def main(processes = 8):
lda_loaded_model = lda.load_lda('model/100_topics_lda.txt')
user_list = db_info.user_list
user_list_list = db.split_item(user_list)
results = multiprocess(processes,lda_loaded_model,user_list_list)
json.dump(results,open("matrix/100_topics_sim_matrix.txt",'w'))
# topic_sim_matrix(lda_loaded_model)
# item_lda_model(num_topics=50,path='model/50_topics_lda.txt')
def main(processes=8):
lda_loaded_model = lda.load_lda('model/100_topics_lda.txt')
user_list = db_info.user_list
user_list_list = db.split_item(user_list)
results = multiprocess(processes, lda_loaded_model, user_list_list)
json.dump(results, open("matrix/100_topics_sim_matrix.txt", 'w'))
# topic_sim_matrix(lda_loaded_model)
# item_lda_model(num_topics=50,path='model/50_topics_lda.txt')
def calulate_user_similarity(processes,topics):
user_list = db_info.user_list
user_list_list = db.split_item(user_list)
user_topic_map = get_user_topic_map(topics)
node = len(user_list_list)/60
for i in range(60):
exe_list = user_list_list[i*node:(i+1)*node]
results = multiprocess(processes,exe_list,user_topic_map)
path = 'user_similarity/user_topic_sim/'+str(topics)+'_topics/' + str(i) + '.pickle'
util.write_file(results,path)
def main(processes=8):
user_list = db_info.user_list
user_list_list = db.split_item(user_list)
results = multiprocess(processes, user_list_list)
json.dump(results, open("matrix/tags_sim_matrix.txt", 'w'))
def main(processes = 8):
user_list = db_info.user_list
user_list_list = db.split_item(user_list)
results = multiprocess(processes,user_list_list)
json.dump(results,open("matrix/user_items_sim_matrix.txt",'w'))