gpytorch.means¶
Standard Means¶
ConstantMean¶
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class
gpytorch.means.
ConstantMean
(constant_prior: Optional[gpytorch.priors.prior.Prior] = None, constant_constraint: Optional[gpytorch.constraints.constraints.Interval] = None, batch_shape: torch.Size = 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: []).
Variables: constant (torch.Tensor) – \(C\) parameter
Specialty Means¶
MultitaskMean¶
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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.