def test_smear_gaussian_trainable():
dist = torch.tensor([[[0.0, 1.0, 1.5, 0.25], [0.5, 1.5, 3.0, 1.0]]])
# smear using 5 Gaussian functions with 0.75 spacing
smear = GaussianSmearing(start=1., stop=4., n_gaussians=5, trainable=True)
# absolute value of centered distances
expt = torch.tensor([[[[1, 1.75, 2.5, 3.25, 4.], [0, 0.75, 1.5, 2.25, 3.],
[0.5, 0.25, 1., 1.75, 2.5], [0.75, 1.5, 2.25, 3., 3.75]],
[[0.5, 1.25, 2., 2.75, 3.5], [0.5, 0.25, 1., 1.75, 2.5],
[2., 1.25, 0.5, 0.25, 1.], [0, 0.75, 1.5, 2.25, 3.]]]])
expt = torch.exp((-0.5 / 0.75**2) * expt**2)
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
params = list(smear.parameters())
assert len(params) == 2
assert len(params[0]) == 5
assert len(params[1]) == 5
# centered = True
smear = GaussianSmearing(start=1., stop=4., n_gaussians=5, trainable=True,
centered=True)
expt = -0.5 / torch.tensor([1, 1.75, 2.5, 3.25, 4])**2
expt = torch.exp(expt * dist[:, :, :, None]**2)
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
params = list(smear.parameters())
assert len(params) == 2
assert len(params[0]) == 5
assert len(params[1]) == 5
def test_smear_gaussian():
dist = torch.tensor([[[0.0, 1.0, 1.5], [0.5, 1.5, 3.0]]])
# smear using 4 Gaussian functions with 1. spacing
smear = GaussianSmearing(start=1., stop=4., n_gaussians=4)
# absolute value of centered distances
expt = torch.tensor([[[[1, 2, 3, 4], [0, 1, 2, 3], [0.5, 0.5, 1.5, 2.5]],
[[.5, 1.5, 2.5, 3.5], [.5, .5, 1.5, 2.5], [2, 1, 0, 1]]]])
expt = torch.exp(-0.5 * expt**2)
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
assert list(smear.parameters()) == []
# centered = True
smear = GaussianSmearing(start=1., stop=4., n_gaussians=4, centered=True)
expt = torch.exp((-0.5 / torch.tensor([1, 2, 3, 4.])**2) * dist[:, :, :, None]**2)
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
assert list(smear.parameters()) == []
def test_smear_gaussian_one_distance():
# case of one distance
dist = torch.tensor([[[1.0]]])
# trainable = False
smear = GaussianSmearing(n_gaussians=6, centered=False, trainable=False)
expt = torch.exp(-0.5 * torch.tensor([[[1., 0., 1., 4., 9., 16.]]]))
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
assert list(smear.parameters()) == []
# trainable = True
smear = GaussianSmearing(n_gaussians=6, centered=False, trainable=True)
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
params = list(smear.parameters())
assert len(params) == 2
assert len(params[0]) == 6
assert len(params[1]) == 6
# centered = True
smear = GaussianSmearing(n_gaussians=6, centered=True)
expt = -0.5 / torch.tensor([0., 1, 2, 3, 4, 5])**2
expt = torch.exp(expt * dist[:, :, :, None]**2)
assert torch.allclose(expt, smear(dist), atol=0.0, rtol=1.0e-7)
assert list(smear.parameters()) == []