def get_docterms_matrix(self):
result = defaultdict(float)
N = self.corpus_size
avg_doc_length = self.avg_doc_length
for id, doc in self.patent_info.items():
for term in self.terms:
tfq = self.get_tf(term)
tfd = doc.get_tf(term)
df = int(self.terms_dictionary[term][0]) if term in self.terms_dictionary else 0.0
cf = int(self.terms_dictionary[term][1]) if term in self.terms_dictionary else 0.0
doc_length = len(doc.terms)
if df > 0.0:
'''
result[id] += (util.bm25(tfd, df, N, doc_length, avg_doc_length) *
util.bm25(tfq, df, N, doc_length, avg_doc_length))
result[id] += util.dfr(tfq, tfd, df, N, doc_length, avg_doc_length)
'''
result[id] += util.es(tfq, tfd, df, N, cf, doc_length, avg_doc_length)
return result
def get_ranked_docs(self):
result = defaultdict(float)
N = self.initial_query.corpus_size
avg_doc_length = self.initial_query.avg_doc_length
for id, doc in self.patent_info.items():
for term in self.terms:
tfq = self.get_tf(term)
tfd = doc.get_tf(term)
df = int(self.terms_dictionary[term][0]) if term in self.terms_dictionary else 0.0
cf = int(self.terms_dictionary[term][1]) if term in self.terms_dictionary else 0.0
doc_length = len(doc.terms)
if df > 0.0:
'''
result[id] += (util.bm25(tfd, df, N, doc_length, avg_doc_length) *
util.bm25(tfq, df, N, doc_length, avg_doc_length))
result[id] += util.dfr(tfq, tfd, df, N, doc_length, avg_doc_length)
'''
result[id] += util.es(tfq, tfd, df, N, cf, doc_length, avg_doc_length)
result = sorted(result, key=result.get, reverse=True)
return result
