aepsych.kernels.rbf_partial_grad module

class aepsych.kernels.rbf_partial_grad.RBFKernelPartialObsGrad(ard_num_dims=None, batch_shape=None, active_dims=None, lengthscale_prior=None, lengthscale_constraint=None, eps=1e-06, **kwargs)[source]

Bases: RBFKernelGrad

An RBF kernel over observations of f, and partial/non-overlapping observations of the gradient of f.

gpytorch.kernels.rbf_kernel_grad assumes a block structure where every partial derivative is observed at the same set of points at which x is observed. This generalizes that by allowing f and any subset of the derivatives of f to be observed at different sets of points.

The final column of x1 and x2 needs to be an index that identifies what is observed at that point. It should be 0 if this observation is of f, and i if it is of df/dxi.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

  • ard_num_dims (Optional[int]) –

  • batch_shape (Optional[torch.Size]) –

  • active_dims (Optional[Tuple[int, ...]]) –

  • lengthscale_prior (Optional[Prior]) –

  • lengthscale_constraint (Optional[Interval]) –

  • eps (float) –

forward(x1, x2, diag=False, **params)[source]

Computes the covariance between \(\mathbf x_1\) and \(\mathbf x_2\). This method should be imlemented by all Kernel subclasses.

  • x1 (Tensor) – First set of data (… x N x D).

  • x2 (Tensor) – Second set of data (… x M x D).

  • diag (bool) – Should the Kernel compute the whole kernel, or just the diag? If True, it must be the case that x1 == x2. (Default: False.)

  • last_dim_is_batch – If True, treat the last dimension of x1 and x2 as another batch dimension. (Useful for additive structure over the dimensions). (Default: False.)

  • params (Any) –


The kernel matrix or vector. The shape depends on the kernel’s evaluation mode:

  • full_covar: … x N x M

  • full_covar with last_dim_is_batch=True: … x K x N x M

  • diag: … x N

  • diag with last_dim_is_batch=True: … x K x N

Return type:


num_outputs_per_input(x1, x2)[source]

For most kernels, num_outputs_per_input = 1.

However, some kernels (e.g. multitask kernels or interdomain kernels) return a num_outputs_per_input x num_outputs_per_input matrix of covariance values for every pair of data points.

I.e. if x1 is size … x N x D and x2 is size … x M x D, then the size of the kernel will be … x (N * num_outputs_per_input) x (M * num_outputs_per_input).


num_outputs_per_input (usually 1).

  • x1 (Tensor) –

  • x2 (Tensor) –

Return type:


Module contents