def __init__(self, train_x, train_y, likelihood):
super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = ConstantMean(constant_bounds=(-1, 1))
self.base_covar_module = RBFKernel(log_lengthscale_bounds=(-3, 3))
self.covar_module = AdditiveGridInterpolationKernel(
self.base_covar_module, grid_size=100, grid_bounds=[(0, 1)], n_components=2
)
def __init__(self, train_x, train_y, likelihood):
super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = ZeroMean()
self.base_covar_module = ScaleKernel(
RBFKernel(log_lengthscale_prior=SmoothedBoxPrior(exp(-3), exp(3), sigma=0.1, log_transform=True))
)
self.covar_module = AdditiveGridInterpolationKernel(
self.base_covar_module, grid_size=100, grid_bounds=[(-0.5, 1.5)], n_components=2
)