Hi,
I have been banging my head against the wall - I know I can get rid of for-loop
here, but can't figure out how. Please help! I suspect I can do np.sum on the
whole adGroup but I am lacking the imagination to visualize it.
for ind in range(adGroup.shape[0]):
row = adGroup.iloc[ind, :]
current_hashes, current_cf_id =
adArrayHashes[row['hash_seq_array'].astype(int)], [row['cf_id']]
# calculaton
hashes_diff = np.array(list(map(lambda x:
np.sum(np.not_equal(start_hashes[x:x +
len(current_hashes)], current_hashes),
axis=1),
range(58)
)))
hashes_diff_scores = np.sum(hashes_diff <= IMG_DIFFER, axis=1)
hashes_diff_mask = hashes_diff_scores >= SEQ_THRESHOLD
if any(hashes_diff_mask):
presumptive_cf_ids.append(current_cf_id[0])
presumptive_scores.append(max(hashes_diff_scores))
if queue != 0: queue.put((presumptive_cf_ids, presumptive_scores))
# return presumptive_cf_ids, presumptive_scores
return
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