freq = int(p.payload)
doclen = index.get_doc_meta(doc_id)['length']
wtd = tfidf(index, t, freq, doclen)
scores[doc_id] += wtq * wtd
doc_norm[doc_id] += wtd * wtd
# `scores` at this points holds the counter of the cosine formula
# we need to perform normslization dividing by sqrt(q_norm * doc_norm)
for doc_id, score in scores.iteritems():
scores[doc_id] = scores[doc_id] / math.sqrt(q_norm * doc_norm[doc_id])
return scores
if __name__ == "__main__":
# Load index
index = Index()
index.load_from_file("../data/index.txt", "../data/meta.txt")
# Input query
query = "financial japan world news"
# Retrieve documents using the vector space model
res = retrieve_vsm(index, query)
# Print relevance scores and document titles for the top 10 results
for doc_id in sorted(res, key=res.get, reverse=True)[:10]:
docmeta = index.get_doc_meta(doc_id)
print res[doc_id], docmeta['title']
