gov.sandia.cognition.learning.algorithm.regression

Class LinearBasisRegression<InputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.regression.LinearBasisRegression<InputType>

Type Parameters:

InputType - Input class for the basis functions, for example, Double, Vector, String.

All Implemented Interfaces:

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

@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-09-02",
            changesNeeded=false,
            comments={"Made minor changes to javadoc","Looks fine."})
 @PublicationReference(author="Wikipedia",
                      title="Linear regression",
                      type=WebPage,
                      year=2008,
                      url="http://en.wikipedia.org/wiki/Linear_regression")
public class LinearBasisRegression<InputType>
extends AbstractCloneableSerializable
implements SupervisedBatchLearner<InputType,java.lang.Double,VectorFunctionLinearDiscriminant<InputType>>

Computes the least-squares regression for a LinearCombinationFunction given a dataset. A LinearCombinationFunction is a weighted linear combination of (potentially) nonlinear basis functions. This looks like y(x) = a0*f0(x) + a1*f1(x) + ... + an*fn(x) and so forth. The internal class LinearRegression.Statistic returns the goodness-of-fit statistics for a set of target-estimate pairs, include a p-value for the null hypothesis significance.

Since:

2.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_PSEUDO_INVERSE_TOLERANCE Tolerance for the pseudo inverse in the learn method, 1.0E-10.

Constructor Summary

Constructors

Constructor and Description
LinearBasisRegression(java.util.Collection<? extends Evaluator<? super InputType,java.lang.Double>> basisFunctions) Creates a new instance of LinearRegression
LinearBasisRegression(Evaluator<? super InputType,Vector> inputToVectorMap) Creates a new instance of LinearRegression
LinearBasisRegression(ScalarBasisSet<InputType> inputToVectorMap) Creates a new instance of LinearRegression

Method Summary

Modifier and Type	Method and Description
LinearBasisRegression<InputType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
Evaluator<? super InputType,Vector>	getInputToVectorMap() Getter for inputToVectorMap
boolean	getUsePseudoInverse() Getter for usePseudoInverse
VectorFunctionLinearDiscriminant<InputType>	learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data) Computes the linear regression for the given Collection of InputOutputPairs.
void	setInputToVectorMap(Evaluator<? super InputType,Vector> inputToVectorMap) Setter for inputToVectorMap
void	setUsePseudoInverse(boolean usePseudoInverse) Setter for usePseudoInverse

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT_PSEUDO_INVERSE_TOLERANCE

public static final double DEFAULT_PSEUDO_INVERSE_TOLERANCE

Tolerance for the pseudo inverse in the learn method, 1.0E-10.

See Also:

Constant Field Values

Constructor Detail

LinearBasisRegression

public LinearBasisRegression(java.util.Collection<? extends Evaluator<? super InputType,java.lang.Double>> basisFunctions)

Creates a new instance of LinearRegression

Parameters:

basisFunctions - Basis functions to create the ScalarBasisSet from

LinearBasisRegression

public LinearBasisRegression(ScalarBasisSet<InputType> inputToVectorMap)

Creates a new instance of LinearRegression

Parameters:

inputToVectorMap - Function that maps the InputType to a Vector

LinearBasisRegression

public LinearBasisRegression(Evaluator<? super InputType,Vector> inputToVectorMap)

Creates a new instance of LinearRegression

Parameters:

inputToVectorMap - Function that maps the InputType to a Vector

Method Detail

clone

public LinearBasisRegression<InputType> 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 AbstractCloneableSerializable

Returns:

A clone of this object.

learn

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

Computes the linear regression for the given Collection of InputOutputPairs. The inputs of the pairs is the independent variable, and the pair output is the dependent variable (variable to predict). The pairs can have an associated weight to bias the regression equation.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,VectorFunctionLinearDiscriminant<InputType>>

Parameters:

data - Collection of InputOutputPairs for the variables. Can be WeightedInputOutputPairs.

Returns:

LinearCombinationFunction that minimizes the RMS error of the outputs.

getInputToVectorMap

public Evaluator<? super InputType,Vector> getInputToVectorMap()

Getter for inputToVectorMap

Returns:

Function that maps the InputType to a Vector

setInputToVectorMap

public void setInputToVectorMap(Evaluator<? super InputType,Vector> inputToVectorMap)

Setter for inputToVectorMap

Parameters:

inputToVectorMap - Function that maps the InputType to a Vector

getUsePseudoInverse

public boolean getUsePseudoInverse()

Getter for usePseudoInverse

Returns:

Flag to use a pseudoinverse. True to use the expensive, but more accurate, pseudoinverse routine. False uses a very fast, but numerically less stable LU solver. Default value is "true".

setUsePseudoInverse

public void setUsePseudoInverse(boolean usePseudoInverse)

Setter for usePseudoInverse

Parameters:

usePseudoInverse - Flag to use a pseudoinverse. True to use the expensive, but more accurate, pseudoinverse routine. False uses a very fast, but numerically less stable LU solver. Default value is "true".