public OutputParameters GetResult(double[] xArray, int[] vArray, double mumin, double mumax, double reso, out double z, double sigmaguess)
{
OutputParameters outputParameters = new OutputParameters();
outputParameters.sigmaguess = sigmaguess;
GetDistribution(xArray, vArray, mumin, mumax, 0.000000000000001, ref outputParameters, out z, sigmaguess);
pub_function.resolution_getReso(StandardSelection.ProcessValue(z), reso, out z);
outputParameters.μ0_final = StandardSelection.GetAvgValue(outputParameters.μ0_final);
return(outputParameters);
}
private void GetDistribution(double[] xArray, int[] vArray, double mumin, double mumax, double reso, ref OutputParameters outputParameters, out double z, double sigmaguess)
{
mumin = StandardSelection.InverseProcessValue(mumin);
mumax = StandardSelection.InverseProcessValue(mumax);
xArray = StandardSelection.InverseProcessArray(xArray);
if (xArray.Length == 0)
{
pub_function.resolution_getReso((mumin + mumax) / 2, reso, out z);
outputParameters.μ0_final = 0;
outputParameters.σ0_final = 0;
}
else
{
int sum = 0;
for (int j = 0; j < xArray.Length; j++)
{
sum += vArray[j];
}
if (sum == 0 || sum == xArray.Length)
{
z = xArray[xArray.Length - 1];
double[] sumi = { (mumin + z) / 2, z - 2 * sigmaguess, 2 * z - xArray[0] };
double[] sum0 = { (mumax + z) / 2, z + 2 * sigmaguess, 2 * z - xArray[0] };
if (sum == xArray.Length)
{
pub_function.resolution_getReso(pub_function.MIN_getMin(sumi), reso, out z);
}
else
{
pub_function.resolution_getReso(pub_function.MAX_getMax(sum0), reso, out z);
}
outputParameters.μ0_final = 0;
outputParameters.σ0_final = 0;
}
else
{
double[] xArray_change = new double[xArray.Length];
int[] vArray_change = new int[xArray.Length];
int ij;
for (ij = 0; ij < xArray.Length; ij++)
{
xArray_change[ij] = xArray[ij];
vArray_change[ij] = vArray[ij];
}
outputParameters.Maxf = pub_function.MAX_getMax(pub_function.getF(xArray_change, vArray_change));
outputParameters.Mins = pub_function.MIN_getMin(pub_function.S_getS(xArray_change, vArray_change));
double[] FZ = pub_function.getF(xArray_change, vArray_change); //失败的刺激量
double[] SZ = pub_function.S_getS(xArray_change, vArray_change); //成功的刺激量
double Diff = outputParameters.Mins - outputParameters.Maxf;
if (DopDistributionSelection.IsValueSize(Diff, sigmaguess))
{
pub_function.resolution_getReso((outputParameters.Mins + outputParameters.Maxf) / 2, reso, out z);
outputParameters.μ0_final = z;
outputParameters.σ0_final = 0;
}
else
{
if (outputParameters.Mins > outputParameters.Maxf)
{
outputParameters.μ0_final = (outputParameters.Mins + outputParameters.Maxf) / 2;
outputParameters.σ0_final = 0;
pub_function.resolution_getReso(DopDistributionSelection.Fisher_getZ_new(xArray_change, vArray_change, outputParameters.μ0_final, sigmaguess), reso, out z);
outputParameters.sigmaguess = 0.8 * sigmaguess;
}
else
{
outputParameters = DistributionSelection.MLS_getMLS(xArray_change, vArray_change);
outputParameters.sigmaguess = sigmaguess;
pub_function.resolution_getReso(DopDistributionSelection.Fisher_getZ_new(xArray_change, vArray_change, outputParameters.μ0_final, outputParameters.σ0_final), reso, out z);
}
}
}
}
DistributionSelection.Interval_estimation(xArray, vArray, ref outputParameters);
}
