package testmptheory;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.math3.analysis.MultivariateMatrixFunction;
import org.apache.commons.math3.analysis.MultivariateVectorFunction;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.PointVectorValuePair;
import org.apache.commons.math3.optim.SimpleBounds;
import org.apache.commons.math3.optim.nonlinear.vector.ModelFunction;
import org.apache.commons.math3.optim.nonlinear.vector.ModelFunctionJacobian;
import org.apache.commons.math3.optim.nonlinear.vector.Target;
import org.apache.commons.math3.optim.nonlinear.vector.Weight;
import org.apache.commons.math3.optim.nonlinear.vector.jacobian.LevenbergMarquardtOptimizer;
/*
* To change this template, choose Tools | Templates
* and open the template in the editor.
*/
/**
*
* @author luca
*/
public class QuadraticProblemNew implements Serializable {
private static final long serialVersionUID = 7072187082052755854L;
private static List<Double> x;
private static List<Double> y;
public static void addPoint(double xp, double yp) {
x.add(xp);
y.add(yp);
}
public static double[] calculateTarget() {
double[] target = new double[y.size()];
for (int i = 0; i < y.size(); i++) {
target[i] = y.get(i).doubleValue();
}
return target;
}
public static void main(String[] a) {
x = new ArrayList<Double>();
y = new ArrayList<Double>();
// data-set
addPoint(1, 34.234064369);
addPoint(2, 68.2681162306);
addPoint(3, 118.6158990846);
addPoint(4, 184.1381972386);
addPoint(5, 266.5998779163);
addPoint(6, 364.1477352516);
addPoint(7, 478.0192260919);
addPoint(8, 608.1409492707);
addPoint(9, 754.5988686671);
addPoint(10, 916.1288180859);
LevenbergMarquardtOptimizer optimizer = new LevenbergMarquardtOptimizer();
MaxEval mEv = new MaxEval(100);
final double[] iSolution = {1, 1, 1};
InitialGuess initialSolution = new InitialGuess(iSolution);
double[] lb = { 0, 0, 0};
double[] ub = { 2, 2, 2};
SimpleBounds bounds = new SimpleBounds(lb,ub);
SimpleBounds unbounds = SimpleBounds.unbounded(iSolution.length);
Target trg = new Target(calculateTarget());
double[] w = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
Weight weights = new Weight(w);
ModelFunction mFun = new ModelFunction(new MultivariateVectorFunction(){
private static final long serialVersionUID = -8673650298627399464L;
@Override
public double[] value(double[] point) throws IllegalArgumentException {
double[] values = new double[x.size()];
for (int i = 0; i < values.length; ++i) {
values[i] = (point[0] * x.get(i) + point[1]) * x.get(i) + point[2];
}
return values;
}
});
ModelFunctionJacobian mFunJ = new ModelFunctionJacobian(new MultivariateMatrixFunction(){
private static final long serialVersionUID = -8673650298627399464L;
@Override
public double[][] value(double[] point) throws IllegalArgumentException {
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;
}
});
PointVectorValuePair optimum = optimizer.optimize(mEv,initialSolution,unbounds,trg,weights,mFun,mFunJ);
double[] optimalValues = optimum.getPoint();
System.out.println("No-bounds estimation:");
System.out.println("A: " + optimalValues[0]);
System.out.println("B: " + optimalValues[1]);
System.out.println("C: " + optimalValues[2]);
optimum = optimizer.optimize(mEv,initialSolution,bounds,trg,weights,mFun,mFunJ);
optimalValues = optimum.getPoint();
System.out.println("\nBounded estimation:");
System.out.println("A: " + optimalValues[0]);
System.out.println("B: " + optimalValues[1]);
System.out.println("C: " + optimalValues[2]);
}
}
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