Models for Exact GP Inference¶
ExactGP(train_inputs, train_targets, likelihood)¶
get_fantasy_model(inputs, targets, **kwargs)¶
Returns a new GP model that incorporates the specified inputs and targets as new training data.
Using this method is more efficient than updating with set_train_data when the number of inputs is relatively small, because any computed test-time caches will be updated in linear time rather than computed from scratch.
If targets is a batch (e.g. b x m), then the GP returned from this method will be a batch mode GP. If inputs is of the same (or lesser) dimension as targets, then it is assumed that the fantasy points are the same for each target batch.
inputs(Tensor b1 x … x bk x m x d or f x b1 x … x bk x m x d): Locations of fantasy
targets(Tensor b1 x … x bk x m or f x b1 x … x bk x m): Labels of fantasy observations.
- An ExactGP model with n + m training examples, where the m fantasy examples have been added and all test-time caches have been updated.
set_train_data(inputs=None, targets=None, strict=True)¶
Set training data (does not re-fit model hyper-parameters).
inputsthe new training inputs
targetsthe new training targets
- if True, the new inputs and targets must have the same shape, dtype, and device as the current inputs and targets. Otherwise, any shape/dtype/device are allowed.