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#include <iostream>
#include <iomanip>
#include <fstream>
#include <string>
#include <sstream>
#include <cmath>
#include <complex>
#include <vector>
#include "MantidKernel/System.h"
#include "MantidKernel/Exception.h"
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template<typename T>
std::ostream&
Geometry::operator<<(std::ostream& of,const Geometry::Matrix<T>& A)
/*!
External Friend :: outputs point to a stream
\param A :: Matrix to write out
\param of :: output stream
\returns The output stream (of)
*/
{
of<<std::endl;
A.write(of,5);
return of;
}
namespace Geometry
{
template<typename T>
Matrix<T>::Matrix(const int nrow,const int ncol)
: nx(0),ny(0),V(0)
/*!
Constructor with pre-set sizes. Matrix is zeroed
\param nrow :: number of rows
\param ncol :: number of columns
*/
{
// Note:: nx,ny zeroed so setMem always works
setMem(nrow,ncol);
zeroMatrix();
}
template<typename T>
Matrix<T>::Matrix(const std::vector<T>& A,const std::vector<T>& B)
: nx(0),ny(0),V(0)
/*!
Constructor to take two vectors and multiply them to
construct a matrix. (assuming that we have columns x row
vector.
\param A :: Column vector to multiply
\param B :: Row vector to multiply
*/
{
// Note:: nx,ny zeroed so setMem always works
setMem(A.size(),B.size());
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]=A[i]*B[j];
}
template<typename T>
Matrix<T>::Matrix(const Matrix<T>& A)
: nx(0),ny(0),V(0)
/*!
Simple copy constructor
\param A :: Object to copy
*/
{
// Note:: nx,ny zeroed so setMem always works
setMem(A.nx,A.ny);
if (nx*ny)
{
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]=A.V[i][j];
}
}
template<typename T>
Matrix<T>&
Matrix<T>::operator=(const Matrix<T>& A)
/*!
Simple assignment operator
\param A :: Object to copy
*/
{
if (&A!=this)
{
setMem(A.nx,A.ny);
if (nx*ny)
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]=A.V[i][j];
}
return *this;
}
template<typename T>
Matrix<T>::~Matrix()
/*!
Delete operator :: removes memory for
matrix
*/
{
deleteMem();
}
template<typename T>
Matrix<T>&
Matrix<T>::operator+=(const Matrix<T>& A)
/*!
Matrix addition THIS + A
If the size is different then 0 is added where appropiate
Matrix A is not expanded.
\param A :: Matrix to add
\returns Matrix(this + A)
*/
{
const int Xpt((nx>A.nx) ? A.nx : nx);
const int Ypt((ny>A.ny) ? A.ny : ny);
for(int i=0;i<Xpt;i++)
for(int j=0;j<Ypt;j++)
V[i][j]+=A.V[i][j];
return *this;
}
template<typename T>
Matrix<T>&
Matrix<T>::operator-=(const Matrix<T>& A)
/*!
Matrix subtractoin THIS - A
If the size is different then 0 is added where appropiate
Matrix A is not expanded.
\param A :: Matrix to add
\returns Ma
*/
{
const int Xpt((nx>A.nx) ? A.nx : nx);
const int Ypt((ny>A.ny) ? A.ny : ny);
for(int i=0;i<Xpt;i++)
for(int j=0;j<Ypt;j++)
V[i][j]-=A.V[i][j];
return *this;
}
template<typename T>
Matrix<T>
Matrix<T>::operator+(const Matrix<T>& A)
/*!
Matrix addition THIS + A
If the size is different then 0 is added where appropiate
Matrix A is not expanded.
\param A :: Matrix to add
\returns Matrix(this + A)
*/
{
Matrix<T> X(*this);
return X+=A;
}
template<typename T>
Matrix<T>
Matrix<T>::operator-(const Matrix<T>& A)
/*!
Matrix subtraction THIS - A
If the size is different then 0 is subtracted where
appropiate. This matrix determines the size
\param A :: Matrix to add
\returns Matrix(this + A)
*/
{
Matrix<T> X(*this);
return X-=A;
}
template<typename T>
Vec3D
Matrix<T>::operator*(const Vec3D& A) const
{
throw Kernel::Exception::NotImplementedError("Operator Mantrix * Vec3D is not implmented");
return A;
}
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Matrix<T>::operator*(const Matrix<T>& A) const
/*!
Matrix multiplication THIS * A
\param A :: Matrix to multiply by (this->row must == A->columns)
\throw MisMatch<int> if there is a size mismatch.
\return Matrix(This * A)
*/
{
if (ny!=A.nx)
throw ColErr::MisMatch<int>(ny,A.nx,"Matrix::operator*(Matrix)");
Matrix<T> X(nx,A.ny);
for(int i=0;i<nx;i++)
for(int j=0;j<A.ny;j++)
for(int kk=0;kk<ny;kk++)
X.V[i][j]+=V[i][kk]*A.V[kk][j];
return X;
}
template<typename T>
std::vector<T>
Matrix<T>::operator*(const std::vector<T>& Vec) const
/*!
Matrix multiplication THIS * Vec to produce a vec
\param Vec :: size of vector > this->nrows
\throw MisMatch<int> if there is a size mismatch.
\return Matrix(This * Vec)
*/
{
std::vector<T> Out;
if (ny>static_cast<int>(Vec.size()))
throw ColErr::MisMatch<int>(ny,Vec.size(),"Matrix::operator*(Vec)");
Out.resize(nx);
for(int i=0;i<nx;i++)
{
Out[i]=0;
for(int j=0;j<ny;j++)
Out[i]+=V[i][j]*Vec[j];
}
return Out;
}
template<typename T>
Matrix<T>
Matrix<T>::operator*(const T& Value) const
/*!
Matrix multiplication THIS * Value
\param Value :: Scalar to multiply by
\return V * (this)
*/
{
Matrix<T> X(*this);
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
X.V[i][j]*=Value;
return X;
}
template<typename T>
Matrix<T>&
Matrix<T>::operator*=(const Matrix<T>& A)
/*!
Matrix multiplication THIS *= A
Note that we call operator* to avoid the problem
of changing matrix size.
\param A :: Matrix to multiply by (this->row must == A->columns)
\return This *= A
*/
{
if (ny!=A.nx)
throw ColErr::MisMatch<int>(ny,A.nx,"Matrix*=(Matrix<T>)");
// This construct to avoid the problem of changing size
*this = this->operator*(A);
return *this;
}
template<typename T>
Matrix<T>&
Matrix<T>::operator*=(const T& Value)
/*!
Matrix multiplication THIS * Value
\param Value :: Scalar to multiply matrix by
\return *this
*/
{
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]*=Value;
return *this;
}
template<typename T>
Matrix<T>&
Matrix<T>::operator/=(const T& Value)
/*!
Matrix divishio THIS / Value
\param Value :: Scalar to multiply matrix by
\return *this
*/
{
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]/=Value;
return *this;
}
template<typename T>
int
Matrix<T>::operator==(const Matrix<T>& A) const
/*!
Element by element comparison within tolerance.
Tolerance means that the value must be > tolerance
and less than (diff/max)>tolerance
Always returns 0 if the Matrix have different sizes
\param A :: matrix to check.
\return 1 on success
*/
{
const double Tolerance(1e-8);
if (&A!=this) // this == A == always true
{
if(A.nx!=nx || A.ny!=ny)
return 0;
double maxS(0.0);
double maxDiff(0.0); // max di
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
{
const T diff=(V[i][j]-A.V[i][j]);
if (fabs(diff)>maxDiff)
maxDiff=fabs(diff);
if (fabs(V[i][j])>maxS)
maxS=fabs(V[i][j]);
}
if (maxDiff<Tolerance)
return 1;
if (maxS>1.0 && (maxDiff/maxS)<Tolerance)
return 1;
return 0;
}
//this == this is true
return 1;
}
template<typename T>
void
Matrix<T>::deleteMem()
/*!
Deletes the memory held in matrix
*/
{
if (V)
{
delete [] *V;
delete [] V;
V=0;
}
nx=0;
ny=0;
return;
}
template<typename T>
void
Matrix<T>::setMem(const int a,const int b)
/*!
Sets the memory held in matrix
\param a :: number of rows
\param b :: number of columns
*/
{
if (a==nx && b==ny)
return;
deleteMem();
if (a<=0 || b<=0)
return;
nx=a;
ny=b;
if (nx*ny)
{
T* tmpX=new T[nx*ny];
V=new T*[nx];
for (int i=0;i<nx;i++)
V[i]=tmpX + (i*ny);
}
return;
}
template<typename T>
void
Matrix<T>::swapRows(const int RowI,const int RowJ)
/*!
Swap rows I and J
\param RowI :: row I to swap
\param RowJ :: row J to swap
*/
{
if (nx*ny && RowI<nx && RowJ<nx &&
RowI!=RowJ)
{
for(int k=0;k<ny;k++)
{
T tmp=V[RowI][k];
V[RowI][k]=V[RowJ][k];
V[RowJ][k]=tmp;
}
}
return;
}
template<typename T>
void
Matrix<T>::swapCols(const int colI,const int colJ)
/*!
Swap columns I and J
\param colI :: col I to swap
\param colJ :: col J to swap
*/
{
if (nx*ny && colI<ny && colJ<ny &&
colI!=colJ)
{
for(int k=0;k<nx;k++)
{
T tmp=V[k][colI];
V[k][colI]=V[k][colJ];
V[k][colJ]=tmp;
}
}
return;
}
template<typename T>
void
Matrix<T>::zeroMatrix()
/*!
Zeros all elements of the matrix
*/
{
if (nx*ny)
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]=static_cast<T>(0);
return;
}
template<typename T>
void
Matrix<T>::identityMatrix()
/*!
Makes the matrix an idenity matrix.
Zeros all the terms outside of the square
*/
{
if (nx*ny)
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
V[i][j]=(j==i) ? 1 : 0;
return;
}
template<typename T>
void
Matrix<T>::rotate(const double tau,const double s,const int i,const int j,
const int k,const int m)
/*!
Applies a rotation to a particular point of tan(theta)=tau
\param tau :: tan(theta)
\param s :: sin(theta)
\param i :: first index (xpos)
\param j :: first index (ypos)
\param k :: second index (xpos)
\param m :: second index (ypos)
*/
{
const T gg=V[i][j];
const T hh=V[k][m];
V[i][j]=static_cast<T>(gg-s*(hh+gg*tau));
V[k][m]=static_cast<T>(hh+s*(gg-hh*tau));
return;
}
template<typename T>
Matrix<T>
Matrix<T>::fDiagonal(const std::vector<T>& Dvec) const
/*!
Construct a matrix based on
A * This, where A is made into a diagonal
matrix.
\param Dvec :: diagonal matrix (just centre points)
\returns a matrix multiplication
*/
{
// Note:: nx,ny zeroed so setMem always works
if (static_cast<int>(Dvec.size())!=nx)
{
std::ostringstream cx;
cx<<"Matrix::fDiagonal Size: "<<Dvec.size()<<" "<<nx<<" "<<ny;
throw ColErr::ExBase(0,cx.str());
}
Matrix<T> X(Dvec.size(),ny);
for(int i=0;i<static_cast<int>(Dvec.size());i++)
for(int j=0;j<ny;j++)
X.V[i][j]=Dvec[i]*V[i][j];
return X;
}
template<typename T>
Matrix<T>
Matrix<T>::bDiagonal(const std::vector<T>& Dvec) const
/*!
Construct a matrix based on
This * A, where A is made into a diagonal
matrix.
\param Dvec :: diagonal matrix (just centre points)
*/
{
// Note:: nx,ny zeroed so setMem always works
if (static_cast<int>(Dvec.size())!=ny)
{
std::ostringstream cx;
cx<<"Error Matrix::bDiaognal size:: "<<Dvec.size()
<<" "<<nx<<" "<<ny;
throw ColErr::ExBase(0,cx.str());
}
Matrix<T> X(nx,Dvec.size());
for(int i=0;i<nx;i++)
for(int j=0;j<static_cast<int>(Dvec.size());j++)
X.V[i][j]=Dvec[j]*V[i][j];
return X;
}
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template<typename T>
Matrix<T>
Matrix<T>::Tprime() const
/*!
Transpose the matrix :
Has transpose for a square matrix case.
\return M^T
*/
{
if (!nx*ny)
return *this;
if (nx==ny) // inplace transpose
{
Matrix<T> MT(*this);
MT.Transpose();
return MT;
}
// irregular matrix
Matrix<T> MT(ny,nx);
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
MT.V[j][i]=V[i][j];
return MT;
}
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template<typename T>
Matrix<T>&
Matrix<T>::Transpose()
/*!
Transpose the matrix :
Has a inplace transpose for a square matrix case.
*/
{
if (!nx*ny)
return *this;
if (nx==ny) // inplace transpose
{
for(int i=0;i<nx;i++)
for(int j=i+1;j<ny;j++)
{
T tmp=V[i][j];
V[i][j]=V[j][i];
V[j][i]=tmp;
}
return *this;
}
// irregular matrix
// get some memory
T* tmpX=new T[ny*nx];
T** Vt=new T*[ny];
for (int i=0;i<ny;i++)
Vt[i]=tmpX + (i*nx);
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
Vt[j][i]=V[i][j];
// remove old memory
const int tx=nx;
const int ty=ny;
deleteMem(); // resets nx,ny
// replace memory
V=Vt;
nx=ty;
ny=tx;
return *this;
}
template<>
int
Matrix<int>::GaussJordan(Geometry::Matrix<int>&)
/*!
Not valid for Integer
*/
{
return 0;
}
template<typename T>
int
Matrix<T>::GaussJordan(Matrix<T>& B)
/*!
Invert this Matrix and solve the
form such that if A.x=B then solve to generate x.
This requires that B is B[A.nx][Any]
The result is placed back in B
*/
{
// check for input errors
if (nx!=ny || B.nx!=nx)
return -1;
// pivoted rows
std::vector<int> pivoted(nx);
fill(pivoted.begin(),pivoted.end(),0);
std::vector<int> indxcol(nx); // Colunm index
std::vector<int> indxrow(nx); // row index
int irow(0),icol(0);
for(int i=0;i<nx;i++)
{
// Get Biggest non-pivoted item
double bigItem=0.0; // get point to pivot over
for(int j=0;j<nx;j++)
{
if (pivoted[j]!= 1) //check only non-pivots
{
for(int k=0;k<nx;k++)
if (!pivoted[k])
{
if (fabs(V[j][k]) >=bigItem)
{
bigItem=fabs(V[j][k]);
irow=j;
icol=k;
}
}
}
else if (pivoted[j]>1)
throw ColErr::ExBase(j,"Error doing G-J elem on a singular matrix");
}
pivoted[icol]++;
// Swap in row/col to make a diagonal item
if (irow != icol)
{
swapRows(irow,icol);
B.swapRows(irow,icol);
}
indxrow[i]=irow;
indxcol[i]=icol;
if (V[icol][icol] == 0.0)
{
std::cerr<<"Error doing G-J elem on a singular matrix"<<std::endl;
return 1;
}
const double pivDiv= 1.0/V[icol][icol];
V[icol][icol]=1;
for(int l=0;l<nx;l++)
V[icol][l] *= pivDiv;
for(int l=0;l<B.ny;l++)
B.V[icol][l] *=pivDiv;
for(int ll=0;ll<nx;ll++)
if (ll!=icol)
{
const double div_num=V[ll][icol];
V[ll][icol]=0.0;
for(int l=0;l<nx;l++)
V[ll][l] -= V[icol][l]*div_num;
for(int l=0;l<B.ny;l++)
B.V[ll][l] -= B.V[icol][l]*div_num;
}
}
// Un-roll interchanges
for(int l=nx-1;l>=0;l--)
if (indxrow[l] !=indxcol[l])
swapCols(indxrow[l],indxcol[l]);
return 0;
}
template<typename T>
std::vector<T>
Matrix<T>::Faddeev(Matrix<T>& InvOut)
/*!
Return the polynominal for the matrix
and the inverse.
The polybnomial is such that
\f[
det(sI-A)=s^n+a_{n-1}s^{n-1} \dots +a_0
\f]
\param InvOut ::: output
*/
{
if (nx!=ny)
throw ColErr::MisMatch<int>(nx,ny,"Matrix::Faddev(Matrix)");
Matrix<T>& A(*this);
Matrix<T> B(A);
Matrix<T> Ident(nx,ny);
T tVal=B.Trace(); // Trace of the matrix
std::vector<T> Poly;
Poly.push_back(1);
Poly.push_back(tVal);
for(int i=0;i<nx-2;i++) // skip first (just copy) and last (to keep B-1)
{
B=A*B - Ident*tVal;
tVal=B.Trace();
Poly.push_back(tVal/(i+1));
}
// Last on need to keep B;
InvOut=B;
B=A*B - Ident*tVal;
tVal=B.Trace();
Poly.push_back(tVal/nx);
InvOut-= Ident* (-Poly[nx-1]);
InvOut/= Poly.back();
return Poly;
}
template<typename T>
T
Matrix<T>::Invert()
/*!
If the Matrix is square then invert the matrix
using LU decomposition
\returns Determinate (0 if the matrix is singular)
*/
{
if (nx!=ny && nx<1)
return 0;
int* indx=new int[nx]; // Set in lubcmp
double* col=new double[nx];
int d;
Matrix<T> Lcomp(*this);
Lcomp.lubcmp(indx,d);
double det=static_cast<double>(d);
for(int j=0;j<nx;j++)
det *= Lcomp.V[j][j];
for(int j=0;j<nx;j++)
{
for(int i=0;i<nx;i++)
col[i]=0.0;
col[j]=1.0;
Lcomp.lubksb(indx,col);
for(int i=0;i<nx;i++)
V[i][j]=static_cast<T>(col[i]);
}
delete [] indx;
delete [] col;
return static_cast<T>(det);
}
template<typename T>
T
Matrix<T>::determinant() const
/*!
Calculate the derminant of the matrix
\return Determinant of matrix.
*/
{
if (nx!=ny)
throw ColErr::MisMatch<int>(nx,ny,
"Determinate error :: Matrix is not NxN");
Matrix<T> Mt(*this); //temp copy
T D=Mt.factor();
return D;
}
template<typename T>
T
Matrix<T>::factor()
/*!
Gauss jordan diagonal factorisation
The diagonal is left as the values,
the lower part is zero.
*/
{
if (nx!=ny || nx<1)
throw ColErr::ExBase(0,"Matirx::fractor Matrix is not NxN");
double Pmax;
double deter=1.0;
for(int i=0;i<nx-1;i++) //loop over each row
{
int jmax=i;
Pmax=fabs(V[i][i]);
for(int j=i+1;j<nx;j++) // find max in Row i
{
if (fabs(V[i][j])>Pmax)
{
Pmax=fabs(V[i][j]);
jmax=j;
}
}
if (Pmax<1e-8) // maxtrix signular
{
std::cerr<<"Matrix Singlular"<<std::endl;
return 0;
}
// Swap Columns
if (i!=jmax)
{
swapCols(i,jmax);
deter*=-1; //change sign.
}
// zero all rows below diagonal
Pmax=V[i][i];
deter*=Pmax;
for(int k=i+1;k<nx;k++) // row index
{
const double scale=V[k][i]/Pmax;
V[k][i]=static_cast<T>(0);
for(int q=i+1;q<nx;q++) //column index
V[k][q]-=static_cast<T>(scale*V[i][q]);
}
}
deter*=V[nx-1][nx-1];
return static_cast<T>(deter);
}
template<typename T>
void
Matrix<T>::normVert()
/*!
Normalise EigenVectors
Assumes that they have already been calculated
*/
{
for(int i=0;i<nx;i++)
{
T sum=0;
for(int j=0;j<ny;j++)
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for(int j=0;j<ny;j++)
V[i][j]/=sum;
}
return;
}
template<typename T>
T
Matrix<T>::compSum() const
/*!
Add up each component sums for the matrix
\return \f$ \sum_i \sum_j V_{ij}^2 \f$
*/
{
T sum(0);
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
sum+=V[i][j]*V[i][j];
return sum;
}
template<typename T>
void
Matrix<T>::lubcmp(int* rowperm,int& interchange)
/*!
Find biggest pivit and move to top row. Then
divide by pivit.
\param interchange :: odd/even nterchange (+/-1)
\param rowperm :: row permuations
*/
{
int imax(0),j,k;
double sum,dum,big,temp;
if (nx!=ny || nx<2)
{
std::cerr<<"Error with lubcmp"<<std::endl;
return;
}
double *vv=new double[nx];
interchange=1;
for(int i=0;i<nx;i++)
{
big=0.0;
for(j=0;j<nx;j++)
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big=temp;
if (big == 0.0)
return;
vv[i]=1.0/big;
}
for (j=0;j<nx;j++)
{
for(int i=0;i<j;i++)
{
sum=V[i][j];
for(k=0;k<i;k++)
sum-= V[i][k] * V[k][j];
V[i][j]=static_cast<T>(sum);
}
big=0.0;
imax=j;
for (int i=j;i<nx;i++)
{
sum=V[i][j];
for (k=0;k<j;k++)
sum -= V[i][k] * V[k][j];
V[i][j]=static_cast<T>(sum);
if ( (dum=vv[i] * fabs(sum)) >=big)
{
big=dum;
imax=i;
}
}
if (j!=imax)
{
for(k=0;k<nx;k++)
{ //Interchange rows
dum=V[imax][k];
V[imax][k]=V[j][k];
V[j][k]=static_cast<T>(dum);
}
interchange *= -1;
vv[imax]=static_cast<T>(vv[j]);
}
rowperm[j]=imax;
if (V[j][j] == 0.0)
V[j][j]=static_cast<T>(1e-14);
if (j != nx-1)
{
dum=1.0/(V[j][j]);
for(int i=j+1;i<nx;i++)
V[i][j] *= static_cast<T>(dum);
}
}
delete [] vv;
return;
}
template<typename T>
void
Matrix<T>::lubksb(const int* rowperm,double* b)
/*!
Impliments a separation of the Matrix
into a triangluar matrix
*/
{
int ii= -1;
for(int i=0;i<nx;i++)
{
int ip=rowperm[i];
double sum=b[ip];
b[ip]=b[i];
if (ii != -1)
for (int j=ii;j<i;j++)
sum -= V[i][j] * b[j];
else if (sum)
ii=i;
b[i]=sum;
}
for (int i=nx-1;i>=0;i--)
{
double sum=static_cast<T>(b[i]);
for (int j=i+1;j<nx;j++)
sum -= V[i][j] * b[j];
b[i]=sum/V[i][i];
}
return;
}
template<typename T>
void
Matrix<T>::averSymmetric()
/*!
Simple function to take an average symmetric matrix
out of the Matrix
*/
{
const int minSize=(nx>ny) ? ny : nx;
for(int i=0;i<minSize;i++)
for(int j=i+1;j<minSize;j++)
{
V[i][j]=(V[i][j]+V[j][i])/2;
V[j][i]=V[i][j];
}
return;
}
template<typename T>
std::vector<T>
Matrix<T>::Diagonal() const
/*!
Returns the diagonal form as a vector
\returns Diagonal elements
*/
{
const int Msize=(ny>nx) ? nx : ny;
std::vector<T> Diag(Msize);
for(int i=0;i<Msize;i++)
Diag[i]=V[i][i];
return Diag;
}
template<typename T>
T
Matrix<T>::Trace() const
/*!
Calculates the trace of the matrix
\returns Trace of matrix
*/
{
const int Msize=(ny>nx) ? nx : ny;
T Trx=0;
for(int i=0;i<Msize;i++)
Trx+=V[i][i];
return Trx;
}
template<typename T>
void
Matrix<T>::sortEigen(Matrix<T>& DiagMatrix)
/*!
Sorts the eigenvalues into increasing
size. Moves the EigenVectors correspondingly
\param DiagMatrix :: matrix of the EigenValues
*/
{
if (ny!=nx || nx!=DiagMatrix.nx || nx!=DiagMatrix.ny)
{
std::cerr<<"Matrix not Eigen Form"<<std::endl;
}
std::vector<int> index;
std::vector<T> X=DiagMatrix.Diagonal();
indexSort(X,index);
Matrix<T> EigenVec(*this);
for(int Icol=0;Icol<nx;Icol++)
{
for(int j=0;j<nx;j++)
V[j][Icol]=EigenVec[j][index[Icol]];
DiagMatrix[Icol][Icol]=X[index[Icol]];
}
return;
}
template<typename T>
int
Matrix<T>::Diagonalise(Matrix<T>& EigenVec,Matrix<T>& DiagMatrix) const
/*!
Attempt to diagonalise the matrix IF symmetric
\param EigenVec :: (output) the Eigenvectors matrix
\param DiagMatrix :: the diagonal matrix of eigenvalues
\returns :: 1 on success 0 on failure
*/
{
double theta,tresh,tanAngle,cosAngle,sinAngle;
if(nx!=ny || nx<1)
{
std::cerr<<"Matrix not square"<<std::endl;
return 0;
}
for(int i=0;i<nx;i++)
for(int j=i+1;j<nx;j++)
if (fabs(V[i][j]-V[j][i])>1e-6)
{
std::cerr<<"Matrix not symmetric"<<std::endl;
std::cerr<< (*this);
return 0;
}
Matrix<T> A(*this);
//Make V an identity matrix
EigenVec.setMem(nx,nx);
EigenVec.identityMatrix();
DiagMatrix.setMem(nx,nx);
DiagMatrix.zeroMatrix();
std::vector<double> Diag(nx);
std::vector<double> B(nx);
std::vector<double> ZeroComp(nx);
//set b and d to the diagonal elements o A
for(int i=0;i<nx;i++)
{
Diag[i]=B[i]= A.V[i][i];
ZeroComp[i]=0;
}
int iteration=0;
double sm;
for(int i=0;i<100;i++) //max 50 iterations
{
sm=0.0; // sum of off-diagonal terms
for(int ip=0;ip<nx-1;ip++)
for(int iq=ip+1;iq<nx;iq++)
sm+=fabs(A.V[ip][iq]);
if (sm==0.0) // Nothing to do return...
{
// Make OUTPUT -- D + A
// sort Output::
std::vector<int> index;
// indexSort(Diag,index);
// for(int ii=0;ii<nx;ii++)
// {
// DiagMatrix.V[ii][ii]=static_cast<T>(Diag[index[ii]]);
// EigenVector
for(int ix=0;ix<nx;ix++)
DiagMatrix.V[ix][ix]=static_cast<T>(Diag[ix]);
return 1;
}
// Threshold large for first 5 sweeps
tresh= (i<6) ? 0.2*sm/(nx*nx) : 0.0;
for(int ip=0;ip<nx-1;ip++)
{
for(int iq=ip+1;iq<nx;iq++)
{
double g=100.0*fabs(A.V[ip][iq]);
// After 4 sweeps skip if off diagonal small
if (i>6 &&
static_cast<float>(fabs(Diag[ip]+g))==static_cast<float>(fabs(Diag[ip])) &&
static_cast<float>(fabs(Diag[iq]+g))==static_cast<float>(fabs(Diag[iq])))
A.V[ip][iq]=0;
else if (fabs(A.V[ip][iq])>tresh)
{
double h=Diag[iq]-Diag[ip];
if (static_cast<float>((fabs(h)+g)) == static_cast<float>(fabs(h)))
tanAngle=A.V[ip][iq]/h; // tanAngle=1/(2theta)
else
{
theta=0.5*h/A.V[ip][iq];
tanAngle=1.0/(fabs(theta)+sqrt(1.0+theta*theta));
if (theta<0.0)
tanAngle = -tanAngle;
}
cosAngle=1.0/sqrt(1+tanAngle*tanAngle);
sinAngle=tanAngle*cosAngle;
double tau=sinAngle/(1.0+cosAngle);
h=tanAngle*A.V[ip][iq];
ZeroComp[ip] -= h;
ZeroComp[iq] += h;
Diag[ip] -= h;
Diag[iq] += h;
A.V[ip][iq]=0;
// Rotations 0<j<p
for(int j=0;j<ip;j++)
A.rotate(tau,sinAngle,j,ip,j,iq);
for(int j=ip+1;j<iq;j++)
A.rotate(tau,sinAngle,ip,j,j,iq);
for(int j=iq+1;j<nx;j++)
A.rotate(tau,sinAngle,ip,j,iq,j);
for(int j=0;j<nx;j++)
EigenVec.rotate(tau,sinAngle,j,ip,j,iq);
iteration++;
}
}
}
for(int j=0;j<nx;j++)
{
B[j]+=ZeroComp[j];
Diag[j]=B[j];
ZeroComp[j]=0.0;
}
}
std::cerr<<"Error :: Iterations are a problem"<<std::endl;
return 0;
}
template<typename T>
void
Matrix<T>::print() const
/*!
Simple print out routine
*/
{
write(std::cout,10);
return;
}
template<typename T>
void
Matrix<T>::write(std::ostream& Fh,const int blockCnt) const
/*!
Write out function for blocks of 10 Columns
\param Fh :: file stream for output
\param blockCnt :: number of columns per line (0 == full)
*/
{
std::ios::fmtflags oldFlags=Fh.flags();
Fh.setf(std::ios::floatfield,std::ios::scientific);
const int blockNumber((blockCnt>0) ? blockCnt : ny);
int BCnt(0);
do
{
const int ACnt=BCnt;
BCnt+=blockNumber;
if (BCnt>ny)
BCnt=ny;
if (ACnt)
Fh<<" ----- "<<ACnt<<" "<<BCnt<<" ------ "<<std::endl;
for(int i=0;i<nx;i++)
{
for(int j=ACnt;j<BCnt;j++)
Fh<<std::setw(10)<<V[i][j]<<" ";
Fh<<std::endl;
}
} while(BCnt<ny);
Fh.flags(oldFlags);
return;
}
template<typename T>
std::string
Matrix<T>::str() const
/*!
Convert the matrix into a simple linear string expression
\returns String value of output
*/
{
std::ostringstream cx;
for(int i=0;i<nx;i++)
for(int j=0;j<ny;j++)
{
cx<<std::setprecision(6)<<V[i][j]<<" ";
}
return cx.str();
}
template class Matrix<double>;
template class Matrix<int>;