def test_complex(self):
c = _AddOneBlock()
d = _AddOneBlock()
e = _TwoInputBlock()
p = pipeline.Pipeline([self.a, (self.b, [c, d]), e])
out = p.process(self.data)
assert out == self.data + 2 + self.data + 3
def test_clear(self):
b = _ClearableBlock()
internal_start = b.internal_var
p = pipeline.Pipeline(b)
out = p.process(self.data)
assert b.internal_var == out
p.clear()
assert b.internal_var == internal_start
def __init__(self, config, record_thread, parent=None):
super(SignalWidget, self).__init__(parent)
self.cfg = config
self.record_thread = record_thread
self.samp_per_read = 1
self.hist = 20
self.plot_items = []
self.plot_data_items = []
self.ui = Ui_SignalWidget()
self.ui.setupUi(self)
self.pipeline = pipeline.Pipeline(self.cfg.conditioner)
self.init_plot()
self.init_button_group()
def test_parallel_to_series(self):
c = _TwoInputBlock()
p = pipeline.Pipeline([(self.a, self.b), c])
out = p.process(self.data)
assert out == self.data + 1 + self.data + 1
def test_series_to_parallel(self):
c = _AddOneBlock()
p = pipeline.Pipeline([self.a, (self.b, c)])
out = p.process(self.data)
assert out == [self.data + 2, self.data + 2]
def test_parallel_simple(self):
p = pipeline.Pipeline((self.a, self.b))
out = p.process(self.data)
assert out == [self.data + 1, self.data + 1]
def test_series_simple(self):
p = pipeline.Pipeline([self.a, self.b])
out = p.process(self.data)
assert out == self.data + 2
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)