gov.sandia.cognition.learning.algorithm.minimization.line
Class DirectionalVectorToDifferentiableScalarFunction

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.math.AbstractScalarFunction<Double>
          extended by gov.sandia.cognition.math.AbstractUnivariateScalarFunction
              extended by gov.sandia.cognition.math.AbstractDifferentiableUnivariateScalarFunction
                  extended by gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction
                      extended by gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToDifferentiableScalarFunction
All Implemented Interfaces:
Evaluator<Double,Double>, DifferentiableEvaluator<Double,Double,Double>, DifferentiableUnivariateScalarFunction, ScalarFunction<Double>, UnivariateScalarFunction, CloneableSerializable, Serializable, Cloneable

@CodeReview(reviewer="Kevin R. Dixon",
            date="2009-07-06",
            changesNeeded=false,
            comments={"Made clone() call super.clone().","Created test class.","Class looks fine."})
public class DirectionalVectorToDifferentiableScalarFunction
extends DirectionalVectorToScalarFunction

Creates a truly differentiable scalar function from a differentiable Vector function, instead of using a forward-differences approximation to the derivative like DirectionalVectorToScalarFunction does.

Since:
2.1
Author:
Kevin R. Dixon
See Also:
Serialized Form

Field Summary
 
Fields inherited from class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction
FORWARD_DIFFERENCE
 
Constructor Summary
DirectionalVectorToDifferentiableScalarFunction(DifferentiableEvaluator<? super Vector,? extends Double,Vector> vectorScalarFunction, Vector vectorOffset, Vector direction)
          Creates a new instance of DirectionalVectorToDifferentiableScalarFunction
 
Method Summary
 DirectionalVectorToDifferentiableScalarFunction clone()
          This makes public the clone method on the Object class and removes the exception that it throws.
 double differentiate(double input)
          Differentiates the output of the function about the given input
 InputOutputPair<Vector,Vector> getLastGradient()
          Getter for lastGradient
 void setLastGradient(InputOutputPair<Vector,Vector> lastGradient)
          Setter for lastGradient
 
Methods inherited from class gov.sandia.cognition.learning.algorithm.minimization.line.DirectionalVectorToScalarFunction
computeVector, evaluate, getDirection, getVectorOffset, getVectorScalarFunction, setDirection, setVectorOffset, setVectorScalarFunction
 
Methods inherited from class gov.sandia.cognition.math.AbstractDifferentiableUnivariateScalarFunction
differentiate
 
Methods inherited from class gov.sandia.cognition.math.AbstractUnivariateScalarFunction
evaluate, evaluateAsDouble
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.math.ScalarFunction
evaluateAsDouble
 
Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator
evaluate
 

Constructor Detail

DirectionalVectorToDifferentiableScalarFunction

public DirectionalVectorToDifferentiableScalarFunction(DifferentiableEvaluator<? super Vector,? extends Double,Vector> vectorScalarFunction,
                                                       Vector vectorOffset,
                                                       Vector direction)
Creates a new instance of DirectionalVectorToDifferentiableScalarFunction

Parameters:
vectorScalarFunction - Function that maps a Vector onto a Double
vectorOffset - offset vector from which to scale along direction to evaluate vectorFunction
direction - Direction to optimize along
Method Detail

clone

public DirectionalVectorToDifferentiableScalarFunction 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 DirectionalVectorToScalarFunction
Returns:
A clone of this object.

differentiate

public double differentiate(double input)
Description copied from interface: DifferentiableUnivariateScalarFunction
Differentiates the output of the function about the given input

Specified by:
differentiate in interface DifferentiableUnivariateScalarFunction
Overrides:
differentiate in class DirectionalVectorToScalarFunction
Parameters:
input - Input about which to compute the derivative of the function output
Returns:
Derivative of the output with respect to the input

getLastGradient

public InputOutputPair<Vector,Vector> getLastGradient()
Getter for lastGradient

Returns:
Last gradient information

setLastGradient

public void setLastGradient(InputOutputPair<Vector,Vector> lastGradient)
Setter for lastGradient

Parameters:
lastGradient - Last gradient information