def updateInfo(self, item, newlinks, oldlinks):
		'''爬虫爬完之后对数据库内容进行更新'''
		cur = self.conn.cursor()
		cur.execute("SELECT wid,did FROM Websites WHERE url=?", (item['url'],))
		wid, did = cur.fetchone()
		# website记录更新
		cur.execute("UPDATE Websites SET title=?,visited=1 WHERE wid=?", (item['title'], wid,))
		# 对应的domain记录中出度也需要更新
		cur.execute("UPDATE Domains SET outdegree=outdegree+? WHERE did=?", (len(item['links']), did,))

		# 对该网页中所有链接涉及的记录进行更新
		# 外部判断未出现过的链接
		for link,domain in newlinks:
			self.insertWebsite(link, domain)

		# 外部判断出现过的链接
		for link,domain in oldlinks:
			# 对对应的domain记录入度增加
			cur.execute("UPDATE Domains SET outdegree=outdegree+1 WHERE domain=?", (domain,))
		# 写入到文件
		self.conn.commit()
def test_sgpr_mean_abs_error_cuda(self):
        # Suppress numerical warnings
        warnings.simplefilter("ignore", NumericalWarning)

        if not torch.cuda.is_available():
            return
        with least_used_cuda_device():
            train_x, train_y, test_x, test_y = make_data(cuda=True)
            likelihood = GaussianLikelihood().cuda()
            gp_model = GPRegressionModel(train_x, train_y, likelihood).cuda()
            mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)

            # Optimize the model
            gp_model.train()
            likelihood.train()

            optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
            optimizer.n_iter = 0
            for _ in range(25):
                optimizer.zero_grad()
                output = gp_model(train_x)
                loss = -mll(output, train_y)
                loss.backward()
                optimizer.n_iter += 1
                optimizer.step()

            for param in gp_model.parameters():
                self.assertTrue(param.grad is not None)
                self.assertGreater(param.grad.norm().item(), 0)

            # Test the model
            gp_model.eval()
            likelihood.eval()
            test_preds = likelihood(gp_model(test_x)).mean
            mean_abs_error = torch.mean(torch.abs(test_y - test_preds))

            self.assertLess(mean_abs_error.squeeze().item(), 0.02)