def parse_kernel(
kernel_type,
x_dim,
rkhs_dim,
length_scale,
sigma_var,
learnable_length_scale=False,
):
if kernel_type == "rbf":
kernel = SeparableKernel(
x_dim,
rkhs_dim,
RBFKernelReparametrised(
x_dim,
log_length_scale=nn.Parameter(
torch.tensor(length_scale).log()) if learnable_length_scale
else torch.tensor(length_scale).log(),
sigma_var=sigma_var,
),
)
elif kernel_type == "divfree":
if (rkhs_dim != x_dim) & rkhs_dim != 2:
raise ValueError(
f"RKHS and X dim for {kernel_type} must be 2. Given {rkhs_dim=}, {x_dim=}."
)
kernel = RBFDivergenceFreeKernelReparametrised(
x_dim,
log_length_scale=nn.Parameter(torch.tensor(length_scale).log())
if learnable_length_scale else torch.tensor(length_scale).log(),
sigma_var=sigma_var,
)
elif kernel_type == "curlfree":
if (rkhs_dim != x_dim) & rkhs_dim != 2:
raise ValueError(
f"RKHS and X dim for {kernel_type} must be 2. Given {rkhs_dim=}, {x_dim=}."
)
kernel = RBFCurlFreeKernelReparametrised(
x_dim,
log_length_scale=nn.Parameter(torch.tensor(length_scale).log())
if learnable_length_scale else torch.tensor(length_scale).log(),
sigma_var=sigma_var,
)
else:
raise ValueError(
f"{kernel_type} is not a recognised kernel type to use.")
return kernel
def get_kernel(self):
if self.kernel_type == "rbf":
kernel = SeparableKernel(
2,
2,
RBFKernel(2,
length_scale=self.length_scale,
sigma_var=self.sigma_var),
)
elif self.kernel_type == "divfree":
kernel = RBFDivergenceFreeKernel(2,
length_scale=self.length_scale,
sigma_var=self.sigma_var)
elif self.kernel_type == "curlfree":
kernel = RBFCurlFreeKernel(2,
length_scale=self.length_scale,
sigma_var=self.sigma_var)
else:
raise ValueError(
f"{self.kernel_type} is not a recognised kernel type to use.")
return kernel
import torch
from steer_cnp.kernel import (
RBFKernel,
SeparableKernel,
DotProductKernel,
RBFDivergenceFreeKernel,
RBFCurlFreeKernel,
kernel_smooth,
)
from steer_cnp.gp import sample_gp_prior, conditional_gp_posterior
from steer_cnp.utils import sample_gp_grid_2d, plot_inference
import matplotlib.pyplot as plt
# %%
rbf = SeparableKernel(3, 3, RBFKernel(3, 3.0))
# div = RBFDivergenceFreeKernel(3, 3.0)
# curl = RBFCurlFreeKernel(3, 3.0)
# %%
x = torch.arange(-4, 4, step=0.5)
x1, x2, x3 = torch.meshgrid(x, x, x)
x1 = x1.flatten()
x2 = x2.flatten()
x3 = x3.flatten()
X_grid = torch.stack([x1, x2, x3], dim=-1)
# %%
Y_rbf = sample_gp_prior(X_grid, rbf)
# plt.quiver(
import torch
from steer_cnp.kernel import (
RBFKernel,
SeparableKernel,
DotProductKernel,
RBFDivergenceFreeKernel,
RBFCurlFreeKernel,
kernel_smooth,
)
from steer_cnp.gp import sample_gp_prior, conditional_gp_posterior
from steer_cnp.utils import sample_gp_grid_2d, plot_inference
import matplotlib.pyplot as plt
# %%
rbf = SeparableKernel(2, 2, RBFKernel(2, 3.0))
div = RBFDivergenceFreeKernel(2, 3.0)
curl = RBFCurlFreeKernel(2, 3.0)
# %%
x = torch.arange(-4, 4, step=0.5)
x1, x2 = torch.meshgrid(x, x)
x1 = x1.flatten()
x2 = x2.flatten()
X_grid = torch.stack([x1, x2], dim=-1)
# %%
Y_rbf = sample_gp_prior(X_grid, rbf)
plt.quiver(
X_grid[:, 0],
from steer_cnp.equiv_deepsets import EquivDeepSet
from steer_cnp.utils import (
get_e2_decoder,
grid_2d,
plot_vector_field,
plot_inference,
plot_embedding,
)
import e2cnn.nn as gnn
import matplotlib.pyplot as plt
# %%
embedding_kernel = SeparableKernel(2, 3, RBFKernel(2, 1.0))
grid_ranges = [-4.0, 4.0]
n_axes = 20
normalise = True
cnn = get_e2_decoder(4,
False,
"regular_small", [1], [1],
activation="normrelu")
output_kernel = SeparableKernel(2, 6, RBFKernel(2, 1.0))
dim = 2
deepset = EquivDeepSet(
grid_ranges=grid_ranges,
n_axes=n_axes,
embedding_kernel=embedding_kernel,
normalise_embedding=normalise,
x, y, n_context = m[np.random.randint(1000)]
# %%
# plt.scatter(m0[0][:, 0], m0[0][:, 1], c=m0[1])
img = points_to_partial(28, x[:n_context].numpy(), y[:n_context].numpy())
plt.imshow(img)
# %%
kernel = SeparableKernel(
2,
2,
RBFKernelReparametrised(
2,
log_length_scale=torch.tensor(3.0).log(),
sigma_var=1.0,
),
)
embedder = DiscretisedRKHSEmbedding([0, 27], 28, dim=2, kernel=kernel, normalise=True)
# %%
grid, Y_target = embedder(x[:n_context].unsqueeze(0), y[:n_context].unsqueeze(0))
grid = grid.squeeze(0).numpy().astype(int)
Y_target = Y_target.squeeze(0).numpy()
img = points_to_img(28, grid, Y_target[:, 0])
plt.imshow(img)
plt.colorbar()
# %%
RBFCurlFreeKernel,
kernel_smooth,
)
# %%
rbf = RBFKernel(3, 1.0)
# %%
X = torch.randn((10, 5, 3))
Y = torch.randn((10, 4, 3))
rbf(X, Y).shape
# %%
sep = SeparableKernel(3, 2, rbf)
# %%
sep(X, Y, flatten=False).shape
# %%
dp = DotProductKernel(3)
# %%
dp(X, Y).shape
# %%
# %%