- class aepsych.kernels.rbf_partial_grad.RBFKernelPartialObsGrad(ard_num_dims=None, batch_shape=torch.Size(), active_dims=None, lengthscale_prior=None, lengthscale_constraint=None, eps=1e-06, **kwargs)[source]¶
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.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
ard_num_dims (Optional[int]) –
batch_shape (Optional[Size]) –
active_dims (Optional[Tuple[int, ...]]) –
lengthscale_prior (Optional[Prior]) –
lengthscale_constraint (Optional[Interval]) –
eps (Optional[float]) –
- forward(x1, x2, diag=False, **params)[source]¶
Computes the covariance between x1 and x2. This method should be imlemented by all Kernel subclasses.
x1 (Tensor n x d or b x n x d) – First set of data
x2 (Tensor m x d or b x m x d) – Second set of data
diag (bool) – Should the Kernel compute the whole kernel, or just the diag?
last_dim_is_batch (tuple, optional) – If this is true, it treats the last dimension of the data as another batch dimension. (Useful for additive structure over the dimensions). Default: False
params (Any) –
The exact size depends on the kernel’s evaluation mode:
full_covar: n x m or b x n x m
full_covar with last_dim_is_batch=True: k x n x m or b x k x n x m
diag: n or b x n
diag with last_dim_is_batch=True: k x n or b x k x n
- Return type
- num_outputs_per_input(x1, x2)[source]¶
How many outputs are produced per input (default 1) if x1 is size n x d and x2 is size m x d, then the size of the kernel will be (n * num_outputs_per_input) x (m * num_outputs_per_input) Default: 1
x1 (Tensor) –
x2 (Tensor) –
- Return type
- training: bool¶