15b3563c3c7f700ee0bf23b0477527dfa5323cea,test/examples/test_simple_gp_regression.py,TestSimpleGPRegression,test_posterior_latent_gp_and_likelihood_without_optimization,#TestSimpleGPRegression#,39

Before Change

class TestSimpleGPRegression(unittest.TestCase):
    def test_posterior_latent_gp_and_likelihood_without_optimization(self):
        // We"re manually going to set the hyperparameters to be ridiculous
        likelihood = GaussianLikelihood(log_noise_bounds=(-3, 3))
        gp_model = ExactGPModel(train_x.data, train_y.data, likelihood)
        // Update bounds to accommodate extreme parameters
        gp_model.covar_module.set_bounds(log_lengthscale=(-10, 10))
        likelihood.set_bounds(log_noise=(-10, 10))
        // Update parameters
        gp_model.covar_module.initialize(log_lengthscale=-10)
        gp_model.mean_module.initialize(constant=0)
        likelihood.initialize(log_noise=-10)

        // Compute posterior distribution
        gp_model.eval()

After Change

class TestSimpleGPRegression(unittest.TestCase):
    def test_posterior_latent_gp_and_likelihood_without_optimization(self):
        // We"re manually going to set the hyperparameters to be ridiculous
        likelihood = GaussianLikelihood(
            log_noise_prior=SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5, log_transform=True)
        )
        gp_model = ExactGPModel(train_x.data, train_y.data, likelihood)
        // Update lengthscale prior to accommodate extreme parameters
        gp_model.covar_module.set_priors(
            log_lengthscale=SmoothedBoxPrior(exp(-10), exp(10), sigma=0.5, log_transform=True)

Instances