def __init__(self, inducing_points, learn_locs=True):
variational_distribution = CholeskyVariationalDistribution(inducing_points.size(-1))
variational_strategy = WhitenedVariationalStrategy(
self, inducing_points, variational_distribution, learn_inducing_locations=learn_locs
)
super(SVGPRegressionModel, self).__init__(variational_strategy)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel(lengthscale_prior=gpytorch.priors.SmoothedBoxPrior(0.001, 1.0, sigma=0.1))
)
def __init__(self, inducing_points):
variational_distribution = CholeskyVariationalDistribution(
inducing_points.size(0))
variational_strategy = WhitenedVariationalStrategy(
self,
inducing_points,
variational_distribution,
learn_inducing_locations=True)
super(GPRegressionLayer, self).__init__(variational_strategy)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(
gpytorch.kernels.RBFKernel())