public override ExpertOpinionSharp.Distributions.IDistribution Fit(string variableName)
{
var variable = Variables.Single(x => x.Name == variableName);
var bounds = GetBounds(variable);
var w = new double[Experts.Count() + 1];
var d = new QuantileDistribution[Experts.Count()];
w [0] = 0;
w [w.Length - 1] = 1;
var weights = UseOptimalAlpha ? GetOptimalWeights() : GetWeights(_alpha);
int i = 0;
foreach (var kv in weights)
{
var p = Estimates [kv.Item1, variable];
var t = new [] { bounds.Item1, p [0], p [1], p [2], bounds.Item2 };
d [i] = new QuantileDistribution(QuantileVector, t);
w [i + 1] = w [i] + kv.Item2;
i++;
}
var dist = new MixtureDistribution(w, d);
return(dist);
}
private Formulator.Formula GetFormula(StringBuilder sb)
{
sb.Length--;
sb.Insert(0, '('); sb.Append(')'); sb.Append('/');
sb.Append(Experts.Count());
return(GetFormula(sb.ToString()));
}
/// <summary>
/// Build the array <c>Pr</c>.
/// </summary>
protected void BuildPr()
{
Pr = new double[M];
for (int h = 0; h < M; h++)
{
double product = 1;
var hashI = h;
for (int i = 0; i < Experts.Count(); i++)
{
var index = hashI % (NbQuantiles + 1);
product *= ExpectedDensity[index];
hashI = hashI / (NbQuantiles + 1);
}
Pr[h] = (S[h] + 1) / (Variables.Count(x => x.Calibration) + (1 / product));
}
Pr = Pr.Select(x => x / Pr.Sum()).ToArray();
}
public override IDistribution Fit(string variableName)
{
/* **** */
orderedExpertNames = new string[Experts.Count()];
int j = 0;
foreach (var e in Experts)
{
orderedExpertNames [j] = e.Name;
j++;
}
/* ---- */
Buildz();
BuildZ();
BuildS();
BuildPr();
/* **** */
var variable = Variables.Single(x => x.Name == variableName);
var bounds = GetBounds(variable);
var min = bounds.Item1;
var max = bounds.Item2;
var xx = Estimates.Get(variable);
// var xx = expertEstimates.SelectMany(t => t);
var xlist = new List <double> (xx);
xlist.Add(min);
xlist.Add(max);
xlist.Sort();
var probabilities = new double[xlist.Count - 1];
for (int i = 0; i < xlist.Count - 1; i++)
{
var v = xlist[i];
probabilities[i] = 1;
var h = 0;
// for getting the correct h, we need to compute it in the opposite
// direction; as h = ((...) * (m + 1) + j ) * (m + 1) + k for [...,j,k]
for (int l = Experts.Count() - 1; l >= 0; l--)
{
var currentExpert = Experts.Single(x => x.Name == orderedExpertNames [l]);
var ll = Estimates [currentExpert, variable].ToList();
// expertEstimates[l].ToList();
var index = ll.FindIndex(y => y > v);
if (index < 0)
{
index = NbQuantiles;
}
h = (h * (NbQuantiles + 1)) + index;
}
probabilities[i] *= Pr[h];
}
var sum = probabilities.Sum();
probabilities = probabilities.Select(t => t / sum).ToArray();
var stops = xlist.ToArray();
var quantiles = new double [probabilities.Length + 1];
quantiles [0] = 0;
int ij;
for (ij = 1; ij < probabilities.Length; ij++)
{
quantiles [ij] = quantiles[ij - 1] + probabilities[ij - 1];
}
quantiles [ij] = 1;
var dist = new QuantileDistribution(quantiles, stops);
return(dist);
}
public override void Calculate()
{
var qCount = Questions.Count();
var eCount = Experts.Count();
var qDict = Questions.ToDictionary(x => x, x => 0d);
int index = 0;
var qIndexDict = Questions.ToDictionary(x => x, x => index++);
index = 0;
var eIndexDict = Experts.ToDictionary(x => x, x => index++);
var matrix = new double[eCount, qCount];
var coeffitient = (double)qCount / MaxNormalizedAnswer;
//Нормализую ответы, относительно максимума, чтобы результат был меньше кол-ва вопросов
foreach (var answer in ExpertAnswer)
{
var qIndex = qIndexDict[answer.Question];
var eIndex = eIndexDict[answer.Expert];
matrix[eIndex, qIndex] = answer.NormalizedAnswer * coeffitient;
}
//Нормализую ответы, убирая одинаковые ответы, не соотносящиеся с рангами
Normalize(matrix);
_normalizedMartix = matrix;
//Сумма ответов по вопросам
var totalSum = 0d;
//Среднее значение ответов от сумм ответов по вопросам
var middleSum = 0d;
//Суммы ответов по вопросам
var qSumm = new double[qCount]; QSumms = qSumm;
//Отклонение от средней суммы ответов по вопросам
var qDSumm = new double[qCount]; QDSumms = qDSumm;
//Квадраты ответов по вопросам
var qDoubleSumm = new double[qCount]; QDoubleSumms = qDoubleSumm;
for (int q = 0; q < qCount; q++)
{
var qSum = 0d;
for (int e = 0; e < eCount; e++)
{
qSum += matrix[e, q];
}
totalSum += qSum;
qSumm[q] = qSum;
}
middleSum = qSumm.Sum() / qSumm.Length;
for (int q = 0; q < qDSumm.Length; q++)
{
qDSumm[q] = qSumm[q] - middleSum;
}
for (int q = 0; q < qDoubleSumm.Length; q++)
{
qDoubleSumm[q] = qDSumm[q] * qDSumm[q];
}
S = qDoubleSumm.Sum();
const string sName = "S";
const string expName = "exp";
const string queName = "que";
CoefficientOfConcordance = GetFormula($"(12*{sName})/(({expName}*{expName})*({queName}*{queName}*{queName}-{queName}))");
CoefficientOfConcordance.TrySetValue(sName, (decimal)S);
CoefficientOfConcordance.TrySetValue(expName, eCount);
CoefficientOfConcordance.TrySetValue(queName, qCount);
}
