gov.sandia.cognition.learning.function.scalar

Class PolynomialFunction.Regression

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.function.scalar.PolynomialFunction.Regression

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends java.lang.Double,java.lang.Double>>,VectorFunctionLinearDiscriminant<java.lang.Double>>, SupervisedBatchLearner<java.lang.Double,java.lang.Double,VectorFunctionLinearDiscriminant<java.lang.Double>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

PolynomialFunction

public static class PolynomialFunction.Regression
extends AbstractCloneableSerializable
implements SupervisedBatchLearner<java.lang.Double,java.lang.Double,VectorFunctionLinearDiscriminant<java.lang.Double>>

Performs Linear Regression using an arbitrary set of PolynomialFunction basis functions

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Regression(double... polynomialExponents) Creates a new instance of Regression

Method Summary

Modifier and Type	Method and Description
ScalarBasisSet<java.lang.Double>	getPolynomials() Getter for polynomials
VectorFunctionLinearDiscriminant<java.lang.Double>	learn(java.util.Collection<? extends InputOutputPair<? extends java.lang.Double,java.lang.Double>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
static VectorFunctionLinearDiscriminant<java.lang.Double>	learn(int maxOrder, java.util.Collection<? extends InputOutputPair<java.lang.Double,java.lang.Double>> data) Performs LinearRegression using all integer-exponent polynomials less than or equal to the maxOrder
void	setPolynomials(ScalarBasisSet<java.lang.Double> polynomials) Setter for polynomials

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

Regression

public Regression(double... polynomialExponents)

Creates a new instance of Regression

Parameters:

polynomialExponents - Set of polynomial exponents to use during the regression

Method Detail

learn

public static VectorFunctionLinearDiscriminant<java.lang.Double> learn(int maxOrder,
                                                                       java.util.Collection<? extends InputOutputPair<java.lang.Double,java.lang.Double>> data)

Performs LinearRegression using all integer-exponent polynomials less than or equal to the maxOrder

Parameters:

maxOrder - Uses all polynomials below the maxOrder: a0*x^0 + a1*x^1 + ... am*a^m

data - Data set to use for the LinearRegression

Returns:

LinearCombinationFunction that combines the desired PolynomialFunctions with weighting coefficients determined by the LinearRegression algorithm

learn

public VectorFunctionLinearDiscriminant<java.lang.Double> learn(java.util.Collection<? extends InputOutputPair<? extends java.lang.Double,java.lang.Double>> 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 java.lang.Double,java.lang.Double>>,VectorFunctionLinearDiscriminant<java.lang.Double>>

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.

getPolynomials

public ScalarBasisSet<java.lang.Double> getPolynomials()

Getter for polynomials

Returns:

Polynomials to use in the regression

setPolynomials

public void setPolynomials(ScalarBasisSet<java.lang.Double> polynomials)

Setter for polynomials

Parameters:

polynomials - Polynomials to use in the regression