bare_data = [0.0 if np.isnan(sample) else sample
for _, sample in data.iteritems()]
# TODO: Put analogous iterator into jumpavg library.
- groups = BitCountingClassifier.classify(bare_data)
+ groups = BitCountingClassifier().classify(bare_data)
groups.reverse() # Just to use .pop() for FIFO.
classification = []
avgs = []
continue
if values_left < 1 or active_group is None:
values_left = 0
- while values_left < 1: # To ignore empty groups.
+ while values_left < 1: # Ignore empty groups (should not happen).
active_group = groups.pop()
values_left = len(active_group.values)
avg = active_group.metadata.avg