public double[] StartMapping(SpotParsInitBox spotParsInitBox)
{
OptBoundVariable[] x = new OptBoundVariable[spotParsInitBox.SpotsNumber * 4];
for (int i = 0; i < x.Length; i++)
{
x[i] = new OptBoundVariable();
}
for (int q = 0; q < this.lcObs.Length; q++)
{
for (int s = 0; s < spotParsInitBox.SpotsNumber; s++)
{
this.modeller[q].StarM.AddUniformCircularSpot(0.0, 0.0, 1.0, 4000, 30, 90);
}
}
for (int s = 0; s < spotParsInitBox.SpotsNumber; s++)
{
x[0 + s * 4].InitialGuess = spotParsInitBox.spots[s].longitude * Math.PI / 180;
x[0 + s * 4].UpperBound = spotParsInitBox.spots[s].longitudeUpperLimit * Math.PI / 180;
x[0 + s * 4].LowerBound = spotParsInitBox.spots[s].longitudeLowerLimit * Math.PI / 180;
x[0 + s * 4].Fixed = spotParsInitBox.spots[s].longitudeFixed;
x[1 + s * 4].InitialGuess = spotParsInitBox.spots[s].colatutude * Math.PI / 180;
x[1 + s * 4].UpperBound = spotParsInitBox.spots[s].colatitudeUpperLimit * Math.PI / 180;
x[1 + s * 4].LowerBound = spotParsInitBox.spots[s].colatitudeLowerLimit * Math.PI / 180;
x[1 + s * 4].Fixed = spotParsInitBox.spots[s].colatitudeFixed;
x[2 + s * 4].InitialGuess = spotParsInitBox.spots[s].radius * Math.PI / 180;
x[2 + s * 4].UpperBound = spotParsInitBox.spots[s].radiusUpperLimit * Math.PI / 180;
x[2 + s * 4].LowerBound = spotParsInitBox.spots[s].radiusLowerLimit * Math.PI / 180;
x[2 + s * 4].Fixed = spotParsInitBox.spots[s].radiusFixed;
x[3 + s * 4].InitialGuess = spotParsInitBox.spots[s].teff;
x[3 + s * 4].UpperBound = spotParsInitBox.spots[s].teffUpperLimit;
x[3 + s * 4].LowerBound = spotParsInitBox.spots[s].teffLowerLimit;
x[3 + s * 4].Fixed = spotParsInitBox.spots[s].teffFixed;
}
double[] res;
Simplex simplex = new Simplex();
DotNumerics.Optimization.TruncatedNewton tn = new TruncatedNewton();
//res = simplex.ComputeMin(this.Khi2, x);
res = tn.ComputeMin(this.Khi2, this.GradKhi2, x);
this.GenerateModLightCurve();
return(res);
}
public void OptimizationLBFGSBConstrained()
{
//This example minimize the function
//f(x0,x2,...,xn)= (x0-0)^2+(x1-1)^2+...(xn-n)^2
//The minimum is at (0,1,2,3...,n) for the unconstrained case.
//using DotNumerics.Optimization;
L_BFGS_B LBFGSB = new L_BFGS_B();
int numVariables = 5;
OptBoundVariable[] variables = new OptBoundVariable[numVariables];
//Constrained Minimization on the interval (-10,10), initial Guess=-2;
for (int i = 0; i < numVariables; i++)
{
variables[i] = new OptBoundVariable("x" + i.ToString(), -2, -10, 10);
}
double[] minimum = LBFGSB.ComputeMin(ObjetiveFunction, Gradient, variables);
ObjectDumper.Write("L-BFGS-B Method. Constrained Minimization on the interval (-10,10)");
for (int i = 0; i < minimum.Length; i++)
{
ObjectDumper.Write("x" + i.ToString() + " = " + minimum[i].ToString());
}
//Constrained Minimization on the interval (-10,3), initial Guess=-2;
for (int i = 0; i < numVariables; i++)
{
variables[i].UpperBound = 3;
}
minimum = LBFGSB.ComputeMin(ObjetiveFunction, Gradient, variables);
ObjectDumper.Write("L-BFGS-B Method. Constrained Minimization on the interval (-10,3)");
for (int i = 0; i < minimum.Length; i++)
{
ObjectDumper.Write("x" + i.ToString() + " = " + minimum[i].ToString());
}
//f(x0,x2,...,xn)= (x0-0)^2+(x1-1)^2+...(xn-n)^2
//private double ObjetiveFunction(double[] x)
//{
// int numVariables = 5;
// double f = 0;
// for (int i = 0; i < numVariables; i++) f += Math.Pow(x[i] - i, 2);
// return f;
//}
//private double[] Gradient(double[] x)
//{
// int numVariables = 5;
// double[] grad = new double[x.Length];
// for (int i = 0; i < numVariables; i++) grad[i] = 2 * (x[i] - i);
// return grad;
//}
}
public void OptimizationTruncatedNewtonConstrained()
{
//This example minimize the function
//f(x0,x2,...,xn)= (x0-0)^2+(x1-1)^2+...(xn-n)^2
//The minimum is at (0,1,2,3...,n) for the unconstrained case.
//using DotNumerics.Optimization;
TruncatedNewton tNewton = new TruncatedNewton();
int numVariables = 5;
OptBoundVariable[] variables = new OptBoundVariable[numVariables];
//Constrained Minimization on the interval (-10,10), initial Guess=-2;
for (int i = 0; i < numVariables; i++)
{
variables[i] = new OptBoundVariable("x" + i.ToString(), -2, -10, 10);
}
double[] minimum = tNewton.ComputeMin(ObjetiveFunction, Gradient, variables);
ObjectDumper.Write("Truncated Newton Method. Constrained Minimization on the interval (-10,10)");
for (int i = 0; i < minimum.Length; i++)
{
ObjectDumper.Write("x" + i.ToString() + " = " + minimum[i].ToString());
}
//Constrained Minimization on the interval (-10,3), initial Guess=-2;
for (int i = 0; i < numVariables; i++)
{
variables[i].UpperBound = 3;
}
minimum = tNewton.ComputeMin(ObjetiveFunction, Gradient, variables);
ObjectDumper.Write("Truncated Newton Method. Constrained Minimization on the interval (-10,3)");
for (int i = 0; i < minimum.Length; i++)
{
ObjectDumper.Write("x" + i.ToString() + " = " + minimum[i].ToString());
}
//f(a,b) = 100*(b-a^2)^2 + (1-a)^2
//private double BananaFunction(double[] x)
//{
// double f = 0;
// f = 100 * Math.Pow((x[1] - x[0] * x[0]), 2) + Math.Pow((1 - x[0]), 2);
// return f;
//}
//double[] bananaGrad = new double[2];
//private double[] BananaGradient(double[] x)
//{
// this.bananaGrad[0] = 200 * (x[1] - x[0] * x[0]) * (-2 * x[0]) - 2 * (1 - x[0]);
// this.bananaGrad[1] = 200 * (x[1] - x[0] * x[0]);
// return bananaGrad;
//}
}
public double[] ComputeMin(OptMultivariateFunction function, OptMultivariateGradient gradient, OptBoundVariable[] variables, double tolerance, double factr, ref int nMax)
{
double[] minimum;
this.Initialize(variables);
if (this._NumFreeVariables == 0) return this._ExternalVariables;
minimum = this.Compute(function, gradient, tolerance, factr, ref nMax);
return minimum;
}
private void Initialize(OptBoundVariable[] variables)
{
this._OptVariable = null;
this._OptBoundVariable = OptBoundVariable.GetClon(variables);
this._ExternalVariables = new double[this._OptBoundVariable.Length];
this._ExternalGradientArray = new double[this._OptBoundVariable.Length];
this.CheckAndSetBounds(this._OptBoundVariable);
int numFreeVariable = 0;
for (int i = 0; i < this._OptBoundVariable.Length; i++)
{
this._ExternalVariables[i] = this._OptBoundVariable[i].InitialGuess;
if (this._OptBoundVariable[i].Fixed == false)
{
numFreeVariable++;
}
}
this._NumFreeVariables = numFreeVariable;
this._FreeVariables = new double[numFreeVariable];
int index = 0;
for (int i = 0; i < this._OptBoundVariable.Length; i++)
{
if (this._OptBoundVariable[i].Fixed == false)
{
this._FreeVariables[index] = this._OptBoundVariable[i].InitialGuess;
index++;
}
}
this._GradientArray = new double[numFreeVariable];
//c nmax is the dimension of the largest problem to be solved.
//c mmax is the maximum number of limited memory corrections.
int NMAX = this._NumFreeVariables;
int MMAX = this.M;
// c wa is a double precision working array of length
// c (2mmax + 4)nmax + 12mmax^2 + 12mmax
int WADimension = (2 * MMAX + 4) * NMAX + 12 * (MMAX * MMAX) + 12 * MMAX;
this.WA = new double[WADimension];
this.IWA = new int[3 * NMAX];
}
private void Initialize(OptMultivariateFunction function, OptMultivariateGradient gradient, OptBoundVariable[] variables)
{
this.internalFunction = new SFUN(function, gradient, variables);
this.MeOptVariable = null;
this.MeOptBoundVariable = OptBoundVariable.GetClon(variables);
this.CheckAndSetBounds(this.MeOptBoundVariable);
this.MeExternalVariables = new double[this.MeOptBoundVariable.Length];
int numFreeVariable = 0;
for (int i = 0; i < this.MeOptBoundVariable.Length; i++)
{
this.MeExternalVariables[i] = variables[i].InitialGuess;
if (this.MeOptBoundVariable[i].Fixed == false)
{
numFreeVariable++;
}
}
this.MeF = function(this.MeExternalVariables);
this.MeNumFreeVariables = numFreeVariable;
this.MeFreeVariables = new double[numFreeVariable];
this.MeGradientArray = new double[numFreeVariable];
int index = 0;
for (int i = 0; i < this.MeOptBoundVariable.Length; i++)
{
if (this.MeOptBoundVariable[i].Fixed == false)
{
this.MeFreeVariables[index] = this.MeOptBoundVariable[i].InitialGuess;
index++;
}
}
//W - (REAL*8)(REAL*8) WORK VECTOR OF LENGTH AT LEAST 14*N
//LW - (INTEGER) THE DECLARED DIMENSION OF W
this.MeLW = 14 * this.MeNumFreeVariables;
this.MeW = new double[this.MeLW];
this.MeMAXIT = Math.Max(1, this.MeNumFreeVariables / 2);
}
internal SFUN(OptMultivariateFunction function, OptMultivariateGradient gradient, OptBoundVariable[] variables)
{
//this.MeNParameters = nParameters;
this.MeFunction = function;
this.MeGradient = gradient;
this.MeOptVariable = null;
this.MeOptBoundVariable = variables;
this.MeExternalVariables = new double[variables.Length];
this.MeExternalGradientArray = new double[variables.Length];
for (int i = 0; i < variables.Length; i++)
{
this.MeExternalVariables[i] = variables[i].InitialGuess;
}
}
private void CheckAndSetBounds(OptBoundVariable[] variables)
{
int numFreeVariable = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false) numFreeVariable++;
}
this._LowerBounds = new double[numFreeVariable];
this._UpperBounds = new double[numFreeVariable];
// c nbd(i)=0 if x(i) is unbounded,
// c 1 if x(i) has only a lower bound,
// c 2 if x(i) has both lower and upper bounds, and
// c 3 if x(i) has only an upper bound.
this._NBD = new int[numFreeVariable];
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
//Se invierten los limites de ser necesario
if (variables[i].LowerBound > variables[i].UpperBound)
{
double tempBound = variables[i].LowerBound;
variables[i].LowerBound = variables[i].UpperBound;
variables[i].UpperBound = tempBound;
}
//Se revisa que la variable inicial este dentro de los limites
if (variables[i].InitialGuess < variables[i].LowerBound)
{
variables[i].InitialGuess = variables[i].LowerBound;
}
if (variables[i].InitialGuess > variables[i].UpperBound)
{
variables[i].InitialGuess = variables[i].UpperBound;
}
this._LowerBounds[index] = variables[i].LowerBound;
this._UpperBounds[index] = variables[i].UpperBound;
if (this._LowerBounds[index] == double.NegativeInfinity && this._UpperBounds[index] == double.PositiveInfinity)
{
this._NBD[index] = 0; //nbd(i)=0 if x(i) is unbounded,
}
else if (this._LowerBounds[index] == double.NegativeInfinity)
{
this._NBD[index] = 3; //3 if x(i) has only an upper bound.
}
else if (this._UpperBounds[index] == double.PositiveInfinity)
{
this._NBD[index] = 3; //1 if x(i) has only a lower bound,
}
else
{
this._NBD[index] = 2; //2 if x(i) has both lower and upper bounds
}
index++;
}
}
}
private void UpdateExternalVariables(OptBoundVariable[] variables, double[] X, int o_x)
{
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
this.MeExternalVariables[i] = X[index + o_x];
index++;
}
}
}
private void UpdateInternalGradient(OptBoundVariable[] variables)
{
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
this._GradientArray[index] = this._ExternalGradientArray[i];
index++;
}
}
}
private void UpdateExternalVariables(OptBoundVariable[] variables)
{
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
this._ExternalVariables[i] = this._FreeVariables[index];
index++;
}
}
}
private void Initialize(OptBoundVariable[] variables)
{
this._OptVariable = null;
this._OptBoundVariable = OptBoundVariable.GetClon(variables);
this._ExternalVariables = new double[this._OptBoundVariable.Length];
this._ExternalGradientArray = new double[this._OptBoundVariable.Length];
this.CheckAndSetBounds(this._OptBoundVariable);
int numFreeVariable = 0;
for (int i = 0; i < this._OptBoundVariable.Length; i++)
{
this._ExternalVariables[i] = this._OptBoundVariable[i].InitialGuess;
if (this._OptBoundVariable[i].Fixed == false) numFreeVariable++;
}
this._NumFreeVariables = numFreeVariable;
this._FreeVariables = new double[numFreeVariable];
int index = 0;
for (int i = 0; i < this._OptBoundVariable.Length; i++)
{
if (this._OptBoundVariable[i].Fixed == false)
{
this._FreeVariables[index] = this._OptBoundVariable[i].InitialGuess;
index++;
}
}
this._GradientArray = new double[numFreeVariable];
//c nmax is the dimension of the largest problem to be solved.
//c mmax is the maximum number of limited memory corrections.
int NMAX = this._NumFreeVariables;
int MMAX = this.M;
// c wa is a double precision working array of length
// c (2mmax + 4)nmax + 12mmax^2 + 12mmax
int WADimension = (2 * MMAX + 4) * NMAX + 12 * (MMAX * MMAX) + 12 * MMAX;
this.WA = new double[WADimension];
this.IWA = new int[3 * NMAX];
}
public double[] StartMapping(SpotParsInitBox spotParsInitBox, string minimizator, double tau)
{
OptBoundVariable[] x = new OptBoundVariable[spotParsInitBox.SpotsNumber * 4];
for (int i = 0; i < x.Length; i++)
{
x[i] = new OptBoundVariable();
}
for (int q = 0; q < this.lcObs.Length; q++)
{
this.modellers[q].StarM.RemoveAllSpots();
for (int s = 0; s < spotParsInitBox.SpotsNumber; s++)
{
this.modellers[q].StarM.AddUniformCircularSpot(
spotParsInitBox.spots[s].longitude * Math.PI / 180,
spotParsInitBox.spots[s].colatutude * Math.PI / 180,
spotParsInitBox.spots[s].radius * Math.PI / 180,
spotParsInitBox.spots[s].teff,
spotParsInitBox.spots[s].beltsCount,
spotParsInitBox.spots[s].nearEquatorialPatchesCount);
}
}
this.fixedParsMask = new bool[x.Length];
for (int s = 0; s < spotParsInitBox.SpotsNumber; s++)
{
x[0 + s * 4].InitialGuess = spotParsInitBox.spots[s].longitude * Math.PI / 180;
x[0 + s * 4].UpperBound = spotParsInitBox.spots[s].longitudeUpperLimit * Math.PI / 180;
x[0 + s * 4].LowerBound = spotParsInitBox.spots[s].longitudeLowerLimit * Math.PI / 180;
x[0 + s * 4].Fixed = spotParsInitBox.spots[s].longitudeFixed;
this.fixedParsMask[0 + s * 4] = spotParsInitBox.spots[s].longitudeFixed;
x[1 + s * 4].InitialGuess = spotParsInitBox.spots[s].colatutude * Math.PI / 180;
x[1 + s * 4].UpperBound = spotParsInitBox.spots[s].colatitudeUpperLimit * Math.PI / 180;
x[1 + s * 4].LowerBound = spotParsInitBox.spots[s].colatitudeLowerLimit * Math.PI / 180;
x[1 + s * 4].Fixed = spotParsInitBox.spots[s].colatitudeFixed;
this.fixedParsMask[1 + s * 4] = spotParsInitBox.spots[s].colatitudeFixed;
x[2 + s * 4].InitialGuess = spotParsInitBox.spots[s].radius * Math.PI / 180;
x[2 + s * 4].UpperBound = spotParsInitBox.spots[s].radiusUpperLimit * Math.PI / 180;
x[2 + s * 4].LowerBound = spotParsInitBox.spots[s].radiusLowerLimit * Math.PI / 180;
x[2 + s * 4].Fixed = spotParsInitBox.spots[s].radiusFixed;
this.fixedParsMask[2 + s * 4] = spotParsInitBox.spots[s].radiusFixed;
x[3 + s * 4].InitialGuess = spotParsInitBox.spots[s].teff;
x[3 + s * 4].UpperBound = spotParsInitBox.spots[s].teffUpperLimit;
x[3 + s * 4].LowerBound = spotParsInitBox.spots[s].teffLowerLimit;
x[3 + s * 4].Fixed = spotParsInitBox.spots[s].teffFixed;
this.fixedParsMask[3 + s * 4] = spotParsInitBox.spots[s].teffFixed;
}
double[] res = new double[4];
if (minimizator == "Simplex")
{
Simplex simplex = new Simplex();
res = simplex.ComputeMin(this.Khi2, x);
}
if (minimizator == "TN")
{
//DotNumerics.Optimization.TruncatedNewton tn = new TruncatedNewton();
DotNumerics.Optimization.L_BFGS_B bfgs = new L_BFGS_B();
//tn.SearchSeverity = 1;
//res = tn.ComputeMin(this.Khi2, this.GradKhi2, x);
res = bfgs.ComputeMin(this.Khi2, this.GradKhi2, x);
}
if (minimizator == "LM")
{
LevenbergMaquard gn = new LevenbergMaquard();
gn.MinimizedFunction = this.ResudalVector;
double[] step = new double[x.Length];
double[] xinit = new double[x.Length];
bool[] isFixed = new bool[x.Length];
double[] lowLimits = new double[x.Length];
double[] highLimits = new double[x.Length];
for (int i = 0; i < xinit.Length; i++)
{
xinit[i] = x[i].InitialGuess;
isFixed[i] = x[i].Fixed;
lowLimits[i] = x[i].LowerBound;
highLimits[i] = x[i].UpperBound;
if (i != 0 && (i + 1) % 4 == 0)
{
step[i] = 10; // step for temperature;
}
else
{
step[i] = 0.02;
}
}
gn.StepForDer = step;
gn.IterMax = 100;
gn.Tau = tau;
res = gn.ComputeMin(xinit, isFixed, lowLimits, highLimits);
}
this.khi2 = this.Khi2(res);
this.GenerateModLightCurve();
return(res);
}
private void UpdateInternalGradient(OptBoundVariable[] variables, double[] G, int o_g)
{
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
G[index + o_g] = this.MeExternalGradientArray[i];
index++;
}
}
}
public double[] ComputeMin(OptMultivariateFunction function, OptMultivariateGradient gradient, OptBoundVariable[] variables, double tolerance, double ACCRCY, ref int nMax)
{
if (this.MeLMQNBC == null) this.MeLMQNBC = new LMQNBC();
this.Initialize(function, gradient, variables);
if (this.MeNumFreeVariables == 0) return this.MeExternalVariables;
int IERROR = 0;
int[] IPIVOT = new int[this.MeNumFreeVariables];
this.MeLMQNBC.Run(ref IERROR, this.MeNumFreeVariables, ref this.MeFreeVariables, 0, ref this.MeF,
ref this.MeGradientArray, 0, ref this.MeW, 0, this.MeLW, this.internalFunction, this.MeLowerBounds, 0, this.MeUpperBounds, 0,
ref this.MeIPIVOT, 0, this.MeMSGLVL, this.MeMAXIT, nMax,this.MeETA, this.MeSTEPMX, ACCRCY, tolerance);
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
this.MeExternalVariables[i] = this.MeFreeVariables[index];
index++;
}
}
nMax = this.internalFunction.FunEvaluations;
return this.MeExternalVariables;
}
private void Initialize(OptMultivariateFunction function, OptMultivariateGradient gradient, OptBoundVariable[] variables)
{
this.internalFunction = new SFUN(function, gradient, variables);
this.MeOptVariable = null;
this.MeOptBoundVariable = OptBoundVariable.GetClon(variables);
this.CheckAndSetBounds(this.MeOptBoundVariable);
this.MeExternalVariables = new double[this.MeOptBoundVariable.Length];
int numFreeVariable = 0;
for (int i = 0; i < this.MeOptBoundVariable.Length; i++)
{
this.MeExternalVariables[i] = variables[i].InitialGuess;
if (this.MeOptBoundVariable[i].Fixed == false) numFreeVariable++;
}
this.MeF = function(this.MeExternalVariables);
this.MeNumFreeVariables = numFreeVariable;
this.MeFreeVariables = new double[numFreeVariable];
this.MeGradientArray = new double[numFreeVariable];
int index = 0;
for (int i = 0; i < this.MeOptBoundVariable.Length; i++)
{
if (this.MeOptBoundVariable[i].Fixed == false)
{
this.MeFreeVariables[index] = this.MeOptBoundVariable[i].InitialGuess;
index++;
}
}
//W - (REAL*8)(REAL*8) WORK VECTOR OF LENGTH AT LEAST 14*N
//LW - (INTEGER) THE DECLARED DIMENSION OF W
this.MeLW = 14 * this.MeNumFreeVariables;
this.MeW = new double[this.MeLW];
this.MeMAXIT = Math.Max(1, this.MeNumFreeVariables / 2);
}
private void CheckAndSetBounds(OptBoundVariable[] variables)
{
int numFreeVariable = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false) numFreeVariable++;
}
this.MeLowerBounds = new double[numFreeVariable];
this.MeUpperBounds = new double[numFreeVariable];
this.MeIPIVOT = new int[numFreeVariable];
int index = 0;
for (int i = 0; i < variables.Length; i++)
{
if (variables[i].Fixed == false)
{
//Se invierten los limites de ser necesario
if (variables[i].LowerBound > variables[i].UpperBound)
{
double tempBound = variables[i].LowerBound;
variables[i].LowerBound = variables[i].UpperBound;
variables[i].UpperBound = tempBound;
}
//Se revisa que la variable inicial este dentro de los limites
if (variables[i].InitialGuess < variables[i].LowerBound)
{
variables[i].InitialGuess = variables[i].LowerBound;
}
if (variables[i].InitialGuess > variables[i].UpperBound)
{
variables[i].InitialGuess = variables[i].UpperBound;
}
this.MeLowerBounds[index] = variables[i].LowerBound;
this.MeUpperBounds[index] = variables[i].UpperBound;
// C LOW, UP - (REAL*8) VECTORS OF LENGTH AT LEAST N CONTAINING
// C THE LOWER AND UPPER BOUNDS ON THE VARIABLES. IF
// C THERE ARE NO BOUNDS ON A PARTICULAR VARIABLE, SET
// C THE BOUNDS TO -1.D38 AND 1.D38, RESPECTIVELY.
if (this.MeLowerBounds[index] == double.NegativeInfinity && this.MeUpperBounds[index] == double.PositiveInfinity)
{
this.MeLowerBounds[index] = 1E-38;
this.MeUpperBounds[index] = 1E38;
}
else if (this.MeLowerBounds[index] == double.NegativeInfinity)
{
this.MeLowerBounds[index] = 1E-38;
}
else if (this.MeUpperBounds[index] == double.PositiveInfinity)
{
this.MeUpperBounds[index] = 1E38;
}
index++;
}
}
}