//The M Step - Much better numeric properties if A and R are put on the same scale at the start
//of the process
private void MaximizeGivenZExpectationsScaled()
{
//Recreating as I didn't know if it remembered the Hessian
QN = new QuasiNewton();
QN.MaxIterations = MaxIterations;
QN.Tolerance = TerminationTolerance;
currentWinner = new CurrentBestSolution()
{
curBestFunction = double.MaxValue
};
// results = QN.MinimizeDetail(new DiffFunc(GetDerivatives), new double[] {InitialPopSize,GrowthRate});
results = QN.MinimizeDetail(new DiffFunc(GetDerivativesScaled), startParams);
pParameters = results.solution;
curVal = results.funcValue;
if (results.quality != Microsoft.SolverFoundation.Solvers.CompactQuasiNewtonSolutionQuality.LocalOptima)
{
//Sometimes it went to the local optimum, but didn't seem to think so.
if (results.quality == Microsoft.SolverFoundation.Solvers.CompactQuasiNewtonSolutionQuality.UserCalculationError &&
currentWinner.GradientL2NormAtBest < .002)
{
pParameters = currentWinner.curBestParameters;
curVal = currentWinner.curBestFunction;
}
else
{
throw new Exception("M Step Failed to Converge during Solving");
}
}
}
protected void FitModel()
{
QN.MaxIterations = 50000;
QN.Tolerance = 1e-10;
results = QN.MinimizeDetail(new DiffFunc(GetDerivatives), Parameters);
Parameters = results.solution;
SetParameterDictionary();
if (results.quality != Microsoft.SolverFoundation.Solvers.CompactQuasiNewtonSolutionQuality.LocalOptima)
{
throw new Exception("Optimization failed\n" + results.quality.ToString());
}
else
{
SuccessfulFit = true;
}
}
protected override void FitModel()
{
///TestGradient();
QN.MaxIterations = 500;
QN.Tolerance = 1e-8;
results = QN.MinimizeDetail(new DiffFunc(GetDerivatives), CreateInitialParameterGuess());
if (results.quality != Microsoft.SolverFoundation.Solvers.CompactQuasiNewtonSolutionQuality.LocalOptima)
{
Console.WriteLine(results.quality.ToString());
this.SuccessfulFit = false;
pParameters = new double[] { Double.NaN, Double.NaN, Double.NaN };
//throw new Exception("Problem in Data Fitting!");
}
else
{
pParameters = results.solution;
SuccessfulFit = true;
}
}
//The M Step
private void MaximizeGivenZExpectations()
{
//Solver doesn't work well, but is here.
//LBFGS solver = new LBFGS(2);
//FunctionEval ff = new FunctionEval(GetDerivativesLBFGS);
//Vector x0 = Vector.FromArray(new double[] { InitialPopSize, GrowthRate });
//solver.debug = true;
//solver.linesearchDebug = true;
//solver.Run(x0,1, ff);
//pParameters = x0.ToArray();
//var b = solver.convergenceCriteria;
//Recreating as I didn't know if it remembered the Hessian
QN = new QuasiNewton();
QN.MaxIterations = MaxIterations;
QN.Tolerance = TerminationTolerance;
currentWinner = new CurrentBestSolution()
{
curBestFunction = double.MaxValue
};
// results = QN.MinimizeDetail(new DiffFunc(GetDerivatives), new double[] {InitialPopSize,GrowthRate});
results = QN.MinimizeDetail(new DiffFunc(GetDerivatives), startParams);
pParameters = results.solution;
curVal = results.funcValue;
if (results.quality != Microsoft.SolverFoundation.Solvers.CompactQuasiNewtonSolutionQuality.LocalOptima)
{
//Sometimes it went to the local optimum, but didn't seem to think so.
if (results.quality == Microsoft.SolverFoundation.Solvers.CompactQuasiNewtonSolutionQuality.UserCalculationError &&
currentWinner.GradientL2NormAtBest < .002)
{
pParameters = currentWinner.curBestParameters;
curVal = currentWinner.curBestFunction;
}
else
{
throw new Exception("M Step Failed to Converge during Solving");
}
}
}
