gov.sandia.cognition.learning.algorithm.nearest

Class KNearestNeighborKDTree.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.AbstractKNearestNeighbor<InputType,OutputType>

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

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

Type Parameters:

InputType - Type of data upon which the KNearestNeighbor 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>>,KNearestNeighborKDTree<InputType,OutputType>>, KNearestNeighbor<InputType,OutputType>, NearestNeighbor<InputType,OutputType>, SupervisedBatchLearner<InputType,OutputType,KNearestNeighborKDTree<InputType,OutputType>>, DivergenceFunctionContainer<InputType,InputType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>

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

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

See Also:

Serialized Form

Nested Class Summary

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

KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>

Field Summary

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

divergenceFunction

Fields inherited from interface gov.sandia.cognition.learning.algorithm.nearest.KNearestNeighbor

DEFAULT_K

Constructor Summary

Constructors

Constructor and Description
Learner() Default constructor.
Learner(int k, Metric<? super Vectorizable> divergenceFunction, Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of Learner
Learner(Summarizer<? super OutputType,? extends OutputType> averager) Creates a new instance of Learner.

Method Summary

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

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

clone, computeNeighborhood, getData, getDivergenceFunction, rebalance, setData, setDivergenceFunction, setDivergenceFunction

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

evaluate, getAverager, getK, setAverager, setK

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

Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator

evaluate

Methods inherited from interface gov.sandia.cognition.learning.algorithm.nearest.NearestNeighbor

add

Constructor Detail

Learner

public Learner()

Default constructor.

Learner

public Learner(Summarizer<? super OutputType,? extends OutputType> averager)

Creates a new instance of Learner.

Parameters:

averager - Creates a single object from a collection of data.

Learner

public Learner(int k,
               Metric<? super Vectorizable> divergenceFunction,
               Summarizer<? super OutputType,? extends OutputType> averager)

Creates a new instance of Learner

Parameters:

k - Number of neighbors to consider, must be greater than zero

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

averager - Creates a single object from a collection of data

Method Detail

learn

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

Creates a new KNearestNeighbor 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>>,KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>>

Parameters:

data - Dataset from which to create a new KNearestNeighbor

Returns:

KNearestNeighbor based on the given dataset with a balanced KDTree.