def process(self):
""" Processes all data that is provided by the input node
Returns a generator that yields the data after being processed by this
node.
"""
assert(self.input_node != None), "No input node specified!"
# Assert that this node has already been trained
assert(not self.is_trainable() or
self.get_remaining_train_phase() == 0), "Node not trained!"
data_generator = \
itertools.imap(lambda (data, label):
(self.execute(data), label),
self.input_node.process())
self.client = TimeSeriesClient(ts_stream = data_generator)
self.client.connect()
self.marker_windower = MarkerWindower(data_client=self.client,
windowdefs=self.window_definition,
stridems=self.nullmarker_stride_ms)
if self.marker_windower == None:
self.window_stream()
# Create a generator that emits the windows
test_data_generator = ((sample, label) \
for (sample, label) in self.marker_windower)
self.data_for_testing = MemoizeGenerator(test_data_generator)
# Return a fresh copy of the generator
return self.data_for_testing.fresh()
def window_stream(self, data):
# Creates a windower that splits the given data data into windows
# based in the window definitions provided
# and assigns correct labels to these windows
self.client = TimeSeriesClient(ts_stream = iter(data))
self.client.connect()
self.marker_windower = MarkerWindower(data_client=self.client,
windowdefs=self.window_definition,
stridems=self.nullmarker_stride_ms)
