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 )