Kinda wish they would use some more letters when writing the sample code. This terse style in general is fine but when the purpose of the code is to try to exp

Kinda wish they would use some more letters when writing the sample code. This terse style in general is fine but when the purpose of the code is to try to explain how something works, it's adding an extra layer of difficulty decoding what the heck 'nns, 'cv', 'd', 'e' and 'ef' is supposed to mean. Like this ChatGPT-looking function nns = [best] visit = set(best) # set of visited nodes candid = [best] # candidate nodes to insert into nearest neighbors # find top-k nearest neighbors while candid: cv = heappop(candid) if nns[-1][0] < cv[0]: break # loop through all nearest neighbors to the candidate vector for e in graph[cv[1]][1]: d = np.linalg.norm(graph[e][0] - query) if (d, e) not in visit: visit.add((d, e)) # push only "better" vectors into candidate heap if d < nns[-1][0] or len(nns) < ef: heappush(candid, (d, e)) insort(nns, (d, e)) if len(nns) > ef: nns.pop() code could have been trivially cleaned up and it would have been so much easier to follow. I'm guessing what some of these variables are supposed to mean. nearest_neighbors = [best] visited = set(best) candidates = [best] # find top-k nearest neighbors while candidates: candidate_vector = heappop(candidates) if nearest_neighbors[-1][0] < candidate_vector[0]: break # loop through all nearest neighbors to the candidate vector for neighbor in graph[candidate_vector[1]][1]: distance = np.linalg.norm(graph[e][0] - query) if (distance, neighbor) in visit: continue visited.add((distance, neighbor)) # push only "better" vectors into candidate heap if distance < nearest_neighbors[-1][0] or len(nearest_neighbors) < epsilon: heappush(candidates, (distance, neighbor)) insort(nns, (distance, neighbor)) if len(nearest_neighbors) > epsilon: nearest_neighbors.pop()