// @(#)root/matrix:$Name:  $:$Id: TMatrix.cxx,v 1.27 2002/09/15 10:16:44 brun Exp $
// Author: Fons Rademakers   03/11/97

/*************************************************************************
 * Copyright (C) 1995-2000, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/

//////////////////////////////////////////////////////////////////////////
//                                                                      //
// Linear Algebra Package                                               //
//                                                                      //
// The present package implements all the basic algorithms dealing      //
// with vectors, matrices, matrix columns, rows, diagonals, etc.        //
//                                                                      //
// Matrix elements are arranged in memory in a COLUMN-wise              //
// fashion (in FORTRAN's spirit). In fact, it makes it very easy to     //
// feed the matrices to FORTRAN procedures, which implement more        //
// elaborate algorithms.                                                //
//                                                                      //
// Unless otherwise specified, matrix and vector indices always start   //
// with 0, spanning up to the specified limit-1. However, there are     //
// constructors to which one can specify aribtrary lower and upper      //
// bounds, e.g. TMatrix m(1,10,1,5) defines a matrix that ranges, and   //
// that can be addresses, from 1..10, 1..5 (a(1,1)..a(10,5)).           //
//                                                                      //
// The present package provides all facilities to completely AVOID      //
// returning matrices. Use "TMatrix A(TMatrix::kTransposed,B);" and     //
// other fancy constructors as much as possible. If one really needs    //
// to return a matrix, return a TLazyMatrix object instead. The         //
// conversion is completely transparent to the end user, e.g.           //
// "TMatrix m = THaarMatrix(5);" and _is_ efficient.                    //
//                                                                      //
// Since TMatrix et al. are fully integrated in ROOT they of course     //
// can be stored in a ROOT database.                                    //
//                                                                      //
//                                                                      //
// How to efficiently use this package                                  //
// -----------------------------------                                  //
//                                                                      //
// 1. Never return complex objects (matrices or vectors)                //
//    Danger: For example, when the following snippet:                  //
//        TMatrix foo(int n)                                            //
//        {                                                             //
//           TMatrix foom(n,n); fill_in(foom); return foom;             //
//        }                                                             //
//        TMatrix m = foo(5);                                           //
//    runs, it constructs matrix foo:foom, copies it onto stack as a    //
//    return value and destroys foo:foom. Return value (a matrix)       //
//    from foo() is then copied over to m (via a copy constructor),     //
//    and the return value is destroyed. So, the matrix constructor is  //
//    called 3 times and the destructor 2 times. For big matrices,      //
//    the cost of multiple constructing/copying/destroying of objects   //
//    may be very large. *Some* optimized compilers can cut down on 1   //
//    copying/destroying, but still it leaves at least two calls to     //
//    the constructor. Note, TLazyMatrices (see below) can construct    //
//    TMatrix m "inplace", with only a _single_ call to the             //
//    constructor.                                                      //
//                                                                      //
// 2. Use "two-address instructions"                                    //
//        "void TMatrix::operator += (const TMatrix &B);"               //
//    as much as possible.                                              //
//    That is, to add two matrices, it's much more efficient to write   //
//        A += B;                                                       //
//    than                                                              //
//        TMatrix C = A + B;                                            //
//    (if both operand should be preserved,                             //
//        TMatrix C = A; C += B;                                        //
//    is still better).                                                 //
//                                                                      //
// 3. Use glorified constructors when returning of an object seems      //
//    inevitable:                                                       //
//        "TMatrix A(TMatrix::kTransposed,B);"                          //
//        "TMatrix C(A,TMatrix::kTransposeMult,B);"                     //
//                                                                      //
//    like in the following snippet (from $ROOTSYS/test/vmatrix.cxx)    //
//    that verifies that for an orthogonal matrix T, T'T = TT' = E.     //
//                                                                      //
//    TMatrix haar = THaarMatrix(5);                                    //
//    TMatrix unit(TMatrix::kUnit,haar);                                //
//    TMatrix haar_t(TMatrix::kTransposed,haar);                        //
//    TMatrix hth(haar,TMatrix::kTransposeMult,haar);                   //
//    TMatrix hht(haar,TMatrix::kMult,haar_t);                          //
//    TMatrix hht1 = haar; hht1 *= haar_t;                              //
//    VerifyMatrixIdentity(unit,hth);                                   //
//    VerifyMatrixIdentity(unit,hht);                                   //
//    VerifyMatrixIdentity(unit,hht1);                                  //
//                                                                      //
// 4. Accessing row/col/diagonal of a matrix without much fuss          //
//    (and without moving a lot of stuff around):                       //
//                                                                      //
//        TMatrix m(n,n); TVector v(n); TMatrixDiag(m) += 4;            //
//        v = TMatrixRow(m,0);                                          //
//        TMatrixColumn m1(m,1); m1(2) = 3; // the same as m(2,1)=3;    //
//    Note, constructing of, say, TMatrixDiag does *not* involve any    //
//    copying of any elements of the source matrix.                     //
//                                                                      //
// 5. It's possible (and encouraged) to use "nested" functions          //
//    For example, creating of a Hilbert matrix can be done as follows: //
//                                                                      //
//    void foo(const TMatrix &m)                                        //
//    {                                                                 //
//       TMatrix m1(TMatrix::kZero,m);                                  //
//       struct MakeHilbert : public TElementPosAction {                //
//          void Operation(Real_t &element) { element = 1./(fI+fJ-1); } //
//       };                                                             //
//       m1.Apply(MakeHilbert());                                       //
//    }                                                                 //
//                                                                      //
//    of course, using a special method TMatrix::HilbertMatrix() is     //
//    still more optimal, but not by a whole lot. And that's right,     //
//    class MakeHilbert is declared *within* a function and local to    //
//    that function. It means one can define another MakeHilbert class  //
//    (within another function or outside of any function, that is, in  //
//    the global scope), and it still will be OK. Note, this currently  //
//    is not yet supported by the interpreter CINT.                     //
//                                                                      //
//    Another example is applying of a simple function to each matrix   //
//    element:                                                          //
//                                                                      //
//    void foo(TMatrix &m, TMatrix &m1)                                 //
//    {                                                                 //
//       typedef  double (*dfunc_t)(double);                            //
//       class ApplyFunction : public TElementAction {                  //
//          dfunc_t fFunc;                                              //
//          void Operation(Real_t &element) { element=fFunc(element); } //
//       public:                                                        //
//          ApplyFunction(dfunc_t func):fFunc(func) {}                  //
//       };                                                             //
//       ApplyFunction x(TMath::Sin);                                   //
//       m.Apply(x);                                                    //
//    }                                                                 //
//                                                                      //
//    Validation code $ROOTSYS/test/vmatrix.cxx and vvector.cxx contain //
//    a few more examples of that kind.                                 //
//                                                                      //
// 6. Lazy matrices: instead of returning an object return a "recipe"   //
//    how to make it. The full matrix would be rolled out only when     //
//    and where it's needed:                                            //
//       TMatrix haar = THaarMatrix(5);                                 //
//    THaarMatrix() is a *class*, not a simple function. However        //
//    similar this looks to a returning of an object (see note #1       //
//    above), it's dramatically different. THaarMatrix() constructs a   //
//    TLazyMatrix, an object of just a few bytes long. A
//    "TMatrix(const TLazyMatrix &recipe)" constructor follows the      //
//    recipe and makes the matrix haar() right in place. No matrix      //
//    element is moved whatsoever!                                      //
//                                                                      //
// The implementation is based on original code by                      //
// Oleg E. Kiselyov (oleg@pobox.com).                                   //
//                                                                      //
//////////////////////////////////////////////////////////////////////////

#include "TMatrix.h"
#include "TROOT.h"
#include "TClass.h"
#include "TPluginManager.h"
#include "TVirtualUtilHist.h"

ClassImp(TMatrix)

//______________________________________________________________________________
 void TMatrix::Allocate(Int_t no_rows, Int_t no_cols, Int_t row_lwb, Int_t col_lwb)
{
   // Allocate new matrix. Arguments are number of rows, columns, row
   // lowerbound (0 default) and column lowerbound (0 default).

   Invalidate();

   if (no_rows <= 0) {
      Error("Allocate", "no of rows has to be positive");
      return;
   }
   if (no_cols <= 0) {
      Error("Allocate", "no of columns has to be positive");
      return;
   }

   fNrows  = no_rows;
   fNcols  = no_cols;
   fRowLwb = row_lwb;
   fColLwb = col_lwb;
   fNelems = fNrows * fNcols;

   fElements = new Real_t[fNelems];
   if (fElements)
      memset(fElements, 0, fNelems*sizeof(Real_t));

   if (fNcols == 1) {          // Only one col - fIndex is dummy actually
      fIndex = &fElements;
      return;
   }

   fIndex = new Real_t*[fNcols];
   if (fIndex)
      memset(fIndex, 0, fNcols*sizeof(Real_t*));

   Int_t i;
   Real_t *col_p;
   for (i = 0, col_p = &fElements[0]; i < fNcols; i++, col_p += fNrows)
      fIndex[i] = col_p;
}

//______________________________________________________________________________
 TMatrix::~TMatrix()
{
   // TMatrix destructor.

   if (IsValid()) {
      if (fNcols != 1)
         delete [] fIndex;
      delete [] fElements;
   }

   Invalidate();
}

//______________________________________________________________________________
 void TMatrix::Draw(Option_t *option)
{
   // Draw this matrix using an intermediate histogram
   // The histogram is named "TMatrix" by default and no title


   //create the hist utility manager (a plugin)
   TVirtualUtilHist *util = (TVirtualUtilHist*)gROOT->GetListOfSpecials()->FindObject("R__TVirtualUtilHist");
   if (!util) {
      TPluginHandler *h;
      if ((h = gROOT->GetPluginManager()->FindHandler("TVirtualUtilHist"))) {
          if (h->LoadPlugin() == -1)
            return;
          h->ExecPlugin(0);
          util = (TVirtualUtilHist*)gROOT->GetListOfSpecials()->FindObject("R__TVirtualUtilHist");
      }
   }
   util->PaintMatrix(*this,option);
}

//______________________________________________________________________________
 void TMatrix::ResizeTo(Int_t nrows, Int_t ncols)
{
   // Erase the old matrix and create a new one according to new boundaries
   // with indexation starting at 0.

   if (IsValid()) {
      if (fNrows == nrows && fNcols == ncols)
         return;

      if (fNcols != 1)
         delete [] fIndex;
      delete [] fElements;
   }

   Allocate(nrows, ncols);
}

//______________________________________________________________________________
 void TMatrix::ResizeTo(Int_t row_lwb, Int_t row_upb, Int_t col_lwb, Int_t col_upb)
{
   // Erase the old matrix and create a new one according to new boudaries.

   Int_t new_nrows = row_upb - row_lwb + 1;
   Int_t new_ncols = col_upb - col_lwb + 1;

   if (IsValid()) {
      fRowLwb = row_lwb;
      fColLwb = col_lwb;

      if (fNrows == new_nrows && fNcols == new_ncols)
         return;

      if (fNcols != 1)
         delete [] fIndex;
      delete [] fElements;
   }

   Allocate(new_nrows, new_ncols, row_lwb, col_lwb);
}

//______________________________________________________________________________
 TMatrix::TMatrix(EMatrixCreatorsOp1 op, const TMatrix &prototype)
{
   // Create a matrix applying a specific operation to the prototype.
   // Example: TMatrix a(10,12); ...; TMatrix b(TMatrix::kTransposed, a);
   // Supported operations are: kZero, kUnit, kTransposed, kInverted and kInvertedPosDef.

   Invalidate();

   if (!prototype.IsValid()) {
      Error("TMatrix(EMatrixCreatorOp1)", "prototype matrix not initialized");
      return;
   }

   switch(op) {
      case kZero:
         Allocate(prototype.fNrows, prototype.fNcols,
                  prototype.fRowLwb, prototype.fColLwb);
         break;

      case kUnit:
         Allocate(prototype.fNrows, prototype.fNcols,
                  prototype.fRowLwb, prototype.fColLwb);
         UnitMatrix();
         break;

      case kTransposed:
         Transpose(prototype);
         break;

      case kInverted:
         Invert(prototype);
         break;

      case kInvertedPosDef:
         InvertPosDef(prototype);
         break;

      default:
         Error("TMatrix(EMatrixCreatorOp1)", "operation %d not yet implemented", op);
   }
}

//______________________________________________________________________________
 TMatrix::TMatrix(const TMatrix &a, EMatrixCreatorsOp2 op, const TMatrix &b)
{
   // Create a matrix applying a specific operation to two prototypes.
   // Example: TMatrix a(10,12), b(12,5); ...; TMatrix c(a, TMatrix::kMult, b);
   // Supported operations are: kMult (a*b), kTransposeMult (a'*b),
   // kInvMult,kInvPosDefMult (a^(-1)*b) and kAtBA (a'*b*a).

   Invalidate();

   if (!a.IsValid()) {
      Error("TMatrix(EMatrixCreatorOp2)", "matrix a not initialized");
      return;
   }
   if (!b.IsValid()) {
      Error("TMatrix(EMatrixCreatorOp2)", "matrix b not initialized");
      return;
   }

   switch(op) {
      case kMult:
         AMultB(a, b);
         break;

      case kTransposeMult:
         AtMultB(a, b);
         break;

      default:
         Error("TMatrix(EMatrixCreatorOp2)", "operation %d not yet implemented", op);
   }
}

//______________________________________________________________________________
 TMatrix &TMatrix::MakeSymmetric()
{
   // symmetrize matrix (matrix needs to be a square one).

   if (!IsValid()) {
      Error("MakeSymmetric", "matrix not initialized");
      return *this;
   }

   if (fNrows != fNcols) {
      Error("MakeSymmetric", "matrix to symmetrize must be square");
      return *this;
   }

   Int_t irow;
   for (irow = 0; irow < fNrows; irow++) {
     Int_t icol;
     for (icol = 0; icol < irow; icol++) {
       fElements[irow*fNrows+icol] += fElements[icol*fNrows+irow];
       fElements[irow*fNrows+icol] /= 2.0;
       fElements[icol*fNrows+irow] = fElements[irow*fNrows+icol];
     }
   }

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::UnitMatrix()
{
   // make a unit matrix (matrix need not be a square one). The matrix
   // is traversed in the natural (that is, column by column) order.

   if (!IsValid()) {
      Error("UnitMatrix", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   Int_t i, j;

   for (j = 0; j < fNcols; j++)
      for (i = 0; i < fNrows; i++)
         *ep++ = (i==j ? 1.0 : 0.0);

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::HilbertMatrix()
{
   // Make a Hilbert matrix. Hilb[i,j] = 1/(i+j-1), i,j=1...max, OR
   // Hilb[i,j] = 1/(i+j+1), i,j=0...max-1 (matrix need not be a square one).
   // The matrix is traversed in the natural (that is, column by column) order.

   if (!IsValid()) {
      Error("HilbertMatrix", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   Int_t i, j;

   for (j = 0; j < fNcols; j++)
      for (i = 0; i < fNrows; i++)
         *ep++ = 1./(i+j+1);

   return *this;
}

//______________________________________________________________________________
TMatrix &TMatrix::operator=(Real_t val)
{
   // Assign val to every element of the matrix.

   if (!IsValid()) {
      Error("operator=", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      *ep++ = val;

   return *this;
}

//______________________________________________________________________________
TMatrix &TMatrix::operator+=(Double_t val)
{
   // Add val to every element of the matrix.

   if (!IsValid()) {
      Error("operator+=", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      *ep++ += val;

   return *this;
}

//______________________________________________________________________________
TMatrix &TMatrix::operator-=(Double_t val)
{
   // Subtract val from every element of the matrix.

   if (!IsValid()) {
      Error("operator-=", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      *ep++ -= val;

   return *this;
}

//______________________________________________________________________________
TMatrix &TMatrix::operator*=(Double_t val)
{
   // Multiply every element of the matrix with val.

   if (!IsValid()) {
      Error("operator*=", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      *ep++ *= val;

   return *this;
}

//______________________________________________________________________________
Bool_t TMatrix::operator==(Real_t val) const
{
   // Are all matrix elements equal to val?

   if (!IsValid()) {
      Error("operator==", "matrix not initialized");
      return kFALSE;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      if (!(*ep++ == val))
         return kFALSE;

   return kTRUE;
}

//______________________________________________________________________________
Bool_t TMatrix::operator!=(Real_t val) const
{
   // Are all matrix elements not equal to val?

   if (!IsValid()) {
      Error("operator!=", "matrix not initialized");
      return kFALSE;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      if (!(*ep++ != val))
         return kFALSE;

   return kTRUE;
}

//______________________________________________________________________________
Bool_t TMatrix::operator<(Real_t val) const
{
   // Are all matrix elements < val?

   if (!IsValid()) {
      Error("operator<", "matrix not initialized");
      return kFALSE;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      if (!(*ep++ < val))
         return kFALSE;

   return kTRUE;
}

//______________________________________________________________________________
Bool_t TMatrix::operator<=(Real_t val) const
{
   // Are all matrix elements <= val?

   if (!IsValid()) {
      Error("operator<=", "matrix not initialized");
      return kFALSE;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      if (!(*ep++ <= val))
         return kFALSE;

   return kTRUE;
}

//______________________________________________________________________________
Bool_t TMatrix::operator>(Real_t val) const
{
   // Are all matrix elements > val?

   if (!IsValid()) {
      Error("operator>", "matrix not initialized");
      return kFALSE;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      if (!(*ep++ > val))
         return kFALSE;

   return kTRUE;
}

//______________________________________________________________________________
Bool_t TMatrix::operator>=(Real_t val) const
{
   // Are all matrix elements >= val?

   if (!IsValid()) {
      Error("operator>=", "matrix not initialized");
      return kFALSE;
   }

   Real_t *ep = fElements;
   while (ep < fElements+fNelems)
      if (!(*ep++ >= val))
         return kFALSE;

   return kTRUE;
}

//______________________________________________________________________________
 TMatrix &TMatrix::Abs()
{
   // Take an absolute value of a matrix, i.e. apply Abs() to each element.

   if (!IsValid()) {
      Error("Abs", "matrix not initialized");
      return *this;
   }

   Real_t *ep;
   for (ep = fElements; ep < fElements+fNelems; ep++)
      *ep = TMath::Abs(*ep);

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::Sqr()
{
   // Square each element of the matrix.

   if (!IsValid()) {
      Error("Sqr", "matrix not initialized");
      return *this;
   }

   Real_t *ep;
   for (ep = fElements; ep < fElements+fNelems; ep++)
      *ep = (*ep) * (*ep);

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::Sqrt()
{
   // Take square root of all elements.

   if (!IsValid()) {
      Error("Sqrt", "matrix not initialized");
      return *this;
   }

   Real_t *ep;
   for (ep = fElements; ep < fElements+fNelems; ep++)
      if (*ep >= 0)
         *ep = TMath::Sqrt(*ep);
      else
         Error("Sqrt", "(%d,%d)-th element, %g, is negative, can't take the square root",
               (ep-fElements) % fNrows + fRowLwb,
               (ep-fElements) / fNrows + fColLwb, *ep);

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::Apply(TElementPosAction &action)
{
   // Apply action to each element of the matrix. In action the location
   // of the current element is known. The matrix is traversed in the
   // natural (that is, column by column) order.

   if (!IsValid()) {
      Error("Apply(TElementPosAction&)", "matrix not initialized");
      return *this;
   }

   Real_t *ep = fElements;
   for (action.fJ = fColLwb; action.fJ < fColLwb+fNcols; action.fJ++)
      for (action.fI = fRowLwb; action.fI < fRowLwb+fNrows; action.fI++)
         action.Operation(*ep++);

   Assert(ep == fElements+fNelems);

   return *this;
}

//______________________________________________________________________________
Bool_t operator==(const TMatrix &im1, const TMatrix &im2)
{
   // Check to see if two matrices are identical.

   if (!AreCompatible(im1, im2)) return kFALSE;
   return (memcmp(im1.fElements, im2.fElements, im1.fNelems*sizeof(Real_t)) == 0);
}

//______________________________________________________________________________
TMatrix &operator+=(TMatrix &target, const TMatrix &source)
{
   // Add the source matrix to the target matrix.

   if (!AreCompatible(target, source)) {
      Error("operator+=", "matrices are not compatible");
      return target;
   }

   Real_t *sp = source.fElements;
   Real_t *tp = target.fElements;
   for ( ; tp < target.fElements+target.fNelems; )
      *tp++ += *sp++;

   return target;
}

//______________________________________________________________________________
TMatrix &operator-=(TMatrix &target, const TMatrix &source)
{
   // Subtract the source matrix from the target matrix.

   if (!AreCompatible(target, source)) {
      Error("operator-=", "matrices are not compatible");
      return target;
   }

   Real_t *sp = source.fElements;
   Real_t *tp = target.fElements;
   for ( ; tp < target.fElements+target.fNelems; )
      *tp++ -= *sp++;

   return target;
}

//______________________________________________________________________________
TMatrix &Add(TMatrix &target, Double_t scalar, const TMatrix &source)
{
   // Modify addition: target += scalar * source.

   if (!AreCompatible(target, source)) {
      Error("Add", "matrices are not compatible");
      return target;
   }

   Real_t *sp = source.fElements;
   Real_t *tp = target.fElements;
   for ( ; tp < target.fElements+target.fNelems; )
      *tp++ += scalar * (*sp++);

   return target;
}

//______________________________________________________________________________
TMatrix operator+(const TMatrix &source1, const TMatrix &source2)
{
  TMatrix target(source1);
  target += source2;
  return target;
}

//______________________________________________________________________________
TMatrix operator-(const TMatrix &source1, const TMatrix &source2)
{
  TMatrix target(source1);
  target -= source2;
  return target;
}

//______________________________________________________________________________
TMatrix operator*(const TMatrix &source1, const TMatrix &source2)
{
  TMatrix target(source1);
  target *= source2;
  return target;
}

//______________________________________________________________________________
TMatrix &ElementMult(TMatrix &target, const TMatrix &source)
{
   // Multiply target by the source, element-by-element.

   if (!AreCompatible(target, source)) {
      Error("ElementMult", "matrices are not compatible");
      return target;
   }

   Real_t *sp = source.fElements;
   Real_t *tp = target.fElements;
   for ( ; tp < target.fElements+target.fNelems; )
      *tp++ *= *sp++;

   return target;
}

//______________________________________________________________________________
TMatrix &ElementDiv(TMatrix &target, const TMatrix &source)
{
   // Divide target by the source, element-by-element.

   if (!AreCompatible(target, source)) {
      Error("ElementDiv", "matrices are not compatible");
      return target;
   }

   Real_t *sp = source.fElements;
   Real_t *tp = target.fElements;
   for ( ; tp < target.fElements+target.fNelems; )
      *tp++ /= *sp++;

   return target;
}

//______________________________________________________________________________
 Double_t TMatrix::RowNorm() const
{
   // Row matrix norm, MAX{ SUM{ |M(i,j)|, over j}, over i}.
   // The norm is induced by the infinity vector norm.

   if (!IsValid()) {
      Error("RowNorm", "matrix not initialized");
      return 0.0;
   }

   Real_t  *ep = fElements;
   Double_t norm = 0;

   // Scan the matrix row-after-row
   while (ep < fElements+fNrows) {
      Int_t j;
      Double_t sum = 0;
      // Scan a row to compute the sum
      for (j = 0; j < fNcols; j++, ep += fNrows)
         sum += TMath::Abs(*ep);
      ep -= fNelems - 1;         // Point ep to the beginning of the next row
      norm = TMath::Max(norm, sum);
   }

   Assert(ep == fElements + fNrows);

   return norm;
}

//______________________________________________________________________________
 Double_t TMatrix::ColNorm() const
{
   // Column matrix norm, MAX{ SUM{ |M(i,j)|, over i}, over j}.
   // The norm is induced by the 1 vector norm.

   if (!IsValid()) {
      Error("ColNorm", "matrix not initialized");
      return 0.0;
   }

   Real_t  *ep = fElements;
   Double_t norm = 0;

   // Scan the matrix col-after-col (i.e. in the natural order of elems)
   while (ep < fElements+fNelems) {
      Int_t i;
      Double_t sum = 0;
      // Scan a col to compute the sum
      for (i = 0; i < fNrows; i++)
         sum += TMath::Abs(*ep++);
      norm = TMath::Max(norm, sum);
   }

   Assert(ep == fElements + fNelems);

   return norm;
}

//______________________________________________________________________________
 Double_t TMatrix::E2Norm() const
{
   // Square of the Euclidian norm, SUM{ m(i,j)^2 }.

   if (!IsValid()) {
      Error("E2Norm", "matrix not initialized");
      return 0.0;
   }

   Real_t  *ep;
   Double_t sum = 0;

   for (ep = fElements; ep < fElements+fNelems; ep++)
      sum += (*ep) * (*ep);

   return sum;
}

//______________________________________________________________________________
Double_t E2Norm(const TMatrix &m1, const TMatrix &m2)
{
   // Square of the Euclidian norm of the difference between two matrices.

   if (!AreCompatible(m1, m2)) {
      Error("E2Norm", "matrices are not compatible");
      return 0.0;
   }

   Real_t  *mp1 = m1.fElements;
   Real_t  *mp2 = m2.fElements;
   Double_t sum = 0;

   for (; mp1 < m1.fElements+m1.fNelems; mp1++, mp2++)
      sum += (*mp1 - *mp2) * (*mp1 - *mp2);

   return sum;
}

//______________________________________________________________________________
 TMatrix &TMatrix::NormByDiag(const TVector &v, Option_t *option)
{
   // b(i,j) = a(i,j)/sqrt(abs*(v(i)*v(j))) 

   if (!IsValid()) {
      Error("NormByDiag", "matrix not initialized");
      return *this;
   }

   if (!v.IsValid()) {
      Error("NormByDiag", "vector is not initialized");
      return *this;
   }

   const Int_t nMax = TMath::Max(fNrows,fNcols);
   if (v.fNrows < nMax) {
      Error("NormByDiag", "norm vector is too short");
      return *this;
   }

   TString opt(option);
   opt.ToUpper();
   const Int_t divide = (opt.Contains("D")) ? 1 : 0;

   const Real_t* pv = v.fElements;
   Real_t* mp = fElements;

   Int_t irow;
   if (divide) {
     for (irow = 0; irow < fNrows; irow++) {
       Int_t icol;
       for (icol = 0; icol < fNcols; icol++) {
         Double_t val = TMath::Sqrt(TMath::Abs(pv[irow]*pv[icol]));
         Assert(val != 0.0);
         mp[irow*fNcols+icol] /= val;
       }
     }
   } else {
     for (irow = 0; irow < fNrows; irow++) {
       Int_t icol;
       for (icol = 0; icol < fNcols; icol++) {
         Double_t val = TMath::Sqrt(TMath::Abs(pv[irow]*pv[icol]));
         mp[irow*fNcols+icol] *= val;
       }
     }
   }

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::NormByColumn(const TVector &v, Option_t *option)
{
   // Multiply/divide a matrix columns with a vector:
   // matrix(i,j) *= v(i)

   if (!IsValid()) {
      Error("NormByColumn", "matrix not initialized");
      return *this;
   }

   if (!v.IsValid()) {
      Error("NormByColumn", "vector is not initialized");
      return *this;
   }

   if (fNcols != v.fNrows) {
      Error("NormByColumn", "matrix cannot be normed column-wise by this vector");
      return *this;
   }

   TString opt;
   opt.ToUpper();
   const Int_t divide = (opt.Contains("D")) ? 1 : 0;

   const Real_t* pv = v.fElements;
   Real_t *mp = fElements;

   Int_t i;
   if (divide) {
     for ( ; mp < fElements + fNelems; pv++)
       for (i = 0; i < fNrows; i++) {
         Assert(*pv != 0.0);
         *mp++ /= *pv;
       }
   } else {
     for ( ; mp < fElements + fNelems; pv++)
       for (i = 0; i < fNrows; i++)
         *mp++ *= *pv;
   }

   return *this;
}

//______________________________________________________________________________
 TMatrix &TMatrix::NormByRow(const TVector &v, Option_t *option)
{
   // Multiply/divide a matrix row with a vector:
   // matrix(i,j) *= v(j)

   if (!IsValid()) {
      Error("NormByRow", "matrix not initialized");
      return *this;
   }

   if (!v.IsValid()) {
      Error("NormByRow", "vector is not initialized");
      return *this;
   }

   if (fNcols != v.fNrows) {
      Error("NormByRow", "matrix cannot be normed column-wise by this vector");
      return *this;
   }

   TString opt;
   opt.ToUpper();
   const Int_t divide = (opt.Contains("D")) ? 1 : 0;

   const Real_t* pv = v.fElements;
   Real_t *mp = fElements;

   Int_t i;
   if (divide) {
     for ( ; mp < fElements + fNelems; pv = v.fElements) {
       for (i = 0; i < fNrows; i++)
       {
         Assert(*pv != 0.0);
         *mp++ /= *pv++;
       }
     }
   } else {
     for ( ; mp < fElements + fNelems; pv = v.fElements)
       for (i = 0; i < fNrows; i++)
         *mp++ *= *pv++;
   }

   return *this;
}

//______________________________________________________________________________
 void TMatrix::Print(Option_t *) const
{
   // Print the matrix as a table of elements (zeros are printed as dots).

   if (!IsValid()) {
      Error("Print", "matrix not initialized");
      return;
   }

   printf("nMatrix %dx%d is as follows", fNrows, fNcols);

   Int_t cols_per_sheet = 5;
   Int_t sheet_counter;

   for (sheet_counter = 1; sheet_counter <= fNcols; sheet_counter += cols_per_sheet) {
      printf("n\n     |");
      Int_t i, j;
      for (j = sheet_counter; j < sheet_counter+cols_per_sheet && j <= fNcols; j++)
         printf("   %6d  |", j+fColLwb-1);
      printf("n%sn",
             "------------------------------------------------------------------");
      for (i = 1; i <= fNrows; i++) {
         printf("%4d |", i+fRowLwb-1);
         for (j = sheet_counter; j < sheet_counter+cols_per_sheet && j <= fNcols; j++)
            printf("%11.4g ", (*this)(i+fRowLwb-1, j+fColLwb-1));
         printf("n");
      }
   }
   printf("n");
}

//______________________________________________________________________________
 void TMatrix::Transpose(const TMatrix &prototype)
{
   // Transpose a matrix.

   if (!prototype.IsValid()) {
      Error("Transpose", "prototype matrix not initialized");
      return;
   }

   Allocate(prototype.fNcols,  prototype.fNrows,
            prototype.fColLwb, prototype.fRowLwb);

   Real_t *rsp    = prototype.fElements;    // Row source pointer
   Real_t *tp     = fElements;

   // (This: target) matrix is traversed in the natural, column-wise way,
   // whilst the source (prototype) matrix is scanned row-by-row
   while (tp < fElements + fNelems) {
      Real_t *sp = rsp++;  // sp = @ms[j,i] for i=0

      // Move tp to the next elem in the col and sp to the next el in the curr row
      while (sp < prototype.fElements + prototype.fNelems)
         *tp++ = *sp, sp += prototype.fNrows;
   }

   Assert(tp == fElements + fNelems &&
          rsp == prototype.fElements + prototype.fNrows);
}

//______________________________________________________________________________
 TMatrix &TMatrix::Invert(Double_t *determ_ptr)
{
   // The most general (Gauss-Jordan) matrix inverse
   //
   // This method works for any matrix (which of course must be square and
   // non-singular). Use this method only if none of specialized algorithms
   // (for symmetric, tridiagonal, etc) matrices isn't applicable/available.
   // Also, the matrix to invert has to be _well_ conditioned:
   // Gauss-Jordan eliminations (even with pivoting) perform poorly for
   // near-singular matrices (e.g., Hilbert matrices).
   //
   // The method inverts matrix inplace and returns the determinant if
   // determ_ptr was specified as not 0. Determinant will be exactly zero
   // if the matrix turns out to be (numerically) singular. If determ_ptr is
   // 0 and matrix happens to be singular, throw up.
   //
   // The algorithm perform inplace Gauss-Jordan eliminations with
   // full pivoting. It was adapted from my Algol-68 "translation" (ca 1986)
   // of the FORTRAN code described in
   // Johnson, K. Jeffrey, "Numerical methods in chemistry", New York,
   // N.Y.: Dekker, c1980, 503 pp, p.221
   //
   // Note, since it's much more efficient to perform operations on matrix
   // columns rather than matrix rows (due to the layout of elements in the
   // matrix), the present method implements a "transposed" algorithm.

   if (!IsValid()) {
      Error("Invert(Double_t*)", "matrix not initialized");
      return *this;
   }

   if (fNrows != fNcols) {
      Error("Invert(Double_t*)", "matrix to invert must be square");
      return *this;
   }

   Double_t determinant = 1;
   const Double_t singularity_tolerance = 1e-35;

   Int_t symmetric = IsSymmetric();

   // condition the matrix
   TVector diag(fNrows);
   if (symmetric) {
     diag = TMatrixDiag(*this);
     this->NormByDiag(diag);
   }

   // Locations of pivots (indices start with 0)
   struct Pivot_t { int row, col; } *const pivots = new Pivot_t[fNcols];
   Bool_t *const was_pivoted = new Bool_t[fNrows];
   memset(was_pivoted, 0, fNrows*sizeof(Bool_t));
   Pivot_t *pivotp;

   for (pivotp = &pivots[0]; pivotp < &pivots[fNcols]; pivotp++) {
      Int_t prow = 0, pcol = 0;         // Location of a pivot to be
      {                                 // Look through all non-pivoted cols
         Real_t max_value = 0;          // (and rows) for a pivot (max elem)
         for (Int_t j = 0; j < fNcols; j++)
            if (!was_pivoted[j]) {
               Real_t *cp;
               Int_t k;
               Real_t curr_value = 0;
               for (k = 0, cp = fIndex[j]; k < fNrows; k++, cp++)
                  if (!was_pivoted[k] && (curr_value = TMath::Abs(*cp)) > max_value)
                     max_value = curr_value, prow = k, pcol = j;
             }
         if (max_value < singularity_tolerance) {
            // free allocated heap memory before returning
            delete [] pivots;
            delete [] was_pivoted;
            if (determ_ptr) {
               *determ_ptr = 0;
               return *this;
            } else {
               Error("Invert(Double_t*)", "matrix turns out to be singular: can't invert");
               return *this;
            }
         }
         pivotp->row = prow;
         pivotp->col = pcol;
     }

     // Swap prow-th and pcol-th columns to bring the pivot to the diagonal
     if (prow != pcol) {
        Real_t *cr = fIndex[prow];
        Real_t *cc = fIndex[pcol];
        for (Int_t k = 0; k < fNrows; k++) {
           Real_t temp = *cr; *cr++ = *cc; *cc++ = temp;
        }
     }
     was_pivoted[prow] = kTRUE;

     {                                       // Normalize the pivot column and
        Real_t *pivot_cp = fIndex[prow];
        Double_t pivot_val = pivot_cp[prow]; // pivot is at the diagonal
        determinant *= pivot_val;            // correct the determinant
        pivot_cp[prow] = kTRUE;
        for (Int_t k=0; k < fNrows; k++)
           *pivot_cp++ /= pivot_val;
     }

     {                                           // Perform eliminations
        Real_t *pivot_rp = fElements + prow;     // pivot row
        for (Int_t k = 0; k < fNrows; k++, pivot_rp += fNrows)
           if (k != prow) {
              Double_t temp = *pivot_rp;
              *pivot_rp = 0;
              Real_t *pivot_cp = fIndex[prow];          // pivot column
              Real_t *elim_cp  = fIndex[k];             // elimination column
              for (Int_t l = 0; l < fNrows; l++)
                 *elim_cp++ -= temp * *pivot_cp++;
           }
      }
   }

   Int_t no_swaps = 0;                   // Swap exchanged *rows* back in place
   for (pivotp = &pivots[fNcols-1]; pivotp >= &pivots[0]; pivotp--)
      if (pivotp->row != pivotp->col) {
         no_swaps++;
         Real_t *rp = fElements + pivotp->row;
         Real_t *cp = fElements + pivotp->col;
         for (Int_t k = 0; k < fNcols; k++, rp += fNrows, cp += fNrows) {
            Real_t temp = *rp; *rp = *cp; *cp = temp;
         }
      }

   // revert our scaling
   if (symmetric) {
      this->NormByDiag(diag);
      Int_t irow;
      for (irow = 0; irow < fNrows; irow++)
         determinant *= TMath::Abs(diag(irow));
   }

   if (determ_ptr)
      *determ_ptr = (no_swaps & 1 ? -determinant : determinant);

   delete [] was_pivoted;
   delete [] pivots;

   return *this;
}

//______________________________________________________________________________
 Bool_t TMatrix::IsSymmetric() const
{
  Int_t irow;
  for (irow = 0; irow < fNrows; irow++) {
    Int_t icol;
    for (icol = 0; icol < irow; icol++) {
      if (fElements[irow*fNrows+icol] != fElements[icol*fNrows+irow]) {
        return 0;
      }
    }
  }
  return 1;
}

//______________________________________________________________________________
 void TMatrix::Invert(const TMatrix &m)
{
   // Allocate new matrix and set it to inv(m).

   if (!m.IsValid()) {
      Error("Invert(const TMatrix&)", "matrix m not initialized");
      return;
   }

   ResizeTo(m);

   *this = m;    // assignment operator

   Invert(0);
}

//______________________________________________________________________________
 TMatrix &TMatrix::InvertPosDef()
{
   if (!IsValid()) {
      Error("InvertPosDef(Real_t*)", "matrix not initialized");
      return *this;
   }

   if (fNrows != fNcols) {
      Error("InvertPosDef(Real_t*)", "matrix to invert must be square");
      return *this;
   }

   Int_t   n  = fNrows;
   Real_t *pa = fElements;
   Real_t *pu = new Real_t[n*n];

   // step 1: Cholesky decomposition
   if (Pdcholesky(pa,pu,n))
     Error("InvertPosDef","matrix not positive definite ?");

   Int_t off_n = (n-1)*n;
   Int_t i,l;
   for (i = 0; i < n; i++)
   {
     Int_t off_i = i*n;

   // step 2: Forward substitution
     for (l = i; l < n; l++)
     {
       if (l == i)
         pa[off_n+l] = 1./pu[l*n+l];
       else
       {
         pa[off_n+l] = 0.;
         for (Int_t k = i; k <= l-1; k++)
           pa[off_n+l] = pa[off_n+l]-pu[k*n+l]*pa[off_n+k];
         pa[off_n+l] = pa[off_n+l]/pu[l*n+l];
       }
     }

   // step 3: Back substitution
     for (l = n-1; l >= i; l--)
     {
       Int_t off_l = l*n;
       if (l == n-1)
         pa[off_i+l] = pa[off_n+l]/pu[off_l+l];
       else
       {
         pa[off_i+l] = pa[off_n+l];
         for (Int_t k = n-1; k >= l+1; k--)
           pa[off_i+l] = pa[off_i+l]-pu[off_l+k]*pa[off_i+k];
         pa[off_i+l] = pa[off_i+l]/pu[off_l+l];
       }
     }
   }

   // Fill lower triangle symmetrically
   if (n > 1)
   {
     for (Int_t i = 0; i < n; i++)
     {
       for (Int_t l = 0; l <= i-1; l++)
         pa[i*n+l] = pa[l*n+i];
     }
   }

   delete [] pu;
   return *this;
}

//______________________________________________________________________________
 Int_t TMatrix::Pdcholesky(
const Real_t *a,
      Real_t *u,
const Int_t   n)
{
  //  Program Pdcholesky inverts a positiv definite (n x n) - matrix A,
  //  using the Cholesky decomposition
  //
  //  Input:	a	- (n x n)- Matrix A
  //  		n	- dimensions n of matrices
  //
  //  Output:	u	- (n x n)- Matrix U so that U^T . U = A
  //		return	- 0 decomposition succesful
  //			- 1 decomposition failed

  memset(u,0,n*n*sizeof(Real_t));

  Int_t status = 0;
  for (Int_t k = 0; k < n; k++)
  {
    Double_t s = 0.;
    Int_t off_k = k*n;
    for (Int_t j = k; j < n; j++)
    {
      if (k > 0)
      {
        s = 0.;
        for (Int_t l = 0; l <= k-1; l++)
        {
          Int_t off_l = l*n;
          s += u[off_l+k]*u[off_l+j];
        }
      }
      u[off_k+j] = a[off_k+j]-s;
      if (k == j)
      {
        if (u[off_k+j] < 0) status = 1;
        u[off_k+j] = TMath::Sqrt(TMath::Abs(u[off_k+j]));
      }
      else
        u[off_k+j] = u[off_k+j]/u[off_k+k];
    }
  }
  return status;
}

//______________________________________________________________________________
 void TMatrix::InvertPosDef(const TMatrix &m)
{
   // Allocate new matrix and set it to inv(m).

   if (!m.IsValid()) {
      Error("InvertPosDef(const TMatrix&)", "matrix m not initialized");
      return;
   }

   ResizeTo(m);

   *this = m;    // assignment operator

   InvertPosDef();
}

//____________________________________________________________________
 TMatrix TMatrix::EigenVectors(
TVector &eigenValues)
{
  // Return a matrix containing the eigen-vectors; also fill the
  // supplied vector with the eigen values.

  if (IsSymmetric()) {
    TMatrix eigenVectors = *this;
    eigenValues.ResizeTo(fNrows);
    TVector offDiag(fNrows);
    // Tridiagonalize matrix
    MakeTridiagonal(eigenVectors,eigenValues,offDiag);

    // Make eigenvectors and -values
    MakeEigenVectors(eigenValues,offDiag,eigenVectors);

    // Order eigenvalues and -vectors
    EigenSort(eigenVectors,eigenValues);
    return eigenVectors;
  }
  else {
    Error("EigenVectors","Not yet implemented for non-symmetric matrix");
    return *this;
  }
}

//____________________________________________________________________
 void TMatrix::MakeTridiagonal(
TMatrix &a,
TVector &d,
TVector &e)
{
  // The comments in this algorithm are modified version of those in
  // "Numerical ...". Please refer to that book (web-page) for more on
  // the algorithm.
  // 
  /*
    
    PRIVATE METHOD:
    
Tridiagonalise the covariance matrix according to the Householder method as described in Numerical Recipes in C section 11.2.

The basic idea is to perform $P-2$ orthogonal transformation, where each transformation eat away the off-diagonal elements, except the inner most. */ // Int_t n = a.fNrows; if (!a.IsValid()) { Error("Maketridiagonal", "matrix not initialized"); return; } if (a.fNrows != a.fNcols) { Error("Maketridiagonal", "matrix to tridiagonalize must be square"); return; } Float_t *pa = a.fElements; Float_t *pd = d.fElements; Float_t *pe = e.fElements; Int_t i; for (i = n-1; i > 0; i--) { Int_t l = i-1; Double_t h = 0; Double_t scale = 0; if (l > 0) { for (Int_t k = 0; k <= l; k++) scale += TMath::Abs(pa[i+k*n]); if (scale == 0) // Skip transformation pe[i] = pa[i+l*n]; else { Int_t k; for (k = 0; k <= l; k++) { // Use scaled elements of a for transformation pa[i+k*n] /= scale; // Calculate sigma in h h += pa[i+k*n]*pa[i+k*n]; } Double_t f = pa[i+l*n]; Double_t g = (f >= 0. ? -TMath::Sqrt(h) : TMath::Sqrt(h)); pe[i] = scale*g; h -= f*g; // Now h is eq. (11.2.4) in "Numerical ..." pa[i+l*n] = f-g; f = 0; Int_t j; for (j = 0; j <= l; j++) { // Store the u/H in ith column of a; pa[j+i*n] = pa[i+j*n]/h; // Form element A dot u in g; g = 0; Int_t k; for (k = 0; k <= j; k++) g += pa[j+k*n]*pa[i+k*n]; for (k = j+1; k <= l; k++) g += pa[k+j*n]*pa[i+k*n]; // Form element of vector p in temporarily unused element of // e pe[j] = g/h; f += pe[j]*pa[i+j*n]; } // Form K eq (11.2.11) Double_t hh = f/(h+h); // Form vector q and store in e overwriting p for (j = 0; j <= l; j++) { f = pa[i+j*n]; pe[j] = g = pe[j]-hh*f; Int_t k; for (k = 0; k <= j; k++) // Reduce a, eq (11.2.13) pa[j+k*n] -= (f*pe[k]+g*pa[i+k*n]); } } } else pe[i] = pa[i+l*n]; pd[i] = h; } pd[0] = 0; pe[0] = 0; for (i = 0; i < n; i++) { // Begin accumulation of transformation matrix Int_t l = i-1; if (pd[i]) { // This block is skipped if i = 0; Int_t j; for (j = 0; j <= l; j++) { Double_t g = 0; Int_t k; for (k = 0; k <= l; k++) // Use vector u/H stored in a to form P dot Q g += pa[i+k*n]*pa[k+j*n]; for (k = 0; k <= l; k++) pa[k+j*n] -= g*pa[k+i*n]; } } pd[i] = pa[i+i*n]; pa[i+i*n] = 1; Int_t j; for (j = 0; j <= l; j++) { pa[j+i*n] = pa[i+j*n] = 0; } } } //____________________________________________________________________ void TMatrix::MakeEigenVectors( TVector &d, TVector &e, TMatrix &z) { // /* PRIVATE METHOD:
Find eigenvalues and vectors of tridiagonalised covariance matrix according to the QL with implicit shift algorithm from Numerical Recipes in C section 11.3.

The basic idea is to find matrices $\mathsf{Q}$ and $\mathsf{L}$ so that $\mathsf{C} = \mathsf{Q} \cdot \mathsf{L}$, where $\mathsf{Q}$ is orthogonal and $\mathsf{L}$ is lower triangular. The QL algorithm consist of a sequence of orthogonal transformations

\begin{displaymath}
    \mathsf{C}_s = \mathsf{Q}_s \cdot \mathsf{L}_s
    \end{displaymath}


\begin{displaymath}
    \mathsf{C}_{s+1} = \mathsf{L}_s \cdot \mathsf{Q}_s
    = \mathsf{Q}_s^T \cdot \mathsf{C}_s \cdot \mathsf{Q}_s
    \end{displaymath}

(1) If $\mathsf{C}$ have eigenvalues with different absolute value $\vert l_i\vert$, then $\mathsf{C}_s \rightarrow$ [lower triangular form] as $s\rightarrow\infty$. The eigenvalues appear on the diagonal in increasing order of absolute magnitude. (2) If If $\mathsf{C}$ has an eigenvalue $\vert l_i\vert$ of multiplicty of order $p$, $\mathsf{C}_s \rightarrow$ [lower triangular form] as $s\rightarrow\infty$, except for a diagona block matrix of order $p$, whose eigenvalues $\rightarrow l_i$. */ // Int_t n = z.fNrows; Float_t *pd = d.fElements; Float_t *pe = e.fElements; Float_t *pz = z.fElements; // It's convenient to renumber the e vector elements Int_t l; for (l = 1; l < n; l++) pe[l-1] = pe[l]; pe[n-1] = 0; for (l = 0; l < n; l++) { Int_t iter = 0; Int_t m = 0; do { for (m = l; m < n-1; m++) { // Look for a single small sub-diagonal element to split the // matrix Double_t dd = TMath::Abs(pd[m])+TMath::Abs(pd[m+1]); if ((Double_t)(TMath::Abs(pe[m])+dd) == dd) break; } if (m != l) { if (iter++ == 30) { Error("MakeEigenVectors","too many iterationsn"); return; } // Form shift Double_t g = (pd[l+1]-pd[l])/(2*pe[l]); Double_t r = TMath::Sqrt(g*g+1); // This is d_m-k_s g = pd[m]-pd[l]+pe[l]/(g+TMath::Sign(r,g)); Double_t s = 1; Double_t c = 1; Double_t p = 0; Int_t i = 0; for (i = m-1; i >= l; i--) { // A plane rotation as in the original QL, followed by // Givens rotations to restore tridiagonal form Double_t f = s*pe[i]; Double_t b = c*pe[i]; r = TMath::Sqrt(f*f+g*g); pe[i+1] = r; if (r == 0) { // Recover from underflow pd[i+1] -= p; pe[m] = 0; break; } s = f/r; c = g/r; g = pd[i+1]-p; r = (pd[i]-g)*s+2*c*b; p = s*r; pd[i+1] = g+p; g = c*r-b; Int_t k; for (k = 0; k < n; k++) { // Form Eigenvectors f = pz[k+(i+1)*n]; pz[k+(i+1)*n] = s*pz[k+i*n]+c*f; pz[k+i*n] = c*pz[k+i*n]-s*f; } } // for (i = m) if (r == 0 && i >= l) continue; pd[l] -= p; pe[l] = g; pe[m] = 0; } // if (m != l) } while (m != l); } // for (l = 0) } //____________________________________________________________________ void TMatrix::EigenSort( TMatrix &eigenVectors, TVector &eigenValues) { // /* PRIVATE METHOD:
Order the eigenvalues and vectors by ascending eigenvalue. The algorithm is a straight insertion. It's taken from Numerical Recipes in C section 11.1. */ // Int_t n = eigenVectors.fNrows; Float_t *pVec = eigenVectors.fElements; Float_t *pVal = eigenValues.fElements; Int_t i; for (i = 0; i < n; i++) { Int_t k = i; Double_t p = pVal[i]; Int_t j; for (j = i + 1; j < n; j++) if (pVal[j] >= p) { k = j; p = pVal[j]; } if (k != i) { pVal[k] = pVal[i]; pVal[i] = p; for (j = 0; j < n; j++) { p = pVec[j+i*n]; pVec[j+i*n] = pVec[j+k*n]; pVec[j+k*n] = p; } } } } //______________________________________________________________________________ TMatrix &TMatrix::operator*=(const TMatrix &source) { // Compute target = target * source inplace. Strictly speaking, it can't be // done inplace, though only the row of the target matrix needs // to be saved. "Inplace" multiplication is only possible // when the 'source' matrix is square. if (!IsValid()) { Error("operator*=(const TMatrix&)", "matrix not initialized"); return *this; } if (!source.IsValid()) { Error("operator*=(const TMatrix&)", "source matrix not initialized"); return *this; } if (fRowLwb != source.fColLwb || fNcols != source.fNrows || fColLwb != source.fColLwb || fNcols != source.fNcols) Error("operator*=(const TMatrix&)", "matrices above are unsuitable for the inplace multiplication"); // One row of the old_target matrix Real_t *const one_row = new Real_t[fNcols]; const Real_t *one_row_end = &one_row[fNcols]; Real_t *trp = fElements; // Pointer to the i-th row for ( ; trp < &fElements[fNrows]; trp++) { // Go row-by-row in the target Real_t *wrp, *orp; // work row pointers for (wrp = trp, orp = one_row; orp < one_row_end; ) *orp++ = *wrp, wrp += fNrows; // Copy a row of old_target Real_t *scp = source.fElements; // Source column pointer for (wrp = trp; wrp < fElements+fNelems; wrp += fNrows) { Double_t sum = 0; // Multiply a row of old_target for (orp = one_row; orp < one_row_end; ) // by each col of source sum += *orp++ * *scp++; // to get a row of new_target *wrp = sum; } } delete [] one_row; return *this; } //______________________________________________________________________________ TMatrix &TMatrix::operator*=(const TMatrixDiag &diag) { // Multiply a matrix by the diagonal of another matrix // opt == "R" : matrix(i,j) *= diag(j) (default) // else : matrix(i,j) *= diag(i) if (!IsValid()) { Error("operator*=(const TMatrixDiag&)", "matrix not initialized"); return *this; } if (!diag.fMatrix->IsValid()) { Error("operator*=(const TMatrixDiag&)", "diag matrix not initialized"); return *this; } if (fNcols != diag.fNdiag) { Error("operator*=(const TMatrixDiag&)", "matrix cannot be multiplied row-wise by the diagonal of the other matrix"); return *this; } Real_t *dp = diag.fPtr; // Diag ptr Real_t *mp = fElements; // Matrix ptr Int_t i; for ( ; mp < fElements + fNelems; dp += diag.fInc) for (i = 0; i < fNrows; i++) *mp++ *= *dp; Assert(dp < diag.fPtr + diag.fMatrix->fNelems + diag.fInc); return *this; } //______________________________________________________________________________ TMatrix &TMatrix::operator/=(const TMatrixDiag &diag) { // Divide a matrix by the diagonal of another matrix // matrix(i,j) *= diag(j) if (!IsValid()) { Error("operator/=(const TMatrixDiag&)", "matrix not initialized"); return *this; } if (!diag.fMatrix->IsValid()) { Error("operator/=(const TMatrixDiag&)", "diag matrix not initialized"); return *this; } if (fNcols != diag.fNdiag) { Error("operator/=(const TMatrixDiag&)", "matrix cannot be divided by the diagonal of the other matrix"); return *this; } Real_t *dp = diag.fPtr; // Diag ptr Real_t *mp = fElements; // Matrix ptr Int_t i; for ( ; mp < fElements + fNelems; dp += diag.fInc) { Assert(*dp != 0.0); for (i = 0; i < fNrows; i++) *mp++ /= *dp; } Assert(dp < diag.fPtr + diag.fMatrix->fNelems + diag.fInc); return *this; } //______________________________________________________________________________ TMatrix &TMatrix::operator*=(const TMatrixColumn &col) { // Multiply a matrix by the column of another matrix // matrix(i,j) *= another(i,k) for fixed k if (!IsValid()) { Error("operator*=(const TMatrixColumn&)", "matrix not initialized"); return *this; } if (!col.fMatrix->IsValid()) { Error("operator*=(const TMatrixColumn&)", "column matrix not initialized"); return *this; } if (fNcols != col.fMatrix->fNcols) { Error("operator*=(const TMatrixColumn&)", "matrix cannot be multiplied by the column of the other matrix"); return *this; } Real_t *cp = col.fPtr; // Column ptr Real_t *mp = fElements; // Matrix ptr Int_t i; for ( ; mp < fElements + fNelems; cp++) for (i = 0; i < fNrows; i++) *mp++ *= *cp; Assert(cp < col.fPtr + col.fMatrix->fNelems); return *this; } //______________________________________________________________________________ TMatrix &TMatrix::operator/=(const TMatrixColumn &col) { // Divide a matrix by the column of another matrix // matrix(i,j) /= another(i,k) for fixed k if (!IsValid()) { Error("operator/=(const TMatrixColumn&)", "matrix not initialized"); return *this; } if (!col.fMatrix->IsValid()) { Error("operator/=(const TMatrixColumn&)", "column matrix not initialized"); return *this; } if (fNcols != col.fMatrix->fNcols) { Error("operator/=(const TMatrixColumn&)", "matrix cannot be divided by the column of the other matrix"); return *this; } Real_t *cp = col.fPtr; // Column ptr Real_t *mp = fElements; // Matrix ptr Int_t i; for ( ; mp < fElements + fNelems; cp++) { Assert(*cp != 0.0); for (i = 0; i < fNrows; i++) *mp++ /= *cp; } Assert(cp < col.fPtr + col.fMatrix->fNelems); return *this; } //______________________________________________________________________________ TMatrix &TMatrix::operator*=(const TMatrixRow &row) { // Multiply a matrix by the row of another matrix // matrix(i,j) *= another(k,j) for fixed k if (!IsValid()) { Error("operator*=(const TMatrixRow&)", "matrix not initialized"); return *this; } if (!row.fMatrix->IsValid()) { Error("operator*=(const TMatrixRow&)", "row matrix not initialized"); return *this; } if (fNrows != row.fMatrix->fNrows) { Error("operator*=(const TMatrixRow&)", "matrix cannot be multiplied by the row of the other matrix"); return *this; } Real_t *rp = row.fPtr; // Row ptr Real_t *mp = fElements; // Matrix ptr Int_t i; for ( ; mp < fElements + fNelems; rp = row.fPtr) { for (i = 0; i < fNrows; i++) { Assert(rp < row.fPtr+row.fMatrix->fNelems); *mp++ *= *rp; rp += row.fInc; } } return *this; } //______________________________________________________________________________ TMatrix &TMatrix::operator/=(const TMatrixRow &row) { // Divide a matrix by the row of another matrix // matrix(i,j) /= another(k,j) for fixed k if (!IsValid()) { Error("operator/=(const TMatrixRow&)", "matrix not initialized"); return *this; } if (!row.fMatrix->IsValid()) { Error("operator/=(const TMatrixRow&)", "row matrix not initialized"); return *this; } if (fNrows != row.fMatrix->fNrows) { Error("operator/=(const TMatrixRow&)", "matrix cannot be divided by the row of the other matrix"); return *this; } Real_t *rp = row.fPtr; // Row ptr Real_t *mp = fElements; // Matrix ptr Int_t i; for ( ; mp < fElements + fNelems; rp = row.fPtr) { for (i = 0; i < fNrows; i++) { Assert(rp < row.fPtr+row.fMatrix->fNelems); Assert(*rp != 0.0); *mp++ /= *rp; rp += row.fInc; } } return *this; } //______________________________________________________________________________ void TMatrix::AMultB(const TMatrix &a, const TMatrix &b) { // General matrix multiplication. Create a matrix C such that C = A * B. // Note, matrix C needs to be allocated. if (!a.IsValid()) { Error("AMultB", "matrix a not initialized"); return; } if (!b.IsValid()) { Error("AMultB", "matrix b not initialized"); return; } if (a.fNcols != b.fNrows || a.fColLwb != b.fRowLwb) { Error("AMultB", "matrices a and b cannot be multiplied"); return; } Allocate(a.fNrows, b.fNcols, a.fRowLwb, b.fColLwb); Real_t *arp; // Pointer to the i-th row of A Real_t *bcp = b.fElements; // Pointer to the j-th col of B Real_t *cp = fElements; // C is to be traversed in the natural while (cp < fElements + fNelems) { // order, col-after-col for (arp = a.fElements; arp < a.fElements + a.fNrows; ) { Double_t cij = 0; Real_t *bccp = bcp; // To scan the jth col of B while (arp < a.fElements + a.fNelems) // Scan the i-th row of A and cij += *bccp++ * *arp, arp += a.fNrows; // the j-th col of B *cp++ = cij; arp -= a.fNelems - 1; // arp points to (i+1)-th row } bcp += b.fNrows; // We're done with j-th col of both } // B and C. Set bcp to the (j+1)-th col Assert(cp == fElements + fNelems && bcp == b.fElements + b.fNelems); } //______________________________________________________________________________ void TMatrix::Mult(const TMatrix &a, const TMatrix &b) { // Compute C = A*B. The same as AMultB(), only matrix C is already // allocated, and it is *this. if (!a.IsValid()) { Error("Mult", "matrix a not initialized"); return; } if (!b.IsValid()) { Error("Mult", "matrix b not initialized"); return; } if (!IsValid()) { Error("Mult", "matrix not initialized"); return; } if (a.fNcols != b.fNrows || a.fColLwb != b.fRowLwb) { Error("Mult", "matrices a and b cannot be multiplied"); return; } if (fNrows != a.fNrows || fNcols != b.fNcols || fRowLwb != a.fRowLwb || fColLwb != b.fColLwb) { Error("Mult", "product A*B is incompatible with the given matrix"); return; } Real_t *arp; // Pointer to the i-th row of A Real_t *bcp = b.fElements; // Pointer to the j-th col of B Real_t *cp = fElements; // C is to be traversed in the natural while (cp < fElements + fNelems) { // order, col-after-col for (arp = a.fElements; arp < a.fElements + a.fNrows; ) { Double_t cij = 0; Real_t *bccp = bcp; // To scan the jth col of B while (arp < a.fElements + a.fNelems) // Scan the i-th row of A and cij += *bccp++ * *arp, arp += a.fNrows; // the j-th col of B *cp++ = cij; arp -= a.fNelems - 1; // arp points to (i+1)-th row } bcp += b.fNrows; // We're done with j-th col of both } // B and C. Set bcp to the (j+1)-th col Assert(cp == fElements + fNelems && bcp == b.fElements + b.fNelems); } //______________________________________________________________________________ void TMatrix::AtMultB(const TMatrix &a, const TMatrix &b) { // Create a matrix C such that C = A' * B. In other words, // c[i,j] = SUM{ a[k,i] * b[k,j] }. Note, matrix C needs to be allocated. if (!a.IsValid()) { Error("AtMultB", "matrix a not initialized"); return; } if (!b.IsValid()) { Error("AtMultB", "matrix b not initialized"); return; } if (a.fNrows != b.fNrows || a.fRowLwb != b.fRowLwb) { Error("AtMultB", "matrices above are unsuitable for A'B multiplication"); return; } Allocate(a.fNcols, b.fNcols, a.fColLwb, b.fColLwb); Real_t *acp; // Pointer to the i-th col of A Real_t *bcp = b.fElements; // Pointer to the j-th col of B Real_t *cp = fElements; // C is to be traversed in the natural while (cp < fElements + fNelems) { // order, col-after-col for (acp = a.fElements; acp < a.fElements + a.fNelems; ) { Double_t cij = 0; // Scan all cols of A Real_t *bccp = bcp; // To scan the jth col of B for (int i = 0; i < a.fNrows; i++) // Scan the i-th row of A and cij += *bccp++ * *acp++; // the j-th col of B *cp++ = cij; } bcp += b.fNrows; // We're done with j-th col of both } // B and C. Set bcp to the (j+1)-th col Assert(cp == fElements + fNelems && bcp == b.fElements + b.fNelems); } //______________________________________________________________________________ Double_t TMatrix::Determinant() const { // Compute the determinant of a general square matrix. // Example: Matrix A; Double_t A.Determinant(); // // Gauss-Jordan transformations of the matrix with a slight // modification to take advantage of the *column*-wise arrangement // of Matrix elements. Thus we eliminate matrix's columns rather than // rows in the Gauss-Jordan transformations. Note that determinant // is invariant to matrix transpositions. // The matrix is copied to a special object of type TMatrixPivoting, // where all Gauss-Jordan eliminations with full pivoting are to // take place. if (!IsValid()) { Error("Determinant", "matrix not initialized"); return 0.0; } if (fNrows != fNcols) { Error("Determinant", "can't obtain determinant of a non-square matrix"); return 0.0; } if (fRowLwb != fColLwb) { Error("Determinant", "row and col lower bounds are inconsistent"); return 0.0; } TMatrixPivoting mp(*this); Double_t det = 1; Int_t k; for (k = 0; k < fNcols && det != 0; k++) det *= mp.PivotingAndElimination(); return det; } //______________________________________________________________________________ void TMatrix::Streamer(TBuffer &R__b) { // Stream an object of class TMatrix. if (R__b.IsReading()) { UInt_t R__s, R__c; Version_t R__v = R__b.ReadVersion(&R__s, &R__c); if (R__v > 1) { TMatrix::Class()->ReadBuffer(R__b, this, R__v, R__s, R__c); if (fNcols == 1) { fIndex = &fElements; } else { if (fNcols <= 0) return; fIndex = new Real_t*[fNcols]; if (fIndex) memset(fIndex, 0, fNcols*sizeof(Real_t*)); Int_t i; Real_t *col_p; for (i = 0, col_p = &fElements[0]; i < fNcols; i++, col_p += fNrows) fIndex[i] = col_p; } return; } //====process old versions before automatic schema evolution TObject::Streamer(R__b); R__b >> fNrows; R__b >> fNcols; R__b >> fRowLwb; R__b >> fColLwb; fNelems = R__b.ReadArray(fElements); if (fNcols == 1) { fIndex = &fElements; } else { fIndex = new Real_t*[fNcols]; if (fIndex) memset(fIndex, 0, fNcols*sizeof(Real_t*)); Int_t i; Real_t *col_p; for (i = 0, col_p = &fElements[0]; i < fNcols; i++, col_p += fNrows) fIndex[i] = col_p; } R__b.CheckByteCount(R__s, R__c, TMatrix::IsA()); //====end of old versions } else { TMatrix::Class()->WriteBuffer(R__b,this); } } //______________________________________________________________________________ void Compare(const TMatrix &matrix1, const TMatrix &matrix2) { // Compare two matrices and print out the result of the comparison. Int_t i, j; if (!AreCompatible(matrix1, matrix2)) { Error("Compare", "matrices are not compatible"); return; } printf("n\nComparison of two TMatrices:n"); Double_t norm1 = 0, norm2 = 0; // Norm of the Matrices Double_t ndiff = 0; // Norm of the difference Int_t imax = 0, jmax = 0; // For the elements that differ most Real_t difmax = -1; Real_t *mp1 = matrix1.fElements; // Matrix element pointers Real_t *mp2 = matrix2.fElements; for (j = 0; j < matrix1.fNcols; j++) // Due to the column-wise arrangement, for (i = 0; i < matrix1.fNrows; i++) { // the row index changes first Real_t mv1 = *mp1++; Real_t mv2 = *mp2++; Real_t diff = TMath::Abs(mv1-mv2); if (diff > difmax) { difmax = diff; imax = i; jmax = j; } norm1 += TMath::Abs(mv1); norm2 += TMath::Abs(mv2); ndiff += TMath::Abs(diff); } imax += matrix1.fRowLwb, jmax += matrix1.fColLwb; printf("nMaximal discrepancy t\t%g", difmax); printf("n occured at the pointt\t(%d,%d)", imax, jmax); const Real_t mv1 = matrix1(imax,jmax); const Real_t mv2 = matrix2(imax,jmax); printf("n Matrix 1 element is t\t%g", mv1); printf("n Matrix 2 element is t\t%g", mv2); printf("n Absolute error v2[i]-v1[i]t\t%g", mv2-mv1); printf("n Relative errort\tt\t%gn", (mv2-mv1)/TMath::Max(TMath::Abs(mv2+mv1)/2,(Real_t)1e-7)); printf("n||Matrix 1|| t\tt%g", norm1); printf("n||Matrix 2|| t\tt%g", norm2); printf("n||Matrix1-Matrix2||t\tt\t%g", ndiff); printf("n||Matrix1-Matrix2||/sqrt(||Matrix1|| ||Matrix2||)t%gn\n", ndiff/TMath::Max(TMath::Sqrt(norm1*norm2), 1e-7)); } //______________________________________________________________________________ void VerifyElementValue(const TMatrix &m, Real_t val) { // Validate that all elements of matrix have value val (within 1.e-5). Int_t imax = 0, jmax = 0; Double_t max_dev = 0; Int_t i, j; for (i = m.GetRowLwb(); i <= m.GetRowUpb(); i++) for (j = m.GetColLwb(); j <= m.GetColUpb(); j++) { Double_t dev = TMath::Abs(m(i,j)-val); if (dev > max_dev) imax = i, jmax = j, max_dev = dev; } if (max_dev == 0) return; else if(max_dev < 1e-5) printf("Element (%d,%d) with value %g differs the most from whatn" "was expected, %g, though the deviation %g is smalln", imax,jmax,m(imax,jmax),val,max_dev); else Error("VerifyElementValue", "a significant difference from the expected value %gn" "encountered for element (%d,%d) with value %g", val,imax,jmax,m(imax,jmax)); } //______________________________________________________________________________ void VerifyMatrixIdentity(const TMatrix &m1, const TMatrix &m2) { // Verify that elements of the two matrices are equal (within 1.e-5). Int_t imax = 0, jmax = 0; Double_t max_dev = 0; Int_t i, j; if (!AreCompatible(m1, m2)) { Error("VerifyMatrixIdentity", "matrices are not compatible"); return; } for (i = m1.GetRowLwb(); i <= m1.GetRowUpb(); i++) for (j = m1.GetColLwb(); j <= m1.GetColUpb(); j++) { Double_t dev = TMath::Abs(m1(i,j)-m2(i,j)); if (dev > max_dev) imax = i, jmax = j, max_dev = dev; } if (max_dev == 0) return; if (max_dev < 1e-5) printf("Two (%d,%d) elements of matrices with values %g and %gn" "differ the most, though the deviation %g is smalln", imax,jmax,m1(imax,jmax),m2(imax,jmax),max_dev); else Error("VerifyMatrixIdentity", "a significant difference between the matrices encounteredn" "at (%d,%d) element, with values %g and %g", imax,jmax,m1(imax,jmax),m2(imax,jmax)); } #if defined(R__HPUX) || defined(R__MACOSX) //______________________________________________________________________________ // These functions should be inline //______________________________________________________________________________ TMatrix::TMatrix(Int_t no_rows, Int_t no_cols) { Allocate(no_rows, no_cols); } TMatrix::TMatrix(Int_t row_lwb, Int_t row_upb, Int_t col_lwb, Int_t col_upb) { Allocate(row_upb-row_lwb+1, col_upb-col_lwb+1, row_lwb, col_lwb); } void TMatrix::SetElements(const Float_t *elements, Option_t *option) { if (!IsValid()) { Error("SetElements", "matrix is not initialized"); return; } TString opt = option; opt.ToUpper(); if (opt.Contains("F")) memcpy(fElements,elements,fNelems*sizeof(Float_t)); else { for (Int_t irow = 0; irow < fNrows; irow++) { for (Int_t icol = 0; icol < fNcols; icol++) fElements[irow+icol*fNrows] = elements[irow*fNcols+icol]; } } } TMatrix::TMatrix(Int_t no_rows, Int_t no_cols, const Float_t *elements, Option_t *option) { // option="F": array elements contains the matrix stored column-wise // like in Fortran, so a[i,j] = elements[i+no_rows*j], // else it is supposed that array elements are stored row-wise // a[i,j] = elements[i*no_cols+j] Allocate(no_rows, no_cols); SetElements(elements,option); } Bool_t TMatrix::IsValid() const { if (fNrows == -1) return kFALSE; return kTRUE; } TMatrix::TMatrix(Int_t row_lwb, Int_t row_upb, Int_t col_lwb, Int_t col_upb, const Float_t *elements, Option_t *option) { Allocate(row_upb-row_lwb+1, col_upb-col_lwb+1, row_lwb, col_lwb); SetElements(elements,option); } void TMatrix::GetElements(Float_t *elements, Option_t *option) const { if (!IsValid()) { Error("GetElements", "matrix is not initialized"); return; } TString opt = option; opt.ToUpper(); if (opt.Contains("F")) memcpy(elements,fElements,fNelems*sizeof(Float_t)); else { for (Int_t irow = 0; irow < fNrows; irow++) { for (Int_t icol = 0; icol < fNcols; icol++) elements[irow+icol*fNrows] = fElements[irow*fNcols+icol]; } } } TMatrix::TMatrix(const TLazyMatrix &lazy_constructor) { Allocate(lazy_constructor.fRowUpb-lazy_constructor.fRowLwb+1, lazy_constructor.fColUpb-lazy_constructor.fColLwb+1, lazy_constructor.fRowLwb, lazy_constructor.fColLwb); lazy_constructor.FillIn(*this); } TMatrix &TMatrix::operator=(const TLazyMatrix &lazy_constructor) { if (!IsValid()) { Error("operator=(const TLazyMatrix&)", "matrix is not initialized"); return *this; } if (lazy_constructor.fRowUpb != GetRowUpb() || lazy_constructor.fColUpb != GetColUpb() || lazy_constructor.fRowLwb != GetRowLwb() || lazy_constructor.fColLwb != GetColLwb()) { Error("operator=(const TLazyMatrix&)", "matrix is incompatible with " "the assigned Lazy matrix"); return *this; } lazy_constructor.FillIn(*this); return *this; } Bool_t AreCompatible(const TMatrix &im1, const TMatrix &im2) { if (!im1.IsValid()) { Error("AreCompatible", "matrix 1 not initialized"); return kFALSE; } if (!im2.IsValid()) { Error("AreCompatible", "matrix 2 not initialized"); return kFALSE; } if (im1.fNrows != im2.fNrows || im1.fNcols != im2.fNcols || im1.fRowLwb != im2.fRowLwb || im1.fColLwb != im2.fColLwb) return kFALSE; return kTRUE; } TMatrix &TMatrix::operator=(const TMatrix &source) { if (this != &source && AreCompatible(*this, source)) { TObject::operator=(source); memcpy(fElements, source.fElements, fNelems*sizeof(Real_t)); } return *this; } TMatrix::TMatrix(const TMatrix &another) : TObject(another) { if (another.IsValid()) { Allocate(another.fNrows, another.fNcols, another.fRowLwb, another.fColLwb); *this = another; } else Error("TMatrix(const TMatrix&)", "other matrix is not valid"); } void TMatrix::ResizeTo(const TMatrix &m) { ResizeTo(m.GetRowLwb(), m.GetRowUpb(), m.GetColLwb(), m.GetColUpb()); } const Real_t &TMatrix::operator()(int rown, int coln) const { static Real_t err; err = 0.0; if (!IsValid()) { Error("operator()", "matrix is not initialized"); return err; } Int_t arown = rown - fRowLwb; // Effective indices Int_t acoln = coln - fColLwb; if (arown >= fNrows || arown < 0) { Error("operator()", "row index %d is out of matrix boundaries [%d,%d]", rown, fRowLwb, fNrows+fRowLwb-1); return err; } if (acoln >= fNcols || acoln < 0) { Error("operator()", "col index %d is out of matrix boundaries [%d,%d]", coln, fColLwb, fNcols+fColLwb-1); return err; } return (fIndex[acoln])[arown]; } Real_t &TMatrix::operator()(Int_t rown, Int_t coln) { return (Real_t&)((*(const TMatrix *)this)(rown,coln)); } const TMatrixRow TMatrix::operator[](int rown) const { return TMatrixRow(*this,rown); } TMatrixRow TMatrix::operator[](int rown) { return TMatrixRow(*this,rown); } TMatrix &TMatrix::Zero() { if (!IsValid()) Error("Zero", "matrix not initialized"); else memset(fElements, 0, fNelems*sizeof(Real_t)); return *this; } TMatrix &TMatrix::Apply(TElementAction &action) { if (!IsValid()) Error("Apply(TElementAction&)", "matrix not initialized"); else for (Real_t *ep = fElements; ep < fElements+fNelems; ep++) action.Operation(*ep); return *this; } #endif


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