#Dataset : https://www.daviddlewis.com/resources/testcollections/reuters21578/

----------------------- How to Run ------------------------------

1. Make sure you have python3 and the following modules installed: -> Numpy -> Sklearn -> Matplotlib -> Pandas -> nltk (with stopword downloaded!)
2. git checkout to main branch(which has LSH and Community detection ) or git checkout to clustering branch (which has clustering method)
3. Open constants.py and modify it with the values you would like
4. Run main.py

#Steps:

1. Pre-processing Reuters dataset : Write regex to parse and extract stories in memory.# Return a iterator (yield) to extracted stories)
2. Character shingle : Length - 7 :Function that takes a string and returns it's shingle id
3. Count and calculate tf and idf scores: i) Python dictionary, add key as we see.
4. Get the tf-idf scores of those shingles.
5. Throw away stop words ( idf > 0.9 ) and do for each key in dictionary : key -> index.
6. Generate 100 hash functions ( ( k * x + r) % c ) G.C.D (k, c) == 1.
7. Get signature matrix for each hash function. i) the value in each element of matrix is bool(tf-idf score > PARAM_threshold_tf_idf).
8. L.S.H : i) PARAM_number_of_bands , PARAM_number_of_rows_in_each_band and split. ii) Generate K bucket hash functions : f(xor of the band per document) mod K. iii) Confirm it with cosine similarity for candidate pairs O(Summation(Candidate pairsC2)) iv) Key : PARAM_number_bands_matched, PARAM_threshold_cosine_similarity : Remove the false positives. v) Pick the ones with the least time stamp -> First story & family detected.

LSH split : Ankit : False positive removal , candidate pair checking and final decision Raaghav : Dynamic bucketing using (Method - ii) Siddhartha : Static bucketing + False postitive removal using similarity score in all pairs per bucket.

#Extension:

1. Extend dynamic method without ML Abhinav:
2. Extend dynamic method using ML ( Transfer Learning)