/// <summary>
/// returns the eigen values and eigen vectors of a matrix
/// IMPORTANT: THIS METHOD NEEDS DEBUGGING.
/// IT RETURNS THE NEGATIVE VALUES OF THE EIGEN VECTORS ON A TOY EXMAPLE
/// double[,] matrix = {
/// { 3.0, -1.0 },
/// { -1.0, 3.0 }
/// };
/// eigen values are correct ie, 2.0, 4.0; but in the wrong order.
/// </summary>
public static Tuple <double[], double[, ]> EigenVectors(double[,] matrix)
{
Evd <double> eigen = DenseMatrix.OfArray(matrix).Evd();
Vector <System.Numerics.Complex> eigenvaluesComplex = eigen.EigenValues;
//WriteArrayOfComplexNumbers(eigenvalues);
double[] eigenvaluesReal = new double[eigenvaluesComplex.Count];
for (int i = 0; i < eigenvaluesComplex.Count; i++)
{
System.Numerics.Complex c = eigenvaluesComplex[i];
double magnitude = c.Magnitude;
Console.WriteLine("eigen value[{0}] {1} Magnitude={2}", i, c.ToString(), magnitude);
}
Matrix <double> eigenvectorsComplex = eigen.EigenVectors;
double[,] eigenvectorsReal = new double[eigenvaluesComplex.Count, matrix.GetLength(0)];
for (int col = 0; col < eigenvectorsComplex.RowCount; col++)
{
Vector <double> ev = eigenvectorsComplex.Column(col);
for (int i = 0; i < ev.Count; i++)
{
eigenvectorsReal[col, i] = ev[i];
Console.WriteLine("eigen vector {0}, value {1} = {2}", col, i, ev[i]);
}
}
return(Tuple.Create(eigenvaluesReal, eigenvectorsReal));
}
public PrincipalComponents(Matrix <double> data)
{
var dt = data.Clone();
dt.Transpose();
sampleCov = dt * data * (1.0 / data.RowCount);
//covDecomposition = new EigenvalueDecomposition(sampleCov);
covDecomposition = sampleCov.Evd();
covDecomposition.Solve(sampleCov);
var evs = new double[sampleCov.RowCount];
var indices = new int[sampleCov.RowCount];
for (var i = 0; i < sampleCov.RowCount; ++i)
{
evs[i] = covDecomposition.EigenValues[i].Real;
indices[i] = i;
}
Array.Sort(evs, indices);
var permutation = Matrix <double> .Build.Dense(sampleCov.RowCount, sampleCov.RowCount);
for (var i = 0; i < sampleCov.RowCount; ++i)
{
permutation[indices[i], i] = 1.0;
}
var v = covDecomposition.EigenVectors;
SortedComponents = v * permutation;
SortedEigenvalues = Vector <double> .Build.DenseOfArray(evs);
}
public void ConstructorThrowsForNotDiagonalizableMatrix()
{
var notDiagonalizable = Matrix <double> .Build
.DenseOfArray(
new[, ]
{
{
0d,
-1d,
},
{
1d,
0d,
},
});
this._covariancesDecomposition = notDiagonalizable.Evd();
Assert.Throws <IndexOutOfRangeException>(
() => new CmaEsElements(
this._configuration,
this._generation,
this._distributionMean,
this._stepSize,
covariances: notDiagonalizable,
covariancesDecomposition: this._covariancesDecomposition,
evolutionPath: this._evolutionPath,
conjugateEvolutionPath: this._conjugateEvolutionPath));
}
/// <summary>
/// Initializes a new instance of the <see cref="CmaEsElementsTest"/> class.
/// </summary>
public CmaEsElementsTest()
{
this._configuration = new CmaEsConfiguration(20, Vector <double> .Build.Random(3), 0.1);
this._distributionMean = Vector <double> .Build.DenseOfArray(new[] { 1.8, 4.9 });
this._evolutionPath = Vector <double> .Build.DenseOfArray(new[] { 2.1, 3.2 });
this._conjugateEvolutionPath = Vector <double> .Build.DenseOfArray(new[] { 1.7, 4.1 });
this._covariances = Matrix <double> .Build
.DenseOfArray(
new[, ]
{
{
3d,
1d,
},
{
1d,
3d,
},
});
this._covariancesDecomposition = this._covariances.Evd(Symmetricity.Symmetric);
this._diagonal = this._covariancesDecomposition.D;
this._eigenvectors = this._covariancesDecomposition.EigenVectors;
}
/*
* public PPTCriterion(QuantumState state)
* {
* State = state;
* StateOperator = StateOperator.FromQuantumState(state);
* }
*
* public PPTCriterion(StateOperator @operator)
* {
* StateOperator = @operator;
* }
*/
public void Evaluate(StateOperator state, SystemBipartition bipartition)
{
EvaluatedMatrix = state.PartialTranspose(bipartition);
Evd <Complex> evd = EvaluatedMatrix.Matrix.Evd();
Eigenvalues = evd.EigenValues.ToArray();
}
//returns the first eigenvector of the matrix
private Matrix <double> getFirstEigenvector(Matrix <double> matrix)
{
Evd <double> eigen = matrix.Evd();
Matrix <double> vectors = eigen.EigenVectors;
return(vectors.SubMatrix(0, vectors.RowCount, vectors.ColumnCount - 1, 1)); //returns only last vector
}
public Vector3[] EigenVectors()
{
Matrix <double> mat = Matrix <double> .Build.Dense(3, 3);
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
mat[i, j] = this[i, j];
}
}
Evd <double> evd = mat.Evd();
Matrix <double> eigenvect = evd.EigenVectors;
Vector3[] eigen_vectors = new Vector3[3];
for (int vect = 0; vect < 3; vect++)
{
for (int comp = 0; comp < 3; comp++)
{
eigen_vectors[vect][comp] = (float)eigenvect[comp, vect];
}
}
return(eigen_vectors);
}
private void CalculateTensilAndCompressive(Matrix <double> stress)
{
Evd <double> evd = stress.Evd();
Matrix <double> tensileComp = Matrix <double> .Build.Dense(3, 3);
Matrix <double> compressiveComp = Matrix <double> .Build.Dense(3, 3);
for (int i = 0; i < 3; ++i)
{
double eval = evd.EigenValues[i].Real;
Matrix <double> m = Utilities.BuildM(evd.EigenVectors.Column(i).Normalize(2));
tensileComp += System.Math.Max(0, eval) * m;
compressiveComp += System.Math.Min(0, eval) * m;
}
for (int i = 0; i < 3; ++i)
{
Vector <double> ftensil = Vector <double> .Build.Dense(3);
Vector <double> fcompressive = Vector <double> .Build.Dense(3);
CalculatePointForce(ftensil, i, tensileComp);
fcompressive = forcesP[i] - ftensil;
FracParticle fp = p[i].GetComponent <FracParticle>();
fp.AddTensilLoad(ftensil);
fp.AddCompressiveLoad(fcompressive);
}
}
public PrincipalComponents(Matrix <double> covMatrix)
{
sampleCov = covMatrix;
// covDecomposition = new EigenvalueDecomposition(sampleCov);
covDecomposition = sampleCov.Evd();
covDecomposition.Solve(sampleCov);
var evs = new double[sampleCov.RowCount];
var indices = new int[sampleCov.RowCount];
for (int i = 0; i < sampleCov.RowCount; ++i)
{
evs[i] = covDecomposition.EigenValues[i].Real;
indices[i] = i;
}
Array.Sort(evs, indices);
var permutation = Matrix <double> .Build.Dense(sampleCov.RowCount, sampleCov.RowCount);
for (int i = 0; i < sampleCov.RowCount; ++i)
{
permutation[indices[i], i] = 1.0;
}
Matrix <double> v = covDecomposition.EigenVectors;
SortedComponents = v * permutation;
SortedEigenvalues = Vector <double> .Build.DenseOfArray(evs);
}
public static void SetInertia(this Link.Inertial.Inertia inertia, Rigidbody rigidbody)
{
Evd <float> Evd = inertia.Unity3DCoordTrafo().ToMatrix().Evd(Symmetricity.Symmetric);
rigidbody.inertiaTensor = Evd.EigenValues.GetReal().ToVector3().FixMinInertia(); // optionally check vector for imaginary part = 0
rigidbody.inertiaTensorRotation = Evd.EigenVectors.ToQuaternion(); // optionally check matrix for determinant = 1
}
/// <summary>
/// Calculates the complex roots of the Polynomial by eigenvalue decomposition
/// </summary>
/// <returns>a vector of complex numbers with the roots</returns>
public Complex[] GetRoots()
{
DenseMatrix A = this.GetEigValMatrix();
Complex[] c_vec;
if (A == null)
{
if (Coeffs.Length < 2)
{
var val = Coeffs.Length == 1 ? Coeffs[0] : Double.NaN;
c_vec = new Complex[1] {
val
};
}
else
{
c_vec = new Complex[1] {
new Complex(-Coeffs[0] / Coeffs[1], 0)
}
};
}
else
{
Evd <double> eigen = A.Evd(Symmetricity.Asymmetric);
c_vec = eigen.EigenValues.ToArray();
}
return(c_vec);
}
/// <summary>
/// Initializes a new instance of the <see cref="CmaEsElements"/> class.
/// </summary>
/// <param name="configuration">Fixed parameters for CMA-ES.</param>
/// <param name="generation">The current generation.</param>
/// <param name="distributionMean">The current distribution mean.</param>
/// <param name="stepSize">The current step size.</param>
/// <param name="covariances">The current covariance matrix.</param>
/// <param name="covariancesDecomposition">The current eigendecomposition of the covariance matrix.</param>
/// <param name="evolutionPath">The current evolution path.</param>
/// <param name="conjugateEvolutionPath">The conjugate evolution path.</param>
/// <exception cref="ArgumentOutOfRangeException">
/// Thrown if <paramref name="generation"/> or <paramref name="stepSize"/> are negative.
/// </exception>
public CmaEsElements(
CmaEsConfiguration configuration,
int generation,
Vector <double> distributionMean,
double stepSize,
Matrix <double> covariances,
Evd <double> covariancesDecomposition,
Vector <double> evolutionPath,
Vector <double> conjugateEvolutionPath)
{
if (generation < 0)
{
throw new ArgumentOutOfRangeException(
nameof(generation),
$"Generation must be nonnegative, but was {generation}.");
}
if (stepSize < 0)
{
throw new ArgumentOutOfRangeException(
nameof(stepSize),
$"Step size must be nonnegatives, but was {stepSize}.");
}
this.Configuration = configuration;
this.Generation = generation;
this._distributionMean = distributionMean?.Clone();
this.StepSize = stepSize;
this._covariances = covariances?.Clone();
this._covariancesDiagonal = covariancesDecomposition == null ? null : DiagonalMatrix.OfMatrix(covariancesDecomposition.D);
this._covariancesEigenVectors = covariancesDecomposition?.EigenVectors.Clone();
this._evolutionPath = evolutionPath?.Clone();
this._conjugateEvolutionPath = conjugateEvolutionPath?.Clone();
}
public bool isFracturing(double toughness, List <Vector <double> > fracPlaneNormals)
{
fracPlaneNormals.Clear();
CalculatePointSeparationTensor();
Evd <double> evd = separationTensor.Evd();
double vp = evd.EigenValues.AbsoluteMaximum().Real;
bool fracturesAtNode = vp > toughness; // does at least one eigenvalue exceeds toughness
//Debug.Log("vp = " + vp + "\n");
// Note : More efficient to simply loop on all since there's just 3 eigenvalue. Sorting is not that useful here.
for (int i = 0; fracturesAtNode && i < evd.EigenValues.Count; ++i)
{
if (evd.EigenValues[i].Real > toughness)
{
Vector <double> fracPlaneNormal = evd.EigenVectors.Column(i);
fracPlaneNormals.Add(fracPlaneNormal);
}
}
return(fracturesAtNode);
}
private void GetEigenNumbersButton_Click(object sender, EventArgs e)
{
Matrix matr1 = Matrix.Parse(baseMatrixBox.Text);
if (!matr1.IsSquare())
{
MessageBox.Show("Матрица должна быть квадратной!");
return;
}
double[] values = new double[matr1.Rows * matr1.Columns];
int counter = 0;
for (int i = 0; i < matr1.Rows; i++)
{
for (int j = 0; j < matr1.Columns; j++)
{
values[counter++] = matr1.Values[i, j];
}
}
Evd <double> evd = MathNet.Numerics.LinearAlgebra.Matrix <double> .Build.Dense(matr1.Rows, matr1.Columns, values).Evd();
eigenNumbersBox.Text = "";
double[] eigenNumbers = new double[matr1.Rows];
counter = 0;
foreach (var eigenvalue in evd.EigenValues.AsEnumerable())
{
//if (eigenvalue.Magnitude.CoerceZero() < 0.001) eigenNumbers[counter++] = 0;
//else eigenNumbers[counter++] = eigenvalue.Real;
eigenNumbersBox.Text += eigenvalue.Magnitude.CoerceZero(0.00001) + "\r\n";
}
}
/*************************************************************************************************
* Private used functions *
*************************************************************************************************/
/* Square root of a matrix using eigenvectors and eigenvalues (assuming it can be applied, undefined if not) */
/// <summary>
/// Square root of a matrix using eigenvectors and eigenvalues (assuming it can be applied, undefined if not)
/// </summary>
/// <param name="M">A Matrix</param>
/// <returns>The square root of M if applicable</returns>
private static Matrix sqrtm(Matrix M)
{
MathNet.Numerics.LinearAlgebra.Matrix <double> sqrtM;
// Calculating M^(1/2);
Evd <double> eigenVs = M.Evd();
DenseVector values = new DenseVector(M.RowCount);
double[] tempValues = new double[M.RowCount];
int i = 0;
foreach (MathNet.Numerics.Complex c in eigenVs.EigenValues.ToArray())
{
tempValues[i] = c.Real;
i++;
}
values.SetValues(tempValues);
values.MapInplace(System.Math.Sqrt);
DiagonalMatrix newValues = new DiagonalMatrix(M.RowCount, M.RowCount, values.ToArray());
sqrtM = (eigenVs.EigenVectors * newValues) * eigenVs.EigenVectors.Inverse();
return((Matrix)sqrtM);
}
public List <double> ComputeEVD(Matrix <double> laplacian)
{
Evd <double> evd = laplacian.Evd();
var column = new List <double>(evd.EigenVectors.Column(1).Storage.ToArray());
return(column);
}
static Matrix <double> GetEigen(List <List <double> > covariance, int N)
{
Matrix <double> matrix = ConvToMatrix(covariance);
Evd <double> eigen = matrix.Evd();
//Vector<Complex> eigenValues = eigen.EigenValues;
//Console.WriteLine(eigenValues.ToString());
Matrix <double> eigenVectors = eigen.EigenVectors;
return(eigenVectors.SubMatrix(0, eigenVectors.RowCount, 0, N));
}
public static double Eigenvalue(double[,] array)
{
Matrix <double> A = DenseMatrix.OfArray(array);
Evd <double> eigen = A.Evd();
Matrix <double> eigenvect = eigen.EigenVectors;
Vector <Complex> eigenvalues = eigen.EigenValues;
return(eigenvalues.AbsoluteMaximum().Real);
}
/// <summary>
/// Compute the plane that best fit a set of input points (least squared orthogonal distance)
/// </summary>
/// <param name="points">The input points</param>
/// <param name="planeOrigin">The output plane origin</param>
/// <param name="planeNormal">The output plane normal vector</param>
/// <returns>0 if the input points are identical, 1 if the input points are colinear, 2 if the input points are coplanar (already on a plane), 3 otherwise</returns>
public static int ComputeBestFitPlane(List <Triple> points, out Triple planeOrigin, out Triple planeNormal)
{
Triple centroid = Triple.Zero;
for (int i = 0; i < points.Count; i++)
{
centroid += points[i];
}
centroid /= points.Count;
float[,] P = new float[points.Count, 3];
for (int i = 0; i < points.Count; i++)
{
P[i, 0] = points[i].X - centroid.X;
P[i, 1] = points[i].Y - centroid.Y;
P[i, 2] = points[i].Z - centroid.Z;
}
Matrix <float> covariance = Matrix <float> .Build.Dense(3, 3);
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
for (int k = 0; k < points.Count; k++)
{
covariance[i, j] += P[k, i] * P[k, j];
}
}
}
Evd <float> evd = covariance.Evd();
planeOrigin = centroid;
if (evd.Rank == 0) // The input points are idendtical, so we just pick an arbitrary normal vector
{
planeNormal = Triple.BasisZ;
}
else if (evd.Rank == 1
) // The input points are colinear, so we just pick an arbitrary vector perpendicular to the only eigen vector
{
planeNormal = new Triple(evd.EigenVectors[0, 1], evd.EigenVectors[1, 1], evd.EigenVectors[2, 1])
.GeneratePerpendicular();
}
else // The normal is perpendicular to the two dominant eigen vectors
{
Triple e1 = new Triple(evd.EigenVectors[0, 1], evd.EigenVectors[1, 1], evd.EigenVectors[2, 1]);
Triple e2 = new Triple(evd.EigenVectors[0, 2], evd.EigenVectors[1, 2], evd.EigenVectors[2, 2]);
planeNormal = e2.Cross(e1).Normalise();
}
return(evd.Rank);
}
// Compute the optimal rotation and translation vectors
// Accepts the Matrix4X4 registration matrix, center of mass of source and target point sets
//outputs the rotationMatrix and the translation vector
public static void GetTransformationVectors
(float[,] registrationMatrix, Vector3 SourceCenterOfMass, Vector3 TargeCenterOfMass, out Vector3 TranslationVector, out Quaternion UnityQuat)
{
//initialise matrix builder
MatrixBuilder <float> regMatrix = Matrix <float> .Build;
//fill matrix builder with contents of our params registrationmatri to create a Matrix
Matrix <float> reg = regMatrix.DenseOfArray(registrationMatrix);
//EVD decompose to generate our eignevalues
Evd <float> registrationevd = reg.Evd();
//Cholesky<float> registrationevd = reg.Cholesky();
Console.WriteLine("Symmetric" + registrationevd.IsSymmetric);
//get inex of maximum eigenvalue for correspond eigenvector
double maxValue = double.NegativeInfinity;
//int maxEigenValue = registrationevd.EigenValues.
int index = 0;
for (int i = 0; i < registrationevd.EigenValues.Count; i++)
{
if (registrationevd.EigenValues[i].Real > maxValue)
{
maxValue = registrationevd.EigenValues[i].Real;
index = i;
}
}
//Get the eigenvalue index and copy the vector as our unit eigenvector.
//Our unit EigenVector below
MathNet.Numerics.LinearAlgebra.Vector <float> unitEigenVector = registrationevd.EigenVectors.Column(index);
float q0 = unitEigenVector.At(0);
float q1 = unitEigenVector.At(1);
float q2 = unitEigenVector.At(2);
float q3 = unitEigenVector.At(3);
UnityQuat = new Quaternion(q1, q2, q3, q0);
Matrix4x4 rotMatrix = Matrix4x4.CreateFromQuaternion(UnityQuat);
Vector3 y = new Vector3(0, 0, 0);
//get optimal translation vector
// Vector3 y = Matrix4x4.Rotate(UnityQuat).MultiplyPoint(SourceCenterOfMass);
Vector3 optimalTranslation = TargeCenterOfMass - y;
TranslationVector = optimalTranslation;
}
public static bool Calculate(Graph g)
{
if (g.order == 0)
{
return(true);
}
Matrix <double> lMatrix = DenseMatrix.OfArray(Utils.Spectral.SignlessLaplacianMatrix(g));
Evd <double> evd = lMatrix.Evd(Symmetricity.Symmetric);
Vector <Complex> eigenvalues = evd.EigenValues;
double x = eigenvalues.ElementAt(g.order - 1).Real;
return(Utils.Spectral.ApproxToInt(x) % 1 == 0);
}
public double Calculate(Graph g)
{
if (g.order == 0)
{
return(0);
}
Matrix <double> lMatrix = DenseMatrix.OfArray(Utils.Spectral.LaplacianMatrix(g));
Evd <double> evd = lMatrix.Evd(Symmetricity.Symmetric);
Vector <Complex> eigenvalues = evd.EigenValues;
double a = eigenvalues.ElementAt(1).Real;
return(Utils.Spectral.ApproxToInt(a));
}
public double Calculate(Graph g)
{
if (g.order <= 1)
{
return(0);
}
Matrix <double> lMatrix = DenseMatrix.OfArray(Utils.Spectral.AdjacencyMatrix(g));
Evd <double> evd = lMatrix.Evd(Symmetricity.Symmetric);
Vector <Complex> eigenvalues = evd.EigenValues;
double x = eigenvalues.ElementAt(g.order - 2).Real;
return(Utils.Spectral.ApproxToInt(x));
}
public double[][] GetEigenVectors(List <User> uList)
{
Matrix <double> diagonalMatrix;
Matrix <double> associationMatrix = FillMatrix(uList, out diagonalMatrix);
Matrix <double> laplacianMatrix = diagonalMatrix - associationMatrix;
Evd <double> eigen = laplacianMatrix.Evd(Symmetricity.Symmetric);
var eigenVectors = eigen.EigenVectors.ToRowArrays();
return(eigenVectors);
}
public ModalSynthesizer(Mesh mesh, SM_Material properties, int sampleRate = 44100)
{
this.sampleRate = sampleRate;
SpringMesh fem = new SpringMesh(mesh, properties);
mMass = fem.GetMassMatrix();
mStiffness = fem.GetGlobalStiffnessMatrix();
if (mStiffness.RowCount > USE_EXT_TOL)
{
/*
* ext = ExternalLinalgHandler.Instance;
* ParentBehaviour?.StartCoroutine(ext.UnityDebugProcessOutput());
* ext.ComputeExternal(mStiffness);
* mGain = ext.EigenVectors;
* mGain_inv = ext.EigenVectors_Inv;
* mEig = ext.EigenValues;
*/
/*
* using (ExternalLinalgHandler ext = new ExternalLinalgHandler())
* {
* ParentBehaviour?.StartCoroutine(ext.UnityDebugProcessOutput());
* ext.ComputeExternal(K);
* mGain = ext.EigenVectors;
* mGain_inv = ext.EigenVectors_Inv;
* mEig = ext.EigenValues;
* }
*/
//ext.Dispose();
}
else
{
Evd <double> evd = mStiffness.Evd(Symmetricity.Hermitian);
mGain = evd.EigenVectors;
mGain_inv = mGain.Inverse();
mEig = evd.EigenValues;
}
props = properties;
nmodes = mEig.Count;
omega_plus = new zdouble[nmodes];
omega_minus = new zdouble[nmodes];
SetModeProperties();
cGains = new zdouble[nmodes];
for (int i = 0; i < nmodes; i++)
{
cGains[i] = new zdouble(0.0, 0.0);
}
}
public void Rescale()
{
Center();
Matrix <double> A = PositionMatrix();
Matrix <double> cov = 1.0f / ((float)A.RowCount) * A.Transpose() * A;
Evd <double> eigen = cov.Evd();
MathNet.Numerics.LinearAlgebra.Vector <System.Numerics.Complex> eigenvals = eigen.EigenValues;
double l1 = eigenvals[0].Real; //1 / Math.Sqrt(eigenvals[0].Real);
double l2 = eigenvals[1].Real; //1 / Math.Sqrt(eigenvals[1].Real);
double l3 = eigenvals[2].Real; // / Math.Sqrt(eigenvals[2].Real);
Debug.Log("l1: " + l1 + ", l2: " + l2 + ", l3: " + l3);
double vol = l1 * l2 * l3;
double max = eigenvals.AbsoluteMaximum().Real;
Debug.Log("max=" + max);
//double det = cov.Determinant();
A = A / Math.Sqrt(max) * 0.2;
Debug.Log(A);
//Debug.Log("determinant:");
for (int i = 0; i < A.RowCount; i++)
{
poses_[i].Translation = new Vector3((float)A[i, 0], (float)A[i, 1], (float)A[i, 2]);
}
/* Matrix<double> A = PositionMatrix();
* Matrix<double> cov = 1.0f / ((float)A.RowCount ) *A.Transpose() * A;
*
*
* Debug.Log(cov.ToString());
* Evd<double> eigen = cov.Evd();
* Matrix<double> m = eigen.EigenVectors;
* Matrix<double> points = m * A * m.Inverse();
*
* points = points * cov.Determinant() / 10;
* points = m * points * m.Inverse();
* for (int i = 0; i < points.RowCount; i++)
* {
* poses_[i].Translation =new Vector3((float)points[i, 0], (float)points[i, 1], (float)points[i, 2]);
* }*/
/*double det = cov.Determinant();
* cov_rotated = cov_rotated / det;
* Matrix<double> cov_recovered = m * cov_rotated * m.Inverse();*/
//Debug.Log(cov_recovered.ToString());
}
// Return the eigenvector associated with the smallest eigenvalue
// and convert it into a Quaternion
static Quaternion SmallestEigenVector(Matrix4x4 m)
{
Matrix <double> mCopy = Matrix <double> .Build.Dense(4, 4);
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 4; j++)
{
mCopy[i, j] = m[i, j];
}
}
Evd <double> evd = mCopy.Evd();
System.Numerics.Complex min = evd.EigenValues[0];
int indiceMin = 0;
for (int i = 1; i < 4; i++)
{
if (evd.EigenValues[i].Imaginary == 0)
{
if ((evd.EigenValues[i].Real > 0 && evd.EigenValues[i].Real < min.Real))
{
min = evd.EigenValues[i];
indiceMin = i;
}
}
}
Quaternion result = new Quaternion();
result.w = (float)evd.EigenVectors[0, indiceMin];
// There could be a problem with quaternion sign, so... :
if (result.w < 0)
{
result.w = -(float)evd.EigenVectors[0, indiceMin];
result.x = -(float)evd.EigenVectors[1, indiceMin];
result.y = -(float)evd.EigenVectors[2, indiceMin];
result.z = -(float)evd.EigenVectors[3, indiceMin];
}
else
{
result.x = (float)evd.EigenVectors[1, indiceMin];
result.y = (float)evd.EigenVectors[2, indiceMin];
result.z = (float)evd.EigenVectors[3, indiceMin];
}
return(result);
}
/*************************************************************************************************
* Private used functions *
*************************************************************************************************/
/* Square root of a matrix using eigenvectors and eigenvalues (assuming it can be applied, undefined if not) */
/// <summary>
/// Square root of a matrix using eigenvectors and eigenvalues (assuming it can be applied, undefined if not)
/// </summary>
/// <param name="M">A Matrix</param>
/// <returns>The square root of M if applicable</returns>
private Matrix sqrtm(Matrix M)
{
MathNet.Numerics.LinearAlgebra.Matrix <double> sqrtM;
// Calculating M^(1/2);
Evd <double> eigenVs = M.Evd();
DenseVector values = new DenseVector(M.RowCount);
double[] tempValues = new double[M.RowCount];
int i = 0;
foreach (MathNet.Numerics.Complex c in eigenVs.EigenValues.ToArray())
{
tempValues[i] = c.Real;
i++;
}
values.SetValues(tempValues);
values.MapInplace(System.Math.Sqrt);
DiagonalMatrix newValues = new DiagonalMatrix(M.RowCount, M.RowCount, values.ToArray());
sqrtM = (eigenVs.EigenVectors * newValues) * eigenVs.EigenVectors.Inverse();
/* This is debug to see what's actually inside M & M^1/2 */
/*
* Debug.Log("Old matrix");
* for (int j = 0; j < M.RowCount; j++)
* {
* string message = "";
* for (int k = 0; k < M.ColumnCount; k++)
* {
* message += M.Row(j).At(k).ToString(null, null) + " ";
* }
* Debug.Log(message);
* }
* Debug.Log("New matrix");
* for (int j = 0; j < sqrtM.RowCount; j++)
* {
* string message = "";
* for (int k = 0; k < sqrtM.ColumnCount; k++)
* {
* message += sqrtM.Row(j).At(k).ToString(null, null) + " ";
* }
* Debug.Log(message);
* }
*/
return((Matrix)sqrtM);
}
private static void AnBacahaytMEthod(int count, Matrix <double> arrayForA, Vector <double> arrayForB, Vector <double> oldVectorForX, Matrix <double> eye3, Matrix <double> c, double tao)
{
Console.WriteLine("\n\n******************* An-Bacahayt-Method *******************");
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
count = 0;
////anbacahayt
Matrix <double> arrayForM = (2 * c) - tao * arrayForA;
Console.WriteLine(arrayForM);
Evd <double> eigenM = arrayForM.Evd();
Vector <Complex> eigenVectroM = eigenM.EigenValues;
if (eigenVectroM.All(item => item.Real > 0))
{
Console.WriteLine(eigenVectroM);
Vector <double> newVectorForX;
while (true)
{
count++;
newVectorForX = (eye3 - tao * c.Inverse() * arrayForA) * oldVectorForX + (tao * c.Inverse() * arrayForB);
if (count == maximalIterationCount || Math.Abs(oldVectorForX.Sum() - newVectorForX.Sum()) < 0.00001)
{
Console.WriteLine("Iteration number count = {0} ", count);
Console.WriteLine(newVectorForX);
break;
}
if (count % 10 == 0)
{
Console.WriteLine("Iteration number count = {0} ", count);
Console.WriteLine(newVectorForX);
}
oldVectorForX = newVectorForX;
}
}
else
{
Console.WriteLine("M matrxi EIG is a NEGATIVE");
}
stopWatch.Start();
TimeSpan ts = stopWatch.Elapsed;
string elaspedTIme = String.Format("{0:00}.{1:00}", ts.Seconds, ts.Milliseconds / 10);
Console.WriteLine("AnBacahayt Method RunTime: {0}", elaspedTIme);
}
private void InitContinuousModel(
Matrix <double> A, Matrix <double> B, Matrix <double> C, Matrix <double> D
)
{
this.A = A;
this.B = B;
this.C = C;
this.D = D;
Order = this.A.ColumnCount;
Evd <double> decomposition = this.A.Evd();
EigenValues = decomposition.EigenValues;
EigenVectors = decomposition.EigenVectors;
I = Matrix <double> .Build.DenseIdentity(order : Order);
}
