gov.sandia.cognition.learning.algorithm.regression

Class LinearRegression.Statistic

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic

gov.sandia.cognition.learning.algorithm.regression.LinearRegression.Statistic

All Implemented Interfaces:

ConfidenceStatistic, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

LinearRegression

public static class LinearRegression.Statistic
extends AbstractConfidenceStatistic

Computes regression statistics using a chi-square measure of the statistical significance of the learned approximator

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic

nullHypothesisProbability

Constructor Summary

Constructors

Constructor and Description
Statistic(java.util.Collection<java.lang.Double> targets, java.util.Collection<java.lang.Double> estimates, java.util.Collection<java.lang.Double> weights, int numParameters) Creates a new instance of Statistic
Statistic(java.util.Collection<java.lang.Double> targets, java.util.Collection<java.lang.Double> estimates, int numParameters) Creates a new instance of Statistic

Method Summary

Modifier and Type	Method and Description
LinearRegression.Statistic	clone() This makes public the clone method on the Object class and removes the exception that it throws.
double	getChiSquare() Getter for chiSquare
double	getDegreesOfFreedom() Getter for degreesOfFreedom
double	getMeanL1Error() Getter for meanL1Error
int	getNumParameters() Getter for numParameters
int	getNumSamples() Getter for numSamples
double	getRootMeanSquaredError() Getter for rootMeanSquaredError
double	getTargetEstimateCorrelation() Getter for targetEstimateCorrelation
double	getTestStatistic() Gets the statistic from which we compute the null-hypothesis probability.
double	getUnpredictedErrorFraction() Getter for unpredictedErrorFraction
void	setChiSquare(double chiSquare) Setter for chiSquare
protected void	setDegreesOfFreedom(double degreesOfFreedom) Setter for degreesOfFreedom
protected void	setMeanL1Error(double meanL1Error) Setter for meanL1Error
void	setNumParameters(int numParameters) Setter for numParameters
protected void	setNumSamples(int numSamples) Setter for numSamples
protected void	setRootMeanSquaredError(double rootMeanSquaredError) Setter fpr rootMeanSquaredError
protected void	setTargetEstimateCorrelation(double targetEstimateCorrelation) Setter for targetEstimateCorrelation
protected void	setUnpredictedErrorFraction(double unpredictedErrorFraction) Setter for unpredictedErrorFraction

Methods inherited from class gov.sandia.cognition.statistics.method.AbstractConfidenceStatistic

getNullHypothesisProbability, setNullHypothesisProbability, toString

Methods inherited from class java.lang.Object

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

Constructor Detail

Statistic

public Statistic(java.util.Collection<java.lang.Double> targets,
                 java.util.Collection<java.lang.Double> estimates,
                 int numParameters)

Creates a new instance of Statistic

Parameters:

targets - Collection of ground-truth targets for the learned approximator

estimates - Collection of estimates from the learned approximator

numParameters - Number of parameters in the learned approximator

Statistic

public Statistic(java.util.Collection<java.lang.Double> targets,
                 java.util.Collection<java.lang.Double> estimates,
                 java.util.Collection<java.lang.Double> weights,
                 int numParameters)

Creates a new instance of Statistic

Parameters:

targets - Collection of ground-truth targets for the learned approximator

estimates - Collection of estimates from the learned approximator

weights - Collection of weights to apply to the corresponding target-estimate pair

numParameters - Number of parameters in the learned approximator

Method Detail

clone

public LinearRegression.Statistic 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.

getRootMeanSquaredError

public double getRootMeanSquaredError()

Getter for rootMeanSquaredError

Returns:

Root mean-squared error of the targets and estimates

setRootMeanSquaredError

protected void setRootMeanSquaredError(double rootMeanSquaredError)

Setter fpr rootMeanSquaredError

Parameters:

rootMeanSquaredError - Root mean-squared error of the targets and estimates

getTargetEstimateCorrelation

public double getTargetEstimateCorrelation()

Getter for targetEstimateCorrelation

Returns:

Pearson Correlation between the targets and estimates, [-1,1]

setTargetEstimateCorrelation

protected void setTargetEstimateCorrelation(double targetEstimateCorrelation)

Setter for targetEstimateCorrelation

Parameters:

targetEstimateCorrelation - Pearson Correlation between the targets and estimates, [-1,1]

getUnpredictedErrorFraction

public double getUnpredictedErrorFraction()

Getter for unpredictedErrorFraction

Returns:

Fraction of variance unaccounted for in the predictions, [0,1]

setUnpredictedErrorFraction

protected void setUnpredictedErrorFraction(double unpredictedErrorFraction)

Setter for unpredictedErrorFraction

Parameters:

unpredictedErrorFraction - Fraction of variance unaccounted for in the predictions, [0,1]

getNumSamples

public int getNumSamples()

Getter for numSamples

Returns:

Number of samples used to create the Regression

setNumSamples

protected void setNumSamples(int numSamples)

Setter for numSamples

Parameters:

numSamples - Number of samples used to create the Regression

getDegreesOfFreedom

public double getDegreesOfFreedom()

Getter for degreesOfFreedom

Returns:

Degrees of freedom in the Regression = numSamples-numParameters

setDegreesOfFreedom

protected void setDegreesOfFreedom(double degreesOfFreedom)

Setter for degreesOfFreedom

Parameters:

degreesOfFreedom - Degrees of freedom in the Regression = numSamples-numParameters

getMeanL1Error

public double getMeanL1Error()

Getter for meanL1Error

Returns:

Average L1-norm error (absolute value difference) between the targets and estimates

setMeanL1Error

protected void setMeanL1Error(double meanL1Error)

Setter for meanL1Error

Parameters:

meanL1Error - Average L1-norm error (absolute value difference) between the targets and estimates

getNumParameters

public int getNumParameters()

Getter for numParameters

Returns:

Number of parameters in the learned approximator

setNumParameters

public void setNumParameters(int numParameters)

Setter for numParameters

Parameters:

numParameters - Number of parameters in the learned approximator

getChiSquare

public double getChiSquare()

Getter for chiSquare

Returns:

Gets the value of the chi-square variable, Total weighted sum-squared error between the targets and estimates

setChiSquare

public void setChiSquare(double chiSquare)

Setter for chiSquare

Parameters:

chiSquare - Gets the value of the chi-square variable, Total weighted sum-squared error between the targets and estimates

getTestStatistic

public double getTestStatistic()

Description copied from interface: ConfidenceStatistic

Gets the statistic from which we compute the null-hypothesis probability. In an ANOVA, this would be the "F" statistic. In a t-test, this would be the "t" value. And so forth.

Returns:

Confidence statistic used to compute the null-hypothesis probability.