/*************************************************************************
Calculation of the determinant of a general matrix
Input parameters:
A - matrix, array[0..N-1, 0..N-1]
N - size of matrix A.
Result: determinant of matrix A.
-- ALGLIB --
Copyright 2005 by Bochkanov Sergey
*************************************************************************/
public static AP.Complex cmatrixdet(AP.Complex[,] a,
int n)
{
AP.Complex result = 0;
int[] pivots = new int[0];
a = (AP.Complex[,])a.Clone();
trfac.cmatrixlu(ref a, n, n, ref pivots);
result = cmatrixludet(ref a, ref pivots, n);
return result;
}
private void InitializeActionPresenter(ActionPresenter ap)
{
var apChangingImg = ap as IActionPresenterChangingResultImage;
if (apChangingImg != null)
{
if (!m_isHereActionChangingResultImage)
{
m_isHereActionChangingResultImage = true;
apChangingImg.NewResultImageIsReady += (sender, e) =>
{
var apChanginImage = sender as IActionPresenterChangingResultImage;
m_commonViewModel.ResultImageSource = apChanginImage.NewResultImage;
ImageSource img = apChanginImage.NewResultImage.Clone();
img.Freeze();
this.LastResultImage = img;
};
}
else
{
return;
}
}
var apNeedingProperty = ap as IActionPresenterNeedingCameraProperty <float>;
if (apNeedingProperty != null)
{
apNeedingProperty.SetNeededCameraProperty += (sender, e) =>
{
var sendr = sender as IActionPresenterNeedingCameraProperty <float>;
sendr.CurrentNeededPropertyValue = m_camera.GetProperty(sendr.NeededPropertyType).absValue;
};
}
ap.IsNeedImageChanged += (sender, e) =>
{
var apChangingImage = sender as IActionPresenterChangingResultImage;
if (apChangingImage != null)
{
m_isHereActionChangingResultImage = false;
}
var AP = sender as ActionPresenter;
m_actions.TryRemove(AP.ID, out AP);
};
m_actions.AddOrUpdate(ap.ID, ap, (str, AP) => AP.Clone() as ActionPresenter);
}
/*************************************************************************
Finding the eigenvalues and eigenvectors of a Hermitian matrix
The algorithm finds eigen pairs of a Hermitian matrix by reducing it to
real tridiagonal form and using the QL/QR algorithm.
Input parameters:
A - Hermitian matrix which is given by its upper or lower
triangular part.
Array whose indexes range within [0..N-1, 0..N-1].
N - size of matrix A.
IsUpper - storage format.
ZNeeded - flag controlling whether the eigenvectors are needed or
not. If ZNeeded is equal to:
* 0, the eigenvectors are not returned;
* 1, the eigenvectors are returned.
Output parameters:
D - eigenvalues in ascending order.
Array whose index ranges within [0..N-1].
Z - if ZNeeded is equal to:
* 0, Z hasn�t changed;
* 1, Z contains the eigenvectors.
Array whose indexes range within [0..N-1, 0..N-1].
The eigenvectors are stored in the matrix columns.
Result:
True, if the algorithm has converged.
False, if the algorithm hasn't converged (rare case).
Note:
eigenvectors of Hermitian matrix are defined up to multiplication by
a complex number L, such that |L|=1.
-- ALGLIB --
Copyright 2005, 23 March 2007 by Bochkanov Sergey
*************************************************************************/
public static bool hmatrixevd(AP.Complex[,] a,
int n,
int zneeded,
bool isupper,
ref double[] d,
ref AP.Complex[,] z)
{
bool result = new bool();
AP.Complex[] tau = new AP.Complex[0];
double[] e = new double[0];
double[] work = new double[0];
double[,] t = new double[0, 0];
AP.Complex[,] q = new AP.Complex[0, 0];
int i = 0;
int k = 0;
double v = 0;
int i_ = 0;
a = (AP.Complex[,])a.Clone();
System.Diagnostics.Debug.Assert(zneeded == 0 | zneeded == 1, "HermitianEVD: incorrect ZNeeded");
//
// Reduce to tridiagonal form
//
ortfac.hmatrixtd(ref a, n, isupper, ref tau, ref d, ref e);
if (zneeded == 1)
{
ortfac.hmatrixtdunpackq(ref a, n, isupper, ref tau, ref q);
zneeded = 2;
}
//
// TDEVD
//
result = smatrixtdevd(ref d, e, n, zneeded, ref t);
//
// Eigenvectors are needed
// Calculate Z = Q*T = Re(Q)*T + i*Im(Q)*T
//
if (result & zneeded != 0)
{
work = new double[n - 1 + 1];
z = new AP.Complex[n - 1 + 1, n - 1 + 1];
for (i = 0; i <= n - 1; i++)
{
//
// Calculate real part
//
for (k = 0; k <= n - 1; k++)
{
work[k] = 0;
}
for (k = 0; k <= n - 1; k++)
{
v = q[i, k].x;
for (i_ = 0; i_ <= n - 1; i_++)
{
work[i_] = work[i_] + v * t[k, i_];
}
}
for (k = 0; k <= n - 1; k++)
{
z[i, k].x = work[k];
}
//
// Calculate imaginary part
//
for (k = 0; k <= n - 1; k++)
{
work[k] = 0;
}
for (k = 0; k <= n - 1; k++)
{
v = q[i, k].y;
for (i_ = 0; i_ <= n - 1; i_++)
{
work[i_] = work[i_] + v * t[k, i_];
}
}
for (k = 0; k <= n - 1; k++)
{
z[i, k].y = work[k];
}
}
}
return result;
}
/*************************************************************************
Estimate of a matrix condition number (infinity-norm).
The algorithm calculates a lower bound of the condition number. In this case,
the algorithm does not return a lower bound of the condition number, but an
inverse number (to avoid an overflow in case of a singular matrix).
Input parameters:
A - matrix. Array whose indexes range within [0..N-1, 0..N-1].
N - size of matrix A.
Result: 1/LowerBound(cond(A))
NOTE:
if k(A) is very large, then matrix is assumed degenerate, k(A)=INF,
0.0 is returned in such cases.
*************************************************************************/
public static double cmatrixrcondinf(AP.Complex[,] a,
int n)
{
double result = 0;
int i = 0;
int j = 0;
double v = 0;
double nrm = 0;
int[] pivots = new int[0];
a = (AP.Complex[,])a.Clone();
System.Diagnostics.Debug.Assert(n >= 1, "CMatrixRCondInf: N<1!");
nrm = 0;
for (i = 0; i <= n - 1; i++)
{
v = 0;
for (j = 0; j <= n - 1; j++)
{
v = v + AP.Math.AbsComplex(a[i, j]);
}
nrm = Math.Max(nrm, v);
}
trfac.cmatrixlu(ref a, n, n, ref pivots);
cmatrixrcondluinternal(ref a, n, false, true, nrm, ref v);
result = v;
return result;
}
/*************************************************************************
Estimate of a matrix condition number (1-norm)
The algorithm calculates a lower bound of the condition number. In this case,
the algorithm does not return a lower bound of the condition number, but an
inverse number (to avoid an overflow in case of a singular matrix).
Input parameters:
A - matrix. Array whose indexes range within [0..N-1, 0..N-1].
N - size of matrix A.
Result: 1/LowerBound(cond(A))
NOTE:
if k(A) is very large, then matrix is assumed degenerate, k(A)=INF,
0.0 is returned in such cases.
*************************************************************************/
public static double cmatrixrcond1(AP.Complex[,] a,
int n)
{
double result = 0;
int i = 0;
int j = 0;
double v = 0;
double nrm = 0;
int[] pivots = new int[0];
double[] t = new double[0];
a = (AP.Complex[,])a.Clone();
System.Diagnostics.Debug.Assert(n >= 1, "CMatrixRCond1: N<1!");
t = new double[n];
for (i = 0; i <= n - 1; i++)
{
t[i] = 0;
}
for (i = 0; i <= n - 1; i++)
{
for (j = 0; j <= n - 1; j++)
{
t[j] = t[j] + AP.Math.AbsComplex(a[i, j]);
}
}
nrm = 0;
for (i = 0; i <= n - 1; i++)
{
nrm = Math.Max(nrm, t[i]);
}
trfac.cmatrixlu(ref a, n, n, ref pivots);
cmatrixrcondluinternal(ref a, n, true, true, nrm, ref v);
result = v;
return result;
}
/*************************************************************************
Condition number estimate of a Hermitian positive definite matrix.
The algorithm calculates a lower bound of the condition number. In this case,
the algorithm does not return a lower bound of the condition number, but an
inverse number (to avoid an overflow in case of a singular matrix).
It should be noted that 1-norm and inf-norm of condition numbers of symmetric
matrices are equal, so the algorithm doesn't take into account the
differences between these types of norms.
Input parameters:
A - Hermitian positive definite matrix which is given by its
upper or lower triangle depending on the value of
IsUpper. Array with elements [0..N-1, 0..N-1].
N - size of matrix A.
IsUpper - storage format.
Result:
1/LowerBound(cond(A)), if matrix A is positive definite,
-1, if matrix A is not positive definite, and its condition number
could not be found by this algorithm.
NOTE:
if k(A) is very large, then matrix is assumed degenerate, k(A)=INF,
0.0 is returned in such cases.
*************************************************************************/
public static double hpdmatrixrcond(AP.Complex[,] a,
int n,
bool isupper)
{
double result = 0;
int i = 0;
int j = 0;
int j1 = 0;
int j2 = 0;
double v = 0;
double nrm = 0;
double[] t = new double[0];
a = (AP.Complex[,])a.Clone();
t = new double[n];
for (i = 0; i <= n - 1; i++)
{
t[i] = 0;
}
for (i = 0; i <= n - 1; i++)
{
if (isupper)
{
j1 = i;
j2 = n - 1;
}
else
{
j1 = 0;
j2 = i;
}
for (j = j1; j <= j2; j++)
{
if (i == j)
{
t[i] = t[i] + AP.Math.AbsComplex(a[i, i]);
}
else
{
t[i] = t[i] + AP.Math.AbsComplex(a[i, j]);
t[j] = t[j] + AP.Math.AbsComplex(a[i, j]);
}
}
}
nrm = 0;
for (i = 0; i <= n - 1; i++)
{
nrm = Math.Max(nrm, t[i]);
}
if (trfac.hpdmatrixcholesky(ref a, n, isupper))
{
hpdmatrixrcondcholeskyinternal(ref a, n, isupper, true, nrm, ref v);
result = v;
}
else
{
result = -1;
}
return result;
}
/*************************************************************************
Tests whether A is HPD
*************************************************************************/
private static bool ishpd(AP.Complex[,] a,
int n)
{
bool result = new bool();
int j = 0;
double ajj = 0;
AP.Complex v = 0;
double r = 0;
AP.Complex[] t = new AP.Complex[0];
AP.Complex[] t2 = new AP.Complex[0];
AP.Complex[] t3 = new AP.Complex[0];
int i = 0;
AP.Complex[,] a1 = new AP.Complex[0,0];
int i_ = 0;
a = (AP.Complex[,])a.Clone();
t = new AP.Complex[n-1+1];
t2 = new AP.Complex[n-1+1];
t3 = new AP.Complex[n-1+1];
result = true;
//
// Compute the Cholesky factorization A = U'*U.
//
for(j=0; j<=n-1; j++)
{
//
// Compute U(J,J) and test for non-positive-definiteness.
//
v = 0.0;
for(i_=0; i_<=j-1;i_++)
{
v += AP.Math.Conj(a[i_,j])*a[i_,j];
}
ajj = (a[j,j]-v).x;
if( (double)(ajj)<=(double)(0) )
{
a[j,j] = ajj;
result = false;
return result;
}
ajj = Math.Sqrt(ajj);
a[j,j] = ajj;
//
// Compute elements J+1:N-1 of row J.
//
if( j<n-1 )
{
for(i_=0; i_<=j-1;i_++)
{
t2[i_] = AP.Math.Conj(a[i_,j]);
}
for(i_=j+1; i_<=n-1;i_++)
{
t3[i_] = a[j,i_];
}
for(i=j+1; i<=n-1; i++)
{
v = 0.0;
for(i_=0; i_<=j-1;i_++)
{
v += a[i_,i]*t2[i_];
}
t3[i] = t3[i]-v;
}
for(i_=j+1; i_<=n-1;i_++)
{
a[j,i_] = t3[i_];
}
r = 1/ajj;
for(i_=j+1; i_<=n-1;i_++)
{
a[j,i_] = r*a[j,i_];
}
}
}
return result;
}
/*************************************************************************
Checks whether inverse is correct
Returns True on success.
*************************************************************************/
private static bool hpdmatrixcheckinverse(AP.Complex[,] a,
AP.Complex[,] inva,
bool isupper,
int n,
double threshold,
int info,
ref matinv.matinvreport rep)
{
bool result = new bool();
int i = 0;
int j = 0;
AP.Complex v = 0;
int i_ = 0;
a = (AP.Complex[,])a.Clone();
inva = (AP.Complex[,])inva.Clone();
for(i=0; i<=n-2; i++)
{
if( isupper )
{
for(i_=i+1; i_<=n-1;i_++)
{
a[i_,i] = AP.Math.Conj(a[i,i_]);
}
for(i_=i+1; i_<=n-1;i_++)
{
inva[i_,i] = AP.Math.Conj(inva[i,i_]);
}
}
else
{
for(i_=i+1; i_<=n-1;i_++)
{
a[i,i_] = AP.Math.Conj(a[i_,i]);
}
for(i_=i+1; i_<=n-1;i_++)
{
inva[i,i_] = AP.Math.Conj(inva[i_,i]);
}
}
}
result = true;
if( info<=0 )
{
result = false;
}
else
{
result = result & !((double)(rep.r1)<(double)(100*AP.Math.MachineEpsilon) | (double)(rep.r1)>(double)(1+1000*AP.Math.MachineEpsilon));
result = result & !((double)(rep.rinf)<(double)(100*AP.Math.MachineEpsilon) | (double)(rep.rinf)>(double)(1+1000*AP.Math.MachineEpsilon));
for(i=0; i<=n-1; i++)
{
for(j=0; j<=n-1; j++)
{
v = 0.0;
for(i_=0; i_<=n-1;i_++)
{
v += a[i,i_]*inva[i_,j];
}
if( i==j )
{
v = v-1;
}
result = result & (double)(AP.Math.AbsComplex(v))<=(double)(threshold);
}
}
}
return result;
}
