/// <summary>
/// Initialization assumptions: LambdaA and LambdaB are 1. ie, both bases are evolving at the average rate.
/// P_A = (1-P_B) = empiricalLeafMargin. Note however that the empiricalLeafMargin is measured for base a; base B could be entirely different.
/// However, we need to keep the interface the same as for the other DiscreteDistributions, so it's not clear how to pass that information
/// in. For now we assume symmetry.
/// </summary>
public override OptimizationParameterList GetParameters(bool useConditionalParameter)
{
//Random random = new Random();
//double pAB = rand.NextDouble();
//double pAb = (1 - pAB) * rand.NextDouble();
//double paB = (1 - pAB - pAb) * rand.NextDouble();
//double lA = 3000 * rand.NextDouble();
//double lB = 3000 * rand.NextDouble();
double pAB = InitialParamVals == null ? EmpiricalEquilibrium / 2 : InitialParamVals[(int)ParameterIndex.P_AB].Value;
double pAb = InitialParamVals == null ? EmpiricalEquilibrium / 2 : InitialParamVals[(int)ParameterIndex.P_Ab].Value;
double paB = InitialParamVals == null ? (1 - EmpiricalEquilibrium) / 2 : InitialParamVals[(int)ParameterIndex.P_aB].Value;
double lA = InitialParamVals == null ? 1 : InitialParamVals[(int)ParameterIndex.Lambda_A].Value;
double lB = InitialParamVals == null ? 1 : InitialParamVals[(int)ParameterIndex.Lambda_B].Value;
OptimizationParameterList optList = OptimizationParameterList.GetInstance();
optList.Add((int)ParameterIndex.P_AB, OptimizationParameter.GetProbabilityInstance("P_TT", pAB, true));
optList.Add((int)ParameterIndex.P_Ab, OptimizationParameter.GetProbabilityInstance("P_TF", pAb, true));
optList.Add((int)ParameterIndex.P_aB, OptimizationParameter.GetProbabilityInstance("P_FT", paB, true));
optList.Add((int)ParameterIndex.Lambda_A, OptimizationParameter.GetPositiveFactorInstance("Lambda_A", lA, true));
optList.Add((int)ParameterIndex.Lambda_B, OptimizationParameter.GetPositiveFactorInstance("Lambda_B", lB, true));
optList.SetCheckConditions(CheckParameterConstraints);
return(optList);
}
protected virtual OptimizationParameterList GetParameters(bool useConditionalParameter, bool zeroVariance)
{
// ignore the initial parameter settings.
OptimizationParameterList paramStart = OptimizationParameterList.GetInstance(
OptimizationParameter.GetPositiveFactorInstance("Alpha", 1, true),
//OptimizationParameter.GetProbabilityInstance("Alpha", .5, true),
OptimizationParameter.GetPositiveFactorInstance("Variance", 10000, true),
//OptimizationParameter.GetPositiveFactorInstance("AlphaVariance", 10000, true),
OptimizationParameter.GetTanInstance("Delta", 0, useConditionalParameter, -1000, 1000),
OptimizationParameter.GetTanInstance("Mean", 0, true, -1000, 1000),
zeroVariance ? OptimizationParameter.GetPositiveFactorInstance("vNoise", 0, false)
: OptimizationParameter.GetPositiveFactorInstance("vNoise", 75, true)
);
//TEMPORARY TESTING!!!!!!!
//paramStart["Alpha"].Value = 0.165267457;
//paramStart["AlphaVariance"].Value = 6952.429217;
//paramStart["Delta"].Value = -23.71002634;
//paramStart["Delta"].DoSearch = true;
paramStart["Mean"].Value = 24.71176471;
paramStart["Variance"].Value = 35.224;
//paramStart["vNoise"].Value = 35.4194535;
return(paramStart);
}
private static void ComputeIidScores(int[] fisherCounts, out List <Score> nullScores, out Score altScore)
{
int tt = fisherCounts[(int)TwoByTwo.ParameterIndex.TT];
int tf = fisherCounts[(int)TwoByTwo.ParameterIndex.TF];
int ft = fisherCounts[(int)TwoByTwo.ParameterIndex.FT];
int ff = fisherCounts[(int)TwoByTwo.ParameterIndex.FF];
int sum = SpecialFunctions.Sum(fisherCounts);
double pi0 = (double)(tt + tf) / sum;
double pi1 = (double)(tt + ft) / sum;
double ptt = (double)tt / sum;
double ptf = (double)tf / sum;
double pft = (double)ft / sum;
double pff = (double)ff / sum;
OptimizationParameterList nullParamsLeft = OptimizationParameterList.GetInstance(
OptimizationParameter.GetProbabilityInstance("Pi", pi0, true));
OptimizationParameterList nullParamsRight = OptimizationParameterList.GetInstance(
OptimizationParameter.GetProbabilityInstance("Pi", pi1, true));
OptimizationParameterList altParams = OptimizationParameterList.GetInstance(
OptimizationParameter.GetProbabilityInstance("P_TT", ptt, true),
OptimizationParameter.GetProbabilityInstance("P_TF", ptf, true),
OptimizationParameter.GetProbabilityInstance("P_FT", pft, true));
ComputeIidScoresGivenParams(fisherCounts, nullParamsLeft, nullParamsRight, altParams, out nullScores, out altScore);
}
//public override bool TryDeriveParametersFromFisherCounts(int[] fisherCounts, out OptimizationParameterList parameters)
//{
// parameters = null;
// if (fisherCounts.Length != 4)
// {
// return false;
// }
// int tt = fisherCounts[(int)TwoByTwo.ParameterIndex.TT];
// int tf = fisherCounts[(int)TwoByTwo.ParameterIndex.TF];
// int ft = fisherCounts[(int)TwoByTwo.ParameterIndex.FT];
// int ff = fisherCounts[(int)TwoByTwo.ParameterIndex.FF];
// int total = tt+tf+ft+ff;
// double predP = (double)(tt + tf) / total;
// double targP = (double)(tt + ft) / total;
// if (Math.Max(predP, targP) != 1 && Math.Min(predP, targP) != 0)
// {
// return false;
// }
// parameters = GetParameters();
// if (targP == 0 || targP == 1)
// {
// parameters[(int)ParameterIndex.Equilibrium].Value = targP;
// parameters[(int)ParameterIndex.Lambda].Value = 0;
// }
//}
private OptimizationParameterList GetParameters(bool useConditionalParameter, double empericalEq, OptimizationParameterList initParams)
{
if (initParams != null)
{
OptimizationParameterList cloneList = (OptimizationParameterList)initParams.Clone();
OptimizationParameter parameter1 = cloneList[(int)ParameterIndex.Predictor1];
parameter1.DoSearch = useConditionalParameter || PredictorParametersInSequence;
OptimizationParameter parameter2 = cloneList[(int)ParameterIndex.Predictor2];
parameter2.DoSearch = useConditionalParameter && Use2PredictorParameters;
if (!useConditionalParameter)
{
if (!PredictorParametersInSequence)
{
parameter1.Value = 0.0;
}
parameter2.Value = 0.0;
}
return(cloneList);
}
else
{
OptimizationParameterList optList = OptimizationParameterList.GetInstance();
optList.Add((int)ParameterIndex.Predictor1, OptimizationParameter.GetProbabilityInstance("selection_pressure", 0, useConditionalParameter || PredictorParametersInSequence));
optList.Add((int)ParameterIndex.Predictor2, OptimizationParameter.GetProbabilityInstance("secondary_selection_pressure", 0, useConditionalParameter && Use2PredictorParameters));
optList.Add((int)ParameterIndex.Lambda, OptimizationParameter.GetPositiveFactorInstance("lambda", 1, true));
optList.Add((int)ParameterIndex.Equilibrium, OptimizationParameter.GetProbabilityInstance("pi", empericalEq, true));
return(optList);
}
}
protected override OptimizationParameterList GetParameters(bool useConditionalParameter, bool zeroVariance)
{
OptimizationParameterList parameters = base.GetParameters(useConditionalParameter, zeroVariance);
OptimizationParameter alpha = parameters["Alpha"];
alpha.Value = 10000;
//alpha.Value = double.PositiveInfinity;
alpha.DoSearch = false;
return(parameters);
}
private IEnumerator <string> GetEnumerator(OptimizationParameter parameter)
{
var stepParameter = parameter as OptimizationStepParameter;
if (stepParameter == null)
{
throw new InvalidOperationException("");
}
return(new OptimizationStepParameterEnumerator(stepParameter));
}
public OptimizationParameterList CloneWithNewValuesForSearch(List <double> point)
{
List <OptimizationParameter> parameterCollection = new List <OptimizationParameter>();
for (int iParam = 0; iParam < Count; ++iParam)
{
OptimizationParameter clone = AsParameterArray[iParam].Clone();
clone.ValueForSearch = point[iParam];
parameterCollection.Add(clone);
}
return(GetInstance2(parameterCollection));
}
public override OptimizationParameterList GetParameters(int[] fisherCounts, OptimizationParameterList initParams)
{
double pAB, pAb, paB, lA, lB;
if (initParams != null)
{
pAB = initParams[(int)ParameterIndex.P_AB].Value;
pAb = initParams[(int)ParameterIndex.P_Ab].Value;
paB = initParams[(int)ParameterIndex.P_aB].Value;
lA = initParams[(int)ParameterIndex.Lambda_A].Value;
lB = initParams[(int)ParameterIndex.Lambda_B].Value;
}
else if (fisherCounts.Length == 4)
{
double sum = (double)SpecialFunctions.Sum(fisherCounts);
lA = lB = 1;
pAB = fisherCounts[(int)TwoByTwo.ParameterIndex.TT];
pAb = fisherCounts[(int)TwoByTwo.ParameterIndex.TF];
paB = fisherCounts[(int)TwoByTwo.ParameterIndex.FT];
}
else if (fisherCounts.Length == 2) // primarily for backward compatability testing
{
double empiricalEquilibrium = (double)fisherCounts[(int)DistributionDiscreteConditional.DistributionClass.True] / (fisherCounts[0] + fisherCounts[1]);
lA = lB = 1;
pAB = empiricalEquilibrium / 2;
pAb = empiricalEquilibrium / 2;
paB = (1 - empiricalEquilibrium) / 2;
}
else
{
throw new ArgumentException("Cannot parse fisher counts of length " + fisherCounts.Length);
}
//double pAB = initParams == null ? empiricalEquilibrium / 2 : initParams[(int)ParameterIndex.P_AB].Value;
//double pAb = initParams == null ? empiricalEquilibrium / 2 : initParams[(int)ParameterIndex.P_Ab].Value;
//double paB = initParams == null ? (1 - empiricalEquilibrium) / 2 : initParams[(int)ParameterIndex.P_aB].Value;
//double lA = initParams == null ? 1 : initParams[(int)ParameterIndex.Lambda_A].Value;
//double lB = initParams == null ? 1 : initParams[(int)ParameterIndex.Lambda_B].Value;
OptimizationParameterList optList = OptimizationParameterList.GetInstance();
optList.Add((int)ParameterIndex.P_AB, OptimizationParameter.GetProbabilityInstance("P_TT", pAB, true));
optList.Add((int)ParameterIndex.P_Ab, OptimizationParameter.GetProbabilityInstance("P_TF", pAb, true));
optList.Add((int)ParameterIndex.P_aB, OptimizationParameter.GetProbabilityInstance("P_FT", paB, true));
optList.Add((int)ParameterIndex.Lambda_A, OptimizationParameter.GetPositiveFactorInstance("Lambda_A", lA, true));
optList.Add((int)ParameterIndex.Lambda_B, OptimizationParameter.GetPositiveFactorInstance("Lambda_B", lB, true));
optList.SetCheckConditions(CheckParameterConstraints);
return(optList);
}
/// <summary>
/// Calculates number od data points for step based optimization parameter based on min/max and step values
/// </summary>
private int Estimate(OptimizationParameter parameter)
{
var stepParameter = parameter as OptimizationStepParameter;
if (stepParameter == null)
{
throw new InvalidOperationException($"Cannot estimate parameter of type {parameter.GetType().FullName}");
}
if (!stepParameter.Step.HasValue)
{
throw new InvalidOperationException("Optimization parameter cannot be estimated due to step value is not initialized");
}
return((int)Math.Floor((stepParameter.MaxValue - stepParameter.MinValue) / stepParameter.Step.Value) + 1);
}
public override OptimizationParameterList GetParameters(bool useConditionalParameter, bool zeroVariance)
{
// alpha is confounded with variance in this case, so we fix it at 1.
OptimizationParameterList parameters = base.GetParameters(useConditionalParameter, zeroVariance);
OptimizationParameter alphaParam = parameters["Alpha"];
alphaParam.ConvertToPositiveFactorInstance();
alphaParam.Value = 1.0;
//alphaParam.Value = 1.0;
//alphaParam.DoSearch = false;
// TEMPORARY!!!!
//parameters["Mean"].Value = 10.13;
//parameters["AlphaVariance"].Value = 2.18 * 2.18;
return(parameters);
}
//private static OptimizationParameterList CreateQmrrParamsX(double causePrior, double leakProbability, Set<string> candidateHlaSet)
//{
// List<Parameter> parameterCollection = new List<Parameter>();
// parameterCollection.Add(Parameter.GetProbabilityInstance("causePrior", causePrior, false));
// foreach (string hla in candidateHlaSet)
// {
// if (hla == "B1599")
// {
// Parameter aParameter = Parameter.GetProbabilityInstance("link" + hla, 0, false);
// parameterCollection.Add(aParameter);
// }
// else
// {
// Parameter aParameter = Parameter.GetProbabilityInstance("link" + hla, .5, true);
// parameterCollection.Add(aParameter);
// }
// }
// parameterCollection.Add(Parameter.GetProbabilityInstance("leakProbability", leakProbability, false));
// parameterCollection.Add(Parameter.GetPositiveFactorInstance("useKnownList", 1, false));
// OptimizationParameterList qmrrParamsStart = OptimizationParameterList.GetInstance2(parameterCollection);
// return qmrrParamsStart;
//}
//private static Dictionary<string, string> LoadOriginalParameters(string directory)
//{
// string parameterFileName = string.Format(@"{0}\{1}", directory, "NoisyOr.Parameters.TwoCause.UseKnown.txt");//!!!const
// string header = "dataset varyFitFactor score causePrior fitFactor leakProbability link useKnownList";
// foreach (Dictionary<string, string> row in SpecialFunctions.TabFileTable(parameterFileName, header, false))
// {
// bool varyFitFactor = bool.Parse(row["varyFitFactor"]);
// if (!varyFitFactor)
// {
// return row;
// }
// }
// Debug.Fail("Didn't find expected line");
// return null;
//}
//static Dictionary<string, Set<string>> LoadOriginalPeptideToHlaSet(string directory)
//{
// string fileName = string.Format(@"{0}\{1}", directory, @"NoisyOr.HlasPerPeptide.hivmodel.TwoCauseFalse.UseKnownTrue.txt");
// Dictionary<string, Set<string>> originalPeptideToHlaSet = new Dictionary<string, Set<string>>();
// string header = "Peptide HLAAssignment LogLikelihood";
// foreach (Dictionary<string, string> row in SpecialFunctions.TabFileTable(fileName, header, false))
// {
// string peptide = row["Peptide"];
// Set<string> hlaSet = Set<string>.GetInstance(row["HLAAssignment"].Split(';'));
// originalPeptideToHlaSet.Add(peptide, hlaSet);
// }
// return originalPeptideToHlaSet;
//}
public static QmrrModelAllPeptides CreateSimpleModel(string directory, string casename, bool useKnownList)
{
OptimizationParameterList qmrrParamsStart = OptimizationParameterList.GetInstance(
OptimizationParameter.GetProbabilityInstance("causePrior", .01, true),
OptimizationParameter.GetProbabilityInstance("link", .5, true),
OptimizationParameter.GetProbabilityInstance("leakProbability", .003, true),
OptimizationParameter.GetPositiveFactorInstance("useKnownList", useKnownList ? 1 : 0, false)
);
ModelLikelihoodFactories modelLikelihoodFactories = ModelLikelihoodFactories.GetInstanceThreeParamSlow(qmrrParamsStart);
double depth = 1.5;
string dataset = string.Format(@"{0}\{1}", directory, casename);
QmrrModelAllPeptides qmrrModel = QmrrModelAllPeptides.GetInstance(modelLikelihoodFactories, dataset, qmrrParamsStart, depth, "noConstraints");
return(qmrrModel);
}
private IEnumerator <string> GetEnumerator(OptimizationParameter parameter)
{
var staticOptimizationParameter = parameter as StaticOptimizationParameter;
if (staticOptimizationParameter != null)
{
return(new List <string> {
staticOptimizationParameter.Value
}.GetEnumerator());
}
var stepParameter = parameter as OptimizationStepParameter;
if (stepParameter == null)
{
throw new InvalidOperationException("");
}
return(new OptimizationStepParameterEnumerator(stepParameter));
}
/// <summary>
/// Initialization assumptions: LambdaA and LambdaB are 1. ie, both bases are evolving at the average rate.
/// P_A = (1-P_B) = empiricalLeafMargin. Note however that the empiricalLeafMargin is measured for base a; base B could be entirely different.
/// However, we need to keep the interface the same as for the other DiscreteDistributions, so it's not clear how to pass that information
/// in. For now we assume symmetry.
/// </summary>
public override OptimizationParameterList GetParameters(bool useConditionalParameter)
{
double pHLA = InitialParamVals == null ? 0.1 : InitialParamVals[(int)ParameterIndex.P_Hla].Value;
double pAA = InitialParamVals == null ? EmpiricalEquilibrium : InitialParamVals[(int)ParameterIndex.P_AA].Value;
double lAA = InitialParamVals == null ? 1 : InitialParamVals[(int)ParameterIndex.ETimesLambaA].Value;
double lHLA = InitialParamVals == null ? 1 : InitialParamVals[(int)ParameterIndex.Lambda_Hla].Value;
double erRatio = InitialParamVals == null ? 0 : InitialParamVals[(int)ParameterIndex.RTimesLambdaA].Value;
//double r = InitialParamVals == null ? 0 : InitialParamVals[(int)ParameterIndex.P_r].Value;
OptimizationParameterList optList = OptimizationParameterList.GetInstance();
optList.Add((int)ParameterIndex.P_AA, OptimizationParameter.GetProbabilityInstance("P_AA", pAA, true));
optList.Add((int)ParameterIndex.P_Hla, OptimizationParameter.GetProbabilityInstance("P_Hla", pHLA, true));
optList.Add((int)ParameterIndex.ETimesLambaA, OptimizationParameter.GetPositiveFactorInstance("Lambda_AA", lAA, true));
optList.Add((int)ParameterIndex.Lambda_Hla, OptimizationParameter.GetPositiveFactorInstance("Lambda_Hla", lHLA, true));
optList.Add((int)ParameterIndex.RTimesLambdaA, OptimizationParameter.GetPositiveFactorInstance("E_R_Ratio", erRatio, true));
//optList.Add((int)ParameterIndex.ERLogRatio, OptimizationParameter.GetTanInstance("E_R_Ratio", erRatio, true, -10, 10));
//optList.Add((int)ParameterIndex.P_r, OptimizationParameter.GetProbabilityInstance("r", r, true)); // DON'T LEARN R FOR NOW
//optList.SetCheckConditions(CheckParameterConstraints);
return(optList);
}
public override OptimizationParameterList GetParameters(bool useConditionalParameter)
{
if (InitialParamVals != null)
{
OptimizationParameterList cloneList = InitialParamVals.Clone();
OptimizationParameter parameter1 = cloneList[(int)ParameterIndex.Predictor1];
parameter1.DoSearch = useConditionalParameter || PredictorParametersInSequence;
OptimizationParameter parameter2 = cloneList[(int)ParameterIndex.Predictor2];
parameter2.DoSearch = useConditionalParameter && Use2PredictorParameters;
if (!useConditionalParameter)
{
if (!PredictorParametersInSequence)
{
parameter1.Value = 0.0;
}
parameter2.Value = 0.0;
}
return(cloneList);
}
//double initLambda = InitialParamVals == null ? 1 : InitialParamVals[(int)ParameterIndex.Lambda].Value;
//double initParam = InitialParamVals == null ? 0 : InitialParamVals[(int)ParameterIndex.Predictor].Value;
//double initEmp = InitialParamVals == null ? EmpiricalEquilibrium : InitialParamVals[(int)ParameterIndex.Equilibrium].Value;
OptimizationParameterList optList = OptimizationParameterList.GetInstance();
//optList.Add((int)ParameterIndex.Predictor, OptimizationParameter.GetProbabilityInstance("parameter", initParam, useConditionalParameter));
//optList.Add((int)ParameterIndex.Lambda, OptimizationParameter.GetPositiveFactorInstance("lambda", initLambda, true));
//optList.Add((int)ParameterIndex.Equilibrium, OptimizationParameter.GetProbabilityInstance("x", initEmp, true));
optList.Add((int)ParameterIndex.Predictor1, OptimizationParameter.GetProbabilityInstance("selection_pressure", 0, useConditionalParameter || PredictorParametersInSequence));
optList.Add((int)ParameterIndex.Predictor2, OptimizationParameter.GetProbabilityInstance("secondary_selection_pressure", 0, useConditionalParameter && Use2PredictorParameters));
optList.Add((int)ParameterIndex.Lambda, OptimizationParameter.GetPositiveFactorInstance("lambda", 1, true));
optList.Add((int)ParameterIndex.Equilibrium, OptimizationParameter.GetProbabilityInstance("pi", EmpiricalEquilibrium, true));
return(optList);
}
protected OptimizationParameterList CreateQmrrParamsStartForTheseCandidateHlas(Set <Hla> trueHlaSet, Set <Hla> hlaWithLinkZero)
{
List <OptimizationParameter> parameterCollection = new List <OptimizationParameter>();
parameterCollection.Add(OptimizationParameter.GetProbabilityInstance("causePrior", .5, false));
foreach (Hla hla in trueHlaSet)
{
bool fixAtZero = hlaWithLinkZero.Contains(hla);
OptimizationParameter aParameter = OptimizationParameter.GetProbabilityInstance("link" + hla, fixAtZero ? 0 : .5, !fixAtZero);
parameterCollection.Add(aParameter);
}
if (LeakProbabilityOrNull == null)
{
parameterCollection.Add(OptimizationParameter.GetProbabilityInstance("leakProbability", .01, true));
}
else
{
parameterCollection.Add(OptimizationParameter.GetProbabilityInstance("leakProbability", (double)LeakProbabilityOrNull, false));
}
parameterCollection.Add(OptimizationParameter.GetPositiveFactorInstance("useKnownList", 1, false));
OptimizationParameterList qmrrParamsStart = OptimizationParameterList.GetInstance2(parameterCollection);
return(qmrrParamsStart);
}
private bool Close(OptimizationParameter myValue, OptimizationParameter otherValue, double eps)
{
return(Math.Abs(myValue.Value - otherValue.Value) < eps);
}
public override OptimizationParameterList GetParameters(int[] fisherCounts, OptimizationParameterList initParams)
{
double pHLA;
double pAA;
double lAA;
double lHLA;
double erRatio;
if (initParams != null)
{
pHLA = initParams[(int)ParameterIndex.P_Hla].Value;
pAA = initParams[(int)ParameterIndex.P_AA].Value;
lAA = initParams[(int)ParameterIndex.ETimesLambaA].Value;
lHLA = initParams[(int)ParameterIndex.Lambda_Hla].Value;
erRatio = initParams[(int)ParameterIndex.RTimesLambdaA].Value;
}
else if (fisherCounts.Length == 4)
{
double sum = (double)SpecialFunctions.Sum(fisherCounts);
int tt = (int)TwoByTwo.ParameterIndex.TT;
int tf = (int)TwoByTwo.ParameterIndex.TF;
int ft = (int)TwoByTwo.ParameterIndex.FT;
lAA = lHLA = 1;
erRatio = 0;
pHLA = (fisherCounts[tt] + fisherCounts[tf]) / sum;
pAA = (fisherCounts[tt] + fisherCounts[ft]) / sum;
}
else if (fisherCounts.Length == 2) // primarily for backward compatability testing
{
double empiricalEquilibrium = (double)fisherCounts[(int)DistributionDiscreteConditional.DistributionClass.True] / (fisherCounts[0] + fisherCounts[1]);
lAA = lHLA = 1;
erRatio = 0;
pHLA = 0.1;
pAA = empiricalEquilibrium;
}
else
{
throw new ArgumentException("Cannot parse fisher counts of length " + fisherCounts.Length);
}
//double pHLA = initParams == null ? 0.1 : initParams[(int)ParameterIndex.P_Hla].Value;
//double pAA = initParams == null ? empiricalEquilibrium : initParams[(int)ParameterIndex.P_AA].Value;
//double lAA = initParams == null ? 1 : initParams[(int)ParameterIndex.ETimesLambaA].Value;
//double lHLA = initParams == null ? 1 : initParams[(int)ParameterIndex.Lambda_Hla].Value;
//double erRatio = initParams == null ? 0 : initParams[(int)ParameterIndex.RTimesLambdaA].Value;
//double r = InitialParamVals == null ? 0 : InitialParamVals[(int)ParameterIndex.P_r].Value;
OptimizationParameterList optList = OptimizationParameterList.GetInstance();
optList.Add((int)ParameterIndex.P_AA, OptimizationParameter.GetProbabilityInstance("P_AA", pAA, true));
optList.Add((int)ParameterIndex.P_Hla, OptimizationParameter.GetProbabilityInstance("P_Hla", pHLA, true));
optList.Add((int)ParameterIndex.ETimesLambaA, OptimizationParameter.GetPositiveFactorInstance("Lambda_AA", lAA, true));
optList.Add((int)ParameterIndex.Lambda_Hla, OptimizationParameter.GetPositiveFactorInstance("Lambda_Hla", lHLA, true));
optList.Add((int)ParameterIndex.RTimesLambdaA, OptimizationParameter.GetPositiveFactorInstance("E_R_Ratio", erRatio, true));
//optList.Add((int)ParameterIndex.ERLogRatio, OptimizationParameter.GetTanInstance("E_R_Ratio", erRatio, true, -10, 10));
//optList.Add((int)ParameterIndex.P_r, OptimizationParameter.GetProbabilityInstance("r", r, true)); // DON'T LEARN R FOR NOW
//optList.SetCheckConditions(CheckParameterConstraints);
return(optList);
}
public void NotIEnumerable(OptimizationParameter optimizationParameter)
{
Assert.IsNotInstanceOf <IEnumerable <string> >(optimizationParameter);
}
