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;
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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.");
}
}
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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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