class Optimizer(QObject):
update = Signal(float, np.ndarray, np.ndarray)
done = Signal(SOM)
stopped = Signal()
def __init__(self, data, widget):
super().__init__()
self.som = SOM(
widget.size_x,
widget.size_y,
hexagonal=widget.hexagonal,
pca_init=widget.initialization == 0,
random_seed=0 if widget.initialization == 2 else None)
self.data = data
self.widget = widget
def callback(self, progress):
self.update.emit(progress, self.som.weights.copy(),
self.som.ssum_weights.copy())
return not self.widget.stop_optimization
def run(self):
try:
self.som.fit(self.data,
N_ITERATIONS,
callback=self.callback)
# Report an exception, but still remove the thread
finally:
self.done.emit(self.som)
self.stopped.emit()
def __init__(self, data, widget):
super().__init__()
self.som = SOM(
widget.size_x, widget.size_y,
hexagonal=widget.hexagonal,
pca_init=widget.initialization == 0,
random_seed=0 if widget.initialization == 2 else None)
self.data = data
self.widget = widget
def _recompute_som(self):
if self.cont_x is None:
return
som = SOM(self.size_x,
self.size_y,
hexagonal=self.hexagonal,
pca_init=self.initialization == 0,
random_seed=0 if self.initialization == 2 else None)
class Optimizer(QObject):
update = Signal(float, np.ndarray, np.ndarray)
done = Signal(SOM)
stopped = Signal()
stop_optimization = False
def __init__(self, data, som):
super().__init__()
self.som = som
self.data = data
def callback(self, progress):
self.update.emit(progress, self.som.weights.copy(),
self.som.ssum_weights.copy())
return not self.stop_optimization
def run(self):
try:
self.som.fit(self.data,
N_ITERATIONS,
callback=self.callback)
# Report an exception, but still remove the thread
finally:
self.done.emit(self.som)
self.stopped.emit()
def thread_finished():
self._optimizer = None
self._optimizer_thread = None
self.progressBarInit()
self._optimizer = Optimizer(self.cont_x, som)
self._optimizer_thread = QThread()
self._optimizer_thread.setStackSize(5 * 2**20)
self._optimizer.update.connect(self.__update)
self._optimizer.done.connect(self.__done)
self._optimizer.stopped.connect(self._optimizer_thread.quit)
self._optimizer.moveToThread(self._optimizer_thread)
self._optimizer_thread.started.connect(self._optimizer.run)
self._optimizer_thread.finished.connect(thread_finished)
self.stop_optimization = False
self._optimizer_thread.start()
def _assign_instances(self, weights, ssum_weights):
if self.cont_x is None:
return # the widget is shutting down while signals still processed
assignments = SOM.winner_from_weights(self.cont_x, weights,
ssum_weights, self.hexagonal)
members = defaultdict(list)
for i, (x, y) in enumerate(assignments):
members[(x, y)].append(i)
members.pop(None, None)
self.cells = np.empty((self.size_x, self.size_y, 2), dtype=int)
self.member_data = np.empty(self.cont_x.shape[0], dtype=int)
index = 0
for x in range(self.size_x):
for y in range(self.size_y):
nmembers = len(members[(x, y)])
self.member_data[index:index + nmembers] = members[(x, y)]
self.cells[x, y] = [index, index + nmembers]
index += nmembers
