EigenvalueDecomposition(
double[] d,
double[] e)
{
/* TODO: unit test missing for EigenvalueDecomposition constructor. */
_n = d.Length;
_v = Matrix.CreateMatrixData(_n, _n);
_d = new double[_n];
Array.Copy(d, 0, _d, 0, _n);
_e = new double[_n];
Array.Copy(e, 0, _e, 1, _n - 1);
for (int i = 0; i < _n; i++)
{
_v[i][i] = 1;
}
SymmetricDiagonalize();
_eigenValuesReal = new Vector(_d);
_eigenValuesImag = new Vector(_e);
_eigenValues = ComplexVector.Create(_d, _e);
InitOnDemandComputations();
}
ComputeOrthogonalFactor()
{
double[][] Q = Matrix.CreateMatrixData(m, Math.Min(m, n));
for (int k = Math.Min(m, n) - 1; k >= 0; k--)
{
Q[k][k] = 1.0;
for (int j = k; j < Math.Min(m, n); j++)
{
if (QR[k][k] == 0.0)
{
continue;
}
double s = 0.0;
for (int i = k; i < m; i++)
{
s += QR[i][k] * Q[i][j];
}
s = (-s) / QR[k][k];
for (int i = k; i < m; i++)
{
Q[i][j] += s * QR[i][k];
}
}
double columnSign = Math.Sign(Rdiag[k]);
for (int i = 0; i < m; i++)
{
Q[i][k] *= columnSign;
}
}
return(new Matrix(Q));
}
ComputeOrthogonalFactor()
{
double[][] q = Matrix.CreateMatrixData(M, Math.Min(M, N));
for (int k = Math.Min(M, N) - 1; k >= 0; k--)
{
q[k][k] = 1.0;
for (int j = k; j < Math.Min(M, N); j++)
{
if (_qr[k][k] == 0.0)
{
continue;
}
double s = 0.0;
for (int i = k; i < M; i++)
{
s += _qr[i][k] * q[i][j];
}
s = (-s) / _qr[k][k];
for (int i = k; i < M; i++)
{
q[i][j] += s * _qr[i][k];
}
}
double columnSign = Math.Sign(_rDiag[k]);
for (int i = 0; i < M; i++)
{
q[i][k] *= columnSign;
}
}
return(new Matrix(q));
}
ComputeOrthogonalFactor()
{
double[][] Q = Matrix.CreateMatrixData(m, n);
for (int k = n - 1; k >= 0; k--)
{
for (int i = 0; i < m; i++)
{
Q[i][k] = 0.0;
}
Q[k][k] = 1.0;
for (int j = k; j < n; j++)
{
if (QR[k][k] != 0)
{
double s = 0.0;
for (int i = k; i < m; i++)
{
s += QR[i][k] * Q[i][j];
}
s = (-s) / QR[k][k];
for (int i = k; i < m; i++)
{
Q[i][j] += s * QR[i][k];
}
}
}
}
return(new Matrix(Q));
}
ComputeUpperTriangularFactor()
{
double[][] R = Matrix.CreateMatrixData(Math.Min(n, m), n);
for (int i = 0; i < Math.Min(n, m); i++)
{
double rowSign = Math.Sign(Rdiag[i]);
double[] Ri = R[i];
for (int j = 0; j < Ri.Length; j++)
{
if (i < j)
{
Ri[j] = rowSign * QR[i][j];
}
else if (i == j)
{
Ri[j] = rowSign * Rdiag[i];
}
else
{
Ri[j] = 0.0;
}
}
}
return(new Matrix(R));
}
ComputeUpperTriangularFactor()
{
double[][] r = Matrix.CreateMatrixData(Math.Min(N, M), N);
for (int i = 0; i < Math.Min(N, M); i++)
{
double rowSign = Math.Sign(_rDiag[i]);
double[] ri = r[i];
for (int j = 0; j < ri.Length; j++)
{
if (i < j)
{
ri[j] = rowSign * _qr[i][j];
}
else if (i == j)
{
ri[j] = rowSign * _rDiag[i];
}
else
{
ri[j] = 0.0;
}
}
}
return(new Matrix(r));
}
EigenvalueDecomposition(
double[] d,
double[] e
)
{
// TODO: unit test missing for EigenvalueDecomposition constructor.
n = d.Length;
V = Matrix.CreateMatrixData(n, n);
this.d = new double[n];
Array.Copy(d, 0, this.d, 0, n);
this.e = new double[n];
Array.Copy(e, 0, this.e, 1, n - 1);
for (int i = 0; i < n; i++)
{
V[i][i] = 1;
}
SymmetricDiagonalize();
InitOnDemandComputations();
}
EigenvalueDecomposition(
Matrix Arg
)
{
double[][] A = Arg;
n = Arg.ColumnCount;
V = Matrix.CreateMatrixData(n, n);
d = new double[n];
e = new double[n];
isSymmetric = true;
for (int j = 0; (j < n) & isSymmetric; j++)
{
for (int i = 0; (i < n) & isSymmetric; i++)
{
isSymmetric &= (A[i][j] == A[j][i]);
}
}
if (isSymmetric)
{
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
V[i][j] = A[i][j];
}
}
SymmetricTridiagonalize();
SymmetricDiagonalize();
}
else
{
H = new double[n][];
for (int i = 0; i < n; i++)
{
double[] Hi = new double[n];
double[] Ai = A[i];
for (int j = 0; j < n; j++)
{
Hi[j] = Ai[j];
}
H[i] = Hi;
}
NonsymmetricReduceToHessenberg();
NonsymmetricReduceHessenberToRealSchur();
}
_eigenValuesReal = new Vector(d);
_eigenValuesImag = new Vector(e);
_eigenValues = ComplexVector.Create(d, e);
InitOnDemandComputations();
}
EigenvalueDecomposition(Matrix arg)
{
double[][] a = arg;
_n = arg.ColumnCount;
_v = Matrix.CreateMatrixData(_n, _n);
_d = new double[_n];
_e = new double[_n];
_isSymmetric = true;
for (int j = 0; (j < _n) & _isSymmetric; j++)
{
for (int i = 0; (i < _n) & _isSymmetric; i++)
{
_isSymmetric &= (a[i][j] == a[j][i]);
}
}
if (_isSymmetric)
{
for (int i = 0; i < _n; i++)
{
for (int j = 0; j < _n; j++)
{
_v[i][j] = a[i][j];
}
}
SymmetricTridiagonalize();
SymmetricDiagonalize();
}
else
{
_h = new double[_n][];
for (int i = 0; i < _n; i++)
{
double[] hi = new double[_n];
double[] ai = a[i];
for (int j = 0; j < _n; j++)
{
hi[j] = ai[j];
}
_h[i] = hi;
}
NonsymmetricReduceToHessenberg();
NonsymmetricReduceHessenberToRealSchur();
}
_eigenValuesReal = new Vector(_d);
_eigenValuesImag = new Vector(_e);
_eigenValues = ComplexVector.Create(_d, _e);
InitOnDemandComputations();
}
ToColumnMatrix()
{
double[][] m = Matrix.CreateMatrixData(_length, 1);
for (int i = 0; i < m.Length; i++)
{
m[i][0] = _data[i];
}
return(new Matrix(m));
}
ToRowMatrix()
{
double[][] m = Matrix.CreateMatrixData(1, _length);
for (int i = 0; i < m.Length; i++)
{
m[0][i] = _data[i];
}
return(new Matrix(m));
}
ComputePermutationMatrix()
{
int[] pivot = Pivot;
double[][] perm = Matrix.CreateMatrixData(pivot.Length, pivot.Length);
for (int i = 0; i < pivot.Length; i++)
{
perm[pivot[i]][i] = 1.0;
}
return(new Matrix(perm));
}
Solve(
Matrix B
)
{
if (B.RowCount != _l.RowCount)
{
throw new ArgumentException(Resources.ArgumentMatrixSameRowDimension, "B");
}
if (!_isSymmetricPositiveDefinite)
{
throw new InvalidOperationException(Resources.ArgumentMatrixSymmetricPositiveDefinite);
}
int nx = B.ColumnCount;
double[][] L = _l;
double[][] BB = B;
double[][] X = Matrix.CreateMatrixData(L.Length, nx);
// Solve L*Y = B
for (int i = 0; i < X.Length; i++)
{
for (int j = 0; j < nx; j++)
{
double sum = BB[i][j];
for (int k = i - 1; k >= 0; k--)
{
sum -= L[i][k] * X[k][j];
}
X[i][j] = sum / L[i][i];
}
}
// Solve L'*x = y
for (int i = X.Length - 1; i >= 0; i--)
{
for (int j = 0; j < nx; j++)
{
double sum = X[i][j];
for (int k = i + 1; k < X.Length; k++)
{
sum -= L[k][i] * X[k][j];
}
X[i][j] = sum / L[i][i];
}
}
return(new Matrix(X));
}
EigenvalueDecomposition(
Matrix Arg
)
{
double[][] A = Arg;
n = Arg.ColumnCount;
V = Matrix.CreateMatrixData(n, n);
d = new double[n];
e = new double[n];
isSymmetric = true;
for (int j = 0; (j < n) & isSymmetric; j++)
{
for (int i = 0; (i < n) & isSymmetric; i++)
{
isSymmetric = (A[i][j] == A[j][i]);
}
}
if (isSymmetric)
{
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
V[i][j] = A[i][j];
}
}
SymmetricTridiagonalize();
SymmetricDiagonalize();
}
else
{
H = Matrix.CreateMatrixData(n, n);
ort = new double[n];
for (int j = 0; j < n; j++)
{
for (int i = 0; i < n; i++)
{
H[i][j] = A[i][j];
}
}
NonsymmetricReduceToHessenberg();
NonsymmetricReduceHessenberToRealSchur();
}
InitOnDemandComputations();
}
Solve(Matrix b)
{
if (b.RowCount != _l.RowCount)
{
throw new ArgumentException(Properties.LocalStrings.ArgumentMatrixSameRowDimension, "B");
}
if (!_isSymmetricPositiveDefinite)
{
throw new InvalidOperationException(Properties.LocalStrings.ArgumentMatrixSymmetricPositiveDefinite);
}
int nx = b.ColumnCount;
double[][] l = _l;
double[][] bb = b;
double[][] x = Matrix.CreateMatrixData(l.Length, nx);
// Solve L*Y = B
for (int i = 0; i < x.Length; i++)
{
for (int j = 0; j < nx; j++)
{
double sum = bb[i][j];
for (int k = i - 1; k >= 0; k--)
{
sum -= l[i][k] * x[k][j];
}
x[i][j] = sum / l[i][i];
}
}
// Solve L'*x = y
for (int i = x.Length - 1; i >= 0; i--)
{
for (int j = 0; j < nx; j++)
{
double sum = x[i][j];
for (int k = i + 1; k < x.Length; k++)
{
sum -= l[k][i] * x[k][j];
}
x[i][j] = sum / l[i][i];
}
}
return(new Matrix(x));
}
ComputeUpperTriangularFactor()
{
double[][] u = Matrix.CreateMatrixData(_columnCount, _columnCount);
for (int i = 0; i < _columnCount; i++)
{
for (int j = 0; j < _columnCount; j++)
{
u[i][j] = (i <= j) ? _lu[i][j] : 0.0;
}
}
return(new Matrix(u));
}
ComputeHouseholderVectors()
{
double[][] h = Matrix.CreateMatrixData(M, N);
for (int i = 0; i < M; i++)
{
for (int j = 0; j < N; j++)
{
h[i][j] = (i >= j) ? _qr[i][j] : 0.0;
}
}
return(new Matrix(h));
}
ComputeDiagonalSingularValues()
{
double[][] X = Matrix.CreateMatrixData(n, n);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
X[i][j] = 0.0;
}
X[i][i] = this.s[i];
}
return(new Matrix(X));
}
DyadicProduct(
IVector u,
IVector v
)
{
double[][] m = Matrix.CreateMatrixData(u.Length, v.Length);
for (int i = 0; i < u.Length; i++)
{
for (int j = 0; j < v.Length; j++)
{
m[i][j] = u[i] * v[j];
}
}
return(new Matrix(m));
}
CholeskyDecomposition(
Matrix m
)
{
if (m.RowCount != m.ColumnCount)
{
throw new InvalidOperationException(Resources.ArgumentMatrixSquare);
}
double[][] A = m;
double[][] L = Matrix.CreateMatrixData(m.RowCount, m.RowCount);
_isSymmetricPositiveDefinite = true; // ensure square
for (int i = 0; i < L.Length; i++)
{
double diagonal = 0.0;
for (int j = 0; j < i; j++)
{
double sum = A[i][j];
for (int k = 0; k < j; k++)
{
sum -= L[j][k] * L[i][k];
}
L[i][j] = sum /= L[j][j];
diagonal += sum * sum;
_isSymmetricPositiveDefinite &= (A[j][i] == A[i][j]); // ensure symmetry
}
diagonal = A[i][i] - diagonal;
L[i][i] = Math.Sqrt(Math.Max(diagonal, 0.0));
_isSymmetricPositiveDefinite &= (diagonal > 0.0); // ensure positive definite
// zero out resulting upper triangle.
for (int j = i + 1; j < L.Length; j++)
{
L[i][j] = 0.0;
}
}
_l = new Matrix(L);
}
CholeskyDecomposition(Matrix m)
{
if (m.RowCount != m.ColumnCount)
{
throw new InvalidOperationException(Properties.LocalStrings.ArgumentMatrixSquare);
}
double[][] a = m;
double[][] l = Matrix.CreateMatrixData(m.RowCount, m.RowCount);
_isSymmetricPositiveDefinite = true; // ensure square
for (int i = 0; i < l.Length; i++)
{
double diagonal = 0.0;
for (int j = 0; j < i; j++)
{
double sum = a[i][j];
for (int k = 0; k < j; k++)
{
sum -= l[j][k] * l[i][k];
}
l[i][j] = sum /= l[j][j];
diagonal += sum * sum;
_isSymmetricPositiveDefinite &= (a[j][i] == a[i][j]); // ensure symmetry
}
diagonal = a[i][i] - diagonal;
l[i][i] = Math.Sqrt(Math.Max(diagonal, 0.0));
_isSymmetricPositiveDefinite &= (diagonal > 0.0); // ensure positive definite
// zero out resulting upper triangle.
for (int j = i + 1; j < l.Length; j++)
{
l[i][j] = 0.0;
}
}
_l = new Matrix(l);
}
ComputeHouseholderVectors()
{
double[][] H = Matrix.CreateMatrixData(m, n);
for (int i = 0; i < m; i++)
{
for (int j = 0; j < n; j++)
{
if (i >= j)
{
H[i][j] = QR[i][j];
}
else
{
H[i][j] = 0.0;
}
}
}
return(new Matrix(H));
}
ComputeUpperTriangularFactor()
{
double[][] U = Matrix.CreateMatrixData(_columnCount, _columnCount);
for (int i = 0; i < _columnCount; i++)
{
for (int j = 0; j < _columnCount; j++)
{
if (i <= j)
{
U[i][j] = LU[i][j];
}
else
{
U[i][j] = 0.0;
}
}
}
return(new Matrix(U));
}
ComputeBlockDiagonalMatrix()
{
double[][] D = Matrix.CreateMatrixData(n, n);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
D[i][j] = 0.0;
}
D[i][i] = d[i];
if (e[i] > 0)
{
D[i][i + 1] = e[i];
}
else if (e[i] < 0)
{
D[i][i - 1] = e[i];
}
}
return(new Matrix(D));
}
ComputeUpperTriangularFactor()
{
double[][] R = Matrix.CreateMatrixData(n, n);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
if (i < j)
{
R[i][j] = QR[i][j];
}
else if (i == j)
{
R[i][j] = Rdiag[i];
}
else
{
R[i][j] = 0.0;
}
}
}
return(new Matrix(R));
}
ComputeLowerTriangularFactor()
{
double[][] L = Matrix.CreateMatrixData(_rowCount, _columnCount);
for (int i = 0; i < L.Length; i++)
{
for (int j = 0; j < _columnCount; j++)
{
if (i > j)
{
L[i][j] = LU[i][j];
}
else if (i == j)
{
L[i][j] = 1.0;
}
else
{
L[i][j] = 0.0;
}
}
}
return(new Matrix(L));
}
ComputeBlockDiagonalMatrix()
{
double[][] d = Matrix.CreateMatrixData(_n, _n);
for (int i = 0; i < _n; i++)
{
for (int j = 0; j < _n; j++)
{
d[i][j] = 0.0;
}
d[i][i] = _d[i];
if (_e[i] > 0)
{
d[i][i + 1] = _e[i];
}
else if (_e[i] < 0)
{
d[i][i - 1] = _e[i];
}
}
return(new Matrix(d));
}
SingularValueDecomposition(IMatrix <double> arg)
{
_transpose = (arg.RowCount < arg.ColumnCount);
// Derived from LINPACK code.
// Initialize.
double[][] a;
if (_transpose)
{
// copy of internal data, independent of Arg
a = Matrix.Transpose(arg).GetArray();
_m = arg.ColumnCount;
_n = arg.RowCount;
}
else
{
a = arg.CopyToJaggedArray();
_m = arg.RowCount;
_n = arg.ColumnCount;
}
int nu = Math.Min(_m, _n);
double[] s = new double[Math.Min(_m + 1, _n)];
double[][] u = Matrix.CreateMatrixData(_m, nu);
double[][] v = Matrix.CreateMatrixData(_n, _n);
double[] e = new double[_n];
double[] work = new double[_m];
/*
* Reduce A to bidiagonal form, storing the diagonal elements
* in s and the super-diagonal elements in e.
*/
int nct = Math.Min(_m - 1, _n);
int nrt = Math.Max(0, Math.Min(_n - 2, _m));
for (int k = 0; k < Math.Max(nct, nrt); k++)
{
if (k < nct)
{
// Compute the transformation for the k-th column and
// place the k-th diagonal in s[k].
// Compute 2-norm of k-th column without under/overflow.
s[k] = 0;
for (int i = k; i < _m; i++)
{
s[k] = Fn.Hypot(s[k], a[i][k]);
}
if (s[k] != 0.0)
{
if (a[k][k] < 0.0)
{
s[k] = -s[k];
}
for (int i = k; i < _m; i++)
{
a[i][k] /= s[k];
}
a[k][k] += 1.0;
}
s[k] = -s[k];
}
for (int j = k + 1; j < _n; j++)
{
if ((k < nct) & (s[k] != 0.0))
{
/* Apply the transformation */
double t = 0;
for (int i = k; i < _m; i++)
{
t += a[i][k] * a[i][j];
}
t = (-t) / a[k][k];
for (int i = k; i < _m; i++)
{
a[i][j] += t * a[i][k];
}
}
/*
* Place the k-th row of A into e for the
* subsequent calculation of the row transformation.
*/
e[j] = a[k][j];
}
if (k < nct)
{
/*
* Place the transformation in U for subsequent back
* multiplication.
*/
for (int i = k; i < _m; i++)
{
u[i][k] = a[i][k];
}
}
if (k < nrt)
{
// Compute the k-th row transformation and place the
// k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0;
for (int i = k + 1; i < _n; i++)
{
e[k] = Fn.Hypot(e[k], e[i]);
}
if (e[k] != 0.0)
{
if (e[k + 1] < 0.0)
{
e[k] = -e[k];
}
for (int i = k + 1; i < _n; i++)
{
e[i] /= e[k];
}
e[k + 1] += 1.0;
}
e[k] = -e[k];
if ((k + 1 < _m) & (e[k] != 0.0))
{
/* Apply the transformation */
for (int i = k + 1; i < _m; i++)
{
work[i] = 0.0;
}
for (int j = k + 1; j < _n; j++)
{
for (int i = k + 1; i < _m; i++)
{
work[i] += e[j] * a[i][j];
}
}
for (int j = k + 1; j < _n; j++)
{
double t = (-e[j]) / e[k + 1];
for (int i = k + 1; i < _m; i++)
{
a[i][j] += t * work[i];
}
}
}
/*
* Place the transformation in V for subsequent
* back multiplication.
*/
for (int i = k + 1; i < _n; i++)
{
v[i][k] = e[i];
}
}
}
/* Set up the final bidiagonal matrix or order p. */
int p = Math.Min(_n, _m + 1);
if (nct < _n)
{
s[nct] = a[nct][nct];
}
if (_m < p)
{
s[p - 1] = 0.0;
}
if (nrt + 1 < p)
{
e[nrt] = a[nrt][p - 1];
}
e[p - 1] = 0.0;
/* If required, generate U */
for (int j = nct; j < nu; j++)
{
for (int i = 0; i < _m; i++)
{
u[i][j] = 0.0;
}
u[j][j] = 1.0;
}
for (int k = nct - 1; k >= 0; k--)
{
if (s[k] != 0.0)
{
for (int j = k + 1; j < nu; j++)
{
double t = 0;
for (int i = k; i < _m; i++)
{
t += u[i][k] * u[i][j];
}
t = (-t) / u[k][k];
for (int i = k; i < _m; i++)
{
u[i][j] += t * u[i][k];
}
}
for (int i = k; i < _m; i++)
{
u[i][k] = -u[i][k];
}
u[k][k] = 1.0 + u[k][k];
for (int i = 0; i < k - 1; i++)
{
u[i][k] = 0.0;
}
}
else
{
for (int i = 0; i < _m; i++)
{
u[i][k] = 0.0;
}
u[k][k] = 1.0;
}
}
/* If required, generate V */
for (int k = _n - 1; k >= 0; k--)
{
if ((k < nrt) & (e[k] != 0.0))
{
for (int j = k + 1; j < nu; j++)
{
double t = 0;
for (int i = k + 1; i < _n; i++)
{
t += v[i][k] * v[i][j];
}
t = (-t) / v[k + 1][k];
for (int i = k + 1; i < _n; i++)
{
v[i][j] += t * v[i][k];
}
}
}
for (int i = 0; i < _n; i++)
{
v[i][k] = 0.0;
}
v[k][k] = 1.0;
}
/* Main iteration loop for the singular values */
int pp = p - 1;
int iter = 0;
double eps = Number.PositiveRelativeAccuracy;
while (p > 0)
{
int k;
IterationStep step;
/* Here is where a test for too many iterations would go */
/*
* This section of the program inspects for
* negligible elements in the s and e arrays. On
* completion the variables kase and k are set as follows.
*
* DeflateNeglible: if s[p] and e[k-1] are negligible and k<p
* SplitAtNeglible: if s[k] is negligible and k<p
* QR: if e[k-1] is negligible, k<p, and s[k], ..., s[p] are not negligible.
* Convergence: if e[p-1] is negligible.
*/
for (k = p - 2; k >= 0; k--)
{
if (Math.Abs(e[k]) <= eps * (Math.Abs(s[k]) + Math.Abs(s[k + 1])))
{
e[k] = 0.0;
break;
}
}
if (k == p - 2)
{
step = IterationStep.Convergence;
}
else
{
int ks;
for (ks = p - 1; ks >= k; ks--)
{
if (ks == k)
{
break;
}
double t = (ks != p ? Math.Abs(e[ks]) : 0.0) + (ks != k + 1 ? Math.Abs(e[ks - 1]) : 0.0);
if (Math.Abs(s[ks]) <= eps * t)
{
s[ks] = 0.0;
break;
}
}
if (ks == k)
{
step = IterationStep.QR;
}
else if (ks == p - 1)
{
step = IterationStep.DeflateNeglible;
}
else
{
step = IterationStep.SplitAtNeglible;
k = ks;
}
}
k++;
/* Perform the task indicated by 'step'. */
switch (step)
{
// Deflate negligible s(p).
case IterationStep.DeflateNeglible:
{
double f = e[p - 2];
e[p - 2] = 0.0;
for (int j = p - 2; j >= k; j--)
{
double t = Fn.Hypot(s[j], f);
double cs = s[j] / t;
double sn = f / t;
s[j] = t;
if (j != k)
{
f = (-sn) * e[j - 1];
e[j - 1] = cs * e[j - 1];
}
for (int i = 0; i < _n; i++)
{
t = (cs * v[i][j]) + (sn * v[i][p - 1]);
v[i][p - 1] = ((-sn) * v[i][j]) + (cs * v[i][p - 1]);
v[i][j] = t;
}
}
}
break;
// Split at negligible s(k)
case IterationStep.SplitAtNeglible:
{
double f = e[k - 1];
e[k - 1] = 0.0;
for (int j = k; j < p; j++)
{
double t = Fn.Hypot(s[j], f);
double cs = s[j] / t;
double sn = f / t;
s[j] = t;
f = (-sn) * e[j];
e[j] = cs * e[j];
for (int i = 0; i < _m; i++)
{
t = (cs * u[i][j]) + (sn * u[i][k - 1]);
u[i][k - 1] = ((-sn) * u[i][j]) + (cs * u[i][k - 1]);
u[i][j] = t;
}
}
}
break;
// Perform one qr step.
case IterationStep.QR:
{
/* Calculate the shift */
double scale = Math.Max(Math.Max(Math.Max(Math.Max(Math.Abs(s[p - 1]), Math.Abs(s[p - 2])), Math.Abs(e[p - 2])), Math.Abs(s[k])), Math.Abs(e[k]));
double sp = s[p - 1] / scale;
double spm1 = s[p - 2] / scale;
double epm1 = e[p - 2] / scale;
double sk = s[k] / scale;
double ek = e[k] / scale;
double b = (((spm1 + sp) * (spm1 - sp)) + (epm1 * epm1)) / 2.0;
double c = (sp * epm1) * (sp * epm1);
double shift = 0.0;
if ((b != 0.0) | (c != 0.0))
{
shift = Math.Sqrt((b * b) + c);
if (b < 0.0)
{
shift = -shift;
}
shift = c / (b + shift);
}
double f = ((sk + sp) * (sk - sp)) + shift;
double g = sk * ek;
/* Chase zeros */
for (int j = k; j < p - 1; j++)
{
double t = Fn.Hypot(f, g);
double cs = f / t;
double sn = g / t;
if (j != k)
{
e[j - 1] = t;
}
f = (cs * s[j]) + (sn * e[j]);
e[j] = (cs * e[j]) - (sn * s[j]);
g = sn * s[j + 1];
s[j + 1] = cs * s[j + 1];
for (int i = 0; i < _n; i++)
{
t = (cs * v[i][j]) + (sn * v[i][j + 1]);
v[i][j + 1] = ((-sn) * v[i][j]) + (cs * v[i][j + 1]);
v[i][j] = t;
}
t = Fn.Hypot(f, g);
cs = f / t;
sn = g / t;
s[j] = t;
f = (cs * e[j]) + (sn * s[j + 1]);
s[j + 1] = ((-sn) * e[j]) + (cs * s[j + 1]);
g = sn * e[j + 1];
e[j + 1] = cs * e[j + 1];
if (j < _m - 1)
{
for (int i = 0; i < _m; i++)
{
t = (cs * u[i][j]) + (sn * u[i][j + 1]);
u[i][j + 1] = ((-sn) * u[i][j]) + (cs * u[i][j + 1]);
u[i][j] = t;
}
}
}
e[p - 2] = f;
iter = iter + 1;
}
break;
// Convergence.
case IterationStep.Convergence:
{
/* Make the singular values positive */
if (s[k] <= 0.0)
{
s[k] = (s[k] < 0.0 ? -s[k] : 0.0);
for (int i = 0; i <= pp; i++)
{
v[i][k] = -v[i][k];
}
}
/* Order the singular values */
while (k < pp)
{
if (s[k] >= s[k + 1])
{
break;
}
double t = s[k];
s[k] = s[k + 1];
s[k + 1] = t;
if (k < _n - 1)
{
for (int i = 0; i < _n; i++)
{
t = v[i][k + 1];
v[i][k + 1] = v[i][k];
v[i][k] = t;
}
}
if (k < _m - 1)
{
for (int i = 0; i < _m; i++)
{
t = u[i][k + 1];
u[i][k + 1] = u[i][k];
u[i][k] = t;
}
}
k++;
}
iter = 0;
p--;
}
break;
}
}
// (vermorel) transposing the results if needed
if (_transpose)
{
// swaping U and V
double[][] temp = v;
v = u;
u = temp;
}
_u = new Matrix(u);
_v = new Matrix(v);
_singular = new Vector(s);
InitOnDemandComputations();
}
SingularValueDecomposition(
Matrix Arg
)
{
transpose = (Arg.RowCount < Arg.ColumnCount);
// Derived from LINPACK code.
// Initialize.
double[][] A;
if (transpose)
{
// copy of internal data, independent of Arg
A = Matrix.Transpose(Arg).GetArray();
m = Arg.ColumnCount;
n = Arg.RowCount;
}
else
{
A = Arg.CopyToJaggedArray();
m = Arg.RowCount;
n = Arg.ColumnCount;
}
int nu = Math.Min(m, n);
double[] s = new double[Math.Min(m + 1, n)];
double[][] U = Matrix.CreateMatrixData(m, nu);
double[][] V = Matrix.CreateMatrixData(n, n);
double[] e = new double[n];
double[] work = new double[m];
bool wantu = true;
bool wantv = true;
// Reduce A to bidiagonal form, storing the diagonal elements
// in s and the super-diagonal elements in e.
int nct = Math.Min(m - 1, n);
int nrt = Math.Max(0, Math.Min(n - 2, m));
for (int k = 0; k < Math.Max(nct, nrt); k++)
{
if (k < nct)
{
// Compute the transformation for the k-th column and
// place the k-th diagonal in s[k].
// Compute 2-norm of k-th column without under/overflow.
s[k] = 0;
for (int i = k; i < m; i++)
{
s[k] = Fn.Hypot(s[k], A[i][k]);
}
if (s[k] != 0.0)
{
if (A[k][k] < 0.0)
{
s[k] = -s[k];
}
for (int i = k; i < m; i++)
{
A[i][k] /= s[k];
}
A[k][k] += 1.0;
}
s[k] = -s[k];
}
for (int j = k + 1; j < n; j++)
{
if ((k < nct) & (s[k] != 0.0))
{
// Apply the transformation.
double t = 0;
for (int i = k; i < m; i++)
{
t += A[i][k] * A[i][j];
}
t = (-t) / A[k][k];
for (int i = k; i < m; i++)
{
A[i][j] += t * A[i][k];
}
}
// Place the k-th row of A into e for the
// subsequent calculation of the row transformation.
e[j] = A[k][j];
}
if (wantu & (k < nct))
{
// Place the transformation in U for subsequent back
// multiplication.
for (int i = k; i < m; i++)
{
U[i][k] = A[i][k];
}
}
if (k < nrt)
{
// Compute the k-th row transformation and place the
// k-th super-diagonal in e[k].
// Compute 2-norm without under/overflow.
e[k] = 0;
for (int i = k + 1; i < n; i++)
{
e[k] = Fn.Hypot(e[k], e[i]);
}
if (e[k] != 0.0)
{
if (e[k + 1] < 0.0)
{
e[k] = -e[k];
}
for (int i = k + 1; i < n; i++)
{
e[i] /= e[k];
}
e[k + 1] += 1.0;
}
e[k] = -e[k];
if ((k + 1 < m) & (e[k] != 0.0))
{
// Apply the transformation.
for (int i = k + 1; i < m; i++)
{
work[i] = 0.0;
}
for (int j = k + 1; j < n; j++)
{
for (int i = k + 1; i < m; i++)
{
work[i] += e[j] * A[i][j];
}
}
for (int j = k + 1; j < n; j++)
{
double t = (-e[j]) / e[k + 1];
for (int i = k + 1; i < m; i++)
{
A[i][j] += t * work[i];
}
}
}
if (wantv)
{
// Place the transformation in V for subsequent
// back multiplication.
for (int i = k + 1; i < n; i++)
{
V[i][k] = e[i];
}
}
}
}
// Set up the final bidiagonal matrix or order p.
int p = Math.Min(n, m + 1);
if (nct < n)
{
s[nct] = A[nct][nct];
}
if (m < p)
{
s[p - 1] = 0.0;
}
if (nrt + 1 < p)
{
e[nrt] = A[nrt][p - 1];
}
e[p - 1] = 0.0;
// If required, generate U.
if (wantu)
{
for (int j = nct; j < nu; j++)
{
for (int i = 0; i < m; i++)
{
U[i][j] = 0.0;
}
U[j][j] = 1.0;
}
for (int k = nct - 1; k >= 0; k--)
{
if (s[k] != 0.0)
{
for (int j = k + 1; j < nu; j++)
{
double t = 0;
for (int i = k; i < m; i++)
{
t += U[i][k] * U[i][j];
}
t = (-t) / U[k][k];
for (int i = k; i < m; i++)
{
U[i][j] += t * U[i][k];
}
}
for (int i = k; i < m; i++)
{
U[i][k] = -U[i][k];
}
U[k][k] = 1.0 + U[k][k];
for (int i = 0; i < k - 1; i++)
{
U[i][k] = 0.0;
}
}
else
{
for (int i = 0; i < m; i++)
{
U[i][k] = 0.0;
}
U[k][k] = 1.0;
}
}
}
// If required, generate V.
if (wantv)
{
for (int k = n - 1; k >= 0; k--)
{
if ((k < nrt) & (e[k] != 0.0))
{
for (int j = k + 1; j < nu; j++)
{
double t = 0;
for (int i = k + 1; i < n; i++)
{
t += V[i][k] * V[i][j];
}
t = (-t) / V[k + 1][k];
for (int i = k + 1; i < n; i++)
{
V[i][j] += t * V[i][k];
}
}
}
for (int i = 0; i < n; i++)
{
V[i][k] = 0.0;
}
V[k][k] = 1.0;
}
}
// Main iteration loop for the singular values.
int pp = p - 1;
int iter = 0;
double eps = Number.PositiveRelativeAccuracy;
while (p > 0)
{
int k, kase;
// Here is where a test for too many iterations would go.
// This section of the program inspects for
// negligible elements in the s and e arrays. On
// completion the variables kase and k are set as follows.
// kase = 1 if s(p) and e[k-1] are negligible and k<p
// kase = 2 if s(k) is negligible and k<p
// kase = 3 if e[k-1] is negligible, k<p, and
// s(k), ..., s(p) are not negligible (qr step).
// kase = 4 if e(p-1) is negligible (convergence).
for (k = p - 2; k >= -1; k--)
{
if (k == -1)
{
break;
}
if (Math.Abs(e[k]) <= eps * (Math.Abs(s[k]) + Math.Abs(s[k + 1])))
{
e[k] = 0.0;
break;
}
}
if (k == p - 2)
{
kase = 4;
}
else
{
int ks;
for (ks = p - 1; ks >= k; ks--)
{
if (ks == k)
{
break;
}
double t = (ks != p ? Math.Abs(e[ks]) : 0.0) + (ks != k + 1 ? Math.Abs(e[ks - 1]) : 0.0);
if (Math.Abs(s[ks]) <= eps * t)
{
s[ks] = 0.0;
break;
}
}
if (ks == k)
{
kase = 3;
}
else if (ks == p - 1)
{
kase = 1;
}
else
{
kase = 2;
k = ks;
}
}
k++;
// Perform the task indicated by kase.
switch (kase)
{
// Deflate negligible s(p).
case 1:
{
double f = e[p - 2];
e[p - 2] = 0.0;
for (int j = p - 2; j >= k; j--)
{
double t = Fn.Hypot(s[j], f);
double cs = s[j] / t;
double sn = f / t;
s[j] = t;
if (j != k)
{
f = (-sn) * e[j - 1];
e[j - 1] = cs * e[j - 1];
}
if (wantv)
{
for (int i = 0; i < n; i++)
{
t = cs * V[i][j] + sn * V[i][p - 1];
V[i][p - 1] = (-sn) * V[i][j] + cs * V[i][p - 1];
V[i][j] = t;
}
}
}
}
break;
// Split at negligible s(k)
case 2:
{
double f = e[k - 1];
e[k - 1] = 0.0;
for (int j = k; j < p; j++)
{
double t = Fn.Hypot(s[j], f);
double cs = s[j] / t;
double sn = f / t;
s[j] = t;
f = (-sn) * e[j];
e[j] = cs * e[j];
if (wantu)
{
for (int i = 0; i < m; i++)
{
t = cs * U[i][j] + sn * U[i][k - 1];
U[i][k - 1] = (-sn) * U[i][j] + cs * U[i][k - 1];
U[i][j] = t;
}
}
}
}
break;
// Perform one qr step.
case 3:
{
// Calculate the shift.
double scale = Math.Max(Math.Max(Math.Max(Math.Max(Math.Abs(s[p - 1]), Math.Abs(s[p - 2])), Math.Abs(e[p - 2])), Math.Abs(s[k])), Math.Abs(e[k]));
double sp = s[p - 1] / scale;
double spm1 = s[p - 2] / scale;
double epm1 = e[p - 2] / scale;
double sk = s[k] / scale;
double ek = e[k] / scale;
double b = ((spm1 + sp) * (spm1 - sp) + epm1 * epm1) / 2.0;
double c = (sp * epm1) * (sp * epm1);
double shift = 0.0;
if ((b != 0.0) | (c != 0.0))
{
shift = Math.Sqrt(b * b + c);
if (b < 0.0)
{
shift = -shift;
}
shift = c / (b + shift);
}
double f = (sk + sp) * (sk - sp) + shift;
double g = sk * ek;
// Chase zeros.
for (int j = k; j < p - 1; j++)
{
double t = Fn.Hypot(f, g);
double cs = f / t;
double sn = g / t;
if (j != k)
{
e[j - 1] = t;
}
f = cs * s[j] + sn * e[j];
e[j] = cs * e[j] - sn * s[j];
g = sn * s[j + 1];
s[j + 1] = cs * s[j + 1];
if (wantv)
{
for (int i = 0; i < n; i++)
{
t = cs * V[i][j] + sn * V[i][j + 1];
V[i][j + 1] = (-sn) * V[i][j] + cs * V[i][j + 1];
V[i][j] = t;
}
}
t = Fn.Hypot(f, g);
cs = f / t;
sn = g / t;
s[j] = t;
f = cs * e[j] + sn * s[j + 1];
s[j + 1] = (-sn) * e[j] + cs * s[j + 1];
g = sn * e[j + 1];
e[j + 1] = cs * e[j + 1];
if (wantu && (j < m - 1))
{
for (int i = 0; i < m; i++)
{
t = cs * U[i][j] + sn * U[i][j + 1];
U[i][j + 1] = (-sn) * U[i][j] + cs * U[i][j + 1];
U[i][j] = t;
}
}
}
e[p - 2] = f;
iter = iter + 1;
}
break;
// Convergence.
case 4:
{
// Make the singular values positive.
if (s[k] <= 0.0)
{
s[k] = (s[k] < 0.0 ? -s[k] : 0.0);
if (wantv)
{
for (int i = 0; i <= pp; i++)
{
V[i][k] = -V[i][k];
}
}
}
// Order the singular values.
while (k < pp)
{
if (s[k] >= s[k + 1])
{
break;
}
double t = s[k];
s[k] = s[k + 1];
s[k + 1] = t;
if (wantv && (k < n - 1))
{
for (int i = 0; i < n; i++)
{
t = V[i][k + 1];
V[i][k + 1] = V[i][k];
V[i][k] = t;
}
}
if (wantu && (k < m - 1))
{
for (int i = 0; i < m; i++)
{
t = U[i][k + 1];
U[i][k + 1] = U[i][k];
U[i][k] = t;
}
}
k++;
}
iter = 0;
p--;
}
break;
}
}
// (vermorel) transposing the results if needed
if (transpose)
{
// swaping U and V
double[][] T = V;
V = U;
U = T;
}
_u = new Matrix(U);
_v = new Matrix(V);
_singular = new Vector(s);
InitOnDemandComputations();
}
