gov.sandia.cognition.learning.function.kernel
Class NormalizedKernel<InputType>

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer<InputType>
          extended by gov.sandia.cognition.learning.function.kernel.NormalizedKernel<InputType>
Type Parameters:
InputType - The type of the input to the Kernel. For example, Vector.
All Implemented Interfaces:
Kernel<InputType>, KernelContainer<InputType>, CloneableSerializable, Serializable, Cloneable

@CodeReview(reviewer="Kevin R. Dixon",
            date="2009-07-08",
            changesNeeded=false,
            comments={"Made clone call super.clone.","Looks fine otherwise."})
public class NormalizedKernel<InputType>
extends DefaultKernelContainer<InputType>
implements Kernel<InputType>

The NormalizedKernel class implements an Kernel that returns a normalized value between 0.0 and 1.0 by normalizing the results of a given kernel. The normalization is done by:
k(x, y) / sqrt( k(x, x) * k(y, y))

Since:
2.0
Author:
Justin Basilico
See Also:
Serialized Form

Field Summary
 
Fields inherited from class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer
kernel
 
Constructor Summary
NormalizedKernel()
          Creates a new instance of NormalizedKernel.
NormalizedKernel(Kernel<? super InputType> kernel)
          Creates a new instance of NormalizedKernel using the given kernel.
NormalizedKernel(NormalizedKernel<? super InputType> other)
          Creates a new copy of a NormalizedKernel.
 
Method Summary
 NormalizedKernel<InputType> clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 double evaluate(InputType x, InputType y)
          Evaluates the normalized kernel by passing the evaluation off to the internal kernel and then normalizing the results.
 
Methods inherited from class gov.sandia.cognition.learning.function.kernel.DefaultKernelContainer
getKernel, setKernel
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

NormalizedKernel

public NormalizedKernel()
Creates a new instance of NormalizedKernel.


NormalizedKernel

public NormalizedKernel(Kernel<? super InputType> kernel)
Creates a new instance of NormalizedKernel using the given kernel.

Parameters:
kernel - The kernel to use.

NormalizedKernel

public NormalizedKernel(NormalizedKernel<? super InputType> other)
Creates a new copy of a NormalizedKernel.

Parameters:
other - The NormalizedKernel to copy.
Method Detail

clone

public NormalizedKernel<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 CloneableSerializable
Overrides:
clone in class DefaultKernelContainer<InputType>
Returns:
A clone of this object.

evaluate

public double evaluate(InputType x,
                       InputType y)
Evaluates the normalized kernel by passing the evaluation off to the internal kernel and then normalizing the results. The kernel is computed as:
k(x, y) / sqrt( k(x, x) * k(y, y))

Specified by:
evaluate in interface Kernel<InputType>
Parameters:
x - The first item.
y - The second item.
Returns:
The kernel evaluated on the two given objects. The value will be between 0.0 and 1.0 since the value is normalized.