gpflow.mean_functions¶

gpflow.mean_functions.Additive¶

class gpflow.mean_functions.Additive(first_part, second_part)[source]¶

Bases: gpflow.mean_functions.MeanFunction

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

gpflow.mean_functions.Constant¶

class gpflow.mean_functions.Constant(c=None)[source]¶

Bases: gpflow.mean_functions.MeanFunction

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

gpflow.mean_functions.Identity¶

class gpflow.mean_functions.Identity(input_dim=None)[source]¶

Bases: gpflow.mean_functions.Linear

y_i = x_i

Attributes

A
b
parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

gpflow.mean_functions.Linear¶

class gpflow.mean_functions.Linear(A=None, b=None)[source]¶

Bases: gpflow.mean_functions.MeanFunction

y_i = A x_i + b

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

__init__(self, A=None, b=None)[source]¶

A is a matrix which maps each element of X to Y, b is an additive constant.

If X has N rows and D columns, and Y is intended to have Q columns, then A must be [D, Q], b must be a vector of length Q.

gpflow.mean_functions.MeanFunction¶

class gpflow.mean_functions.MeanFunction(*args, **kwargs)[source]¶

Bases: gpflow.base.Module

The base mean function class. To implement a mean function, write the __call__ method. This takes a tensor X and returns a tensor m(X). In accordance with the GPflow standard, each row of X represents one datum, and each row of Y is computed independently for each row of X.

MeanFunction classes can have parameters, see the Linear class for an example.

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

gpflow.mean_functions.Product¶

class gpflow.mean_functions.Product(first_part, second_part)[source]¶

Bases: gpflow.mean_functions.MeanFunction

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

gpflow.mean_functions.SwitchedMeanFunction¶

class gpflow.mean_functions.SwitchedMeanFunction(meanfunction_list)[source]¶

Bases: gpflow.mean_functions.MeanFunction

This class enables to use different (independent) mean_functions respective to the data ‘label’. We assume the ‘label’ is stored in the extra column of X.

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.

gpflow.mean_functions.Zero¶

class gpflow.mean_functions.Zero(output_dim=1)[source]¶

Bases: gpflow.mean_functions.Constant

Attributes

parameters
trainable_parameters

Methods

__call__(self, X)

Call self as a function.