gov.sandia.cognition.learning.algorithm.regression

Class LocallyWeightedFunction.Learner<InputType,OutputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.regression.LocallyWeightedFunction.Learner<InputType,OutputType>

Type Parameters:

InputType - Input class to map onto the Output class

OutputType - Output of the Evaluator

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,LocallyWeightedFunction<? super InputType,OutputType>>, SupervisedBatchLearner<InputType,OutputType,LocallyWeightedFunction<? super InputType,OutputType>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

LocallyWeightedFunction<InputType,OutputType>

public static class LocallyWeightedFunction.Learner<InputType,OutputType>
extends AbstractCloneableSerializable
implements SupervisedBatchLearner<InputType,OutputType,LocallyWeightedFunction<? super InputType,OutputType>>

Learning algorithm for creating LocallyWeightedFunctions. This is essentially just a pass through, as no learning takes place, but a model is fitted to the data about each point on an evaluate() call

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Learner(Kernel<? super InputType> kernel, SupervisedBatchLearner<InputType,OutputType,?> learner) Creates a new instance of LocallyWeightedFunction

Method Summary

Modifier and Type	Method and Description
Kernel<? super InputType>	getKernel() Getter for kernel
SupervisedBatchLearner<InputType,OutputType,?>	getLearner() Getter for learner
LocallyWeightedFunction<InputType,OutputType>	learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setKernel(Kernel<? super InputType> kernel) Setter for kernel
void	setLearner(SupervisedBatchLearner<InputType,OutputType,?> learner) Setter for learner

Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

clone

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(Kernel<? super InputType> kernel,
               SupervisedBatchLearner<InputType,OutputType,?> learner)

Creates a new instance of LocallyWeightedFunction

Parameters:

kernel - Kernel that provides the weights between an input and each sample in the input dataset

learner - Learner that takes the Collection of WeightedInputOutputPairs from the Kernel reweighting and creates a local function approximation at the given input. I would strongly recommend using fast or closed-form learners for this.

Method Detail

learn

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

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

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

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

getKernel

public Kernel<? super InputType> getKernel()

Getter for kernel

Returns:

Kernel that provides the weights between an input and each sample in the input dataset

setKernel

public void setKernel(Kernel<? super InputType> kernel)

Setter for kernel

Parameters:

kernel - Kernel that provides the weights between an input and each sample in the input dataset

getLearner

public SupervisedBatchLearner<InputType,OutputType,?> getLearner()

Getter for learner

Returns:

Learner that takes the Collection of WeightedInputOutputPairs from the Kernel reweighting and creates a local function approximation at the given input. I would strongly recommend using fast or closed-form learners for this.

setLearner

public void setLearner(SupervisedBatchLearner<InputType,OutputType,?> learner)

Setter for learner

Parameters:

learner - The learner to use