public Parameter()
{
this.metadata = null;
this.eigenvalue = null;
this.transform = null;
this.resources = null;
}
public Parameter(Parameter param)
{
this.metadata = param.metadata;
this.eigenvalue = param.eigenvalue;
this.transform = param.transform;
this.resources = param.resources;
}
public Parameter(MetaData metadata, Eigenvalue eigenvalue, Transform transform, Resources resources)
{
this.metadata = metadata;
this.eigenvalue = eigenvalue;
this.transform = transform;
this.resources = resources;
}
public static BasicObjectProgram Generator(WorldController controller)
{
var id = ObjectId.NewGuid();
var metadata = new MetaData(null, null, 0, 0);
var resources = new Resources(100);
var eigenvalue = new Eigenvalue();
var transform = new Transform();
var parameter = new Parameter(metadata, eigenvalue, transform, resources);
var maskedParam = new MaskedParameter(parameter, MaskedParameter.Mask.EIGENVALUE | MaskedParameter.Mask.TRANSFORM);
var knowledge = new Knowledge(id, maskedParam);
controller.credentialTable.Add(id, Credential.HashPassword(id.ToString(), Credential.HashAlgorithm.NOOP));
var data = new BasicObjectProgram();
data.controller = controller;
data.knowledge = knowledge;
controller.objectList.Add(id, parameter);
var objectIdPair = new ObjectIdPair();
objectIdPair.Add(id, id);
controller.maskedObjectList.Add(objectIdPair, maskedParam);
return(data);
}
public static void TestTridiagonalEigenvalues()
{
MatrixR A = new MatrixR(new double[, ] {
{ 5, 1, 2, 2, 4 },
{ 1, 1, 2, 1, 0 },
{ 2, 2, 0, 2, 1 },
{ 2, 1, 2, 1, 2 },
{ 4, 0, 1, 2, 4 }
});
int nn = 5;
MatrixR xx = new MatrixR(A.GetCols(), nn);
MatrixR V = Eigenvalue.Tridiagonalize(A);
double[] lambda = Eigenvalue.TridiagonalEigenvalues(nn);
for (int i = 0; i < nn; i++)
{
double s = lambda[i] * 1.001;
double lam;
VectorR x = Eigenvalue.TridiagonalEigenvector(s, 1e-8, out lam);
for (int j = 0; j < A.GetCols(); j++)
{
xx[j, i] = x[j];
}
}
xx = V * xx;
Console.WriteLine("\n Results from the tridiagonalization method:");
Console.WriteLine("\n Eigenvalues: \n ({0,10:n6} {1,10:n6} {2,10:n6} {3,10:n6} {4,10:n6})", lambda[0], lambda[1], lambda[2], lambda[3], lambda[4]);
Console.WriteLine("\n Eigenvectors:");
for (int i = 0; i < 5; i++)
{
Console.WriteLine(" ({0,10:n6} {1,10:n6} {2,10:n6} {3,10:n6} {4,10:n6})", xx[i, 0], xx[i, 1], xx[i, 2], xx[i, 3], xx[i, 4]);
}
A = new MatrixR(new double[, ] {
{ 5, 1, 2, 2, 4 },
{ 1, 1, 2, 1, 0 },
{ 2, 2, 0, 2, 1 },
{ 2, 1, 2, 1, 2 },
{ 4, 0, 1, 2, 4 }
});
MatrixR xm;
VectorR lamb;
Eigenvalue.Jacobi(A, 1e-8, out xm, out lamb);
Console.WriteLine("\n\n Results from the Jacobi method:");
Console.WriteLine("\n Eigenvalues: \n ({0,10:n6} {1,10:n6} {2,10:n6} {3,10:n6} {4,10:n6})", lamb[4], lamb[3], lamb[2], lamb[1], lamb[0]);
Console.WriteLine("\n Eigenvectors:");
for (int i = 0; i < 5; i++)
{
Console.WriteLine(" ({0,10:n6} {1,10:n6} {2,10:n6} {3,10:n6} {4,10:n6})", xm[i, 4], xm[i, 3], xm[i, 2], xm[i, 1], xm[i, 0]);
}
}
public Eigenvalue(Eigenvalue value)
{
this.value = value.value;
}
