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(self):
        # Suppress numerical warnings
        warnings.simplefilter("ignore", NumericalWarning)

        train_x, train_y, test_x, test_y = make_data()
        likelihood = GaussianLikelihood()
        gp_model = GPRegressionModel(train_x, train_y, likelihood)
        mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)

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

        optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
        for _ in range(30):
            optimizer.zero_grad()
            output = gp_model(train_x)
            loss = -mll(output, train_y)
            loss.backward()
            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(), 0.05)