compute Jaccard similarities between all pairs of documents (so, if you have 4 docs, you need to compute the similarities between doc1-doc2, doc1-doc3, doc1-doc4, doc2-doc3, doc2-doc4, and doc3-doc4, you can use double loops for this purpose

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Learning Goal: I’m working on a python project and need an explanation and answer to help me learn.– Big Data AnalyticsModify Colab2A_LSH_withtoydata.ipynb so that the program become more
general; means that instead of only can accept three documents:the program should be able to
accept any number of documents. In doing so, you must make sure to be able to generate shingle-by-doc matrix from the input documents
generate the signature matrix (you do not need to modify program to generate the 20 permutation vectors, just stick with these permutation vectors)
compute Jaccard similarities between all pairs of documents (so, if you have 4 docs, you need to compute the similarities between doc1-doc2, doc1-doc3, doc1-doc4, doc2-doc3, doc2-doc4, and doc3-doc4, you can use double loops for this purpose)
split the signature matrix into b bands (this can be done using method split_vector without any modification, but you need to modify the codes that call the method split_vector as those codes only works for the original three documents), and find the candidate pairs by checking whether they share the identical bands (if there are identical bands, you need to print them)
Requirements: 010

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