def test_escrita_campo_num_decimal(self):
        # aceitar somente tipo Decimal
        with self.assertRaises(errors.TipoError):
            self.seg_p.valor_titulo = 10.0
        with self.assertRaises(errors.TipoError):
            self.seg_p.valor_titulo = ''

        # Testa se as casas decimais estao sendo verificadas
        with self.assertRaises(errors.NumDecimaisError):
            self.seg_p.valor_titulo = Decimal('100.2')
        with self.assertRaises(errors.NumDecimaisError):
            self.seg_p.valor_titulo = Decimal('1001')
        with self.assertRaises(errors.NumDecimaisError):
            self.seg_p.valor_titulo = Decimal('1.000')

        # verifica se o numero de digitos esta sendo verificado
        with self.assertRaises(errors.NumDigitosExcedidoError):
            self.seg_p.valor_titulo = Decimal('10000000008100.21')

        # armazemamento correto de um decimal
        self.seg_p.valor_titulo = Decimal('2.13')
        self.assertEqual(self.seg_p.valor_titulo, Decimal('2.13'))
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)