gov.sandia.cognition.learning.algorithm.nearest

Class NearestNeighborKDTree.Learner<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>

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

Type Parameters:

InputType - Type of data upon which the NearestNeighbor operates, something like Vector, Double, or String

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

All Implemented Interfaces:

Evaluator<InputType,OutputType>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,NearestNeighborKDTree<InputType,OutputType>>, NearestNeighbor<InputType,OutputType>, SupervisedBatchLearner<InputType,OutputType,NearestNeighborKDTree<InputType,OutputType>>, DivergenceFunctionContainer<InputType,InputType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

NearestNeighborKDTree<InputType extends Vectorizable,OutputType>

public static class NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
extends NearestNeighborKDTree<InputType,OutputType>
implements SupervisedBatchLearner<InputType,OutputType,NearestNeighborKDTree<InputType,OutputType>>

This is a BatchLearner interface for creating a new NearestNeighbor from a given dataset, simply a pass-through to the constructor of NearestNeighbor

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class gov.sandia.cognition.learning.algorithm.nearest.NearestNeighborKDTree

NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>

Field Summary

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

divergenceFunction

Constructor Summary

Constructors

Constructor and Description
Learner() Default constructor.
Learner(Metric<? super Vectorizable> divergenceFunction) Creates a new instance of Learner

Method Summary

Modifier and Type	Method and Description
NearestNeighborKDTree<InputType,OutputType>	learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a new NearestNeighbor from a Collection of InputType.

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

clone, evaluate, getData, getDivergenceFunction, setData, setDivergenceFunction, setDivergenceFunction

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

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Constructor Detail

Learner

public Learner()

Default constructor.

Learner

public Learner(Metric<? super Vectorizable> divergenceFunction)

Creates a new instance of Learner

Parameters:

divergenceFunction - Divergence function that determines how "far" two objects are apart, where lower values indicate two objects are more similar

Method Detail

learn

public NearestNeighborKDTree<InputType,OutputType> learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)

Creates a new NearestNeighbor from a Collection of InputType. We build a balanced KDTree with the data, which is an O(n log(n)) operator for n data points.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType extends Vectorizable,OutputType>>,NearestNeighborKDTree<InputType extends Vectorizable,OutputType>>

Parameters:

data - Dataset from which to create a new NearestNeighbor

Returns:

NearestNeighbor based on the given dataset with a balanced KDTree.