def search(self, text, isSvd = False):
if isSvd:
print "Search with SVD"
else:
print "Search with LDA"
start = time.time()
vector = text.split()
cleanedVector = cleanVector(vector)
#cleanedVector = self.wordsToAsk
print cleanedVector
if isSvd:
bagOfWords, indices = createBagOfWordsFromVector(cleanedVector, self.amountOfWords, self.dictOfWords, self.idfs)
b = fasterCorrelations(self.matrix, indices, bagOfWords, self.amountOfFiles)
else:
bagOfWords = createBagOfWordsForLDA(cleanedVector, self.amountOfWords, self.dictOfWords)
#print 'bow shape:'
#print bagOfWords.shape
#print 'topic word shape:'
#print np.transpose(self.model.topic_word_).shape
res = np.dot(bagOfWords, np.transpose(self.model.topic_word_))
#print 'first mul shape:'
#print res.shape
#print res
#print 'doc topic shape:'
#print np.transpose(self.model.doc_topic_).shape
res2 = np.dot(res, np.transpose(self.model.doc_topic_))
#print 'final shape:'
#print res2.shape
bestValues = sorted(list(res2[0]), reverse=True)[:5]
b = []
for bestVal in bestValues:
for ind, val in enumerate(list(res2[0])):
if val == bestVal:
b.append((ind, val))
results = []
dbMan = DatabaseManager()
for x in b:
results.append(dbMan.get_link(self.listOfArticles[x[0]]))
for res in results:
print res.url
print res.title
stop = time.time()
return results, stop - start
def getArticles(self, drillDownPath):
fileName = ''
formerFileName = ''
for ind in drillDownPath:
formerFileName = fileName
fileName+='_' + str(ind)
with open(CLUST_DIR + 'b' + formerFileName + '.pickle', 'rb') as handle:
formerClust = pickle.load(handle)
artNumbers = formerClust[drillDownPath[-1]]
results = []
dbMan = DatabaseManager()
for x in artNumbers:
results.append(dbMan.get_link(self.listOfArticles[x]))
return results
