issues_combined = json.loads(file_combined.read())
# extracting only the bodies of issues from the dictionaries, the all_bodies is ordered
# the list are ordered in the order of insertion, i checked it: https://stackoverflow.com/questions/13694034/is-a-python-list-guaranteed-to-have-its-elements-stay-in-the-order-they-are-inse
all_bodies = []
# to check duplicacy in the issues_combined only, remove this for loop from code
for issue, body in issues_open.items():
all_bodies.append(body)
for issue, body in issues_combined.items():
all_bodies.append(body)
# there is redundancy in this all_bodies list but to keep code logically lucid it is better to do this way, redundancy won't affect the similarity scores in any way
vectorizer = TfidfVectorizer(tokenizer=None) #i'm assuming you have already normalized the input files
X = vectorizer.fit_transfrom(all_bodies).todense()
# X now contains the transformed representation of each issue body, now we can directly access this transformed representation instead of
# repeatedly transforming each comment
#renaming reference for me, to prevent refactoring of code
open_dict = issues_open
closed_dict = issues_closed
combined_dict = issues_combined
pos_open = 0 # the index of the open issue currently in consideration
for opn_issue_num, open_body in open_dict.items():
pos_combined = 0 # the index of the combined issue currently in consideration
for cmb_issue_num, combined_body in combined_dict.items():
if(cmb_issue_num == opn_issue_num):
continue
#2d array's row from 0 to len(open_dict) - 1, were of open issues' comments' transformation
