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https://issues.apache.org/jira/browse/MATH-303?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Luc Maisonobe resolved MATH-303.
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Resolution: Invalid
The problem is not in the solver but in the implementation of the gradient
method in your SimpleInverseFunction class. The length of the returned array
must match the length of the second argument to the method (which is called
parameters in the interface and doubles in your class). In your implementation,
the array always has length 1 since it is created by statement:
{code}
double[]gradientVector = new double[1];
{code}
Also note that the value of the gradient is wrong. The gradient vector is
computed with respect to the parameters (which is the reason why lengths must
match), not with respect to the independent variable x. So for a function with
two parameters p[0] / x + p[1], the gradient is { 1/x, 1 } and not { -p[0]/x^2,
0 }.
> CurveFitter.fit(ParametricRealFunction, double[]) used with
> LevenbergMarquardtOptimizer throws ArrayIndexOutOfBoundsException when
> double[] length > 1 (AbstractLeastSquaresOptimizer.java:187)
> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: MATH-303
> URL: https://issues.apache.org/jira/browse/MATH-303
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 2.0
> Environment: Java, Linux Ubuntu 9.04 (64 bit)
> Reporter: Daren Drummond
>
> CurveFitter.fit(ParametricRealFunction, double[]) throws
> ArrayIndexOutOfBoundsException at AbstractLeastSquaresOptimizer.java:187 when
> used with the LevenbergMarquardtOptimizer and the length of the initial
> guess array is greater than 1. The code will run if the initialGuess array
> is of length 1, but then CurveFitter.fit() just returns the same value as the
> initialGuess array (I'll file this as a separate issue). Here is my example
> code:
> {code:title=CurveFitter with LevenbergMarquardtOptimizer and
> SimpleInverseFunction|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[2];
> initialguess[0] = 1.0d;
> initialguess[1] = .5d;
> double[] bestCoefficients = fitter.fit(sif, initialguess); // <---- throws
> exception here
> /**
> * 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}
> This is the resulting stack trace:
> java.lang.ArrayIndexOutOfBoundsException: 1
> at
> org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer.updateJacobian(AbstractLeastSquaresOptimizer.java:187)
> at
> org.apache.commons.math.optimization.general.LevenbergMarquardtOptimizer.doOptimize(LevenbergMarquardtOptimizer.java:241)
> at
> org.apache.commons.math.optimization.general.AbstractLeastSquaresOptimizer.optimize(AbstractLeastSquaresOptimizer.java:346)
> at
> org.apache.commons.math.optimization.fitting.CurveFitter.fit(CurveFitter.java:134)
> at
> com.yieldsoftware.analyticstest.tasks.ppcbidder.CurveFittingTest.testFitnessRankCurveIntercept(CurveFittingTest.java:181)
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