Examples of org.apache.commons.math.optimization.general.GaussNewtonOptimizer

Package org.apache.commons.math.optimization.general

Examples of org.apache.commons.math.optimization.general.GaussNewtonOptimizer

org.apache.commons.math.optimization.general.GaussNewtonOptimizer
Gauss-Newton least-squares solver.
This class solve a least-square problem by solving the normal equations of the linearized problem at each iteration. Either LU decomposition or QR decomposition can be used to solve the normal equations. LU decomposition is faster but QR decomposition is more robust for difficult problems.
@version $Revision: 1073158 $ $Date: 2011-02-21 22:46:52 +0100 (lun. 21 févr. 2011) $ @since 2.0

  }


  private PolynomialFunction getPolynomialFunction() {
    Collections.sort(data, new RTs());
    PolynomialFitter fitter = new PolynomialFitter(3,
        new GaussNewtonOptimizer(true));
    for (RTs rt : data) {
      fitter.addObservedPoint(1, rt.RT, rt.RT2);
    }
    try {
      return fitter.fit();

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      xval[i] = rt.RT;
      yval[i++] = rt.RT2;
    }


    PolynomialFitter fitter = new PolynomialFitter(3,
        new GaussNewtonOptimizer(true));
    for (RTs rt : data) {
      fitter.addObservedPoint(1, rt.RT, rt.RT2);
    }
    try {
      return fitter.fit();

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    if (linear) {
      degree = 1;
    }


    PolynomialFitter fitter = new PolynomialFitter(degree,
        new GaussNewtonOptimizer(true));
    for (int i = 0; i < data.size(); i++) {
      AlignStructMol point = data.get(i);
      if (point.ransacMaybeInLiers) {
        points.add(point);
        fitter.addObservedPoint(1, point.RT, point.RT2);

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    if (linear) {
      degree = 1;
    }


    PolynomialFitter fitter = new PolynomialFitter(degree,
        new GaussNewtonOptimizer(true));
    for (RTs rt : data) {
      fitter.addObservedPoint(1, rt.RT, rt.RT2);
    }
    try {
      return fitter.fit();

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