if __name__ == '__main__':
# NetData = np.loadtxt("./Datasets/raw/wikivote.txt")
f = open("./Datasets/raw/wikivote.txt", "r")
# train, test = train_test_split.k_fold_split(f, 10)
# sim = np.dot(train, train)
# score = AUC.Calculation_AUC(train, test, sim, train.shape[0])
# print(score)
# train, test = train_test_split.time_based_split(f, 0.9)
f_tr = open("train.txt", "r")
f_te = open("test.txt", "r")
train, test = train_test_split.read_from_txt(f_tr, f_te)
sim_1 = np.dot(train, train)
score_1 = AUC.Calculation_AUC(train, test, sim_1, train.shape[0], 10000)
print(score_1)
sim_2 = np.dot(np.dot(train, train.T), train)
score_2 = AUC.Calculation_AUC(train, test, sim_2, train.shape[0], 10000)
print(score_2)
sim_3 = basic_measures.IP(train, 0.3)
score_3 = AUC.Calculation_AUC(train, test, sim_3, train.shape[0], 10000)
print(score_3)
# n_folds = 10
# linklist = []
# train_list = []
# test_list = []
# f = open("./Datasets/temporal_sort/email-Eu-core-temporal_sorted.txt", "r")
# train_list, test_list = train_test_split.train_test_split(f, 0.8)
# # f_t = open("train.txt", "r")
from preprocess import train_test_split
from metrics import evaluationMetric
from metrics import AUC
from algorithms import basic_measures
import numpy as np
if __name__ == '__main__':
# f = open("./Datasets/raw/wikivote.txt", "r")
# linklist = prehandle_dataset.gen_linklist_from_txt(f)
# f = open("linklist.txt", "r")
# linklist = prehandle_dataset.gen_linklist_from_txt(f)
# adj_train, adj_test = train_test_split.k_fold_split(linklist, 10)
network = "PB"
N_exp = 5
auc = np.zeros((1, N_exp))
pre = np.zeros((1, N_exp))
for ith_exp in range(N_exp):
f_tr = open("./divided_dataset/"+network+"_tr_0.9_"+str(ith_exp+1)+".txt", "r")
f_te = open("./divided_dataset/"+network+"_te_0.9_"+str(ith_exp+1)+".txt", "r")
adj_train, adj_test = train_test_split.read_from_txt(f_tr, f_te)
sim = basic_measures.Bifan(adj_train)
auc[0, ith_exp] = AUC.Calculation_AUC(adj_train, adj_test, sim, adj_train.shape[0], 10000)
pre[0, ith_exp] = evaluationMetric.cal_precision(adj_train, adj_test, sim, 100)
print(auc.mean())
print(pre.mean())