'LOUVAIN_CRSRA', #31
]
# 实验中使用的方法的id
method_ids = [
0
] #,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
sim_method_list = [sim_methods[i] for i in method_ids]
# 按照数据集，分别计算
for i in range(len(graph_file_list)):
graph_file = graph_file_list[i]
result_file = result_file_list[i]
out_file = open(result_file, 'w') # 打开结果文件
print(graph_file)
# 输出标题
# out_file.write('Method\tAUC\tRanking_Score\ttime (ms)\tPrecision (10)\n')
out_file.write('Method\tAUC\tRanking_Score\ttime (ms)\n')
# 按照不同的相似度方法分别计算
for method in sim_method_list:
print(method)
out_file.write(method + '\t')
lp.LP(graph_file, out_file, method, t, p)
out_file.flush()
# end for
out_file.close()
# end for
###############################################################################
def __init__(self, p, _code, **kwargs):
super().__init__(lp.LP(_code.parity_mtx, **kwargs))