gov.sandia.cognition.learning.function.cost

Class SumSquaredErrorCostFunction

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.performance.AbstractSupervisedPerformanceEvaluator<InputType,TargetType,TargetType,java.lang.Double>

gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction<Vector,Vector>

gov.sandia.cognition.learning.function.cost.AbstractParallelizableCostFunction

gov.sandia.cognition.learning.function.cost.SumSquaredErrorCostFunction

All Implemented Interfaces:

Evaluator<Evaluator<? super Vector,? extends Vector>,java.lang.Double>, CostFunction<Evaluator<? super Vector,? extends Vector>,java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>>, DifferentiableCostFunction, ParallelizableCostFunction, SupervisedCostFunction<Vector,Vector>, PerformanceEvaluator<Evaluator<? super Vector,? extends Vector>,java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,java.lang.Double>, SupervisedPerformanceEvaluator<Vector,Vector,Vector,java.lang.Double>, CloneableSerializable, Summarizer<TargetEstimatePair<? extends Vector,? extends Vector>,java.lang.Double>, java.io.Serializable, java.lang.Cloneable

public class SumSquaredErrorCostFunction
extends AbstractParallelizableCostFunction

This is the sum-squared error cost function

Since:

2.1

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SumSquaredErrorCostFunction.Cache Caches often-used values for the Cost Function
static class	SumSquaredErrorCostFunction.GradientPartialSSE Partial result from the SSE gradient computation

Constructor Summary

Constructors

Constructor and Description
SumSquaredErrorCostFunction() Creates a new instance of SumSquaredErrorCostFunction
SumSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset) Creates a new instance of MeanSquaredErrorCostFunction

Method Summary

Modifier and Type	Method and Description
SumSquaredErrorCostFunction	clone() This makes public the clone method on the Object class and removes the exception that it throws.
Vector	computeParameterGradientAmalgamate(java.util.Collection<java.lang.Object> partialResults) Amalgamates the linear components of the cost gradient function into a single Vector.
java.lang.Object	computeParameterGradientPartial(GradientDescendable function) Computes the partial (linear) component of the cost function gradient.
java.lang.Double	evaluateAmalgamate(java.util.Collection<java.lang.Object> partialResults) Amalgamates the linear components of the cost function into a single Double.
java.lang.Object	evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator) Computes the partial (linear) component of the cost function.
java.lang.Double	evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data) Evaluates the performance accuracy of the given estimates against the given targets.

Methods inherited from class gov.sandia.cognition.learning.function.cost.AbstractParallelizableCostFunction

computeParameterGradient, evaluate

Methods inherited from class gov.sandia.cognition.learning.function.cost.AbstractSupervisedCostFunction

getCostParameters, setCostParameters, summarize

Methods inherited from class gov.sandia.cognition.learning.performance.AbstractSupervisedPerformanceEvaluator

evaluatePerformance

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.learning.function.cost.CostFunction

getCostParameters, setCostParameters

Methods inherited from interface gov.sandia.cognition.util.Summarizer

summarize

Methods inherited from interface gov.sandia.cognition.learning.performance.PerformanceEvaluator

evaluatePerformance

Constructor Detail

SumSquaredErrorCostFunction

public SumSquaredErrorCostFunction()

Creates a new instance of SumSquaredErrorCostFunction

SumSquaredErrorCostFunction

public SumSquaredErrorCostFunction(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> dataset)

Creates a new instance of MeanSquaredErrorCostFunction

Parameters:

dataset - The dataset of examples to use to compute the error.

Method Detail

clone

public SumSquaredErrorCostFunction 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 CostFunction<Evaluator<? super Vector,? extends Vector>,java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>>

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractSupervisedCostFunction<Vector,Vector>

Returns:

A clone of this object.

evaluatePartial

public java.lang.Object evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator)

Description copied from interface: ParallelizableCostFunction

Computes the partial (linear) component of the cost function. This portion will be performed in parallel.

Parameters:

evaluator - Evaluator to compute the cost of

Returns:

Object that contains the linear component of the cost function

evaluateAmalgamate

public java.lang.Double evaluateAmalgamate(java.util.Collection<java.lang.Object> partialResults)

Description copied from interface: ParallelizableCostFunction

Amalgamates the linear components of the cost function into a single Double. This portion will be performed in sequence.

Parameters:

partialResults - Collection of partial (linear) results

Returns:

Cost function of the partial results

computeParameterGradientPartial

public java.lang.Object computeParameterGradientPartial(GradientDescendable function)

Description copied from interface: ParallelizableCostFunction

Computes the partial (linear) component of the cost function gradient. This portion will be performed in parallel.

Parameters:

function - GradientDescendable to compute the gradient of

Returns:

Object that contains the linear component of the gradient

computeParameterGradientAmalgamate

public Vector computeParameterGradientAmalgamate(java.util.Collection<java.lang.Object> partialResults)

Description copied from interface: ParallelizableCostFunction

Amalgamates the linear components of the cost gradient function into a single Vector. This portion will be performed in sequence.

Parameters:

partialResults - Collection of partial (linear) gradient components

Returns:

Vector describing the gradient

evaluatePerformance

public java.lang.Double evaluatePerformance(java.util.Collection<? extends TargetEstimatePair<? extends Vector,? extends Vector>> data)

Description copied from interface: SupervisedPerformanceEvaluator

Evaluates the performance accuracy of the given estimates against the given targets.

Specified by:

evaluatePerformance in interface SupervisedPerformanceEvaluator<Vector,Vector,Vector,java.lang.Double>

Specified by:

evaluatePerformance in class AbstractSupervisedCostFunction<Vector,Vector>

Parameters:

data - The target-estimate pairs to use to evaluate performance.

Returns:

The performance evaluation result.