0b056a895e7729f6ade61a30e9fcb853dc4e371e,test/examples/test_white_noise_regression.py,TestWhiteNoiseGPRegression,test_posterior_latent_gp_and_likelihood_without_optimization,#TestWhiteNoiseGPRegression#Any#,56

Before Change

        train_x, test_x, train_y, test_y = self._get_data(cuda=cuda)
        with gpytorch.settings.debug(False):
            // We"re manually going to set the hyperparameters to be ridiculous
            likelihood = GaussianLikelihood(
                noise_prior=SmoothedBoxPrior(exp(-10), exp(10), sigma=0.25),
                noise_constraint=Positive(),
            )
            gp_model = ExactGPModel(train_x, train_y, likelihood)
            // Update lengthscale prior to accommodate extreme parameters
            gp_model.rbf_covar_module.register_prior(
                "lengthscale_prior", SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5), "raw_lengthscale"
            )
            gp_model.rbf_covar_module.initialize(lengthscale=exp(-10))
            gp_model.mean_module.initialize(constant=0)
            likelihood.initialize(noise=exp(-10))

            if cuda:
                gp_model.cuda()
                likelihood.cuda()

After Change

        train_x, test_x, train_y, test_y = self._get_data(cuda=cuda)
        with gpytorch.settings.debug(False):
            // We"re manually going to set the hyperparameters to be ridiculous
            likelihood = FixedNoiseGaussianLikelihood(torch.ones(11) * 1e-8)
            gp_model = ExactGPModel(train_x, train_y, likelihood)
            // Update lengthscale prior to accommodate extreme parameters
            gp_model.rbf_covar_module.initialize(lengthscale=exp(-6))
            gp_model.mean_module.initialize(constant=0)

Instances