public static string DistributionState(DoptimizeExperimentTable det)
{
LangleyAlgorithm lr = SelectState(det);
return(lr.Discription());
}
private static string LangleyIgnition(List <LangleyDataTable> ldts, LangleyAlgorithm lr, double probability)
{
return(ldts[ldts.Count - 1].ldt_StandardDeviation == 0 ? "0" : "" + lr.ResponsePointCalculate(probability, ldts[ldts.Count - 1].ldt_Mean, ldts[ldts.Count - 1].ldt_StandardDeviation) + "");
}
private static string DoptimizeIgnition(List <DoptimizeDataTable> ddts, LangleyAlgorithm lr, double probability)
{
return(ddts[ddts.Count - 1].ddt_StandardDeviation == 0 ? "0" : "" + lr.ResponsePointCalculate(probability, ddts[ddts.Count - 1].ddt_Mean, ddts[ddts.Count - 1].ddt_StandardDeviation) + "");
}
//修改数据值
public static LangleyDataTable UpdateLangleyDataTable(LangleyExperimentTable langlryExpTable, LangleyAlgorithm langleyAlgorithm, double[] xArray, int[] vArray, LangleyDataTable ldt)
{
ldt.ldt_Response = vArray[vArray.Length - 1];
ldt.ldt_StimulusQuantity = xArray[xArray.Length - 1];
vArray = IsFlipTheResponse(langlryExpTable, vArray);
var pointCalculateValue = langleyAlgorithm.GetResult(xArray, vArray);
ldt.ldt_Mean = double.Parse(pointCalculateValue.μ0_final.ToString("f13"));
if (langlryExpTable.let_Correction == 0)
{
pointCalculateValue.σ0_final = langleyAlgorithm.CorrectionAlgorithm(pointCalculateValue.σ0_final, xArray.Length);
}
ldt.ldt_StandardDeviation = pointCalculateValue.σ0_final;
if (double.IsNaN(pointCalculateValue.varmu))
{
ldt.ldt_MeanVariance = 0;
}
else
{
ldt.ldt_MeanVariance = pointCalculateValue.varmu;
}
if (double.IsNaN(pointCalculateValue.varsigma))
{
ldt.ldt_StandardDeviationVariance = 0;
}
else
{
ldt.ldt_StandardDeviationVariance = pointCalculateValue.varsigma;
}
if (double.IsNaN(pointCalculateValue.covmusigma))
{
ldt.ldt_Covmusigma = 0;
}
else
{
ldt.ldt_Covmusigma = pointCalculateValue.covmusigma;
}
return(ldt);
}
