# gpytorch.means¶

## Mean¶

class gpytorch.means.Mean[source]

Mean function.

## Standard Means¶

### ZeroMean¶

class gpytorch.means.ZeroMean(batch_shape=torch.Size([]), **kwargs)[source]

### 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¶

class gpytorch.means.LinearMean(input_size, batch_shape=torch.Size([]), bias=True)[source]

## Specialty Means¶

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 in forward() and returned as an n x t matrix of means, one for each task.

forward(input)[source]

Evaluate each mean in self.base_means on the input data, and return as an n x t matrix of means.

A (non-zero) constant prior mean function and its first and second derivatives, i.e.:

$\begin{split}\mu(\mathbf x) &= C \\ \nabla \mu(\mathbf x) &= \mathbf 0 \\ \nabla^2 \mu(\mathbf x) &= \mathbf 0\end{split}$

where $$C$$ is a learned constant.

Parameters:
• prior (Prior, optional) – Prior 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

A linear prior mean function and its first derivative, i.e.:

$\begin{split}\mu(\mathbf x) &= \mathbf W \cdot \mathbf x + B \\ \nabla \mu(\mathbf x) &= \mathbf W\end{split}$

where $$\mathbf W$$ and $$B$$ are learned constants.

Parameters:
• input_size (int) – dimension of input $$\mathbf x$$.

• batch_shape (torch.Size, optional) – The batch shape of the learned constant(s) (default: []).

• bias (bool, optional) – True/False flag for whether the bias: $$B$$ should be used in the mean (default: True).

Variables:

A linear prior mean function and its first and second derivatives, i.e.:

$\begin{split}\mu(\mathbf x) &= \mathbf W \cdot \mathbf x + B \\ \nabla \mu(\mathbf x) &= \mathbf W \\ \nabla^2 \mu(\mathbf x) &= \mathbf 0 \\\end{split}$

where $$\mathbf W$$ and $$B$$ are learned constants.

Parameters:
• input_size (int) – dimension of input $$\mathbf x$$.

• batch_shape (torch.Size, optional) – The batch shape of the learned constant(s) (default: []).

• bias (bool, optional) – True/False flag for whether the bias: $$B$$ should be used in the mean (default: True).

Variables: