ca.nengo.model.nef

Interface NEFEnsembleFactory

All Known Implementing Classes:

NEFEnsembleFactoryImpl

public interface NEFEnsembleFactory

Provides a convenient and configurable way to create NEFEnsembles.

Method Summary

Methods

Modifier and Type	Method and Description
ApproximatorFactory	getApproximatorFactory()
VectorGenerator	getEncoderFactory()
VectorGenerator	getEvalPointFactory()
NodeFactory	getNodeFactory()
NEFEnsemble	make(java.lang.String name, int n, float[] radii)
NEFEnsemble	make(java.lang.String name, int n, float[] radii, java.lang.String storageName, boolean overwrite) Loads an NEFEnsemble, or creates and saves it.
NEFEnsemble	make(java.lang.String name, int n, int dim)
NEFEnsemble	make(java.lang.String name, int n, int dim, java.lang.String storageName, boolean overwrite) Loads an NEFEnsemble, or creates and saves it.
void	setApproximatorFactory(ApproximatorFactory factory)
void	setEncoderFactory(VectorGenerator factory)
void	setEvalPointFactory(VectorGenerator factory)
void	setNodeFactory(NodeFactory factory)

Method Detail

getNodeFactory

NodeFactory getNodeFactory()

Returns:

The NodeFactory used to create Nodes that make up new Ensembles

setNodeFactory

void setNodeFactory(NodeFactory factory)

Parameters:

factory - NodeFactory to be used to create Nodes that make up new Ensembles

getEncoderFactory

VectorGenerator getEncoderFactory()

Returns:

The VectorGenerator used to create encoding vectors that are associated with each Node in a new Ensemble

setEncoderFactory

void setEncoderFactory(VectorGenerator factory)

Parameters:

factory - A VectorGenerator to be used to create encoding vectors that are associated with each Node in a new Ensemble

getEvalPointFactory

VectorGenerator getEvalPointFactory()

Returns:

The VectorGenerator used to generate the vector states at which decoding functions are evaluated

setEvalPointFactory

void setEvalPointFactory(VectorGenerator factory)

Parameters:

factory - A VectorGenerator to be used to generate the vector states at which decoding functions are evaluated

getApproximatorFactory

ApproximatorFactory getApproximatorFactory()

Returns:

The factory that creates LinearApproximators used in decoding ensemble output

setApproximatorFactory

void setApproximatorFactory(ApproximatorFactory factory)

Parameters:

factory - A factory for creating the LinearApproximators used in decoding ensemble output

make

NEFEnsemble make(java.lang.String name,
               int n,
               int dim)
                 throws StructuralException

Parameters:

name - Name of the NEFEnsemble

n - Number of neurons in the ensemble

dim - Dimension of the ensemble.

Returns:

NEFEnsemble containing Neurons generated with the default NeuronFactory

Throws:

StructuralException - if there is any error attempting to create the ensemble

make

NEFEnsemble make(java.lang.String name,
               int n,
               float[] radii)
                 throws StructuralException

Parameters:

name - Name of the NEFEnsemble

n - Number of neurons in the ensemble

radii - Radius of encoded region in each dimension

Returns:

NEFEnsemble containing Neurons generated with the default NeuronFactory

Throws:

StructuralException - if there is any error attempting to create the ensemble

make

NEFEnsemble make(java.lang.String name,
               int n,
               float[] radii,
               java.lang.String storageName,
               boolean overwrite)
                 throws StructuralException

Loads an NEFEnsemble, or creates and saves it.

Parameters:

name - Name of the NEFEnsemble

n - Number of neurons in the ensemble

radii - Radius of encoded region in each dimension.

storageName - Name for storage (eg filename, db key; may have to be more fully qualified than name param, if ensembles belonging to multiple networks are stored in the same place)

overwrite - If false, loads the ensemble if it can be found in storage. If true, creates a new ensemble regardless and overwrites any existing ensemble.

Returns:

Either new NEFEnsemble generated according to specs and with default NeuronFactory, or a previously-created ensemble loaded from storage

Throws:

StructuralException - if there is any error attempting to create the ensemble

make

NEFEnsemble make(java.lang.String name,
               int n,
               int dim,
               java.lang.String storageName,
               boolean overwrite)
                 throws StructuralException

Loads an NEFEnsemble, or creates and saves it.

Parameters:

name - Name of the NEFEnsemble

n - Number of neurons in the ensemble

dim - Dimension of the ensemble.

storageName - Name for storage (eg filename, db key; may have to be more fully qualified than name param, if ensembles belonging to multiple networks are stored in the same place)

overwrite - If false, loads the ensemble if it can be found in storage. If true, creates a new ensemble regardless and overwrites any existing ensemble.

Returns:

Either new NEFEnsemble generated according to specs and with default NeuronFactory, or a previously-created ensemble loaded from storage

Throws:

StructuralException - if there is any error attempting to create the ensemble