gov.sandia.cognition.learning.function.vector

Class GeneralizedLinearModel

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.vector.GeneralizedLinearModel

All Implemented Interfaces:

Evaluator<Vector,Vector>, VectorFunction, VectorInputEvaluator<Vector,Vector>, Vectorizable, VectorizableVectorFunction, VectorOutputEvaluator<Vector,Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

DifferentiableGeneralizedLinearModel

@PublicationReference(author="Wikipedia",
                      title="Generalized linear model",
                      type=WebPage,
                      year=2011,
                      url="http://en.wikipedia.org/wiki/Generalized_linear_model")
public class GeneralizedLinearModel
extends AbstractCloneableSerializable
implements VectorizableVectorFunction, VectorInputEvaluator<Vector,Vector>, VectorOutputEvaluator<Vector,Vector>

A VectorizableVectorFunction that is a matrix multiply followed by a VectorFunction... a no-hidden-layer neural network

Since:

1.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GeneralizedLinearModel() Default constructor.
GeneralizedLinearModel(GeneralizedLinearModel other) Creates a new instance of GeneralizedLinearModel
GeneralizedLinearModel(int numInputs, int numOutputs, UnivariateScalarFunction scalarFunction) Creates a new instance of GeneralizedLinearModel
GeneralizedLinearModel(MultivariateDiscriminant matrixMultiply, UnivariateScalarFunction scalarSquashingFunction) Creates a new instance of GeneralizedLinearModel
GeneralizedLinearModel(MultivariateDiscriminant discriminant, VectorFunction squashingFunction) Creates a new instance of GeneralizedLinearModel

Method Summary

Modifier and Type	Method and Description
GeneralizedLinearModel	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
Vector	evaluate(Vector input) Evaluates the function on the given input and returns the output.
MultivariateDiscriminant	getDiscriminant() Getter for discriminant
int	getInputDimensionality() Gets the expected dimensionality of the input vector to the evaluator, if it is known.
int	getOutputDimensionality() Gets the expected dimensionality of the output vector of the evaluator, if it is known.
VectorFunction	getSquashingFunction() Getter for squashingFunction
void	setDiscriminant(MultivariateDiscriminant discriminant) Setter for discriminant
void	setSquashingFunction(VectorFunction squashingFunction) Setter for squashingFunction
java.lang.String	toString()

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

GeneralizedLinearModel

public GeneralizedLinearModel()

Default constructor. Creates a 1x1 model with an identity function for the output.

GeneralizedLinearModel

public GeneralizedLinearModel(int numInputs,
                              int numOutputs,
                              UnivariateScalarFunction scalarFunction)

Creates a new instance of GeneralizedLinearModel

Parameters:

numInputs - Number of inputs of the function (number of matrix columns)

numOutputs - Number of outputs of the function (number of matrix rows)

scalarFunction - Function to apply to each output

GeneralizedLinearModel

public GeneralizedLinearModel(MultivariateDiscriminant discriminant,
                              VectorFunction squashingFunction)

Creates a new instance of GeneralizedLinearModel

Parameters:

discriminant - GradientDescendable that multiplies an input by the internal matrix

squashingFunction - VectorFunction that is applied to the output of the matrix multiply

GeneralizedLinearModel

public GeneralizedLinearModel(GeneralizedLinearModel other)

Creates a new instance of GeneralizedLinearModel

Parameters:

other - GeneralizedLinearModel to copy

GeneralizedLinearModel

public GeneralizedLinearModel(MultivariateDiscriminant matrixMultiply,
                              UnivariateScalarFunction scalarSquashingFunction)

Creates a new instance of GeneralizedLinearModel

Parameters:

matrixMultiply - GradientDescendable that multiplies an input by the internal matrix

scalarSquashingFunction - scalar function that is applied to the output of the matrix multiply

Method Detail

getDiscriminant

public MultivariateDiscriminant getDiscriminant()

Getter for discriminant

Returns:

GradientDescendable that multiplies an input by the internal matrix

setDiscriminant

public void setDiscriminant(MultivariateDiscriminant discriminant)

Setter for discriminant

Parameters:

discriminant - GradientDescendable that multiplies an input by the internal matrix

getSquashingFunction

public VectorFunction getSquashingFunction()

Getter for squashingFunction

Returns:

VectorFunction that is applied to the output of the matrix multiply

setSquashingFunction

public void setSquashingFunction(VectorFunction squashingFunction)

Setter for squashingFunction

Parameters:

squashingFunction - VectorFunction that is applied to the output of the matrix multiply

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

evaluate

public Vector evaluate(Vector input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<Vector,Vector>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

clone

public GeneralizedLinearModel clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface Vectorizable

Specified by:

clone in interface VectorizableVectorFunction

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object

getInputDimensionality

public int getInputDimensionality()

Description copied from interface: VectorInputEvaluator

Gets the expected dimensionality of the input vector to the evaluator, if it is known. If it is not known, -1 is returned.

Specified by:

getInputDimensionality in interface VectorInputEvaluator<Vector,Vector>

Returns:

The expected dimensionality of the input vector to the evaluator, or -1 if it is not known.

getOutputDimensionality

public int getOutputDimensionality()

Description copied from interface: VectorOutputEvaluator

Gets the expected dimensionality of the output vector of the evaluator, if it is known. If it is not known, -1 is returned.

Specified by:

getOutputDimensionality in interface VectorOutputEvaluator<Vector,Vector>

Returns:

The expected dimensionality of the output vector of the evaluator, or -1 if it is not known.