gpflow.mean_functions¶

Throughout GPflow, by default, latent functions being modelled with Gaussian processes are assumed to have zero mean, f ~ GP(0, k(x,x’)).

In some cases we may wish to model only the deviation from a fixed function with a Gaussian process. For flexibility this fixed function could be both input dependent and parameterised function, μ(x; θ), with some unknown parameters θ, resulting in f ~ GP(μ(x;θ), k(x,x’)).

The GPflow MeanFunction class allows this to be done whilst additionally learning parameters of the parametric function.

gpflow.mean_functions.Additive¶

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

Bases: gpflow.mean_functions.MeanFunction

Attributes

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters

first_part (MeanFunction) –
second_part (MeanFunction) –

gpflow.mean_functions.Constant¶

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

Bases: gpflow.mean_functions.MeanFunction

Attributes

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters: c (Union[ndarray, Tensor, Variable, Parameter, None]) –

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
name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters: input_dim (Optional[int]) –

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

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters

A (Union[ndarray, Tensor, Variable, Parameter, None]) –
b (Union[ndarray, Tensor, Variable, Parameter, None]) –

gpflow.mean_functions.MeanFunction¶

class gpflow.mean_functions.MeanFunction(name=None)[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

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

gpflow.mean_functions.Product¶

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

Bases: gpflow.mean_functions.MeanFunction

Attributes

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters

first_part (MeanFunction) –
second_part (MeanFunction) –

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

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters: meanfunction_list (Collection[MeanFunction]) –

gpflow.mean_functions.Zero¶

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

Bases: gpflow.mean_functions.Constant

Attributes

name: Returns the name of this module as passed or determined in the ctor.
name_scope: Returns a tf.name_scope instance for this class.
non_trainable_variables: Sequence of non-trainable variables owned by this module and its submodules.
parameters
submodules: Sequence of all sub-modules.
trainable_parameters
trainable_variables: Sequence of trainable variables owned by this module and its submodules.
variables: Sequence of variables owned by this module and its submodules.

Methods

`__call__`(X)	Call self as a function.
`with_name_scope`(method)	Decorator to automatically enter the module name scope.

Parameters: output_dim (int) –