CurveFitter.fit(ParametricRealFunction, double[]) always returns the same value
as the initial guess when used with the LevenbergMarquardtOptimizer
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Key: MATH-304
URL: https://issues.apache.org/jira/browse/MATH-304
Project: Commons Math
Issue Type: Bug
Affects Versions: 2.0
Environment: Java, Ubuntu 9.04 (64 bit)
Reporter: Daren Drummond
CurveFitter.fit(ParametricRealFunction, double[]) always returns the same value
as the initial guess when used with the LevenbergMarquardtOptimizer and the
length of the initial guess array is 1. Here is my example code:
{code:title=CurveFitter with LevenbergMarquardtOptimizer|borderStyle=solid}
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
CurveFitter fitter = new CurveFitter(optimizer);
fitter.addObservedPoint(2.805d, 0.6934785852953367d);
fitter.addObservedPoint(2.74333333333333d, 0.6306772025518496d);
fitter.addObservedPoint(1.655d, 0.9474675497289684);
fitter.addObservedPoint(1.725d, 0.9013594835804194d);
SimpleInverseFunction sif = new SimpleInverseFunction(); // Class provided
below
double[] initialguess = new double[1];
initialguess[0] = 1.0d;
double[] bestCoefficients = fitter.fit(sif, initialguess); // <---- ALWAYS
RETURNS A VALUE OF initialguess !
/**
* This is my implementation of ParametricRealFunction
* Implements y = ax^-1 + b for use with an Apache CurveFitter
implementation
*/
private class SimpleInverseFunction implements ParametricRealFunction
{
public double value(double x, double[] doubles) throws
FunctionEvaluationException
{
//y = ax^-1 + b
//"double[] must include at least 1 but not more than 2
coefficients."
if(doubles == null || doubles.length ==0 || doubles.length > 2)
throw new FunctionEvaluationException(doubles);
double a = doubles[0];
double b = 0;
if(doubles.length >= 2) b = doubles[1];
return a * Math.pow(x, -1d) + b;
}
public double[] gradient(double x, double[] doubles) throws
FunctionEvaluationException
{
//derivative: -ax^-2
//"double[] must include at least 1 but not more than 2
coefficients."
if(doubles == null || doubles.length ==0 || doubles.length > 2)
throw new FunctionEvaluationException(doubles);
double a = doubles[0];
double b = 0;
if(doubles.length >= 2) b = doubles[1];
double derivative = -a * Math.pow(x, -2d);
double[]gradientVector = new double[1];
gradientVector[0] = derivative;
return gradientVector;
}
}
{code}
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