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  1. Commons Math
  2. MATH-304

CurveFitter.fit(ParametricRealFunction, double[]) always returns the same value as the initial guess when used with the LevenbergMarquardtOptimizer

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Details

    • Bug
    • Status: Closed
    • Major
    • Resolution: Invalid
    • 2.0
    • 2.1
    • None
    • Java, Ubuntu 9.04 (64 bit)

    Description

      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:

      CurveFitter with LevenbergMarquardtOptimizer
        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; 
              }
          }
      

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            Unassigned Unassigned
            ddrummond Daren Drummond
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              Created:
              Updated:
              Resolved: