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Posted to issues@commons.apache.org by "Luc Maisonobe (JIRA)" <ji...@apache.org> on 2008/03/23 14:41:24 UTC
[jira] Resolved: (MATH-199) exception in
LevenbergMarquardtEstimator
[ https://issues.apache.org/jira/browse/MATH-199?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Luc Maisonobe resolved MATH-199.
--------------------------------
Resolution: Fixed
Fixed in svn as of r640205
The problem was due to an overflow in Q.R decomposition. One of the transformed columns had both infinite and NaN elements, so the test of the norm was never met and a column index was never set.
The fix consist in detecting non-numeric norms and throwing an EstimationException stating the Q.R decomposition could not be performed.
> exception in LevenbergMarquardtEstimator
> ----------------------------------------
>
> Key: MATH-199
> URL: https://issues.apache.org/jira/browse/MATH-199
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 1.2
> Environment: Windows XP
> Java 6
> Reporter: Mick
>
> I get this exception:
> Exception in thread "main" java.lang.ArrayIndexOutOfBoundsException: -1
> at org.apache.commons.math.estimation.LevenbergMarquardtEstimator.qrDecomposition(LevenbergMarquardtEstimator.java:772)
> at org.apache.commons.math.estimation.LevenbergMarquardtEstimator.estimate(LevenbergMarquardtEstimator.java:232)
> at quadraticFitterProblem.QuadraticFitterProblem.<init>(QuadraticFitterProblem.java:27)
> at quadraticFitterProblem.QuadraticFitterProblem.main(QuadraticFitterProblem.java:40)
> on the code below.
> The exception does not occur all the weights in the quadraticFitter are 0.0;
> ---------------------------------------------------------------------------------------------
> package quadraticFitterProblem;
> import org.apache.commons.math.estimation.EstimationException;
> import org.apache.commons.math.estimation.LevenbergMarquardtEstimator;
> //import org.apache.commons.math.estimation.WeightedMeasurement;
> import com.strategicanalytics.dtd.data.smoothers.QuadraticFitter;
> public class QuadraticFitterProblem {
> private QuadraticFitter quadraticFitter;
> public QuadraticFitterProblem() {
> // create the uninitialized fitting problem
> quadraticFitter = new QuadraticFitter();
> quadraticFitter.addPoint (0, -3.182591015485607, 0.0);
> quadraticFitter.addPoint (1, -2.5581184967730577, 4.4E-323);
> quadraticFitter.addPoint (2, -2.1488478161387325, 1.0);
> quadraticFitter.addPoint (3, -1.9122489313410047, 4.4E-323);
> quadraticFitter.addPoint (4, 1.7785661310051026, 0.0);
> try {
> // solve the problem, using a Levenberg-Marquardt algorithm with
> default settings
> LevenbergMarquardtEstimator estimator = new LevenbergMarquardtEstimator();
> //WeightedMeasurement[] wm = quadraticFitter.getMeasurements();
> estimator.estimate(quadraticFitter);
> } catch (EstimationException ee) {
> System.err.println(ee.getMessage());
> }
> }
> /**
> * @param args
> *
> */
> public static void main(String[] args) {
> new QuadraticFitterProblem();
> System.out.println ("Done.");
> }
> }
> ----------------------------------------------------------------------------------------------
> import org.apache.commons.math.estimation.EstimatedParameter;
> //import org.apache.commons.math.estimation.EstimationException;
> //import org.apache.commons.math.estimation.LevenbergMarquardtEstimator;
> import org.apache.commons.math.estimation.SimpleEstimationProblem;
> import org.apache.commons.math.estimation.WeightedMeasurement;
> public class QuadraticFitter extends SimpleEstimationProblem {
> // y = a x<sup>2</sup> + b x + c
> private EstimatedParameter a;
> private EstimatedParameter b;
> private EstimatedParameter c;
> /**
> * constructor
> *
> *Fitter for a quadratic model to a sample of 2D points.
> * <p>The model is y(x) = a x<sup>2</sup> + b x + c
> * its three parameters of the model are a, b and c.</p>
> */
> public QuadraticFitter() {
> // three parameters of the model
> a = new EstimatedParameter("a", 0.0);
> b = new EstimatedParameter("b", 0.0);
> c = new EstimatedParameter("c", 0.0);
> // provide the parameters to the base class which
> // implements the getAllParameters and getUnboundParameters methods
> addParameter(a);
> addParameter(b);
> addParameter(c);
> }
> /**
> * Add a sample point
> *
> * @param x abscissa
> * @param y ordinate
> * @param w weight
> */
> public void addPoint(double x, double y, double w) {
> addMeasurement(new LocalMeasurement(x, y, w));
> }
> /**
> * Get the value of the quadratic coefficient.
> *
> * @return the value of a for the quadratic model
> * y = a x<sup>2</sup> + b x + c
> */
> public double getA() {
> return a.getEstimate();
> }
> /**
> * Get the value of the linear coefficient.
> *
> * @return the value of b for the quadratic model
> * y = a x<sup>2</sup> + b x + c
> */
> public double getB() {
> return b.getEstimate();
> }
> /**
> * Get the value of the constant coefficient.
> *
> * @return the value of ac for the quadratic model
> * y = a x<sup>2</sup> + b x + c
> */
> public double getC() {
> return c.getEstimate();
> }
> /**
> * Get the theoretical value of the model for some x.
> * <p>The theoretical value is the value computed using
> * the current state of the problem parameters.</p>
> *
> * Note the use of Hörner's method (synthetic division) for
> evaluating polynomials,
> * (more efficient)
> *
> * @param x explanatory variable
> * @return the theoretical value y = a x<sup>2</sup> + b x + c
> */
> public double theoreticalValue(double x) {
> //System.out.println ("x = " + x + " a.getEstimate() = " +
> a.getEstimate() + " b.getEstimate() = " + b.getEstimate() + "
> c.getEstimate() = " + c.getEstimate());
> return ( (a.getEstimate() * x + b.getEstimate() ) * x +
> c.getEstimate());
> }
> /**
> * Get the partial derivative of the theoretical value
> * of the model for some x.
> * <p>The derivative is computed using
> * the current state of the problem parameters.</p>
> *
> * @param x explanatory variable
> * @param parameter estimated parameter (either a, b, or c)
> * @return the partial derivative dy/dp
> */
> private double partial(double x, EstimatedParameter parameter) {
> // since we know the only parameters are a, b and c in this
> // class we simply use "==" for efficiency
> if (parameter == a) {
> return x * x;
> } else if (parameter == b) {
> return x;
> } else {
> return 1.0;
> }
> }
> /** Internal measurements class.
> * <p>The measurement is the y value for a fixed specified x.</p>
> */
> private class LocalMeasurement extends WeightedMeasurement {
> static final long serialVersionUID = 1;
> private final double x;
> // constructor
> public LocalMeasurement(double x, double y, double w) {
> super(w, y);
> this.x = x;
> }
> public double getTheoreticalValue() {
> // the value is provided by the model for the local x
> return theoreticalValue(x);
> }
> public double getPartial(EstimatedParameter parameter) {
> // the value is provided by the model for the local x
> return partial(x, parameter);
> }
> }
> }
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