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_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)