Examples of org.apache.commons.math.estimation.EstimatedParameter

Package org.apache.commons.math.estimation

Examples of org.apache.commons.math.estimation.EstimatedParameter

org.apache.commons.math.estimation.EstimatedParameter
This class represents the estimated parameters of an estimation problem.
The parameters of an estimation problem have a name, a value and a bound flag. The value of bound parameters is considered trusted and the solvers should not adjust them. On the other hand, the solvers should adjust the value of unbounds parameters until they satisfy convergence criterions specific to each solver.
@version $Revision: 922710 $ $Date: 2010-03-14 02:20:56 +0100 (dim. 14 mars 2010) $ @since 1.2 @deprecated as of 2.0, everything in package org.apache.commons.math.estimation hasbeen deprecated and replaced by package org.apache.commons.math.optimization.general


  }


  public void testNoDependency() throws EstimationException {
    EstimatedParameter[] p = new EstimatedParameter[] {
      new EstimatedParameter("p0", 0),
      new EstimatedParameter("p1", 0),
      new EstimatedParameter("p2", 0),
      new EstimatedParameter("p3", 0),
      new EstimatedParameter("p4", 0),
      new EstimatedParameter("p5", 0)
    };
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[0] }, 0.0),
      new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[1] }, 1.1),
      new LinearMeasurement(new double[] {2}, new EstimatedParameter[] { p[2] }, 2.2),

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}


  public void testOneSet() throws EstimationException {


    EstimatedParameter[] p = {
       new EstimatedParameter("p0", 0),
       new EstimatedParameter("p1", 0),
       new EstimatedParameter("p2", 0)
    };
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] { 1.0 },
                            new EstimatedParameter[] { p[0] },
                            1.0),

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  }


  public void testTwoSets() throws EstimationException {
    EstimatedParameter[] p = {
      new EstimatedParameter("p0", 0),
      new EstimatedParameter("p1", 1),
      new EstimatedParameter("p2", 2),
      new EstimatedParameter("p3", 3),
      new EstimatedParameter("p4", 4),
      new EstimatedParameter("p5", 5)
    };


    double epsilon = 1.0e-7;
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {

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  }


  public void testNonInversible() throws EstimationException {


    EstimatedParameter[] p = {
       new EstimatedParameter("p0", 0),
       new EstimatedParameter("p1", 0),
       new EstimatedParameter("p2", 0)
    };
    LinearMeasurement[] m = new LinearMeasurement[] {
      new LinearMeasurement(new double[] {  1.0, 2.0, -3.0 },
                            new EstimatedParameter[] { p[0], p[1], p[2] },
                            1.0),

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  }


  public void testIllConditioned() throws EstimationException {
    EstimatedParameter[] p = {
      new EstimatedParameter("p0", 0),
      new EstimatedParameter("p1", 1),
      new EstimatedParameter("p2", 2),
      new EstimatedParameter("p3", 3)
    };


    LinearProblem problem1 = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] { 10.0, 7.0,  8.0,  7.0 },
                            new EstimatedParameter[] { p[0], p[1], p[2], p[3] },

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  }


  public void testMoreEstimatedParametersSimple() throws EstimationException {


    EstimatedParameter[] p = {
       new EstimatedParameter("p0", 7),
       new EstimatedParameter("p1", 6),
       new EstimatedParameter("p2", 5),
       new EstimatedParameter("p3", 4)
     };
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] { 3.0, 2.0 },
                             new EstimatedParameter[] { p[0], p[1] },
                             7.0),

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  }


  public void testMoreEstimatedParametersUnsorted() throws EstimationException {
    EstimatedParameter[] p = {
      new EstimatedParameter("p0", 2),
      new EstimatedParameter("p1", 2),
      new EstimatedParameter("p2", 2),
      new EstimatedParameter("p3", 2),
      new EstimatedParameter("p4", 2),
      new EstimatedParameter("p5", 2)
    };
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] { 1.0, 1.0 },
                           new EstimatedParameter[] { p[0], p[1] },
                           3.0),

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  }


  public void testRedundantEquations() throws EstimationException {
    EstimatedParameter[] p = {
      new EstimatedParameter("p0", 1),
      new EstimatedParameter("p1", 1)
    };
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] { 1.0, 1.0 },
                             new EstimatedParameter[] { p[0], p[1] },
                             3.0),

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  }


  public void testInconsistentEquations() throws EstimationException {
    EstimatedParameter[] p = {
      new EstimatedParameter("p0", 1),
      new EstimatedParameter("p1", 1)
    };
    LinearProblem problem = new LinearProblem(new LinearMeasurement[] {
      new LinearMeasurement(new double[] { 1.0, 1.0 },
                            new EstimatedParameter[] { p[0], p[1] },
                            3.0),

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