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https://issues.apache.org/jira/browse/MATH-304?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Phil Steitz closed MATH-304.
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> CurveFitter.fit(ParametricRealFunction, double[]) always returns the same
> value as the initial guess when used with the LevenbergMarquardtOptimizer
> ---------------------------------------------------------------------------------------------------------------------------------------------------
>
> 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
> Fix For: 2.1
>
>
> 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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