def test_mav(self):
mapping = {0: '0', 1: '1'}
controller = control.DBVRController(mapping, ramp_length=1, boosts=1)
out = controller.process((0.8, 0))
assert out == {'0': 0.8, '1': 0}
def test_ramp_length_1(self):
mapping = {0: '0', 1: '1'}
controller = control.DBVRController(mapping, ramp_length=1, boosts=1)
out = controller.process((1, 0))
assert out == {'0': 1, '1': 0}
out = controller.process((1, 1))
assert out == {'0': 0, '1': 1}
def test_complex(self):
mapping = {0: 'no-contraction', 1: '1', 2: '2'}
controller = control.DBVRController(mapping, ramp_length=5, boosts=1)
out = controller.process((1, 0))
assert out == 'no-contraction'
out = controller.process((1, 1))
assert out['1'] > 0
assert out['2'] == 0
out = controller.process((1, 2))
assert out['1'] == 0
assert out['2'] > 0
def test_boosts(self):
mapping = {0: '0', 1: '1'}
boosts = {0: 2, 1: 1}
labels = [0] * 7 + [1] * 7
mav = [1] * len(labels)
data = zip(mav, labels)
controller = control.DBVRController(mapping,
ramp_length=5,
boosts=boosts)
out = _run_controller(controller, data)
assert out[5]['0'] == 1
assert out[-1]['1'] == 1
def test_simple(self):
mapping = {0: 0, 1: 1, 2: 2}
labels = [0] * 10
mav = [1] * len(labels)
data = zip(mav, labels)
controller = control.DBVRController(mapping, ramp_length=5, boosts=1)
out = _run_controller(controller, data)
on_ramp = out[3][0]
saturated = out[5][0]
assert on_ramp > 0 and on_ramp < 1
assert saturated == 1
features.SSC(thresh=0.003)
], len(channels))
learner = pipeline.Classifier(
Pipeline([('preproc', StandardScaler()), ('clf', LDA())]))
post_processor = processing.Processor(
conditioner=conditioner,
windower=windower,
feature_extractor=feature_extractor,
rest_bounds=None,
gesture_bounds=(int((prompt_times[0] + 0.5) * daq_st['f_proc']),
int((prompt_times[1] - 0.5) * daq_st['f_proc'])))
controller = control.DBVRController(
mapping={g.label: g.action
for g in gestures}, ramp_length=5)
tac_sessions = {
'3 active, 1 target':
experiment.TACSession([g for g in gestures if g.dof in [1, 2, 3]],
simul=1,
rep=2,
timeout=15,
dist=60,
tol=10,
dwell=2),
'3 active, 2 target':
experiment.TACSession([g for g in gestures if g.dof in [1, 2, 3]],
simul=(1, 2),
rep=1,
def build_pipeline(self):
"""
Builds the processing pipeline. Most of the pipeline is specified by
the config, but we need to gather training data, build a classifier
with that data, and insert the classifier into the pipeline.
"""
train_list = []
for i in range(self.ui.trainingList.count()):
item = self.ui.trainingList.item(i)
if item.checkState():
train_list.append(str(item.text()))
self.training_sessions = train_list
if not train_list:
QtWidgets.QMessageBox().critical(
self, "Error", "No sessions selected for training.",
QtWidgets.QMessageBox.Ok)
return
# get only the labels for the selected TAC session
# need to loop over available gestures to catch those with no dof
labels = []
mapping = {}
for gesture in self.cfg.gestures:
if gesture.dof is None:
labels.append(gesture.label)
mapping[gesture.label] = gesture.action
else:
if gesture in self.tac_session.gestures:
labels.append(gesture.label)
mapping[gesture.label] = gesture.action
file_list = filestruct.get_feature_file_list(self.cfg.data_path,
self.pid, train_list)
training_data = processing.read_feature_file_list(file_list,
labels=labels)
# get average MAV for each gesture label to auto-set boosts
# warning: super hacky
j = 0
start = 0
for i, feature in enumerate(self.cfg.feature_extractor.features):
if 'MAV' in str(feature):
start = j
break
else:
j += feature.dim_per_channel * len(self.cfg.channels)
X, y = training_data
X = X[:, start:len(self.cfg.channels)]
boosts = dict()
for label in labels:
mav_avg = np.mean(X[y == label, :], axis=1)
# -np.partition(-data, N) gets N largest elements of data
boosts[label] = 1 / np.mean(-np.partition(-mav_avg, 10)[:10])
self.boosts = boosts
# re-create the controller to make sure it has the correct mapping
self.controller = control.DBVRController(
mapping=mapping,
ramp_length=self.cfg.controller.ramp_length,
boosts=1 if self.test else boosts)
self.cfg.learner.fit(*training_data)
self.pipeline = pipeline.Pipeline([
self.cfg.conditioner, self.cfg.windower,
(
features.FeatureExtractor([features.MAV()],
len(self.cfg.channels)),
[self.cfg.feature_extractor, self.cfg.learner],
)
])
self.record_thread.set_pipeline(self.pipeline)