Hello.
On Thu, 13 Oct 2016 12:28:30 +0200, Liudmila Belenki wrote:
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.
It's a compilation problem.
But I can't see right-away what causes it.
Which environment do you use to compile this class (ant, maven,
other?)?
First thing I'd suggest is to not define your own classes
within the library's namespace (see below).
Thanks.
Regards,
Liudmila Belenki
package org.apache.commons.math3.fitting.leastsquares;
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
You should not define the classes in _our_ package, as it could
obscure the source of the problem.
Use something like
package my;
or
package liudmila;
Regards,
Gilles
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);
}
};
}
}
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscr...@commons.apache.org
For additional commands, e-mail: user-h...@commons.apache.org
