gov.sandia.cognition.learning.algorithm.nearest

Class NearestNeighborKDTree<InputType extends Vectorizable,OutputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer<InputType,InputType>

gov.sandia.cognition.learning.algorithm.nearest.AbstractNearestNeighbor<InputType,OutputType>

gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree<InputType,OutputType>

Type Parameters:

InputType - Type of Vectorizable data upon which we determine similarity.

OutputType - Output of the evaluator, like Matrix, Double, String

All Implemented Interfaces:

Evaluator<InputType,OutputType>, NearestNeighbor<InputType,OutputType>, DivergenceFunctionContainer<InputType,InputType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

NearestNeighborKDTree.Learner

public class NearestNeighborKDTree<InputType extends Vectorizable,OutputType>
extends AbstractNearestNeighbor<InputType,OutputType>

A KDTree-based implementation of the nearest neighbor algorithm. This algorithm has a O(n log(n)) construction time and a O(log(n)) evaluate time.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType> This is a BatchLearner interface for creating a new NearestNeighbor from a given dataset, simply a pass-through to the constructor of NearestNeighbor

Field Summary

Fields inherited from class gov.sandia.cognition.learning.function.distance.DefaultDivergenceFunctionContainer

divergenceFunction

Constructor Summary

Constructors

Constructor and Description
NearestNeighborKDTree() Creates a new instance of NearestNeighborKDTree.
NearestNeighborKDTree(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data, DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Creates a new instance of NearestNeighborKDTree

Method Summary

Modifier and Type	Method and Description
NearestNeighborKDTree<InputType,OutputType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
OutputType	evaluate(InputType input) Evaluates the function on the given input and returns the output.
KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>>	getData() Getter for data
Metric<? super InputType>	getDivergenceFunction() Setter for distanceFunction
void	setData(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data) Setter for data
void	setDivergenceFunction(DivergenceFunction<? super InputType,? super InputType> divergenceFunction) Sets the divergence function used by this object.
void	setDivergenceFunction(Metric<? super InputType> divergenceFunction) Sets the Metric to use.

Methods inherited from class gov.sandia.cognition.learning.algorithm.nearest.AbstractNearestNeighbor

add

Methods inherited from class java.lang.Object

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

Constructor Detail

NearestNeighborKDTree

public NearestNeighborKDTree()

Creates a new instance of NearestNeighborKDTree.

NearestNeighborKDTree

public NearestNeighborKDTree(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data,
                             DivergenceFunction<? super InputType,? super InputType> divergenceFunction)

Creates a new instance of NearestNeighborKDTree

Parameters:

data - Underlying data for the classifier

divergenceFunction - Divergence function that determines how "far" two objects are apart

Method Detail

clone

public NearestNeighborKDTree<InputType,OutputType> 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 DefaultDivergenceFunctionContainer<InputType extends Vectorizable,InputType extends Vectorizable>

Returns:

A clone of this object.

getDivergenceFunction

public Metric<? super InputType> getDivergenceFunction()

Setter for distanceFunction

Specified by:

getDivergenceFunction in interface NearestNeighbor<InputType extends Vectorizable,OutputType>

Specified by:

getDivergenceFunction in interface DivergenceFunctionContainer<InputType extends Vectorizable,InputType extends Vectorizable>

Overrides:

getDivergenceFunction in class DefaultDivergenceFunctionContainer<InputType extends Vectorizable,InputType extends Vectorizable>

Returns:

Distance metric that determines how "far" two objects are apart, where lower values indicate two objects are more similar.

setDivergenceFunction

public void setDivergenceFunction(DivergenceFunction<? super InputType,? super InputType> divergenceFunction)

Description copied from class: DefaultDivergenceFunctionContainer

Sets the divergence function used by this object.

Overrides:

setDivergenceFunction in class DefaultDivergenceFunctionContainer<InputType extends Vectorizable,InputType extends Vectorizable>

Parameters:

divergenceFunction - The divergence function.

setDivergenceFunction

public void setDivergenceFunction(Metric<? super InputType> divergenceFunction)

Sets the Metric to use.

Parameters:

divergenceFunction - Metric that determines closeness.

getData

public KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> getData()

Getter for data

Returns:

KDTree that holds the data to search for neighbors.

setData

public void setData(KDTree<InputType,OutputType,InputOutputPair<? extends InputType,OutputType>> data)

Setter for data

Parameters:

data - KDTree that holds the data to search for neighbors.

evaluate

public OutputType evaluate(InputType input)

Description copied from interface: Evaluator

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

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.