def search(self, query: str, **opt) -> List[Score]:
"""
Performs a recommendation-based search.
Returns an array of score objects, containing the
relevant pages and their associated scores.
"""
scores = []
queryWords = self.__parse_query_string(query)
for page in self.__pages:
# create and calculate scores
pageScore = Score(page)
wordFreq = self.__metrics.get_word_frequency_score(page, queryWords)
docLoc = self.__metrics.get_document_location_score(page, queryWords)
pageScore.set_content_score(wordFreq)
pageScore.set_location_score(docLoc)
scores.append(pageScore)
# normalize scores
self.__metrics.normalize_scores(scores)
# generate results
relevantScores = list(filter(lambda x : x.get_content_score() > 0, scores))
# sort list
relevantScores.sort(key=lambda x : x.get_weighted_score(), reverse=True)
if COUNT_ID in opt:
return relevantScores[0:opt[COUNT_ID]]
else:
return relevantScores
