public IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel)
{
var outputParameters = MLS_getMLS(xArray, vArray);
MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "normal", outputParameters.μ0_final, outputParameters.σ0_final, reponseProbability, confidenceLevel, out var final_result);
return(IntervalEstimation.Parse(final_result));
}
private IntervalEstimation GetIntervalEstimationValue(IntervalEstimation rt)
{
rt.Confidence.Down = StandardSelection.ProcessValue(rt.Confidence.Down);
rt.Confidence.Up = StandardSelection.ProcessValue(rt.Confidence.Up);
rt.Mu.Down = StandardSelection.ProcessValue(rt.Mu.Down);
rt.Mu.Up = StandardSelection.ProcessValue(rt.Mu.Up);
return(rt);
}
public static IntervalEstimation Parse(double[] finalResult)
{
IntervalEstimation ret = new IntervalEstimation();
ret.Confidence.Down = finalResult[5];
ret.Confidence.Up = finalResult[4];
ret.Mu.Down = finalResult[1];
ret.Mu.Up = finalResult[0];
ret.Sigma.Down = finalResult[3];
ret.Sigma.Up = finalResult[2];
return(ret);
}
public override IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel)
{
MLR_polar.Max_Likelihood_Estimate(xArray, vArray, "logistic", out var mu, out var sigma, out var L);
MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "logistic", mu, sigma, reponseProbability, confidenceLevel, out var final_result);
IntervalEstimation ret = new IntervalEstimation();
ret.Confidence.Down = final_result[5];
ret.Confidence.Up = final_result[4];
ret.Mu.Down = final_result[1];
ret.Mu.Up = final_result[0];
ret.Sigma.Down = final_result[3];
ret.Sigma.Up = final_result[2];
return(ret);
}
public override IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel)
{
OutputParameters outputParameters = new OutputParameters();
pub_function.norm_MLS_getMLS(xArray, vArray, out outputParameters.varmu, out outputParameters.varsigma, out outputParameters.Maxf, out outputParameters.Mins);
MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "normal", outputParameters.varmu, outputParameters.varsigma, reponseProbability, confidenceLevel, out var final_result);
IntervalEstimation ret = new IntervalEstimation();
ret.Confidence.Down = final_result[5];
ret.Confidence.Up = final_result[4];
ret.Mu.Down = final_result[1];
ret.Mu.Up = final_result[0];
ret.Sigma.Down = final_result[3];
ret.Sigma.Up = final_result[2];
return(ret);
}
public SideReturnData BatchIntervalCalculate(double Y_Ceiling, double Y_LowerLimit, int Y_PartitionNumber, double ConfidenceLevel, double favg, double fsigma, double[] xArray, int[] vArray, int intervalChoose)
{
SideReturnData sideReturnData = new SideReturnData();
double Y_ScaleLength = (DistributionSelection.QnormAndQlogisDistribution(Y_Ceiling) - DistributionSelection.QnormAndQlogisDistribution(Y_LowerLimit)) / Y_PartitionNumber;
sideReturnData.responseProbability = new double[Y_PartitionNumber + 1];
for (int i = 0; i <= Y_PartitionNumber; i++)
{
if (i == 0)
{
sideReturnData.responseProbability[i] = Y_LowerLimit;
}
else
{
sideReturnData.responseProbability[i] = DistributionSelection.PointIntervalDistribution(DistributionSelection.QnormAndQlogisDistribution(Y_LowerLimit) + i * Y_ScaleLength, 0, 1);
}
}
sideReturnData.Y_Ceilings = new double[sideReturnData.responseProbability.Length];
sideReturnData.Y_LowerLimits = new double[sideReturnData.responseProbability.Length];
sideReturnData.responsePoints = new double[sideReturnData.responseProbability.Length];
for (int i = 0; i < sideReturnData.responseProbability.Length; i++)
{
IntervalEstimation ie = new IntervalEstimation();
if (intervalChoose == 0)
{
ie = SingleSideEstimation(xArray, vArray, sideReturnData.responseProbability[i], ConfidenceLevel);
}
else
{
ie = DoubleSideEstimation(xArray, vArray, sideReturnData.responseProbability[i], ConfidenceLevel);
}
sideReturnData.Y_LowerLimits[i] = ie.Confidence.Down;
sideReturnData.Y_Ceilings[i] = ie.Confidence.Up;
double fq = sideReturnData.responseProbability[i];
sideReturnData.responsePoints[i] = StandardSelection.ProcessValue(StandardSelection.InverseProcessValue(favg) + (DistributionSelection.QnormAndQlogisDistribution(fq) * fsigma));
}
return(sideReturnData);
}
public IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel)
{
MLR_polar.Max_Likelihood_Estimate(xArray, vArray, "logistic", out var mu, out var sigma, out var L);
MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "logistic", mu, sigma, reponseProbability, confidenceLevel, out var final_result);
return(IntervalEstimation.Parse(final_result));
}
