private static double[] GetH6ByMGK(Matrix R)
{
var l = LaverierFadeevaMethod.Solve(R, LaverierFadeevaSolvingOptions.FullSolving);
LaverierFadeevaExtendedResult[] ext = LaverierFadeevaExtendedResult.ConvertToExtendedLaverierFadeevaResult(l);
ext = ext.OrderByDescending(v => v.EigenValue.Abs()).ToArray();
int w = ext.Count(v => v.EigenValue.Abs() > 1);
var neededVectors = ext.Take(w).Select(el => el.eigenVector).ToList();
double[] result = new double[ext.Length];
for (int i = 0; i < ext.Length; i++)
{
double sum = 0;
for (int j = 0; j < w; j++)
{
sum += neededVectors[j][i];
}
result[i] = sum;
}
return(result);
}
//7семестр
public LaverierFadeevaExtendedResult[] MGKGetinfo()
{
Matrix dc = new Matrix(DC);
var laverierFadeevaResult = LaverierFadeevaMethod.Solve(dc, LaverierFadeevaSolvingOptions.FullSolving);
var extended = MGKInfo = LaverierFadeevaExtendedResult.ConvertToExtendedLaverierFadeevaResult(laverierFadeevaResult);
return(extended);
}
static int getW(Matrix R)
{
var l = LaverierFadeevaMethod.Solve(R, LaverierFadeevaSolvingOptions.FullSolving);
LaverierFadeevaExtendedResult[] ext = LaverierFadeevaExtendedResult.ConvertToExtendedLaverierFadeevaResult(l);
ext = ext.OrderByDescending(v => v.EigenValue.Abs()).ToArray();
var eigenAverage = ext.Sum(v => v.EigenValue) / ext.Length;
int w = ext.Count(v => v.EigenValue > eigenAverage);
return(w);
}
private static Matrix GetA(Matrix R, out int w, int wMin)
{
var l = LaverierFadeevaMethod.Solve(R, LaverierFadeevaSolvingOptions.FullSolving);
LaverierFadeevaExtendedResult[] ext = LaverierFadeevaExtendedResult.ConvertToExtendedLaverierFadeevaResult(l);
ext = ext.OrderByDescending(v => v.EigenValue.Abs()).ToArray();
var eigenAverage = ext.Sum(v => v.EigenValue) / ext.Length;
w = ext.Count(v => v.EigenValue > eigenAverage);
w = Math.Max(w, wMin);
var neededVectors = ext.Take(w).Select(el => el.eigenVector).ToList();
Matrix A = Matrix.Create.JoinVectors(neededVectors);
return(A);
}