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; }