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

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.math.AbstractScalarFunction<InputType>
          extended by gov.sandia.cognition.learning.function.regression.AbstractRegressor<InputType>
              extended by gov.sandia.cognition.learning.function.scalar.VectorFunctionLinearDiscriminant<InputType>
Type Parameters:
InputType - Must map this class onto a Vector
All Implemented Interfaces:
Evaluator<InputType,Double>, Vectorizable, ScalarFunction<InputType>, CloneableSerializable, Serializable, 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
VectorFunctionLinearDiscriminant(Evaluator<? super InputType,Vector> vectorFunction, LinearDiscriminant discriminant)
          Creates a new instance of VectorFunctionLinearDiscriminant
 
Method Summary
 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 gov.sandia.cognition.math.AbstractScalarFunction
evaluate
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

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.