def add_query(self, query_id = '', query_class='n/a', query_terms=[]):
my_query_terms = SuperList()
my_query_terms.do_padding(new_len=len(self.terms), padding_data=0)
new_terms_count = 0
for term in query_terms:
try:
my_query_terms.insert_after_padding(self.terms.index(term))
except:
# Term not obtaied in traing phase
new_terms_count += 1
self.queries.append({'id': query_id, 'class': query_class, 'terms': my_query_terms, 'new_terms_count': new_terms_count})
def add_vectors(self, a=[], b=[], log_tf_a = True, log_tf_b = True): if not b: b = SuperList() b.do_padding(new_len=a.__len__(), padding_data=0) elif a.__len__() != b.__len__(): if self.verbose: print "add_vectors:", a.__len__(), "!=", b.__len__() raise Exception sum_vector = SuperList() for i in range(0,a.__len__()): sum_vector.append(self.log_tf(a[i], do_nothing = not log_tf_a) + self.log_tf(b[i], do_nothing = not log_tf_b)) return sum_vector
def add_query(self, query_id = '', query_class='n/a', query_terms=[]):
my_query_terms = SuperList()
my_query_terms.do_padding(new_len=len(self.terms), padding_data=0)
for term in query_terms:
try:
my_query_terms.insert_after_padding(self.terms.index(term))
except:
# Term not obtaied in traing phase, ignore it
pass
# Calling add_vectors to convert my_query_terms to log_tf values
self.add_vectors(a=my_query_terms, log_tf_a = True)
self.queries.append({'id': query_id, 'class': query_class, 'terms': my_query_terms})
