaneous_noise"])
A = A_bll + A_sa + A_pm + inst_noise #chunk.activation is the manually specified activation, potentially used by the modeller
if utilities.retrieval_success(A, model_parameters["retrieval_threshold"]) and max_A < A:
self.spreading_activation = A_sa
max_A = A
self.activation = max_A
retrieved = chunk
extra_time = utilities.retrieval_latency(A, model_parameters["latency_factor"], model_parameters["latency_exponent"])
if model_parameters["activation_trace"]:
print("(Partially) matching chunk:", chunk)
print("Base level learning:", A_bll)
print("Spreading activation", A_sa)
print("Partial matching", A_pm)
print("Noise:", inst_noise)
print("Total activation", A)
print("Time to retrieve", extra_time)
else: #otherwise, just standard time for rule firing, so no extra calculation needed
if chunk_tobe_matched <= chunk and self.dm[chunk][0] != time: #the second condition ensures that the chunk that was created are not retrieved at the same time
retrieved = chunk
extra_time = model_parameters["rule_firing"]
if not retrieved:
self.activation, self.spreading_activation = None, None
if model_parameters["subsymbolic"]:
extra_time = utilities.retrieval_latency(model_parameters["retrieval_threshold"], model_parameters["latency_factor"], model_parameters["latency_exponent"])
else:
extra_time = model_parameters["rule_firing"]
if self.__finst:
self.recent.append(retrieved)
if self.__finst < len(self.recent):
self.recent.popleft()
return retrieved, extra_time
def retrieve(self, time, otherchunk, actrvariables, buffers, extra_tests,
model_parameters):
"""
Retrieve a chunk from declarative memory that matches otherchunk.
"""
model_parameters = model_parameters.copy()
model_parameters.update(self.model_parameters)
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {
x[0]: utilities.check_bound_vars(actrvariables, x[1])
for x in otherchunk.removeunused()
}
except utilities.ACTRError as arg:
raise utilities.ACTRError(
"The chunk '%s' is not defined correctly; %s" %
(otherchunk, arg))
chunk_tobe_matched = chunks.Chunk(otherchunk.typename, **mod_attr_val)
max_A = float("-inf")
retrieved = None
for chunk in self.dm:
try:
if extra_tests["recently_retrieved"] == False or extra_tests[
"recently_retrieved"] == 'False':
if self.__finst and chunk in self.recent:
continue
else:
if self.__finst and chunk not in self.recent:
continue
except KeyError:
pass
if model_parameters[
"subsymbolic"]: #if subsymbolic, check activation
A_pm = 0
if model_parameters["partial_matching"]:
A_pm = chunk_tobe_matched.match(
chunk,
partialmatching=True,
mismatch_penalty=model_parameters["mismatch_penalty"])
else:
if not chunk_tobe_matched <= chunk:
continue
if chunk in self.dm.activations:
A_bll = utilities.baselevel_learning(
time,
self.dm[chunk],
model_parameters["baselevel_learning"],
model_parameters["decay"],
self.dm.activations[chunk],
optimized_learning=model_parameters[
"optimized_learning"]) #bll
else:
A_bll = utilities.baselevel_learning(
time,
self.dm[chunk],
model_parameters["baselevel_learning"],
model_parameters["decay"],
optimized_learning=model_parameters[
"optimized_learning"]) #bll
A_sa = utilities.spreading_activation(
chunk, buffers, self.dm,
model_parameters["buffer_spreading_activation"],
model_parameters["strength_of_association"],
model_parameters["spreading_activation_restricted"],
model_parameters["association_only_from_chunks"])
inst_noise = utilities.calculate_instantanoues_noise(
model_parameters["instantaneous_noise"])
A = A_bll + A_sa + A_pm + inst_noise #chunk.activation is the manually specified activation, potentially used by the modeller
if utilities.retrieval_success(
A,
model_parameters["retrieval_threshold"]) and max_A < A:
max_A = A
self.activation = max_A
retrieved = chunk
extra_time = utilities.retrieval_latency(
A, model_parameters["latency_factor"],
model_parameters["latency_exponent"])
if model_parameters["activation_trace"]:
print("(Partially) matching chunk:", chunk)
print("Base level learning:", A_bll)
print("Spreading activation", A_sa)
print("Partial matching", A_pm)
print("Noise:", inst_noise)
print("Total activation", A)
print("Time to retrieve", extra_time)
else: #otherwise, just standard time for rule firing
if chunk_tobe_matched <= chunk:
retrieved = chunk
extra_time = model_parameters["rule_firing"]
if not retrieved:
if model_parameters["subsymbolic"]:
extra_time = utilities.retrieval_latency(
model_parameters["retrieval_threshold"],
model_parameters["latency_factor"],
model_parameters["latency_exponent"])
else:
extra_time = model_parameters["rule_firing"]
if self.__finst:
self.recent.append(retrieved)
if self.__finst < len(self.recent):
self.recent.popleft()
return retrieved, extra_time
def find(self, otherchunk, actrvariables=None, extra_tests=None):
"""
Set a chunk in vision based on what is on the screen.
"""
if extra_tests == None:
extra_tests = {}
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1]) for x in otherchunk.removeunused()}
except utilities.ACTRError as arg:
raise utilities.ACTRError("The chunk '%s' is not defined correctly; %s" % (otherchunk, arg))
chunk_used_for_search = chunks.Chunk(utilities.VISUALLOCATION, **mod_attr_val)
found = None
found_stim = None
closest = float("inf")
x_closest = float("inf")
y_closest = float("inf")
current_x = None
current_y = None
for each in self.environment.stimulus:
position = (int(self.environment.stimulus[each]['position'][0]), int(self.environment.stimulus[each]['position'][1]))
try: #checks absolute position
if chunk_used_for_search.screen_x and int(chunk_used_for_search.screen_x) != position[0]:
continue
except (TypeError, ValueError):
pass
try: #checks absolute position
if chunk_used_for_search.screen_y and int(chunk_used_for_search.screen_y) != position[1]:
continue
except (TypeError, ValueError):
pass
try: #checks on x and y relative positions
if chunk_used_for_search.screen_x[0] == utilities.VISIONSMALLER and int(chunk_used_for_search.screen_x[1:]) <= position[0]:
continue
elif chunk_used_for_search.screen_x[0] == utilities.VISIONGREATER and int(chunk_used_for_search.screen_x[1:]) >= position[0]:
continue
except (TypeError, IndexError):
pass
try: #checks on x and y relative positions
if chunk_used_for_search.screen_y[0] == utilities.VISIONSMALLER and int(chunk_used_for_search.screen_y[1:]) <= position[1]:
continue
elif chunk_used_for_search.screen_y[0] == utilities.VISIONGREATER and int(chunk_used_for_search.screen_y[1:]) >= position[1]:
continue
except (TypeError, IndexError):
pass
try: #checks on x and y absolute positions
if chunk_used_for_search.screen_x == utilities.VISIONLOWEST and current_x != None and position[0] > current_x:
continue
elif chunk_used_for_search.screen_x == utilities.VISIONHIGHEST and current_x != None and position[0] < current_x:
continue
except TypeError:
pass
try: #checks on x and y absolute positions
if chunk_used_for_search.screen_y == utilities.VISIONLOWEST and current_y != None and position[1] > current_y:
continue
elif chunk_used_for_search.screen_y == utilities.VISIONHIGHEST and current_y != None and position[1] < current_y:
continue
except TypeError:
pass
try:
if extra_tests["attended"] == False or extra_tests["attended"] == 'False':
if self.finst and self.environment.stimulus[each] in self.recent:
continue
else:
if self.finst and self.environment.stimulus[each] not in self.recent:
continue
except KeyError:
pass
try: #checks on closest
if (chunk_used_for_search.screen_x == utilities.VISIONCLOSEST or chunk_used_for_search.screen_y == utilities.VISIONCLOSEST) and utilities.calculate_pythagorian_distance(self.environment.current_focus, position) > closest:
continue
except TypeError:
pass
try: #checks on onewayclosest
if (chunk_used_for_search.screen_x == utilities.VISIONONEWAYCLOSEST) and utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=True) > x_closest:
continue
except TypeError:
pass
try: #checks on onewayclosest
if (chunk_used_for_search.screen_y == utilities.VISIONONEWAYCLOSEST) and utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=False) > y_closest:
continue
except TypeError:
pass
found_stim = self.environment.stimulus[each]
visible_chunk = chunks.makechunk(nameofchunk="vis1", typename="_visuallocation", **{key: each[key] for key in self.environment.stimulus[each] if key != 'position' and key != 'text' and key != 'vis_delay'})
if visible_chunk <= chunk_used_for_search:
temp_dict = visible_chunk._asdict()
temp_dict.update({"screen_x":position[0], "screen_y":position[1]})
found = chunks.Chunk(utilities.VISUALLOCATION, **temp_dict)
current_x = position[0]
current_y = position[1]
closest = utilities.calculate_pythagorian_distance(self.environment.current_focus, position)
x_closest = utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=True)
y_closest = utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=False)
return found, found_stim
def find(self, otherchunk, actrvariables=None, extra_tests=None):
"""
Set a chunk in vision based on what is on the screen.
"""
if extra_tests == None:
extra_tests = {}
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1], negative_impossible=False) for x in otherchunk.removeunused()}
except utilities.ACTRError as arg:
raise utilities.ACTRError("The chunk '%s' is not defined correctly; %s" % (otherchunk, arg))
chunk_used_for_search = chunks.Chunk(utilities.VISUALLOCATION, **mod_attr_val)
found = None
found_stim = None
closest = float("inf")
x_closest = float("inf")
y_closest = float("inf")
current_x = None
current_y = None
for each in self.environment.stimulus:
#extra test applied first
try:
if extra_tests["attended"] == False or extra_tests["attended"] == 'False':
if self.finst and self.environment.stimulus[each] in self.recent:
continue
else:
if self.finst and self.environment.stimulus[each] not in self.recent:
continue
except KeyError:
pass
#check value in text; in principle, you can search based on any value, so this is more powerful than actual visual search
if chunk_used_for_search.value != chunk_used_for_search.EmptyValue() and chunk_used_for_search.value.values != self.environment.stimulus[each]["text"]:
continue
position = (int(self.environment.stimulus[each]['position'][0]), int(self.environment.stimulus[each]['position'][1]))
#check absolute position; exception on AttributeError is to avoid the case in which the slot has empty value (in that case, the attribute "values" is undefined)
try:
if chunk_used_for_search.screen_x.values and int(chunk_used_for_search.screen_x.values) != position[0]:
continue
except (TypeError, ValueError, AttributeError):
pass
try:
if chunk_used_for_search.screen_y.values and int(chunk_used_for_search.screen_y.values) != position[1]:
continue
except (TypeError, ValueError, AttributeError):
pass
#check on x and y relative positions
try:
if chunk_used_for_search.screen_x.values[0] == utilities.VISIONSMALLER and int(chunk_used_for_search.screen_x.values[1:]) <= position[0]:
continue
elif chunk_used_for_search.screen_x.values[0] == utilities.VISIONGREATER and int(chunk_used_for_search.screen_x.values[1:]) >= position[0]:
continue
except (TypeError, IndexError, AttributeError):
pass
try:
if chunk_used_for_search.screen_y.values[0] == utilities.VISIONSMALLER and int(chunk_used_for_search.screen_y.values[1:]) <= position[1]:
continue
elif chunk_used_for_search.screen_y.values[0] == utilities.VISIONGREATER and int(chunk_used_for_search.screen_y.values[1:]) >= position[1]:
continue
except (TypeError, IndexError, AttributeError):
pass
#check on x and y absolute positions
try:
if chunk_used_for_search.screen_x.values == utilities.VISIONLOWEST and current_x != None and position[0] > current_x:
continue
elif chunk_used_for_search.screen_x.values == utilities.VISIONHIGHEST and current_x != None and position[0] < current_x:
continue
except (TypeError, AttributeError):
pass
try:
if chunk_used_for_search.screen_y.values == utilities.VISIONLOWEST and current_y != None and position[1] > current_y:
continue
elif chunk_used_for_search.screen_y.values == utilities.VISIONHIGHEST and current_y != None and position[1] < current_y:
continue
except (TypeError, AttributeError):
pass
#check on closest
try:
if (chunk_used_for_search.screen_x.values == utilities.VISIONCLOSEST or chunk_used_for_search.screen_y.values == utilities.VISIONCLOSEST) and utilities.calculate_pythagorian_distance(self.environment.current_focus, position) > closest:
continue
except (TypeError, AttributeError):
pass
#check on onewayclosest
try:
if (chunk_used_for_search.screen_x.values == utilities.VISIONONEWAYCLOSEST) and utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=True) > x_closest:
continue
except (TypeError, AttributeError):
pass
try:
if (chunk_used_for_search.screen_y.values == utilities.VISIONONEWAYCLOSEST) and utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=False) > y_closest:
continue
except (TypeError, AttributeError):
pass
found_stim = self.environment.stimulus[each]
visible_chunk = chunks.makechunk(nameofchunk="vis1", typename="_visuallocation", **{key: each[key] for key in self.environment.stimulus[each] if key != 'position' and key != 'text' and key != 'vis_delay'})
if visible_chunk <= chunk_used_for_search:
temp_dict = visible_chunk._asdict()
temp_dict.update({"screen_x":position[0], "screen_y":position[1]})
found = chunks.Chunk(utilities.VISUALLOCATION, **temp_dict)
current_x = position[0]
current_y = position[1]
closest = utilities.calculate_pythagorian_distance(self.environment.current_focus, position)
x_closest = utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=True)
y_closest = utilities.calculate_onedimensional_distance(self.environment.current_focus, position, horizontal=False)
return found, found_stim
def retrieve(self, time, otherchunk, actrvariables, buffers, extra_tests, model_parameters):
"""
Retrieve a chunk from declarative memory that matches otherchunk.
"""
model_parameters = model_parameters.copy()
model_parameters.update(self.model_parameters)
if actrvariables == None:
actrvariables = {}
try:
mod_attr_val = {x[0]: utilities.check_bound_vars(actrvariables, x[1], negative_impossible=False) for x in otherchunk.removeunused()}
except utilities.ACTRError as arg:
raise utilities.ACTRError("Retrieving the chunk '%s' is impossible; %s" % (otherchunk, arg))
chunk_tobe_matched = chunks.Chunk(otherchunk.typename, **mod_attr_val)
max_A = float("-inf")
retrieved = None
for chunk in self.dm:
try:
if extra_tests["recently_retrieved"] == False or extra_tests["recently_retrieved"] == 'False':
if self.__finst and chunk in self.recent:
continue
else:
if self.__finst and chunk not in self.recent:
continue
except KeyError:
pass
if model_parameters["subsymbolic"]: #if subsymbolic, check activation
A_pm = 0
if model_parameters["partial_matching"]:
A_pm = chunk_tobe_matched.match(chunk, partialmatching=True, mismatch_penalty=model_parameters["mismatch_penalty"])
else:
if not chunk_tobe_matched <= chunk:
continue
try:
A_bll = utilities.baselevel_learning(time, self.dm[chunk], model_parameters["baselevel_learning"], model_parameters["decay"], self.dm.activations.get(chunk), optimized_learning=model_parameters["optimized_learning"]) #bll
except UnboundLocalError:
continue
if math.isnan(A_bll):
raise utilities.ACTRError("The following chunk cannot receive base activation: %s. The reason is that one of its traces did not appear in a past moment." % chunk)
A_sa = utilities.spreading_activation(chunk, buffers, self.dm, model_parameters["buffer_spreading_activation"], model_parameters["strength_of_association"], model_parameters["spreading_activation_restricted"], model_parameters["association_only_from_chunks"])
inst_noise = utilities.calculate_instantaneous_noise(model_parameters["instantaneous_noise"])
A = A_bll + A_sa + A_pm + inst_noise #chunk.activation is the manually specified activation, potentially used by the modeller
if utilities.retrieval_success(A, model_parameters["retrieval_threshold"]) and max_A < A:
max_A = A
self.activation = max_A
retrieved = chunk
extra_time = utilities.retrieval_latency(A, model_parameters["latency_factor"], model_parameters["latency_exponent"])
if model_parameters["activation_trace"]:
print("(Partially) matching chunk:", chunk)
print("Base level learning:", A_bll)
print("Spreading activation", A_sa)
print("Partial matching", A_pm)
print("Noise:", inst_noise)
print("Total activation", A)
print("Time to retrieve", extra_time)
else: #otherwise, just standard time for rule firing, so no extra calculation needed
if chunk_tobe_matched <= chunk and self.dm[chunk][0] != time: #the second condition ensures that the chunk that was created are not retrieved at the same time
retrieved = chunk
extra_time = model_parameters["rule_firing"]
if not retrieved:
if model_parameters["subsymbolic"]:
extra_time = utilities.retrieval_latency(model_parameters["retrieval_threshold"], model_parameters["latency_factor"], model_parameters["latency_exponent"])
else:
extra_time = model_parameters["rule_firing"]
if self.__finst:
self.recent.append(retrieved)
if self.__finst < len(self.recent):
self.recent.popleft()
return retrieved, extra_time