private void computeAllKrits(int n, int m, double fullR2, double sigma2, TettaRSS tettaRss)
{
ForOptMdl.TettaRss.Add(tettaRss);
ForOptMdl.Mellous.Add(tettaRss.Rss / sigma2 + 2.0 * m - n);
double newR2 = calcR2(tettaRss.Y, n);
ForOptMdl.ManyKrit.Add(newR2 / fullR2);
var msep = tettaRss.Rss / (n * (n - m)) * (1 + n + m * (n + 1) / (n - m - 2));
ForOptMdl.MSEP.Add(msep);
var aev = tettaRss.Rss * m / (n * (n - m));
ForOptMdl.AEV.Add(aev);
var fullSum = 0.0;
for (int d = 0; d < n; d++)
{
var dif = Math.Abs(_Y[d][0, 0] - tettaRss.Y[d][0, 0]);
fullSum += dif;
}
ForOptMdl.E.Add(fullSum);
}
private void exceptionAlgorithm(int n, int fullM, double fullR2, double sigma2)
{
bool[] needFunc = new bool[TrueFunc.Count];
for (int i = 0; i < TrueFunc.Count; i++)
{
needFunc[i] = TrueFunc[i];
}
var m = fullM; var nu2 = 1;
for (int i = 0; i < fullM - 1; i++)
{
Dictionary <int, double> FCrit = new Dictionary <int, double>();
var nu1 = n - m + 1; double nu2_nu1 = ((double)nu2 / (double)nu1);
TettaRSS tettaRssBefore = TettaRSS.Compute(generateX(n, m, needFunc), _Y);
computeAllKrits(n, m, fullR2, sigma2, tettaRssBefore);
ForOptMdl.Functions.Add(needFunc.ToArray());
for (int q = fullM - m, index = 0, newM = m - 1; q < fullM; q++, index++)
{
var newNeedToAdd = new bool[TrueFunc.Count];
for (int w = 0; w < TrueFunc.Count; w++)
{
newNeedToAdd[w] = needFunc[w];
}
while (!newNeedToAdd[index] || !TrueFunc[index])
{
index++;
}
newNeedToAdd[index] = false;
TettaRSS newTettaRss = TettaRSS.Compute(generateX(n, newM, newNeedToAdd), _Y);
var fCrit = nu2_nu1 * (newTettaRss.Rss - tettaRssBefore.Rss) / newTettaRss.Rss;
FCrit.Add(index, fCrit);
}
ForOptMdl.AllFCrits.Add(FCrit.ToList());
var sorted = FCrit.OrderBy(x => x.Value).ToList();
m--; needFunc[sorted[0].Key] = false;
}
computeAllKrits(n, m, fullR2, sigma2, TettaRSS.Compute(generateX(n, m, needFunc), _Y));
ForOptMdl.Functions.Add(needFunc.ToArray());
}
public void GenerateOptimalModel(OptimalModelAlgorithm alg)
{
if (!_initialized)
{
return;
}
ForOptMdl.Clear();
var n = Data.Count;
int fullM = 0;
foreach (bool b in TrueFunc)
{
if (b)
{
fullM++;
}
}
double fullR2 = calcR2(_Y, n);
TettaRSS fullXTettaRss = TettaRSS.Compute(generateX(Data.Count, fullM, TrueFunc.ToArray()), _Y);
var sigma2 = fullXTettaRss.Rss / (n - fullM);
if (alg == OptimalModelAlgorithm.Exception)
{
exceptionAlgorithm(n, fullM, fullR2, sigma2);
}
else
{
insertionAlgorithm(n, fullM, fullR2, sigma2);
}
selectBestF(ForOptMdl.Mellous, ref ForOptMdl.BestFMell);
selectBestF(ForOptMdl.MSEP, ref ForOptMdl.BestFMSEP);
selectBestF(ForOptMdl.AEV, ref ForOptMdl.BestFAEV);
selectBestF(ForOptMdl.E, ref ForOptMdl.BestFE);
selectBestFR(ForOptMdl.ManyKrit, ref ForOptMdl.BestFR);
}
private void computeAllKrits(int n, int m, double fullR2, double sigma2, TettaRSS tettaRss)
{
ForOptMdl.TettaRss.Add(tettaRss);
ForOptMdl.Mellous.Add(tettaRss.Rss / sigma2 + 2.0 * m - n);
double newR2 = calcR2(tettaRss.Y, n);
ForOptMdl.ManyKrit.Add(newR2 / fullR2);
var msep = tettaRss.Rss / (n * (n - m)) * (1 + n + m * (n + 1) / (n - m - 2));
ForOptMdl.MSEP.Add(msep);
var aev = tettaRss.Rss * m / (n * (n - m));
ForOptMdl.AEV.Add(aev);
var fullSum = 0.0;
for (int d = 0; d < n; d++) {
var dif = Math.Abs(_Y[d][0, 0] - tettaRss.Y[d][0, 0]);
fullSum += dif;
}
ForOptMdl.E.Add(fullSum);
}
public void CheckMultiColliniar()
{
if (!_initialized) return;
int n = Data.Count;
int m = 0; foreach (bool b in TrueFunc) if (b) m++;
_X = generateX(n, m, TrueFunc.ToArray());
_TettaEst = TettaRSS.Compute(_X,_Y);
var XTX = _X.Transpose() * _X;
var XTXTrace = XTX * 1/XTX.Trace();
ForMultCol.DetXTXTrace = XTXTrace.Determinant();
ForMultCol.MinLambda = XTXTrace.MinEiganValue();
ForMultCol.NeimanGoldstein = XTXTrace.MaxEiganValue() / ForMultCol.MinLambda;
Matrix R = new Matrix(m, m);
double r_max = 0.0;
for (int j = 0; j < m; j++) {
for (int i = 0; i < m; i++) {
double sum_up = 0.0, sum_d1 = 0.0, sum_d2 = 0.0;
for (int k = 0; k < n; k++) {
sum_up += _X[k, i] * _X[k, j];
sum_d1 += _X[k, i] * _X[k, i];
sum_d2 += _X[k, j] * _X[k, j];
}
double rij = sum_up / (Math.Sqrt(sum_d1) * Math.Sqrt(sum_d2));
if (i != j && Math.Abs(rij) > r_max)
r_max = Math.Abs(rij);
R[i, j] = rij;
}
R[j, j] = 1.0;
}
ForMultCol.MaxPairConjugation = r_max;
R = R.Inverse();
r_max = 0.0;
for (int i = 0; i < m; i++) {
double ri = Math.Sqrt(1.0 - 1.0 / R[i, i]);
if (ri > r_max)
r_max = ri;
}
ForMultCol.MaxConjugation = r_max;
}
public void CheckMultiColliniar()
{
if (!_initialized)
{
return;
}
int n = Data.Count;
int m = 0; foreach (bool b in TrueFunc)
{
if (b)
{
m++;
}
}
_X = generateX(n, m, TrueFunc.ToArray());
_TettaEst = TettaRSS.Compute(_X, _Y);
var XTX = _X.Transpose() * _X;
var XTXTrace = XTX * 1 / XTX.Trace();
ForMultCol.DetXTXTrace = XTXTrace.Determinant();
ForMultCol.MinLambda = XTXTrace.MinEiganValue();
ForMultCol.NeimanGoldstein = XTXTrace.MaxEiganValue() / ForMultCol.MinLambda;
Matrix R = new Matrix(m, m);
double r_max = 0.0;
for (int j = 0; j < m; j++)
{
for (int i = 0; i < m; i++)
{
double sum_up = 0.0, sum_d1 = 0.0, sum_d2 = 0.0;
for (int k = 0; k < n; k++)
{
sum_up += _X[k, i] * _X[k, j];
sum_d1 += _X[k, i] * _X[k, i];
sum_d2 += _X[k, j] * _X[k, j];
}
double rij = sum_up / (Math.Sqrt(sum_d1) * Math.Sqrt(sum_d2));
if (i != j && Math.Abs(rij) > r_max)
{
r_max = Math.Abs(rij);
}
R[i, j] = rij;
}
R[j, j] = 1.0;
}
ForMultCol.MaxPairConjugation = r_max;
R = R.Inverse();
r_max = 0.0;
for (int i = 0; i < m; i++)
{
double ri = Math.Sqrt(1.0 - 1.0 / R[i, i]);
if (ri > r_max)
{
r_max = ri;
}
}
ForMultCol.MaxConjugation = r_max;
}
