gov.sandia.cognition.learning.function.scalar

Class VectorFunctionLinearDiscriminant<InputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.math.AbstractScalarFunction<InputType>

gov.sandia.cognition.learning.function.regression.AbstractRegressor<InputType>

gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant<InputType>

Type Parameters:

InputType - Must map this class onto a Vector

All Implemented Interfaces:

Evaluator<InputType,java.lang.Double>, Vectorizable, ScalarFunction<InputType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class VectorFunctionLinearDiscriminant<InputType>
extends AbstractRegressor<InputType>
implements Vectorizable

This class takes a function that maps a generic InputType to a Vector. We then take the dot product of that Vector with a weight Vector to yield the scalar output. The parameters of this class are in the weight Vector.

Author:

Kevin R. Dixon

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
VectorFunctionLinearDiscriminant(Evaluator<? super InputType,Vector> vectorFunction, LinearDiscriminant discriminant) Creates a new instance of VectorFunctionLinearDiscriminant

Method Summary

Modifier and Type	Method and Description
VectorFunctionLinearDiscriminant<InputType>	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.
double	evaluateAsDouble(InputType input) Evaluates the scalar function as a double.
LinearDiscriminant	getDiscriminant() Getter for discriminant
Evaluator<? super InputType,Vector>	getVectorFunction() Getter for vectorFunction
void	setDiscriminant(LinearDiscriminant discriminant) Setter for discriminant
void	setVectorFunction(Evaluator<? super InputType,Vector> vectorFunction) Setter for vectorFunction

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.math.ScalarFunction

evaluate

Constructor Detail

VectorFunctionLinearDiscriminant

public VectorFunctionLinearDiscriminant(Evaluator<? super InputType,Vector> vectorFunction,
                                        LinearDiscriminant discriminant)

Creates a new instance of VectorFunctionLinearDiscriminant

Parameters:

vectorFunction - Maps the input space to a Vector

discriminant - The dot product of the discriminant with the output of the vectorFunction is the output (scalar) value. Must have the same dimensions as the outputDimensionality of vectorFunction.

Method Detail

clone

public VectorFunctionLinearDiscriminant<InputType> 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 CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

getDiscriminant

public LinearDiscriminant getDiscriminant()

Getter for discriminant

Returns:

The dot product of the discriminant with the output of the vectorFunction is the output (scalar) value. Must have the same dimensions as the outputDimensionality of vectorFunction.

setDiscriminant

public void setDiscriminant(LinearDiscriminant discriminant)

Setter for discriminant

Parameters:

discriminant - The dot product of the discriminant with the output of the vectorFunction is the output (scalar) value. Must have the same dimensions as the outputDimensionality of vectorFunction.

getVectorFunction

public Evaluator<? super InputType,Vector> getVectorFunction()

Getter for vectorFunction

Returns:

Maps the input space to a Vector

setVectorFunction

public void setVectorFunction(Evaluator<? super InputType,Vector> vectorFunction)

Setter for vectorFunction

Parameters:

vectorFunction - Maps the input space to a Vector

evaluateAsDouble

public double evaluateAsDouble(InputType input)

Description copied from interface: ScalarFunction

Evaluates the scalar function as a double.

Specified by:

evaluateAsDouble in interface ScalarFunction<InputType>

Parameters:

input - The input value.

Returns:

The scalar output calculated from the given input.

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.