Dear Sirs or Madams,
I use the library org.apache.commons.math3 for the approximation of data set
by Levenberg-Marquard method.
As example, I try to run a test program QuadraticProblem.
There is an error by calling of a class PointVectorValuePair
Exception in thread "main" java.lang.Error: Unresolved compilation problems:
The type
org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem cannot be
resolved. It is indirectly referenced from required .class files
The method optimize(LeastSquaresProblem) from the type
LevenbergMarquardtOptimizer refers to the missing type LeastSquaresProblem
at
org.apache.commons.math3.fitting.leastsquares.QuadraticProblem.main(QuadraticProblem.java:79)
Please explain me, how I can the error eliminate.
Thanks.
Regards,
Liudmila Belenki
package org.apache.commons.math3.fitting.leastsquares;
import java.util.*;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.analysis.DifferentiableMultivariateFunction;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.exception.ConvergenceException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.optim.PointValuePair;
import org.apache.commons.math3.util.Incrementor;
import org.apache.commons.math3.util.Precision;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.stat.descriptive.UnivariateStatistic;
import org.apache.commons.math3.stat.descriptive.rank.Median;
import org.apache.commons.math3.stat.descriptive.rank.Percentile;
import org.apache.commons.math3.stat.correlation.*;
import org.apache.commons.math3.fitting.*;
import org.apache.commons.math3.optimization.fitting.*;
import org.apache.commons.math3.stat.clustering.*;
import org.apache.commons.math3.stat.*;
import org.apache.commons.math3.fitting.leastsquares.RecognizerLibrary;
import org.apache.commons.math3.stat.descriptive.UnivariateStatistic;
import org.apache.commons.math3.stat.descriptive.MultivariateSummaryStatistics;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
//import
org.apache.commons.math3.fitting.leastsquares.LeastSquaresProblem.Evaluation;
import org.apache.commons.math3.linear.Array2DRowRealMatrix;
import org.apache.commons.math3.linear.ArrayRealVector;
import org.apache.commons.math3.linear.DiagonalMatrix;
import org.apache.commons.math3.linear.EigenDecomposition;
import org.apache.commons.math3.linear.RealMatrix;
import org.apache.commons.math3.linear.RealVector;
import org.apache.commons.math3.optim.AbstractOptimizationProblem;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Incrementor;
import org.apache.commons.math3.util.Pair;
//private static class QuadraticProblem
public class QuadraticProblem {
public static void main(String[] args) {
QuadraticProblem problem = new QuadraticProblem();
problem.addPoint(1, 34.234064369);
problem.addPoint(2, 68.2681162306);
problem.addPoint(3, 118.6158990846);
problem.addPoint(4, 184.1381972386);
problem.addPoint(5, 266.5998779163);
problem.addPoint(6, 364.1477352516);
problem.addPoint(7, 478.0192260919);
problem.addPoint(8, 608.1409492707);
problem.addPoint(9, 754.5988686671);
problem.addPoint(10, 916.1288180859);
LevenbergMarquardtOptimizer optimizer = new
LevenbergMarquardtOptimizer(100, 1e-10, 1e-10, 1e-10, 0);
final double[] weights = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
final double[] initialSolution = {1, 1, 1};
PointVectorValuePair optimum =
optimizer.optimize(100,
problem,
problem.calculateTarget(),
weights,
initialSolution);
final double[] optimalValues = optimum.getPoint();
System.out.println("A: " + optimalValues[0]);
System.out.println("B: " + optimalValues[1]);
System.out.println("C: " + optimalValues[2]);
}
//private static final long serialVersionUID = 7072187082052755854L;
Serializable
private List<Double> x;
private List<Double> y;
public QuadraticProblem() {
x = new ArrayList<Double>();
y = new ArrayList<Double>();
}
public void addPoint(double x, double y) {
this.x.add(x);
this.y.add(y);
}
public double[] calculateTarget() {
double[] target = new double[y.size()];
for (int i = 0; i < y.size(); i++) {
target[i] = y.get(i).doubleValue();
}
return target;
}
private double[][] jacobian(double[] variables) {
double[][] jacobian = new double[x.size()][3];
for (int i = 0; i < jacobian.length; ++i) {
jacobian[i][0] = x.get(i) * x.get(i);
jacobian[i][1] = x.get(i);
jacobian[i][2] = 1.0;
}
return jacobian;
}
public double[] value(double[] variables) {
double[] values = new double[x.size()];
for (int i = 0; i < values.length; ++i) {
values[i] = (variables[0] * x.get(i) + variables[1]) * x.get(i) +
variables[2];
}
return values;
}
public MultivariateMatrixFunction jacobian() {
return new MultivariateMatrixFunction() {
private static final long serialVersionUID = -8673650298627399464L;
public double[][] value(double[] point) {
return jacobian(point);
}
};
}
}
