def dump_clusters():
args = get_args()
if args['-train'] == '':
args['-train'] = 'src/resources/output' + args['-k']
w2vobj = W2V(args['-input'], args['-train'], args['-k'])
news = News()
articles = news.get_articles()
w2vobj.train()
# Sentence vectorization by averaging
article_vecs = [w2vobj.get_sentence_vector_avg(article['cleaned_title']) for article in articles]
# Sentence vectorization by "newtonian" method
'''article_vecs = []
for article in articles:
newtonian_vec = w2vobj.get_sentence_vector_newtonian(article['cleaned_title'])
if newtonian_vec is not None:
article_vecs.append(newtonian_vec)'''
cluster_obj = Clustering(article_vecs, w2vobj)
r_conn = redis.from_url(os.getenv('REDIS_URL',"redis://localhost:6379/"))
if args['-cluster'] == 'agg':
if args['-prune'] == 'true' or args['-prune'] == 'True':
utilities.redis_kmeans_clusters(cluster_obj, articles, True, int(args['-limit']), r_conn)
print("redis dump complete")
else:
utilities.redis_kmeans_clusters(cluster_obj, articles, False, int(args['-limit']), r_conn)
print("redis dump complete")
else:
#TODO dump to redis
utilties.print_ann_clusters(cluster_obj, articles)
