I have a Sqlite database that contains following type of schema:
termcount(doc_num, term , count)
This table contains terms with their respective counts in the document.
like
(doc1 , term1 ,12)
(doc1, term 22, 2)
.
.
(docn,term1 , 10)
This matrix can be considered as sparse matrix as each documents contains very few terms that will have a non-zero value.
How would I create a dense matrix from this sparse matrix using numpy as I have to calculate the similarity among documents using cosine similarity.
This dense matrix will look like a table that have docid as the first column and all the terms will be listed as the first row.and remaining cells will contain counts.
Best Answer
this is an example of how to convert a sparse matrix to a dense matrix taken from scipy