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/*
* File: NumericalDifferentiator.java
* Authors: Kevin R. Dixon
* Company: Sandia National Laboratories
* Project: Cognitive Foundry
*
* Copyright Jun 30, 2008, Sandia Corporation.
* Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive
* license for use of this work by or on behalf of the U.S. Government.
* Export of this program may require a license from the United States
* Government. See CopyrightHistory.txt for complete details.
*
*/
package gov.sandia.cognition.math.matrix;
import gov.sandia.cognition.annotation.PublicationReference;
import gov.sandia.cognition.annotation.PublicationType;
import gov.sandia.cognition.evaluator.Evaluator;
import gov.sandia.cognition.math.DifferentiableEvaluator;
import gov.sandia.cognition.util.AbstractCloneableSerializable;
import gov.sandia.cognition.util.ObjectUtil;
/**
* Automatically differentiates a function by the method of forward differences.
*
* @param Class of the input to the Evaluator
* @param Class of the output from the Evaluator
* @param Class of the derivative of the Evaluator
* @author Kevin R. Dixon
* @since 2.1
*/
@PublicationReference(
author="Wikipedia",
title="Numerical differentiation",
type=PublicationType.WebPage,
url="http://en.wikipedia.org/wiki/Numerical_differentiation",
year=2008
)
public abstract class NumericalDifferentiator
extends AbstractCloneableSerializable
implements DifferentiableEvaluator
{
/**
* Default value for x-value differencing, {@value}
*/
static final double DEFAULT_DELTA = 1e-10;
/**
* Value for x-value differencing, must be greater than 0.0
*/
private double delta;
/**
* Internal function to numerically differencing.
*/
private Evaluator internalFunction;
/**
* Creates a new instance of NumericalDifferentiator
* @param internalFunction
* Internal function to numerically differencing.
* @param delta
* Value for x-value differencing, must be greater than 0.0
*/
public NumericalDifferentiator(
Evaluator internalFunction,
double delta )
{
this.setInternalFunction( internalFunction );
this.setDelta( delta );
}
@Override
public NumericalDifferentiator clone()
{
@SuppressWarnings("unchecked")
NumericalDifferentiator clone =
(NumericalDifferentiator) super.clone();
clone.setInternalFunction( ObjectUtil.cloneSmart( this.getInternalFunction() ) );
return clone;
}
/**
* Getter for internalFunction
* @return
* Internal function to numerically differencing.
*/
public Evaluator getInternalFunction()
{
return this.internalFunction;
}
/**
* Setter for internalFunction
* @param internalFunction
* Internal function to numerically differencing.
*/
public void setInternalFunction(
Evaluator internalFunction )
{
this.internalFunction = internalFunction;
}
/**
* Getter for delta
* @return
* Value for x-value differencing, must be greater than 0.0
*/
public double getDelta()
{
return this.delta;
}
/**
* Setter for delta
* @param delta
* Value for x-value differencing, must be greater than 0.0
*/
public void setDelta(
double delta )
{
if( delta <= 0.0 )
{
throw new IllegalArgumentException(
"delta must be > 0.0" );
}
this.delta = delta;
}
public OutputType evaluate(
InputType input )
{
return this.getInternalFunction().evaluate( input );
}
/**
* Numerical differentiator based on a Vector Jacobian.
*/
public static class DoubleJacobian
extends NumericalDifferentiator
{
/**
* Default constructor
*/
public DoubleJacobian()
{
this( null );
}
/**
* Creates a new instance of VectorJacobian
* @param internalFunction
* Internal function to numerically differencing.
*/
public DoubleJacobian(
Evaluator internalFunction )
{
this( internalFunction, DEFAULT_DELTA );
}
/**
* Create a new instance of VectorJacobian
* @param internalFunction
* Internal function to numerically differencing.
* @param delta
* Value for x-value differencing
*/
public DoubleJacobian(
Evaluator internalFunction,
double delta )
{
super( internalFunction, delta );
}
/**
* Static access to the numerical differentiation procedure.
* @param input
* Input about which to approximate the derivative.
* @param f
* Function of which to approximate the derivative.
* @return
* Approximated Jacobian, of the same dimension as input
*/
public static Double differentiate(
double input,
Evaluator f )
{
return DoubleJacobian.differentiate( input, f, DEFAULT_DELTA );
}
/**
* Static access to the numerical differentiation procedure.
* @param input
* Input about which to approximate the derivative.
* @param f
* Function of which to approximate the derivative.
* @param h
* Value for x-value differencing
* @return
* Approximated Jacobian, of the same dimension as input
*/
public static Double differentiate(
double input,
Evaluator f,
double h )
{
double forig = f.evaluate( input );
double fdelta = f.evaluate( input + h );
return (fdelta-forig) / h;
}
public Double differentiate(
Double input )
{
return DoubleJacobian.differentiate(
input, this.getInternalFunction(), this.getDelta() );
}
}
/**
* Numerical differentiator based on a Vector Jacobian.
*/
public static class VectorJacobian
extends NumericalDifferentiator
{
/**
* Default constructor
*/
public VectorJacobian()
{
this( null );
}
/**
* Creates a new instance of VectorJacobian
* @param internalFunction
* Internal function to numerically differencing.
*/
public VectorJacobian(
Evaluator internalFunction )
{
this( internalFunction, DEFAULT_DELTA );
}
/**
* Create a new instance of VectorJacobian
* @param internalFunction
* Internal function to numerically differencing.
* @param delta
* Value for x-value differencing
*/
public VectorJacobian(
Evaluator internalFunction,
double delta )
{
super( internalFunction, delta );
}
/**
* Static access to the numerical differentiation procedure.
* @param input
* Input about which to approximate the derivative.
* @param f
* Function of which to approximate the derivative.
* @return
* Approximated Jacobian, of the same dimension as input
*/
public static Vector differentiate(
Vectorizable input,
Evaluator f )
{
return VectorJacobian.differentiate( input, f, DEFAULT_DELTA );
}
/**
* Static access to the numerical differentiation procedure.
* @param input
* Input about which to approximate the derivative.
* @param f
* Function of which to approximate the derivative.
* @param h
* Value for x-value differencing
* @return
* Approximated Jacobian, of the same dimension as input
*/
public static Vector differentiate(
final Vectorizable input,
final Evaluator f,
final double h )
{
final Vector x = input.convertToVector();
final double forig = f.evaluate( x );
final Vector inputPlusDeltai = x.clone();
int M = x.getDimensionality();
Vector J = VectorFactory.getDefault().createVector( M );
for( int i = 0; i < M; i++ )
{
final double xi = x.getElement( i );
inputPlusDeltai.setElement( i, xi+h );
final double fi = f.evaluate( inputPlusDeltai );
final double di = (fi - forig) / h;
inputPlusDeltai.setElement( i, xi );
J.setElement( i, di );
}
return J;
}
public Vector differentiate(
Vector input )
{
return VectorJacobian.differentiate(
input, this.getInternalFunction(), this.getDelta() );
}
}
/**
* Numerical differentiator based on a Matrix Jacobian.
*/
public static class MatrixJacobian
extends NumericalDifferentiator
{
/**
* Default constructor
*/
public MatrixJacobian()
{
this( (Evaluator) null );
}
/**
* Creates a new instance of VectorJacobian
* @param internalFunction
* Internal function to numerically differencing.
*/
public MatrixJacobian(
Evaluator internalFunction )
{
this( internalFunction, DEFAULT_DELTA );
}
/**
* Create a new instance of VectorJacobian
* @param internalFunction
* Internal function to numerically differencing.
* @param delta
* Value for x-value differencing
*/
public MatrixJacobian(
Evaluator internalFunction,
double delta )
{
super( internalFunction, delta );
}
/**
* Static access to the numerical differentiation procedure.
* @param input
* Input about which to approximate the derivative.
* @param f
* Function of which to approximate the derivative.
* @return
* Approximated Jacobian, of the same dimension as input
*/
public static Matrix differentiate(
Vector input,
Evaluator f )
{
return MatrixJacobian.differentiate( input, f, DEFAULT_DELTA );
}
/**
* Static access to the numerical differentiation procedure.
* @param input
* Input about which to approximate the derivative.
* @param f
* Function of which to approximate the derivative.
* @param h
* Value for x-value differencing
* @return
* Approximated Jacobian, of the same dimension as input
*/
public static Matrix differentiate(
final Vectorizable input,
final Evaluator f,
final double h )
{
final Vector x = input.convertToVector();
final Vector forig = f.evaluate( x );
final Vector inputPlusDeltaj = x.clone();
final int M = forig.getDimensionality();
final int N = x.getDimensionality();
final Matrix J = MatrixFactory.getDefault().createMatrix( M, N );
double Jij;
double xj;
for( int j = 0; j < N; j++ )
{
xj = x.getElement(j);
inputPlusDeltaj.setElement( j, xj+h );
Vector fj = f.evaluate( inputPlusDeltaj );
for( int i = 0; i < M; i++ )
{
Jij = (fj.getElement(i) - forig.getElement(i)) / h;
J.setElement( i, j, Jij);
}
inputPlusDeltaj.setElement( j, xj );
}
return J;
}
public Matrix differentiate(
Vector input )
{
return MatrixJacobian.differentiate(
input, this.getInternalFunction(), this.getDelta() );
}
}
}
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