def __init__(self, base_kernel, grid, interpolation_mode=False, active_dims=None):
super(GridKernel, self).__init__(active_dims=active_dims)
self.interpolation_mode = interpolation_mode
self.base_kernel = base_kernel
self.register_buffer("grid", grid)
if not self.interpolation_mode:
self.register_buffer("full_grid", create_data_from_grid(grid))
def __init__(
self, base_kernel: Kernel, grid: List[Tensor], interpolation_mode: bool = False, active_dims: bool = None
):
super(GridKernel, self).__init__(active_dims=active_dims)
if torch.is_tensor(grid):
grid = convert_legacy_grid(grid)
self.interpolation_mode = interpolation_mode
self.base_kernel = base_kernel
self.num_dims = len(grid)
self.register_buffer_list("grid", grid)
if not self.interpolation_mode:
self.register_buffer("full_grid", create_data_from_grid(grid))
def update_grid(self, grid):
"""
Supply a new `grid` if it ever changes.
"""
self.grid.detach().resize_(grid.size()).copy_(grid)
if not self.interpolation_mode:
full_grid = create_data_from_grid(self.grid)
self.full_grid.detach().resize_(full_grid).copy_(full_grid)
if hasattr(self, "_cached_kernel_mat"):
del self._cached_kernel_mat
return self
def update_grid(self, grid):
"""
Supply a new `grid` if it ever changes.
"""
if torch.is_tensor(grid):
grid = convert_legacy_grid(grid)
if len(grid) != self.num_dims:
raise RuntimeError("New grid should have the same number of dimensions as before.")
for i in range(self.num_dims):
setattr(self, f"grid_{i}", grid[i])
if not self.interpolation_mode:
self.full_grid = create_data_from_grid(self.grid)
if hasattr(self, "_cached_kernel_mat"):
del self._cached_kernel_mat
return self
#!/usr/bin/env python3
import torch
import unittest
from gpytorch.kernels import RBFKernel, GridKernel
from gpytorch.lazy import KroneckerProductLazyTensor
from gpytorch.utils.grid import create_data_from_grid
grid = [torch.linspace(0, 1, 5), torch.linspace(0, 2, 3)]
d = len(grid)
grid_data = create_data_from_grid(grid)
class TestGridKernel(unittest.TestCase):
def test_grid_grid(self):
base_kernel = RBFKernel()
kernel = GridKernel(base_kernel, grid)
grid_covar = kernel(grid_data, grid_data).evaluate_kernel()
self.assertIsInstance(grid_covar, KroneckerProductLazyTensor)
grid_eval = kernel(grid_data, grid_data).evaluate()
actual_eval = base_kernel(grid_data, grid_data).evaluate()
self.assertLess(torch.norm(grid_eval - actual_eval), 2e-5)
def test_nongrid_grid(self):
base_kernel = RBFKernel()
data = torch.randn(5, d)
kernel = GridKernel(base_kernel, grid)
grid_eval = kernel(grid_data, data).evaluate()
actual_eval = base_kernel(grid_data, data).evaluate()
self.assertLess(torch.norm(grid_eval - actual_eval), 1e-5)