def insertWebsite(self, url, domain):
		'''增加一个网页,标记为未访问,并对相应的domain增加其入度'''
		cur = self.conn.cursor()
		cur.execute("SELECT 1 FROM Domains WHERE domain=?", (domain,))
		result = cur.fetchone()
		if not result:
			# 未有对应domain记录, 先创建domain, 把入度设为1
			if VERBOSE:
				print 'Spot Domain:',domain
			self.insertDomain(domain, indegree=1)
			cur.execute("SELECT did FROM Domains WHERE domain=?", (domain,))
			did = cur.fetchone()[0]
		else:
			did = result[0]
			# 对应的domain记录已经存在, 对其入度+1
			cur.execute("UPDATE Domains SET outdegree=outdegree+1 WHERE domain=?", (domain,))

		cur.execute("INSERT INTO Websites VALUES (NULL,?,?,NULL,0)", (did, url,))
		# 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)