gpytorch.means¶
Mean¶
Standard Means¶
ZeroMean¶
ConstantMean¶
- class gpytorch.means.ConstantMean(constant_prior=None, constant_constraint=None, batch_shape=torch.Size([]), **kwargs)[source]¶
A (non-zero) constant prior mean function, i.e.:
\[\mu(\mathbf x) = C\]where \(C\) is a learned constant.
- Parameters
constant_prior (Prior, optional) – Prior for constant parameter \(C\).
constant_constraint (Interval, optional) – Constraint for constant parameter \(C\).
batch_shape (torch.Size, optional) – The batch shape of the learned constant(s) (default: []).
kwargs –
- Variables
constant (torch.Tensor) – \(C\) parameter
LinearMean¶
Specialty Means¶
MultitaskMean¶
- class gpytorch.means.MultitaskMean(base_means, num_tasks)[source]¶
Convenience
gpytorch.means.Mean
implementation for defining a different mean for each task in a multitask model. Expects a list of num_tasks different mean functions, each of which is applied to the given data inforward()
and returned as an n x t matrix of means, one for each task.