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); }