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#ifndef VIGRA_MATRIX_HXX
#define VIGRA_MATRIX_HXX

#include <cmath>
#include <iosfwd>
#include <iomanip>
#include "multi_array.hxx"
#include "mathutil.hxx"
#include "numerictraits.hxx"
#include "multi_pointoperators.hxx"


namespace vigra
{

/** \defgroup LinearAlgebraModule Linear Algebra

    \brief Classes and functions for matrix algebra, linear equations systems, eigen systems, least squares etc.
*/

/** \ingroup LinearAlgebraModule

    Namespace <tt>vigra/linalg</tt> hold VIGRA's linear algebra functionality. But most of its contents
    is exported into namespace <tt>vigra</tt> via <tt>using</tt> directives.
*/
namespace linalg
{

template <class T, class C>
inline MultiArrayIndex 
rowCount(const MultiArrayView<2, T, C> &x);

template <class T, class C>
inline MultiArrayIndex 
columnCount(const MultiArrayView<2, T, C> &x);

template <class T, class C>
inline MultiArrayView <2, T, C>
rowVector(MultiArrayView <2, T, C> const & m, MultiArrayIndex d);

template <class T, class C>
inline MultiArrayView <2, T, C>
columnVector(MultiArrayView<2, T, C> const & m, MultiArrayIndex d);

template <class T, class ALLOC = std::allocator<T> >
class TemporaryMatrix;

template <class T, class C1, class C2>
void transpose(const MultiArrayView<2, T, C1> &v, MultiArrayView<2, T, C2> &r);

template <class T, class C>
bool isSymmetric(const MultiArrayView<2, T, C> &v);

enum RawArrayMemoryLayout { RowMajor, ColumnMajor };

/********************************************************/
/*                                                      */
/*                        Matrix                        */
/*                                                      */
/********************************************************/

/** Matrix class. 

    \ingroup LinearAlgebraModule 

    This is the basic class for all linear algebra computations. Matrices are
    stored in a <i>column-major</i> format, i.e. the row index is varying fastest.
    This is the same format as in the lapack and gmm++ libraries, so it will
    be easy to interface these libraries. In fact, if you need optimized
    high performance code, you should use them. The VIGRA linear algebra
    functionality is provided for smaller problems and rapid prototyping
    (no one wants to spend half the day installing a new library just to
    discover that the new algorithm idea didn't work anyway).

    <b>See also:</b>
    <ul>
    <li> \ref LinearAlgebraFunctions
    </ul>

    <b>\#include</b> \<vigra/matrix.hxx\> or<br>
    <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
    Namespaces: vigra and vigra::linalg
*/
template <class T, class ALLOC = std::allocator<T> >
class Matrix
: public MultiArray<2, T, ALLOC>
{
    typedef MultiArray<2, T, ALLOC> BaseType;

  public:
    typedef Matrix<T, ALLOC>                        matrix_type;
    typedef TemporaryMatrix<T, ALLOC>               temp_type;
    typedef MultiArrayView<2, T>                    view_type;
    typedef typename BaseType::value_type           value_type;
    typedef typename BaseType::pointer              pointer;
    typedef typename BaseType::const_pointer        const_pointer;
    typedef typename BaseType::reference            reference;
    typedef typename BaseType::const_reference      const_reference;
    typedef typename BaseType::difference_type      difference_type;
    typedef typename BaseType::difference_type_1    difference_type_1;
    typedef ALLOC                                   allocator_type;

        /** default constructor
         */
    Matrix()
    {}

        /** construct with given allocator
         */
    explicit Matrix(ALLOC const & alloc)
    : BaseType(alloc)
    {}

        /** construct with given shape and init all
            elements with zero. Note that the order of the axes is
            <tt>difference_type(rows, columns)</tt> which
            is the opposite of the usual VIGRA convention.
         */
    explicit Matrix(const difference_type &aShape,
                    ALLOC const & alloc = allocator_type())
    : BaseType(aShape, alloc)
    {}

        /** construct with given shape and init all
            elements with zero. Note that the order of the axes is
            <tt>(rows, columns)</tt> which
            is the opposite of the usual VIGRA convention.
         */
    Matrix(difference_type_1 rows, difference_type_1 columns,
                    ALLOC const & alloc = allocator_type())
    : BaseType(difference_type(rows, columns), alloc)
    {}

        /** construct with given shape and init all
            elements with the constant \a init. Note that the order of the axes is
            <tt>difference_type(rows, columns)</tt> which
            is the opposite of the usual VIGRA convention.
         */
    Matrix(const difference_type &aShape, const_reference init,
           allocator_type const & alloc = allocator_type())
    : BaseType(aShape, init, alloc)
    {}

        /** construct with given shape and init all
            elements with the constant \a init. Note that the order of the axes is
            <tt>(rows, columns)</tt> which
            is the opposite of the usual VIGRA convention.
         */
    Matrix(difference_type_1 rows, difference_type_1 columns, const_reference init,
           allocator_type const & alloc = allocator_type())
    : BaseType(difference_type(rows, columns), init, alloc)
    {}

        /** construct with given shape and copy data from C-style array \a init.
            Unless \a layout is <tt>ColumnMajor</tt>, the elements in this array
            are assumed to be given in row-major order (the C standard order) and
            will automatically be converted to the required column-major format.
            Note that the order of the axes is <tt>difference_type(rows, columns)</tt> which
            is the opposite of the usual VIGRA convention.
         */
    Matrix(const difference_type &shape, const_pointer init, RawArrayMemoryLayout layout = RowMajor,
           allocator_type const & alloc = allocator_type())
    : BaseType(shape, alloc) // FIXME: this function initializes the memory twice
    {
        if(layout == RowMajor)
        {
            difference_type trans(shape[1], shape[0]);
            linalg::transpose(MultiArrayView<2, T>(trans, const_cast<pointer>(init)), *this);
        }
        else
        {
            std::copy(init, init + elementCount(), this->data());
        }
    }

        /** construct with given shape and copy data from C-style array \a init.
            Unless \a layout is <tt>ColumnMajor</tt>, the elements in this array
            are assumed to be given in row-major order (the C standard order) and
            will automatically be converted to the required column-major format.
            Note that the order of the axes is <tt>(rows, columns)</tt> which
            is the opposite of the usual VIGRA convention.
         */
    Matrix(difference_type_1 rows, difference_type_1 columns, const_pointer init, RawArrayMemoryLayout layout = RowMajor,
           allocator_type const & alloc = allocator_type())
    : BaseType(difference_type(rows, columns), alloc) // FIXME: this function initializes the memory twice
    {
        if(layout == RowMajor)
        {
            difference_type trans(columns, rows);
            linalg::transpose(MultiArrayView<2, T>(trans, const_cast<pointer>(init)), *this);
        }
        else
        {
            std::copy(init, init + elementCount(), this->data());
        }
    }

        /** copy constructor. Allocates new memory and
            copies tha data.
         */
    Matrix(const Matrix &rhs)
    : BaseType(rhs)
    {}

        /** construct from temporary matrix, which looses its data.

            This operation is equivalent to
            \code
                TemporaryMatrix<T> temp = ...;

                Matrix<T> m;
                m.swap(temp);
            \endcode
         */
    Matrix(const TemporaryMatrix<T, ALLOC> &rhs)
    : BaseType(rhs.allocator())
    {
        this->swap(const_cast<TemporaryMatrix<T, ALLOC> &>(rhs));
    }

        /** construct from a MultiArrayView. Allocates new memory and
            copies tha data. \a rhs is assumed to be in column-major order already.
         */
    template<class U, class C>
    Matrix(const MultiArrayView<2, U, C> &rhs)
    : BaseType(rhs)
    {}

        /** assignment.
            If the size of \a rhs is the same as the matrix's old size, only the data
            are copied. Otherwise, new storage is allocated, which invalidates
            all objects (array views, iterators) depending on the matrix.
         */
    Matrix & operator=(const Matrix &rhs)
    {
        BaseType::operator=(rhs); // has the correct semantics already
        return *this;
    }

        /** assign a temporary matrix. If the shapes of the two matrices match,
            only the data are copied (in order to not invalidate views and iterators
            depending on this matrix). Otherwise, the memory is swapped
            between the two matrices, so that all depending objects
            (array views, iterators) ar invalidated.
         */
    Matrix & operator=(const TemporaryMatrix<T, ALLOC> &rhs)
    {
        if(this->shape() == rhs.shape())
            this->copy(rhs);
        else
            this->swap(const_cast<TemporaryMatrix<T, ALLOC> &>(rhs));
        return *this;
    }

        /** assignment from arbitrary 2-dimensional MultiArrayView.<br>
            If the size of \a rhs is the same as the matrix's old size, only the data
            are copied. Otherwise, new storage is allocated, which invalidates
            all objects (array views, iterators) depending on the matrix.
            \a rhs is assumed to be in column-major order already.
         */
    template <class U, class C>
    Matrix & operator=(const MultiArrayView<2, U, C> &rhs)
    {
        BaseType::operator=(rhs); // has the correct semantics already
        return *this;
    }

        /** assignment from scalar.<br>
            Equivalent to Matrix::init(v).
         */
    Matrix & operator=(value_type const & v)
    {
        return init(v);
    }

         /** init elements with a constant
         */
    template <class U>
    Matrix & init(const U & init)
    {
        BaseType::init(init);
        return *this;
    }

       /** reshape to the given shape and initialize with zero.
         */
    void reshape(difference_type_1 rows, difference_type_1 columns)
    {
        BaseType::reshape(difference_type(rows, columns));
    }

        /** reshape to the given shape and initialize with \a init.
         */
    void reshape(difference_type_1 rows, difference_type_1 columns, const_reference init)
    {
        BaseType::reshape(difference_type(rows, columns), init);
    }

        /** reshape to the given shape and initialize with zero.
         */
    void reshape(difference_type const & newShape)
    {
        BaseType::reshape(newShape);
    }

        /** reshape to the given shape and initialize with \a init.
         */
    void reshape(difference_type const & newShape, const_reference init)
    {
        BaseType::reshape(newShape, init);
    }

        /** Create a matrix view that represents the row vector of row \a d.
         */
    view_type rowVector(difference_type_1 d) const
    {
        return vigra::linalg::rowVector(*this, d);
    }

        /** Create a matrix view that represents the column vector of column \a d.
         */
    view_type columnVector(difference_type_1 d) const
    {
        return vigra::linalg::columnVector(*this, d);
    }

        /** number of rows (height) of the matrix.
        */
    difference_type_1 rowCount() const
    {
        return this->m_shape[0];
    }

        /** number of columns (width) of the matrix.
        */
    difference_type_1 columnCount() const
    {
        return this->m_shape[1];
    }

        /** number of elements (width*height) of the matrix.
        */
    difference_type_1 elementCount() const
    {
        return rowCount()*columnCount();
    }

        /** check whether the matrix is symmetric.
        */
    bool isSymmetric() const
    {
        return vigra::linalg::isSymmetric(*this);
    }

        /** sums over the matrix.
        */
    TemporaryMatrix<T> sum() const
    {
        TemporaryMatrix<T> result(1, 1);
        vigra::transformMultiArray(srcMultiArrayRange(*this),
                               destMultiArrayRange(result),
                               vigra::FindSum<T>() );
        return result;
    }
    
        /** sums over dimension \a d of the matrix.
        */
    TemporaryMatrix<T> sum(difference_type_1 d) const
    {
        difference_type shape(d==0 ? 1 : this->m_shape[0], d==0 ? this->m_shape[1] : 1);
        TemporaryMatrix<T> result(shape);
        vigra::transformMultiArray(srcMultiArrayRange(*this),
                               destMultiArrayRange(result),
                               vigra::FindSum<T>() );
        return result;
    }

        /** sums over the matrix.
        */
    TemporaryMatrix<T> mean() const
    {
        TemporaryMatrix<T> result(1, 1);
        vigra::transformMultiArray(srcMultiArrayRange(*this),
                               destMultiArrayRange(result),
                               vigra::FindAverage<T>() );
        return result;
    }
    
        /** calculates mean over dimension \a d of the matrix.
        */
    TemporaryMatrix<T> mean(difference_type_1 d) const
    {
        difference_type shape(d==0 ? 1 : this->m_shape[0], d==0 ? this->m_shape[1] : 1);
        TemporaryMatrix<T> result(shape);
        vigra::transformMultiArray(srcMultiArrayRange(*this),
                               destMultiArrayRange(result),
                               vigra::FindAverage<T>() );
        return result;
    }


#ifdef DOXYGEN
// repeat the following functions for documentation. In real code, they are inherited.

        /** read/write access to matrix element <tt>(row, column)</tt>.
            Note that the order of the argument is the opposite of the usual
            VIGRA convention due to column-major matrix order.
        */
    value_type & operator()(difference_type_1 row, difference_type_1 column);

        /** read access to matrix element <tt>(row, column)</tt>.
            Note that the order of the argument is the opposite of the usual
            VIGRA convention due to column-major matrix order.
        */
    value_type operator()(difference_type_1 row, difference_type_1 column) const;

        /** squared Frobenius norm. Sum of squares of the matrix elements.
        */
    typename NormTraits<Matrix>::SquaredNormType squaredNorm() const;

        /** Frobenius norm. Root of sum of squares of the matrix elements.
        */
    typename NormTraits<Matrix>::NormType norm() const;

        /** create a transposed view of this matrix.
            No data are copied. If you want to transpose this matrix permanently, 
            you have to assign the transposed view:
            
            \code
            a = a.transpose();
            \endcode
         */
    MultiArrayView<2, vluae_type, StridedArrayTag> transpose() const;
#endif

        /** add \a other to this (sizes must match).
         */
    template <class U, class C>
    Matrix & operator+=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator+=(other);
        return *this;
    }

        /** subtract \a other from this (sizes must match).
         */
    template <class U, class C>
    Matrix & operator-=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator-=(other);
        return *this;
    }

        /** multiply \a other element-wise with this matrix (sizes must match).
         */
    template <class U, class C>
    Matrix & operator*=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator*=(other);
        return *this;
    }

        /** divide this matrix element-wise by \a other (sizes must match).
         */
    template <class U, class C>
    Matrix & operator/=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator/=(other);
        return *this;
    }

        /** add \a other to each element of this matrix
         */
    Matrix & operator+=(T other)
    {
        BaseType::operator+=(other);
        return *this;
    }

        /** subtract \a other from each element of this matrix
         */
    Matrix & operator-=(T other)
    {
        BaseType::operator-=(other);
        return *this;
    }

        /** scalar multiply this with \a other
         */
    Matrix & operator*=(T other)
    {
        BaseType::operator*=(other);
        return *this;
    }

        /** scalar divide this by \a other
         */
    Matrix & operator/=(T other)
    {
        BaseType::operator/=(other);
        return *this;
    }
};

// TemporaryMatrix is provided as an optimization: Functions returning a matrix can
// use TemporaryMatrix to make explicit that it was allocated as a temporary data structure.
// Functions receiving a TemporaryMatrix can thus often avoid to allocate new temporary
// memory.
template <class T, class ALLOC>  // default ALLOC already declared above
class TemporaryMatrix
: public Matrix<T, ALLOC>
{
    typedef Matrix<T, ALLOC> BaseType;
  public:
    typedef Matrix<T, ALLOC>                        matrix_type;
    typedef TemporaryMatrix<T, ALLOC>               temp_type;
    typedef MultiArrayView<2, T, StridedArrayTag>   view_type;
    typedef typename BaseType::value_type           value_type;
    typedef typename BaseType::pointer              pointer;
    typedef typename BaseType::const_pointer        const_pointer;
    typedef typename BaseType::reference            reference;
    typedef typename BaseType::const_reference      const_reference;
    typedef typename BaseType::difference_type      difference_type;
    typedef typename BaseType::difference_type_1    difference_type_1;
    typedef ALLOC                                   allocator_type;

    TemporaryMatrix(difference_type const & shape)
    : BaseType(shape, ALLOC())
    {}

    TemporaryMatrix(difference_type const & shape, const_reference init)
    : BaseType(shape, init, ALLOC())
    {}

    TemporaryMatrix(difference_type_1 rows, difference_type_1 columns)
    : BaseType(rows, columns, ALLOC())
    {}

    TemporaryMatrix(difference_type_1 rows, difference_type_1 columns, const_reference init)
    : BaseType(rows, columns, init, ALLOC())
    {}

    template<class U, class C>
    TemporaryMatrix(const MultiArrayView<2, U, C> &rhs)
    : BaseType(rhs)
    {}

    TemporaryMatrix(const TemporaryMatrix &rhs)
    : BaseType()
    {
        this->swap(const_cast<TemporaryMatrix &>(rhs));
    }
    
    template <class U>
    TemporaryMatrix & init(const U & init)
    {
        BaseType::init(init);
        return *this;
    }

    template <class U, class C>
    TemporaryMatrix & operator+=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator+=(other);
        return *this;
    }

    template <class U, class C>
    TemporaryMatrix & operator-=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator-=(other);
        return *this;
    }

    template <class U, class C>
    TemporaryMatrix & operator*=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator*=(other);
        return *this;
    }

    template <class U, class C>
    TemporaryMatrix & operator/=(MultiArrayView<2, U, C> const & other)
    {
        BaseType::operator/=(other);
        return *this;
    }

    TemporaryMatrix & operator+=(T other)
    {
        BaseType::operator+=(other);
        return *this;
    }

    TemporaryMatrix & operator-=(T other)
    {
        BaseType::operator-=(other);
        return *this;
    }

    TemporaryMatrix & operator*=(T other)
    {
        BaseType::operator*=(other);
        return *this;
    }

    TemporaryMatrix & operator/=(T other)
    {
        BaseType::operator/=(other);
        return *this;
    }
  private:

    TemporaryMatrix &operator=(const TemporaryMatrix &rhs); // intentionally not implemented
};

/** \defgroup LinearAlgebraFunctions Matrix Functions

    \brief Basic matrix algebra, element-wise mathematical functions, row and columns statistics, data normalization etc.
    
    \ingroup LinearAlgebraModule
 */
//@{

    /** Number of rows of a matrix represented as a <tt>MultiArrayView<2, ...></tt>

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
inline MultiArrayIndex 
rowCount(const MultiArrayView<2, T, C> &x)
{
    return x.shape(0);
}

    /** Number of columns of a matrix represented as a <tt>MultiArrayView<2, ...></tt>

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
inline MultiArrayIndex 
columnCount(const MultiArrayView<2, T, C> &x)
{
    return x.shape(1);
}

    /** Create a row vector view for row \a d of the matrix \a m

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
inline MultiArrayView <2, T, C>
rowVector(MultiArrayView <2, T, C> const & m, MultiArrayIndex d)
{
    typedef typename MultiArrayView <2, T, C>::difference_type Shape;
    return m.subarray(Shape(d, 0), Shape(d+1, columnCount(m)));
}


    /** Create a row vector view of the matrix \a m starting at element \a first and ranging 
        to column \a end (non-inclusive).

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
inline MultiArrayView <2, T, C>
rowVector(MultiArrayView <2, T, C> const & m, MultiArrayShape<2>::type first, MultiArrayIndex end)
{
    typedef typename MultiArrayView <2, T, C>::difference_type Shape;
    return m.subarray(first, Shape(first[0]+1, end));
}

    /** Create a column vector view for column \a d of the matrix \a m

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
inline MultiArrayView <2, T, C>
columnVector(MultiArrayView<2, T, C> const & m, MultiArrayIndex d)
{
    typedef typename MultiArrayView <2, T, C>::difference_type Shape;
    return m.subarray(Shape(0, d), Shape(rowCount(m), d+1));
}

    /** Create a column vector view of the matrix \a m starting at element \a first and 
        ranging to row \a end (non-inclusive).

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     **/
template <class T, class C>
inline MultiArrayView <2, T, C>
columnVector(MultiArrayView<2, T, C> const & m, MultiArrayShape<2>::type first, int end)
{
    typedef typename MultiArrayView <2, T, C>::difference_type Shape;
    return m.subarray(first, Shape(end, first[1]+1));
}

    /** Create a sub vector view of the vector \a m starting at element \a first and 
        ranging to row \a end (non-inclusive).
        
        Note: This function may only be called when either <tt>rowCount(m) == 1</tt> or
        <tt>columnCount(m) == 1</tt>, i.e. when \a m really represents a vector. 
        Otherwise, a PreconditionViolation exception is raised.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     **/
template <class T, class C>
inline MultiArrayView <2, T, C>
subVector(MultiArrayView<2, T, C> const & m, int first, int end)
{
    typedef typename MultiArrayView <2, T, C>::difference_type Shape;
    if(columnCount(m) == 1)
        return m.subarray(Shape(first, 0), Shape(end, 1));
    vigra_precondition(rowCount(m) == 1, 
                       "linalg::subVector(): Input must be a vector (1xN or Nx1).");
    return m.subarray(Shape(0, first), Shape(1, end));
}

    /** Check whether matrix \a m is symmetric.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
bool
isSymmetric(MultiArrayView<2, T, C> const & m)
{
    const MultiArrayIndex size = rowCount(m);
    if(size != columnCount(m))
        return false;

    for(MultiArrayIndex i = 0; i < size; ++i)
        for(MultiArrayIndex j = i+1; j < size; ++j)
            if(m(j, i) != m(i, j))
                return false;
    return true;
}


    /** Compute the trace of a square matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
typename NumericTraits<T>::Promote
trace(MultiArrayView<2, T, C> const & m)
{
    typedef typename NumericTraits<T>::Promote SumType;
    
    const MultiArrayIndex size = rowCount(m);
    vigra_precondition(size == columnCount(m), "linalg::trace(): Matrix must be square.");

    SumType sum = NumericTraits<SumType>::zero();
    for(MultiArrayIndex i = 0; i < size; ++i)
        sum += m(i, i);
    return sum;
}

#ifdef DOXYGEN // documentation only -- function is already defined in vigra/multi_array.hxx

    /** calculate the squared Frobenius norm of a matrix.
        Equal to the sum of squares of the matrix elements.

        <b>\#include</b> \<vigra/matrix.hxx\>
        Namespace: vigra
     */
template <class T, class ALLOC>
typename Matrix<T, ALLLOC>::SquaredNormType
squaredNorm(const Matrix<T, ALLLOC> &a);

    /** calculate the Frobenius norm of a matrix.
        Equal to the root of the sum of squares of the matrix elements.

        <b>\#include</b> \<vigra/matrix.hxx\>
        Namespace: vigra
     */
template <class T, class ALLOC>
typename Matrix<T, ALLLOC>::NormType
norm(const Matrix<T, ALLLOC> &a);

#endif // DOXYGEN

    /** initialize the given square matrix as an identity matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
void identityMatrix(MultiArrayView<2, T, C> &r)
{
    const MultiArrayIndex rows = rowCount(r);
    vigra_precondition(rows == columnCount(r),
       "identityMatrix(): Matrix must be square.");
    for(MultiArrayIndex i = 0; i < rows; ++i) {
        for(MultiArrayIndex j = 0; j < rows; ++j)
            r(j, i) = NumericTraits<T>::zero();
        r(i, i) = NumericTraits<T>::one();
    }
}

    /** create an identity matrix of the given size.
        Usage:

        \code
        vigra::Matrix<double> m = vigra::identityMatrix<double>(size);
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T>
TemporaryMatrix<T> identityMatrix(MultiArrayIndex size)
{
    TemporaryMatrix<T> ret(size, size, NumericTraits<T>::zero());
    for(MultiArrayIndex i = 0; i < size; ++i)
        ret(i, i) = NumericTraits<T>::one();
    return ret;
}

    /** create matrix of ones of the given size.
        Usage:

        \code
        vigra::Matrix<double> m = vigra::ones<double>(rows, cols);
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T>
TemporaryMatrix<T> ones(MultiArrayIndex rows, MultiArrayIndex cols)
{
    return TemporaryMatrix<T>(rows, cols, NumericTraits<T>::one());
}



template <class T, class C1, class C2>
void diagonalMatrixImpl(MultiArrayView<1, T, C1> const & v, MultiArrayView<2, T, C2> &r)
{
    const MultiArrayIndex size = v.elementCount();
    vigra_precondition(rowCount(r) == size && columnCount(r) == size,
        "diagonalMatrix(): result must be a square matrix.");
    for(MultiArrayIndex i = 0; i < size; ++i)
        r(i, i) = v(i);
}

    /** make a diagonal matrix from a vector.
        The vector is given as matrix \a v, which must either have a single
        row or column. The result is written into the square matrix \a r.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2>
void diagonalMatrix(MultiArrayView<2, T, C1> const & v, MultiArrayView<2, T, C2> &r)
{
    vigra_precondition(rowCount(v) == 1 || columnCount(v) == 1,
        "diagonalMatrix(): input must be a vector.");
    r.init(NumericTraits<T>::zero());
    if(rowCount(v) == 1)
        diagonalMatrixImpl(v.bindInner(0), r);
    else
        diagonalMatrixImpl(v.bindOuter(0), r);
}

    /** create a diagonal matrix from a vector.
        The vector is given as matrix \a v, which must either have a single
        row or column. The result is returned as a temporary matrix.
        Usage:

        \code
        vigra::Matrix<double> v(1, len);
        v = ...;

        vigra::Matrix<double> m = diagonalMatrix(v);
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
TemporaryMatrix<T> diagonalMatrix(MultiArrayView<2, T, C> const & v)
{
    vigra_precondition(rowCount(v) == 1 || columnCount(v) == 1,
        "diagonalMatrix(): input must be a vector.");
    MultiArrayIndex size = v.elementCount();
    TemporaryMatrix<T> ret(size, size, NumericTraits<T>::zero());
    if(rowCount(v) == 1)
        diagonalMatrixImpl(v.bindInner(0), ret);
    else
        diagonalMatrixImpl(v.bindOuter(0), ret);
    return ret;
}

    /** transpose matrix \a v.
        The result is written into \a r which must have the correct (i.e.
        transposed) shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2>
void transpose(const MultiArrayView<2, T, C1> &v, MultiArrayView<2, T, C2> &r)
{
    const MultiArrayIndex rows = rowCount(r);
    const MultiArrayIndex cols = columnCount(r);
    vigra_precondition(rows == columnCount(v) && cols == rowCount(v),
       "transpose(): arrays must have transposed shapes.");
    for(MultiArrayIndex i = 0; i < cols; ++i)
        for(MultiArrayIndex j = 0; j < rows; ++j)
            r(j, i) = v(i, j);
}

    /** create the transpose of matrix \a v.
        This does not copy any data, but only creates a transposed view 
        to the original matrix. A copy is only made when the transposed view
        is assigned to another matrix.
        Usage:

        \code
        vigra::Matrix<double> v(rows, cols);
        v = ...;

        vigra::Matrix<double> m = transpose(v);
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
inline MultiArrayView<2, T, StridedArrayTag> 
transpose(MultiArrayView<2, T, C> const & v)
{
    return v.transpose();
}

    /** Create new matrix by concatenating two matrices \a a and \a b vertically, i.e. on top of each other.
        The two matrices must have the same number of columns.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
joinVertically(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    typedef typename TemporaryMatrix<T>::difference_type Shape;
    
    MultiArrayIndex n = columnCount(a);
    vigra_precondition(n == columnCount(b),
       "joinVertically(): shape mismatch.");
    
    MultiArrayIndex ma = rowCount(a);
    MultiArrayIndex mb = rowCount(b);
    TemporaryMatrix<T> t(ma + mb, n, T());
    t.subarray(Shape(0,0), Shape(ma, n)) = a;
    t.subarray(Shape(ma,0), Shape(ma+mb, n)) = b;
    return t;
}

    /** Create new matrix by concatenating two matrices \a a and \a b horizontally, i.e. side by side.
        The two matrices must have the same number of rows.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
joinHorizontally(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    typedef typename TemporaryMatrix<T>::difference_type Shape;
    
    MultiArrayIndex m = rowCount(a);
    vigra_precondition(m == rowCount(b),
       "joinHorizontally(): shape mismatch.");
    
    MultiArrayIndex na = columnCount(a);
    MultiArrayIndex nb = columnCount(b);
    TemporaryMatrix<T> t(m, na + nb, T());
    t.subarray(Shape(0,0), Shape(m, na)) = a;
    t.subarray(Shape(0, na), Shape(m, na + nb)) = b;
    return t;
}

    /** Initialize a matrix with repeated copies of a given matrix.
    
        Matrix \a r will consist of \a verticalCount downward repetitions of \a v,
        and \a horizontalCount side-by-side repetitions. When \a v has size <tt>m</tt> by <tt>n</tt>,
        \a r must have size <tt>(m*verticalCount)</tt> by <tt>(n*horizontalCount)</tt>.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
void repeatMatrix(MultiArrayView<2, T, C1> const & v, MultiArrayView<2, T, C2> &r, 
                  unsigned int verticalCount, unsigned int horizontalCount)
{
    typedef typename Matrix<T>::difference_type Shape;

    MultiArrayIndex m = rowCount(v), n = columnCount(v);
    vigra_precondition(m*verticalCount == rowCount(r) && n*horizontalCount == columnCount(r),
        "repeatMatrix(): Shape mismatch.");
        
    for(MultiArrayIndex l=0; l<static_cast<MultiArrayIndex>(horizontalCount); ++l)
    {
        for(MultiArrayIndex k=0; k<static_cast<MultiArrayIndex>(verticalCount); ++k)
        {
            r.subarray(Shape(k*m, l*n), Shape((k+1)*m, (l+1)*n)) = v;
        }
    }
}

    /** Create a new matrix by repeating a given matrix.
    
        The resulting matrix \a r will consist of \a verticalCount downward repetitions of \a v,
        and \a horizontalCount side-by-side repetitions, i.e. it will be of size 
        <tt>(m*verticalCount)</tt> by <tt>(n*horizontalCount)</tt> when \a v has size <tt>m</tt> by <tt>n</tt>.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
TemporaryMatrix<T> 
repeatMatrix(MultiArrayView<2, T, C> const & v, unsigned int verticalCount, unsigned int horizontalCount)
{
    MultiArrayIndex m = rowCount(v), n = columnCount(v);
    TemporaryMatrix<T> ret(verticalCount*m, horizontalCount*n);
    repeatMatrix(v, ret, verticalCount, horizontalCount);
    return ret;
}

    /** add matrices \a a and \a b.
        The result is written into \a r. All three matrices must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void add(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b,
              MultiArrayView<2, T, C3> &r)
{
    const MultiArrayIndex rrows = rowCount(r);
    const MultiArrayIndex rcols = columnCount(r);
    vigra_precondition(rrows == rowCount(a) && rcols == columnCount(a) &&
                       rrows == rowCount(b) && rcols == columnCount(b),
                       "add(): Matrix shapes must agree.");

    for(MultiArrayIndex i = 0; i < rcols; ++i) {
        for(MultiArrayIndex j = 0; j < rrows; ++j) {
            r(j, i) = a(j, i) + b(j, i);
        }
    }
}

    /** add matrices \a a and \a b.
        The two matrices must have the same shape.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
operator+(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    return TemporaryMatrix<T>(a) += b;
}

template <class T, class C>
inline TemporaryMatrix<T>
operator+(const TemporaryMatrix<T> &a, const MultiArrayView<2, T, C> &b)
{
    return const_cast<TemporaryMatrix<T> &>(a) += b;
}

template <class T, class C>
inline TemporaryMatrix<T>
operator+(const MultiArrayView<2, T, C> &a, const TemporaryMatrix<T> &b)
{
    return const_cast<TemporaryMatrix<T> &>(b) += a;
}

template <class T>
inline TemporaryMatrix<T>
operator+(const TemporaryMatrix<T> &a, const TemporaryMatrix<T> &b)
{
    return const_cast<TemporaryMatrix<T> &>(a) += b;
}

    /** add scalar \a b to every element of the matrix \a a.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator+(const MultiArrayView<2, T, C> &a, T b)
{
    return TemporaryMatrix<T>(a) += b;
}

template <class T>
inline TemporaryMatrix<T>
operator+(const TemporaryMatrix<T> &a, T b)
{
    return const_cast<TemporaryMatrix<T> &>(a) += b;
}

    /** add scalar \a a to every element of the matrix \a b.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator+(T a, const MultiArrayView<2, T, C> &b)
{
    return TemporaryMatrix<T>(b) += a;
}

template <class T>
inline TemporaryMatrix<T>
operator+(T a, const TemporaryMatrix<T> &b)
{
    return const_cast<TemporaryMatrix<T> &>(b) += a;
}

    /** subtract matrix \a b from \a a.
        The result is written into \a r. All three matrices must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void sub(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b,
              MultiArrayView<2, T, C3> &r)
{
    const MultiArrayIndex rrows = rowCount(r);
    const MultiArrayIndex rcols = columnCount(r);
    vigra_precondition(rrows == rowCount(a) && rcols == columnCount(a) &&
                       rrows == rowCount(b) && rcols == columnCount(b),
                       "subtract(): Matrix shapes must agree.");

    for(MultiArrayIndex i = 0; i < rcols; ++i) {
        for(MultiArrayIndex j = 0; j < rrows; ++j) {
            r(j, i) = a(j, i) - b(j, i);
        }
    }
}

    /** subtract matrix \a b from \a a.
        The two matrices must have the same shape.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
operator-(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    return TemporaryMatrix<T>(a) -= b;
}

template <class T, class C>
inline TemporaryMatrix<T>
operator-(const TemporaryMatrix<T> &a, const MultiArrayView<2, T, C> &b)
{
    return const_cast<TemporaryMatrix<T> &>(a) -= b;
}

template <class T, class C>
TemporaryMatrix<T>
operator-(const MultiArrayView<2, T, C> &a, const TemporaryMatrix<T> &b)
{
    const MultiArrayIndex rows = rowCount(a);
    const MultiArrayIndex cols = columnCount(a);
    vigra_precondition(rows == b.rowCount() && cols == b.columnCount(),
       "Matrix::operator-(): Shape mismatch.");

    for(MultiArrayIndex i = 0; i < cols; ++i)
        for(MultiArrayIndex j = 0; j < rows; ++j)
            const_cast<TemporaryMatrix<T> &>(b)(j, i) = a(j, i) - b(j, i);
    return b;
}

template <class T>
inline TemporaryMatrix<T>
operator-(const TemporaryMatrix<T> &a, const TemporaryMatrix<T> &b)
{
    return const_cast<TemporaryMatrix<T> &>(a) -= b;
}

    /** negate matrix \a a.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator-(const MultiArrayView<2, T, C> &a)
{
    return TemporaryMatrix<T>(a) *= -NumericTraits<T>::one();
}

template <class T>
inline TemporaryMatrix<T>
operator-(const TemporaryMatrix<T> &a)
{
    return const_cast<TemporaryMatrix<T> &>(a) *= -NumericTraits<T>::one();
}

    /** subtract scalar \a b from every element of the matrix \a a.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator-(const MultiArrayView<2, T, C> &a, T b)
{
    return TemporaryMatrix<T>(a) -= b;
}

template <class T>
inline TemporaryMatrix<T>
operator-(const TemporaryMatrix<T> &a, T b)
{
    return const_cast<TemporaryMatrix<T> &>(a) -= b;
}

    /** subtract every element of the matrix \a b from scalar \a a.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator-(T a, const MultiArrayView<2, T, C> &b)
{
    return TemporaryMatrix<T>(b.shape(), a) -= b;
}

    /** calculate the inner product of two matrices representing vectors.
        Typically, matrix \a x has a single row, and matrix \a y has
        a single column, and the other dimensions match. In addition, this
        function handles the cases when either or both of the two inputs are 
        transposed (e.g. it can compute the dot product of two column vectors). 
        A <tt>PreconditionViolation</tt> exception is thrown when
        the shape conditions are violated. 

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2>
typename NormTraits<T>::SquaredNormType 
dot(const MultiArrayView<2, T, C1> &x, const MultiArrayView<2, T, C2> &y)
{
    typename NormTraits<T>::SquaredNormType ret = 
           NumericTraits<typename NormTraits<T>::SquaredNormType>::zero();
    if(y.shape(1) == 1)
    {
        std::ptrdiff_t size = y.shape(0);
        if(x.shape(0) == 1 && x.shape(1) == size) // proper scalar product
            for(std::ptrdiff_t i = 0; i < size; ++i)
                ret += x(0, i) * y(i, 0);
        else if(x.shape(1) == 1u && x.shape(0) == size) // two column vectors
            for(std::ptrdiff_t i = 0; i < size; ++i)
                ret += x(i, 0) * y(i, 0);
        else    
            vigra_precondition(false, "dot(): wrong matrix shapes.");
    }
    else if(y.shape(0) == 1)
    {
        std::ptrdiff_t size = y.shape(1);
        if(x.shape(0) == 1u && x.shape(1) == size) // two row vectors
            for(std::ptrdiff_t i = 0; i < size; ++i)
                ret += x(0, i) * y(0, i);
        else if(x.shape(1) == 1u && x.shape(0) == size) // column dot row
            for(std::ptrdiff_t i = 0; i < size; ++i)
                ret += x(i, 0) * y(0, i);
        else    
            vigra_precondition(false, "dot(): wrong matrix shapes.");
    }
    else
            vigra_precondition(false, "dot(): wrong matrix shapes.");
    return ret;
}

    /** calculate the inner product of two vectors. The vector
        lengths must match.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2>
typename NormTraits<T>::SquaredNormType 
dot(const MultiArrayView<1, T, C1> &x, const MultiArrayView<1, T, C2> &y)
{
    const MultiArrayIndex n = x.elementCount();
    vigra_precondition(n == y.elementCount(),
       "dot(): shape mismatch.");
    typename NormTraits<T>::SquaredNormType ret = 
                NumericTraits<typename NormTraits<T>::SquaredNormType>::zero();
    for(MultiArrayIndex i = 0; i < n; ++i)
        ret += x(i) * y(i);
    return ret;
}

    /** calculate the cross product of two vectors of length 3.
        The result is written into \a r.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void cross(const MultiArrayView<1, T, C1> &x, const MultiArrayView<1, T, C2> &y,
           MultiArrayView<1, T, C3> &r)
{
    vigra_precondition(3 == x.elementCount() && 3 == y.elementCount() && 3 == r.elementCount(),
       "cross(): vectors must have length 3.");
    r(0) = x(1)*y(2) - x(2)*y(1);
    r(1) = x(2)*y(0) - x(0)*y(2);
    r(2) = x(0)*y(1) - x(1)*y(0);
}

    /** calculate the cross product of two matrices representing vectors.
        That is, \a x, \a y, and \a r must have a single column of length 3. The result
        is written into \a r.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void cross(const MultiArrayView<2, T, C1> &x, const MultiArrayView<2, T, C2> &y,
           MultiArrayView<2, T, C3> &r)
{
    vigra_precondition(3 == rowCount(x) && 3 == rowCount(y) && 3 == rowCount(r) ,
       "cross(): vectors must have length 3.");
    r(0,0) = x(1,0)*y(2,0) - x(2,0)*y(1,0);
    r(1,0) = x(2,0)*y(0,0) - x(0,0)*y(2,0);
    r(2,0) = x(0,0)*y(1,0) - x(1,0)*y(0,0);
}

    /** calculate the cross product of two matrices representing vectors.
        That is, \a x, and \a y must have a single column of length 3. The result
        is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2>
TemporaryMatrix<T>
cross(const MultiArrayView<2, T, C1> &x, const MultiArrayView<2, T, C2> &y)
{
    TemporaryMatrix<T> ret(3, 1);
    cross(x, y, ret);
    return ret;
}
    /** calculate the outer product of two matrices representing vectors.
        That is, matrix \a x must have a single column, and matrix \a y must
        have a single row, and the other dimensions must match. The result
        is written into \a r.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void outer(const MultiArrayView<2, T, C1> &x, const MultiArrayView<2, T, C2> &y,
      MultiArrayView<2, T, C3> &r)
{
    const MultiArrayIndex rows = rowCount(r);
    const MultiArrayIndex cols = columnCount(r);
    vigra_precondition(rows == rowCount(x) && cols == columnCount(y) &&
                       1 == columnCount(x) && 1 == rowCount(y),
       "outer(): shape mismatch.");
    for(MultiArrayIndex i = 0; i < cols; ++i)
        for(MultiArrayIndex j = 0; j < rows; ++j)
            r(j, i) = x(j, 0) * y(0, i);
}

    /** calculate the outer product of two matrices representing vectors.
        That is, matrix \a x must have a single column, and matrix \a y must
        have a single row, and the other dimensions must match. The result
        is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C1, class C2>
TemporaryMatrix<T>
outer(const MultiArrayView<2, T, C1> &x, const MultiArrayView<2, T, C2> &y)
{
    const MultiArrayIndex rows = rowCount(x);
    const MultiArrayIndex cols = columnCount(y);
    vigra_precondition(1 == columnCount(x) && 1 == rowCount(y),
       "outer(): shape mismatch.");
    TemporaryMatrix<T> ret(rows, cols);
    outer(x, y, ret);
    return ret;
}

    /** calculate the outer product of a matrix (representing a vector) with itself.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, class C>
TemporaryMatrix<T>
outer(const MultiArrayView<2, T, C> &x)
{
    const MultiArrayIndex rows = rowCount(x);
    const MultiArrayIndex cols = columnCount(x);
    vigra_precondition(rows == 1 || cols == 1,
       "outer(): matrix does not represent a vector.");
    const MultiArrayIndex size = std::max(rows, cols);
    TemporaryMatrix<T> ret(size, size);

    if(rows == 1)
    {
        for(MultiArrayIndex i = 0; i < size; ++i)
            for(MultiArrayIndex j = 0; j < size; ++j)
                ret(j, i) = x(0, j) * x(0, i);
    }
    else
    {
        for(MultiArrayIndex i = 0; i < size; ++i)
            for(MultiArrayIndex j = 0; j < size; ++j)
                ret(j, i) = x(j, 0) * x(i, 0);
    }
    return ret;
}

    /** calculate the outer product of a TinyVector with itself.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespaces: vigra and vigra::linalg
     */
template <class T, int N>
TemporaryMatrix<T>
outer(const TinyVector<T, N> &x)
{
    TemporaryMatrix<T> ret(N, N);

    for(MultiArrayIndex i = 0; i < N; ++i)
        for(MultiArrayIndex j = 0; j < N; ++j)
            ret(j, i) = x[j] * x[i];
    return ret;
}

template <class T>
class PointWise
{
  public:
    T const & t;
    
    PointWise(T const & it)
    : t(it)
    {}
};

template <class T>
PointWise<T> pointWise(T const & t)
{
    return PointWise<T>(t);
}


    /** multiply matrix \a a with scalar \a b.
        The result is written into \a r. \a a and \a r must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
void smul(const MultiArrayView<2, T, C1> &a, T b, MultiArrayView<2, T, C2> &r)
{
    const MultiArrayIndex rows = rowCount(a);
    const MultiArrayIndex cols = columnCount(a);
    vigra_precondition(rows == rowCount(r) && cols == columnCount(r),
                       "smul(): Matrix sizes must agree.");

    for(MultiArrayIndex i = 0; i < cols; ++i)
        for(MultiArrayIndex j = 0; j < rows; ++j)
            r(j, i) = a(j, i) * b;
}

    /** multiply scalar \a a with matrix \a b.
        The result is written into \a r. \a b and \a r must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C2, class C3>
void smul(T a, const MultiArrayView<2, T, C2> &b, MultiArrayView<2, T, C3> &r)
{
    smul(b, a, r);
}

    /** perform matrix multiplication of matrices \a a and \a b.
        The result is written into \a r. The three matrices must have matching shapes.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void mmul(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b,
         MultiArrayView<2, T, C3> &r)
{
    const MultiArrayIndex rrows = rowCount(r);
    const MultiArrayIndex rcols = columnCount(r);
    const MultiArrayIndex acols = columnCount(a);
    vigra_precondition(rrows == rowCount(a) && rcols == columnCount(b) && acols == rowCount(b),
                       "mmul(): Matrix shapes must agree.");

    // order of loops ensures that inner loop goes down columns
    for(MultiArrayIndex i = 0; i < rcols; ++i) 
    {
        for(MultiArrayIndex j = 0; j < rrows; ++j) 
            r(j, i) = a(j, 0) * b(0, i);
        for(MultiArrayIndex k = 1; k < acols; ++k) 
            for(MultiArrayIndex j = 0; j < rrows; ++j) 
                r(j, i) += a(j, k) * b(k, i);
    }
}

    /** perform matrix multiplication of matrices \a a and \a b.
        \a a and \a b must have matching shapes.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
mmul(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    TemporaryMatrix<T> ret(rowCount(a), columnCount(b));
    mmul(a, b, ret);
    return ret;
}

    /** multiply two matrices \a a and \a b pointwise.
        The result is written into \a r. All three matrices must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void pmul(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b,
              MultiArrayView<2, T, C3> &r)
{
    const MultiArrayIndex rrows = rowCount(r);
    const MultiArrayIndex rcols = columnCount(r);
    vigra_precondition(rrows == rowCount(a) && rcols == columnCount(a) &&
                       rrows == rowCount(b) && rcols == columnCount(b),
                       "pmul(): Matrix shapes must agree.");

    for(MultiArrayIndex i = 0; i < rcols; ++i) {
        for(MultiArrayIndex j = 0; j < rrows; ++j) {
            r(j, i) = a(j, i) * b(j, i);
        }
    }
}

    /** multiply matrices \a a and \a b pointwise.
        \a a and \a b must have matching shapes.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
pmul(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    TemporaryMatrix<T> ret(a.shape());
    pmul(a, b, ret);
    return ret;
}

    /** multiply matrices \a a and \a b pointwise.
        \a a and \a b must have matching shapes.
        The result is returned as a temporary matrix.
        
        Usage:
        
        \code
        Matrix<double> a(m,n), b(m,n);
        
        Matrix<double> c = a * pointWise(b);
        // is equivalent to
        // Matrix<double> c = pmul(a, b);
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C, class U>
inline TemporaryMatrix<T>
operator*(const MultiArrayView<2, T, C> &a, PointWise<U> b)
{
    return pmul(a, b.t);
}

    /** multiply matrix \a a with scalar \a b.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator*(const MultiArrayView<2, T, C> &a, T b)
{
    return TemporaryMatrix<T>(a) *= b;
}

template <class T>
inline TemporaryMatrix<T>
operator*(const TemporaryMatrix<T> &a, T b)
{
    return const_cast<TemporaryMatrix<T> &>(a) *= b;
}

    /** multiply scalar \a a with matrix \a b.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator*(T a, const MultiArrayView<2, T, C> &b)
{
    return TemporaryMatrix<T>(b) *= a;
}

template <class T>
inline TemporaryMatrix<T>
operator*(T a, const TemporaryMatrix<T> &b)
{
    return const_cast<TemporaryMatrix<T> &>(b) *= a;
}

    /** multiply matrix \a a with TinyVector \a b.
        \a a must be of size <tt>N x N</tt>. Vector \a b and the result
        vector are interpreted as column vectors.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class A, int N, class DATA, class DERIVED>
TinyVector<T, N>
operator*(const Matrix<T, A> &a, const TinyVectorBase<T, N, DATA, DERIVED> &b)
{
    vigra_precondition(N == rowCount(a) && N == columnCount(a),
         "operator*(Matrix, TinyVector): Shape mismatch.");

    TinyVector<T, N> res = TinyVectorView<T, N>(&a(0,0)) * b[0];
    for(MultiArrayIndex i = 1; i < N; ++i)
        res += TinyVectorView<T, N>(&a(0,i)) * b[i];
    return res;
}

    /** multiply TinyVector \a a with matrix \a b.
        \a b must be of size <tt>N x N</tt>. Vector \a a and the result
        vector are interpreted as row vectors.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, int N, class DATA, class DERIVED, class A>
TinyVector<T, N>
operator*(const TinyVectorBase<T, N, DATA, DERIVED> &a, const Matrix<T, A> &b)
{
    vigra_precondition(N == rowCount(b) && N == columnCount(b),
         "operator*(TinyVector, Matrix): Shape mismatch.");

    TinyVector<T, N> res;
    for(MultiArrayIndex i = 0; i < N; ++i)
        res[i] = dot(a, TinyVectorView<T, N>(&b(0,i)));
    return res;
}

    /** perform matrix multiplication of matrices \a a and \a b.
        \a a and \a b must have matching shapes.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
operator*(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    TemporaryMatrix<T> ret(rowCount(a), columnCount(b));
    mmul(a, b, ret);
    return ret;
}

    /** divide matrix \a a by scalar \a b.
        The result is written into \a r. \a a and \a r must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
void sdiv(const MultiArrayView<2, T, C1> &a, T b, MultiArrayView<2, T, C2> &r)
{
    const MultiArrayIndex rows = rowCount(a);
    const MultiArrayIndex cols = columnCount(a);
    vigra_precondition(rows == rowCount(r) && cols == columnCount(r),
                       "sdiv(): Matrix sizes must agree.");

    for(MultiArrayIndex i = 0; i < cols; ++i)
        for(MultiArrayIndex j = 0; j < rows; ++j)
            r(j, i) = a(j, i) / b;
}

    /** divide two matrices \a a and \a b pointwise.
        The result is written into \a r. All three matrices must have the same shape.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2, class C3>
void pdiv(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b,
              MultiArrayView<2, T, C3> &r)
{
    const MultiArrayIndex rrows = rowCount(r);
    const MultiArrayIndex rcols = columnCount(r);
    vigra_precondition(rrows == rowCount(a) && rcols == columnCount(a) &&
                       rrows == rowCount(b) && rcols == columnCount(b),
                       "pdiv(): Matrix shapes must agree.");

    for(MultiArrayIndex i = 0; i < rcols; ++i) {
        for(MultiArrayIndex j = 0; j < rrows; ++j) {
            r(j, i) = a(j, i) / b(j, i);
        }
    }
}

    /** divide matrices \a a and \a b pointwise.
        \a a and \a b must have matching shapes.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C1, class C2>
inline TemporaryMatrix<T>
pdiv(const MultiArrayView<2, T, C1> &a, const MultiArrayView<2, T, C2> &b)
{
    TemporaryMatrix<T> ret(a.shape());
    pdiv(a, b, ret);
    return ret;
}

    /** divide matrices \a a and \a b pointwise.
        \a a and \a b must have matching shapes.
        The result is returned as a temporary matrix.
        
        Usage:
        
        \code
        Matrix<double> a(m,n), b(m,n);
        
        Matrix<double> c = a / pointWise(b);
        // is equivalent to
        // Matrix<double> c = pdiv(a, b);
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C, class U>
inline TemporaryMatrix<T>
operator/(const MultiArrayView<2, T, C> &a, PointWise<U> b)
{
    return pdiv(a, b.t);
}

    /** divide matrix \a a by scalar \a b.
        The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator/(const MultiArrayView<2, T, C> &a, T b)
{
    return TemporaryMatrix<T>(a) /= b;
}

template <class T>
inline TemporaryMatrix<T>
operator/(const TemporaryMatrix<T> &a, T b)
{
    return const_cast<TemporaryMatrix<T> &>(a) /= b;
}

    /** Create a matrix whose elements are the quotients between scalar \a a and
        matrix \a b. The result is returned as a temporary matrix.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: vigra::linalg
     */
template <class T, class C>
inline TemporaryMatrix<T>
operator/(T a, const MultiArrayView<2, T, C> &b)
{
    return TemporaryMatrix<T>(b.shape(), a) / pointWise(b);
}

using vigra::argMin;
using vigra::argMinIf;
using vigra::argMax;
using vigra::argMaxIf;

    /** \brief Find the index of the minimum element in a matrix.
    
        The function returns the index in column-major scan-order sense,
        i.e. according to the order used by <tt>MultiArrayView::operator[]</tt>.
        If the matrix is actually a vector, this is just the row or columns index.
        In case of a truly 2-dimensional matrix, the index can be converted to an 
        index pair by calling <tt>MultiArrayView::scanOrderIndexToCoordinate()</tt>
        
        <b>Required Interface:</b>
        
        \code
        bool f = a[0] < NumericTraits<T>::max();
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T, class C>
int argMin(MultiArrayView<2, T, C> const & a)
{
    T vopt = NumericTraits<T>::max();
    int best = -1;
    for(int k=0; k < a.size(); ++k)
    {
        if(a[k] < vopt)
        {
            vopt = a[k];
            best = k;
        }
    }
    return best;
}

    /** \brief Find the index of the maximum element in a matrix.
    
        The function returns the index in column-major scan-order sense,
        i.e. according to the order used by <tt>MultiArrayView::operator[]</tt>.
        If the matrix is actually a vector, this is just the row or columns index.
        In case of a truly 2-dimensional matrix, the index can be converted to an 
        index pair by calling <tt>MultiArrayView::scanOrderIndexToCoordinate()</tt>
        
        <b>Required Interface:</b>
        
        \code
        bool f = NumericTraits<T>::min() < a[0];
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T, class C>
int argMax(MultiArrayView<2, T, C> const & a)
{
    T vopt = NumericTraits<T>::min();
    int best = -1;
    for(int k=0; k < a.size(); ++k)
    {
        if(vopt < a[k])
        {
            vopt = a[k];
            best = k;
        }
    }
    return best;
}

    /** \brief Find the index of the minimum element in a matrix subject to a condition.
    
        The function returns <tt>-1</tt> if no element conforms to \a condition.
        Otherwise, the index of the maximum element is returned in column-major scan-order sense,
        i.e. according to the order used by <tt>MultiArrayView::operator[]</tt>.
        If the matrix is actually a vector, this is just the row or columns index.
        In case of a truly 2-dimensional matrix, the index can be converted to an 
        index pair by calling <tt>MultiArrayView::scanOrderIndexToCoordinate()</tt>
        
        <b>Required Interface:</b>
        
        \code
        bool c = condition(a[0]);
        bool f = a[0] < NumericTraits<T>::max();
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T, class C, class UnaryFunctor>
int argMinIf(MultiArrayView<2, T, C> const & a, UnaryFunctor condition)
{
    T vopt = NumericTraits<T>::max();
    int best = -1;
    for(int k=0; k < a.size(); ++k)
    {
        if(condition(a[k]) && a[k] < vopt)
        {
            vopt = a[k];
            best = k;
        }
    }
    return best;
}

    /** \brief Find the index of the maximum element in a matrix subject to a condition.
    
        The function returns <tt>-1</tt> if no element conforms to \a condition.
        Otherwise, the index of the maximum element is returned in column-major scan-order sense,
        i.e. according to the order used by <tt>MultiArrayView::operator[]</tt>.
        If the matrix is actually a vector, this is just the row or columns index.
        In case of a truly 2-dimensional matrix, the index can be converted to an 
        index pair by calling <tt>MultiArrayView::scanOrderIndexToCoordinate()</tt>
        
        <b>Required Interface:</b>
        
        \code
        bool c = condition(a[0]);
        bool f = NumericTraits<T>::min() < a[0];
        \endcode

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T, class C, class UnaryFunctor>
int argMaxIf(MultiArrayView<2, T, C> const & a, UnaryFunctor condition)
{
    T vopt = NumericTraits<T>::min();
    int best = -1;
    for(int k=0; k < a.size(); ++k)
    {
        if(condition(a[k]) && vopt < a[k])
        {
            vopt = a[k];
            best = k;
        }
    }
    return best;
}

/** Matrix point-wise power.
*/
template <class T, class C>
linalg::TemporaryMatrix<T> pow(MultiArrayView<2, T, C> const & v, T exponent)
{
    linalg::TemporaryMatrix<T> t(v.shape());
    MultiArrayIndex m = rowCount(v), n = columnCount(v);

    for(MultiArrayIndex i = 0; i < n; ++i)
        for(MultiArrayIndex j = 0; j < m; ++j)
            t(j, i) = vigra::pow(v(j, i), exponent);
    return t;
}

template <class T>
linalg::TemporaryMatrix<T> pow(linalg::TemporaryMatrix<T> const & v, T exponent)
{
    linalg::TemporaryMatrix<T> & t = const_cast<linalg::TemporaryMatrix<T> &>(v);
    MultiArrayIndex m = rowCount(t), n = columnCount(t);

    for(MultiArrayIndex i = 0; i < n; ++i)
        for(MultiArrayIndex j = 0; j < m; ++j)
            t(j, i) = vigra::pow(t(j, i), exponent);
    return t;
}

template <class T, class C>
linalg::TemporaryMatrix<T> pow(MultiArrayView<2, T, C> const & v, int exponent)
{
    linalg::TemporaryMatrix<T> t(v.shape());
    MultiArrayIndex m = rowCount(v), n = columnCount(v);

    for(MultiArrayIndex i = 0; i < n; ++i)
        for(MultiArrayIndex j = 0; j < m; ++j)
            t(j, i) = vigra::pow(v(j, i), exponent);
    return t;
}

template <class T>
linalg::TemporaryMatrix<T> pow(linalg::TemporaryMatrix<T> const & v, int exponent)
{
    linalg::TemporaryMatrix<T> & t = const_cast<linalg::TemporaryMatrix<T> &>(v);
    MultiArrayIndex m = rowCount(t), n = columnCount(t);

    for(MultiArrayIndex i = 0; i < n; ++i)
        for(MultiArrayIndex j = 0; j < m; ++j)
            t(j, i) = vigra::pow(t(j, i), exponent);
    return t;
}

template <class C>
linalg::TemporaryMatrix<int> pow(MultiArrayView<2, int, C> const & v, int exponent)
{
    linalg::TemporaryMatrix<int> t(v.shape());
    MultiArrayIndex m = rowCount(v), n = columnCount(v);

    for(MultiArrayIndex i = 0; i < n; ++i)
        for(MultiArrayIndex j = 0; j < m; ++j)
            t(j, i) = static_cast<int>(vigra::pow(static_cast<double>(v(j, i)), exponent));
    return t;
}

inline
linalg::TemporaryMatrix<int> pow(linalg::TemporaryMatrix<int> const & v, int exponent)
{
    linalg::TemporaryMatrix<int> & t = const_cast<linalg::TemporaryMatrix<int> &>(v);
    MultiArrayIndex m = rowCount(t), n = columnCount(t);

    for(MultiArrayIndex i = 0; i < n; ++i)
        for(MultiArrayIndex j = 0; j < m; ++j)
            t(j, i) = static_cast<int>(vigra::pow(static_cast<double>(t(j, i)), exponent));
    return t;
}

    /** Matrix point-wise sqrt. */
template <class T, class C>
linalg::TemporaryMatrix<T> sqrt(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise exp. */
template <class T, class C>
linalg::TemporaryMatrix<T> exp(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise log. */
template <class T, class C>
linalg::TemporaryMatrix<T> log(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise log10. */
template <class T, class C>
linalg::TemporaryMatrix<T> log10(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise sin. */
template <class T, class C>
linalg::TemporaryMatrix<T> sin(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise asin. */
template <class T, class C>
linalg::TemporaryMatrix<T> asin(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise cos. */
template <class T, class C>
linalg::TemporaryMatrix<T> cos(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise acos. */
template <class T, class C>
linalg::TemporaryMatrix<T> acos(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise tan. */
template <class T, class C>
linalg::TemporaryMatrix<T> tan(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise atan. */
template <class T, class C>
linalg::TemporaryMatrix<T> atan(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise round. */
template <class T, class C>
linalg::TemporaryMatrix<T> round(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise floor. */
template <class T, class C>
linalg::TemporaryMatrix<T> floor(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise ceil. */
template <class T, class C>
linalg::TemporaryMatrix<T> ceil(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise abs. */
template <class T, class C>
linalg::TemporaryMatrix<T> abs(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise square. */
template <class T, class C>
linalg::TemporaryMatrix<T> sq(MultiArrayView<2, T, C> const & v);
    /** Matrix point-wise sign. */
template <class T, class C>
linalg::TemporaryMatrix<T> sign(MultiArrayView<2, T, C> const & v);

#define VIGRA_MATRIX_UNARY_FUNCTION(FUNCTION, NAMESPACE) \
using NAMESPACE::FUNCTION; \
template <class T, class C> \
linalg::TemporaryMatrix<T> FUNCTION(MultiArrayView<2, T, C> const & v) \
{ \
    linalg::TemporaryMatrix<T> t(v.shape()); \
    MultiArrayIndex m = rowCount(v), n = columnCount(v); \
 \
    for(MultiArrayIndex i = 0; i < n; ++i) \
        for(MultiArrayIndex j = 0; j < m; ++j) \
            t(j, i) = NAMESPACE::FUNCTION(v(j, i)); \
    return t; \
} \
 \
template <class T> \
linalg::TemporaryMatrix<T> FUNCTION(linalg::Matrix<T> const & v) \
{ \
    linalg::TemporaryMatrix<T> t(v.shape()); \
    MultiArrayIndex m = rowCount(v), n = columnCount(v); \
 \
    for(MultiArrayIndex i = 0; i < n; ++i) \
        for(MultiArrayIndex j = 0; j < m; ++j) \
            t(j, i) = NAMESPACE::FUNCTION(v(j, i)); \
    return t; \
} \
 \
template <class T> \
linalg::TemporaryMatrix<T> FUNCTION(linalg::TemporaryMatrix<T> const & v) \
{ \
    linalg::TemporaryMatrix<T> & t = const_cast<linalg::TemporaryMatrix<T> &>(v); \
    MultiArrayIndex m = rowCount(t), n = columnCount(t); \
 \
    for(MultiArrayIndex i = 0; i < n; ++i) \
        for(MultiArrayIndex j = 0; j < m; ++j) \
            t(j, i) = NAMESPACE::FUNCTION(t(j, i)); \
    return v; \
}\
}\
using linalg::FUNCTION;\
namespace linalg {

VIGRA_MATRIX_UNARY_FUNCTION(sqrt, std)
VIGRA_MATRIX_UNARY_FUNCTION(exp, std)
VIGRA_MATRIX_UNARY_FUNCTION(log, std)
VIGRA_MATRIX_UNARY_FUNCTION(log10, std)
VIGRA_MATRIX_UNARY_FUNCTION(sin, std)
VIGRA_MATRIX_UNARY_FUNCTION(asin, std)
VIGRA_MATRIX_UNARY_FUNCTION(cos, std)
VIGRA_MATRIX_UNARY_FUNCTION(acos, std)
VIGRA_MATRIX_UNARY_FUNCTION(tan, std)
VIGRA_MATRIX_UNARY_FUNCTION(atan, std)
VIGRA_MATRIX_UNARY_FUNCTION(round, vigra)
VIGRA_MATRIX_UNARY_FUNCTION(floor, vigra)
VIGRA_MATRIX_UNARY_FUNCTION(ceil, vigra)
VIGRA_MATRIX_UNARY_FUNCTION(abs, vigra)
VIGRA_MATRIX_UNARY_FUNCTION(sq, vigra)
VIGRA_MATRIX_UNARY_FUNCTION(sign, vigra)

#undef VIGRA_MATRIX_UNARY_FUNCTION

//@}

} // namespace linalg

using linalg::RowMajor;
using linalg::ColumnMajor;
using linalg::Matrix;
using linalg::identityMatrix;
using linalg::diagonalMatrix;
using linalg::transpose;
using linalg::pointWise;
using linalg::trace;
using linalg::dot;
using linalg::cross;
using linalg::outer;
using linalg::rowCount;
using linalg::columnCount;
using linalg::rowVector;
using linalg::columnVector;
using linalg::subVector;
using linalg::isSymmetric;
using linalg::joinHorizontally;
using linalg::joinVertically;
using linalg::argMin;
using linalg::argMinIf;
using linalg::argMax;
using linalg::argMaxIf;

/********************************************************/
/*                                                      */
/*                       NormTraits                     */
/*                                                      */
/********************************************************/

template <class T, class ALLOC>
struct NormTraits<Matrix<T, ALLOC> >
: public NormTraits<MultiArray<2, T, ALLOC> >
{
    typedef NormTraits<MultiArray<2, T, ALLOC> > BaseType;
    typedef Matrix<T, ALLOC>                     Type;
    typedef typename BaseType::SquaredNormType   SquaredNormType;
    typedef typename BaseType::NormType          NormType;
};

template <class T, class ALLOC>
struct NormTraits<linalg::TemporaryMatrix<T, ALLOC> >
: public NormTraits<Matrix<T, ALLOC> >
{
    typedef NormTraits<Matrix<T, ALLOC> >        BaseType;
    typedef linalg::TemporaryMatrix<T, ALLOC>    Type;
    typedef typename BaseType::SquaredNormType   SquaredNormType;
    typedef typename BaseType::NormType          NormType;
};

} // namespace vigra

namespace std {

/** \addtogroup LinearAlgebraFunctions
 */
//@{

    /** print a matrix \a m to the stream \a s.

        <b>\#include</b> \<vigra/matrix.hxx\> or<br>
        <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
        Namespace: std
     */
template <class T, class C>
ostream &
operator<<(ostream & s, const vigra::MultiArrayView<2, T, C> &m)
{
    const vigra::MultiArrayIndex rows = vigra::linalg::rowCount(m);
    const vigra::MultiArrayIndex cols = vigra::linalg::columnCount(m);
    ios::fmtflags flags = s.setf(ios::right | ios::fixed, ios::adjustfield | ios::floatfield);
    for(vigra::MultiArrayIndex j = 0; j < rows; ++j)
    {
        for(vigra::MultiArrayIndex i = 0; i < cols; ++i)
        {
            s << m(j, i) << " ";
        }
        s << endl;
    }
    s.setf(flags);
    return s;
}

//@}

} // namespace std

namespace vigra {

namespace linalg {

namespace detail {

template <class T1, class C1, class T2, class C2, class T3, class C3>
void
columnStatisticsImpl(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & sumOfSquaredDifferences)
{
    MultiArrayIndex m = rowCount(A);
    MultiArrayIndex n = columnCount(A);
    vigra_precondition(1 == rowCount(mean) && n == columnCount(mean) &&
                       1 == rowCount(sumOfSquaredDifferences) && n == columnCount(sumOfSquaredDifferences),
                       "columnStatistics(): Shape mismatch between input and output.");

    // West's algorithm for incremental variance computation
    mean.init(NumericTraits<T2>::zero());
    sumOfSquaredDifferences.init(NumericTraits<T3>::zero());
    
    for(MultiArrayIndex k=0; k<m; ++k)
    {
        typedef typename NumericTraits<T2>::RealPromote TmpType;
        Matrix<T2> t = rowVector(A, k) - mean;
        TmpType f  = TmpType(1.0 / (k + 1.0)),
                f1 = TmpType(1.0 - f);
        mean += f*t;
        sumOfSquaredDifferences += f1*sq(t);
    }
}

template <class T1, class C1, class T2, class C2, class T3, class C3>
void
columnStatistics2PassImpl(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & sumOfSquaredDifferences)
{
    MultiArrayIndex m = rowCount(A);
    MultiArrayIndex n = columnCount(A);
    vigra_precondition(1 == rowCount(mean) && n == columnCount(mean) &&
                       1 == rowCount(sumOfSquaredDifferences) && n == columnCount(sumOfSquaredDifferences),
                       "columnStatistics(): Shape mismatch between input and output.");

    // two-pass algorithm for incremental variance computation
    mean.init(NumericTraits<T2>::zero());    
    for(MultiArrayIndex k=0; k<m; ++k)
    {
        mean += rowVector(A, k);
    }
    mean /= static_cast<double>(m);
    
    sumOfSquaredDifferences.init(NumericTraits<T3>::zero());
    for(MultiArrayIndex k=0; k<m; ++k)
    {
        sumOfSquaredDifferences += sq(rowVector(A, k) - mean);
    }
}

} // namespace detail

/** \addtogroup LinearAlgebraFunctions
 */
//@{
    /** Compute statistics of every column of matrix \a A.
    
    The result matrices must be row vectors with as many columns as \a A.

    <b> Declarations:</b>

    compute only the mean:
    \code
    namespace vigra { namespace linalg {
        template <class T1, class C1, class T2, class C2>
        void
        columnStatistics(MultiArrayView<2, T1, C1> const & A, 
                         MultiArrayView<2, T2, C2> & mean);
    } }
    \endcode

    compute mean and standard deviation:
    \code
    namespace vigra { namespace linalg {
        template <class T1, class C1, class T2, class C2, class T3, class C3>
        void
        columnStatistics(MultiArrayView<2, T1, C1> const & A, 
                         MultiArrayView<2, T2, C2> & mean, 
                         MultiArrayView<2, T3, C3> & stdDev);
    } }
    \endcode

    compute mean, standard deviation, and norm:
    \code
    namespace vigra { namespace linalg {
        template <class T1, class C1, class T2, class C2, class T3, class C3, class T4, class C4>
        void
        columnStatistics(MultiArrayView<2, T1, C1> const & A, 
                         MultiArrayView<2, T2, C2> & mean, 
                         MultiArrayView<2, T3, C3> & stdDev, 
                         MultiArrayView<2, T4, C4> & norm);
    } }
    \endcode

    <b> Usage:</b>

    <b>\#include</b> \<vigra/matrix.hxx\> or<br>
    <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
    Namespaces: vigra and vigra::linalg

    \code
    Matrix A(rows, columns);
    .. // fill A
    Matrix columnMean(1, columns), columnStdDev(1, columns), columnNorm(1, columns);

    columnStatistics(A, columnMean, columnStdDev, columnNorm);

    \endcode
    */
doxygen_overloaded_function(template <...> void columnStatistics)

template <class T1, class C1, class T2, class C2>
void
columnStatistics(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean)
{
    MultiArrayIndex m = rowCount(A);
    MultiArrayIndex n = columnCount(A);
    vigra_precondition(1 == rowCount(mean) && n == columnCount(mean),
                       "columnStatistics(): Shape mismatch between input and output.");

    mean.init(NumericTraits<T2>::zero());
    
    for(MultiArrayIndex k=0; k<m; ++k)
    {
        mean += rowVector(A, k);
    }
    mean /= T2(m);
}

template <class T1, class C1, class T2, class C2, class T3, class C3>
void
columnStatistics(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & stdDev)
{
    detail::columnStatisticsImpl(A, mean, stdDev);
    
    if(rowCount(A) > 1)
        stdDev = sqrt(stdDev / T3(rowCount(A) - 1.0));
}

template <class T1, class C1, class T2, class C2, class T3, class C3, class T4, class C4>
void
columnStatistics(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & stdDev, MultiArrayView<2, T4, C4> & norm)
{
    MultiArrayIndex m = rowCount(A);
    MultiArrayIndex n = columnCount(A);
    vigra_precondition(1 == rowCount(mean) && n == columnCount(mean) &&
                       1 == rowCount(stdDev) && n == columnCount(stdDev) &&
                       1 == rowCount(norm) && n == columnCount(norm),
                       "columnStatistics(): Shape mismatch between input and output.");

    detail::columnStatisticsImpl(A, mean, stdDev);
    norm = sqrt(stdDev + T2(m) * sq(mean));
    stdDev = sqrt(stdDev / T3(m - 1.0));
}

    /** Compute statistics of every row of matrix \a A.
    
    The result matrices must be column vectors with as many rows as \a A.

    <b> Declarations:</b>

    compute only the mean:
    \code
    namespace vigra { namespace linalg {
        template <class T1, class C1, class T2, class C2>
        void
        rowStatistics(MultiArrayView<2, T1, C1> const & A, 
                      MultiArrayView<2, T2, C2> & mean);
    } }
    \endcode

    compute mean and standard deviation:
    \code
    namespace vigra { namespace linalg {
        template <class T1, class C1, class T2, class C2, class T3, class C3>
        void
        rowStatistics(MultiArrayView<2, T1, C1> const & A, 
                      MultiArrayView<2, T2, C2> & mean, 
                      MultiArrayView<2, T3, C3> & stdDev);
    } }
    \endcode

    compute mean, standard deviation, and norm:
    \code
    namespace vigra { namespace linalg {
        template <class T1, class C1, class T2, class C2, class T3, class C3, class T4, class C4>
        void
        rowStatistics(MultiArrayView<2, T1, C1> const & A, 
                      MultiArrayView<2, T2, C2> & mean, 
                      MultiArrayView<2, T3, C3> & stdDev, 
                      MultiArrayView<2, T4, C4> & norm);
    } }
    \endcode

    <b> Usage:</b>

    <b>\#include</b> \<vigra/matrix.hxx\> or<br>
    <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
    Namespaces: vigra and vigra::linalg

    \code
    Matrix A(rows, columns);
    .. // fill A
    Matrix rowMean(rows, 1), rowStdDev(rows, 1), rowNorm(rows, 1);

    rowStatistics(a, rowMean, rowStdDev, rowNorm);

    \endcode
     */
doxygen_overloaded_function(template <...> void rowStatistics)

template <class T1, class C1, class T2, class C2>
void
rowStatistics(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean)
{
    vigra_precondition(1 == columnCount(mean) && rowCount(A) == rowCount(mean),
                       "rowStatistics(): Shape mismatch between input and output.");
    MultiArrayView<2, T2, StridedArrayTag> tm = transpose(mean);
    columnStatistics(transpose(A), tm);
}

template <class T1, class C1, class T2, class C2, class T3, class C3>
void
rowStatistics(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & stdDev)
{
    vigra_precondition(1 == columnCount(mean) && rowCount(A) == rowCount(mean) &&
                       1 == columnCount(stdDev) && rowCount(A) == rowCount(stdDev),
                       "rowStatistics(): Shape mismatch between input and output.");
    MultiArrayView<2, T2, StridedArrayTag> tm = transpose(mean);
    MultiArrayView<2, T3, StridedArrayTag> ts = transpose(stdDev);
    columnStatistics(transpose(A), tm, ts);
}

template <class T1, class C1, class T2, class C2, class T3, class C3, class T4, class C4>
void
rowStatistics(MultiArrayView<2, T1, C1> const & A, 
                 MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & stdDev, MultiArrayView<2, T4, C4> & norm)
{
    vigra_precondition(1 == columnCount(mean) && rowCount(A) == rowCount(mean) &&
                       1 == columnCount(stdDev) && rowCount(A) == rowCount(stdDev) &&
                       1 == columnCount(norm) && rowCount(A) == rowCount(norm),
                       "rowStatistics(): Shape mismatch between input and output.");
    MultiArrayView<2, T2, StridedArrayTag> tm = transpose(mean);
    MultiArrayView<2, T3, StridedArrayTag> ts = transpose(stdDev); 
    MultiArrayView<2, T4, StridedArrayTag> tn = transpose(norm);
    columnStatistics(transpose(A), tm, ts, tn);
}

namespace detail {

template <class T1, class C1, class U, class T2, class C2, class T3, class C3>
void updateCovarianceMatrix(MultiArrayView<2, T1, C1> const & features,
                       U & count, MultiArrayView<2, T2, C2> & mean, MultiArrayView<2, T3, C3> & covariance)
{
    MultiArrayIndex n = std::max(rowCount(features), columnCount(features));
    vigra_precondition(std::min(rowCount(features), columnCount(features)) == 1,
          "updateCovarianceMatrix(): Features must be a row or column vector.");
    vigra_precondition(mean.shape() == features.shape(),
          "updateCovarianceMatrix(): Shape mismatch between feature vector and mean vector.");
    vigra_precondition(n == rowCount(covariance) && n == columnCount(covariance),
          "updateCovarianceMatrix(): Shape mismatch between feature vector and covariance matrix.");
    
    // West's algorithm for incremental covariance matrix computation
    Matrix<T2> t = features - mean;
    ++count;
    T2 f  = T2(1.0) / count,
       f1 = T2(1.0) - f;
    mean += f*t;
    
    if(rowCount(features) == 1) // update column covariance from current row
    {
        for(MultiArrayIndex k=0; k<n; ++k)
        {
            covariance(k, k) += f1*sq(t(0, k));
            for(MultiArrayIndex l=k+1; l<n; ++l)
            {
                covariance(k, l) += f1*t(0, k)*t(0, l);
                covariance(l, k) = covariance(k, l);
            }
        }
    }
    else // update row covariance from current column
    {
        for(MultiArrayIndex k=0; k<n; ++k)
        {
            covariance(k, k) += f1*sq(t(k, 0));
            for(MultiArrayIndex l=k+1; l<n; ++l)
            {
                covariance(k, l) += f1*t(k, 0)*t(l, 0);
                covariance(l, k) = covariance(k, l);
            }
        }
    }
}

} // namespace detail

    /** \brief Compute the covariance matrix between the columns of a matrix \a features.
    
        The result matrix \a covariance must by a square matrix with as many rows and
        columns as the number of columns in matrix \a features.

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T1, class C1, class T2, class C2>
void covarianceMatrixOfColumns(MultiArrayView<2, T1, C1> const & features,
                               MultiArrayView<2, T2, C2> & covariance)
{
    MultiArrayIndex m = rowCount(features), n = columnCount(features);
    vigra_precondition(n == rowCount(covariance) && n == columnCount(covariance),
          "covarianceMatrixOfColumns(): Shape mismatch between feature matrix and covariance matrix.");
    MultiArrayIndex count = 0;
    Matrix<T2> means(1, n);
    covariance.init(NumericTraits<T2>::zero());
    for(MultiArrayIndex k=0; k<m; ++k)
        detail::updateCovarianceMatrix(rowVector(features, k), count, means, covariance);
    covariance /= T2(m - 1);
}

    /** \brief Compute the covariance matrix between the columns of a matrix \a features.
    
        The result is returned as a square temporary matrix with as many rows and
        columns as the number of columns in matrix \a features.

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T, class C>
TemporaryMatrix<T> 
covarianceMatrixOfColumns(MultiArrayView<2, T, C> const & features)
{
    TemporaryMatrix<T> res(columnCount(features), columnCount(features));
    covarianceMatrixOfColumns(features, res);
    return res;
}

    /** \brief Compute the covariance matrix between the rows of a matrix \a features.
    
        The result matrix \a covariance must by a square matrix with as many rows and
        columns as the number of rows in matrix \a features.

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T1, class C1, class T2, class C2>
void covarianceMatrixOfRows(MultiArrayView<2, T1, C1> const & features,
                            MultiArrayView<2, T2, C2> & covariance)
{
    MultiArrayIndex m = rowCount(features), n = columnCount(features);
    vigra_precondition(m == rowCount(covariance) && m == columnCount(covariance),
          "covarianceMatrixOfRows(): Shape mismatch between feature matrix and covariance matrix.");
    MultiArrayIndex count = 0;
    Matrix<T2> means(m, 1);
    covariance.init(NumericTraits<T2>::zero());
    for(MultiArrayIndex k=0; k<n; ++k)
        detail::updateCovarianceMatrix(columnVector(features, k), count, means, covariance);
    covariance /= T2(n - 1);
}

    /** \brief Compute the covariance matrix between the rows of a matrix \a features.
    
        The result is returned as a square temporary matrix with as many rows and
        columns as the number of rows in matrix \a features.

        <b>\#include</b> \<vigra/matrix.hxx\><br>
        Namespace: vigra
    */
template <class T, class C>
TemporaryMatrix<T> 
covarianceMatrixOfRows(MultiArrayView<2, T, C> const & features)
{
    TemporaryMatrix<T> res(rowCount(features), rowCount(features));
    covarianceMatrixOfRows(features, res);
    return res;
}

enum DataPreparationGoals { ZeroMean = 1, UnitVariance = 2, UnitNorm = 4, UnitSum = 8 };

inline DataPreparationGoals operator|(DataPreparationGoals l, DataPreparationGoals r)
{
    return DataPreparationGoals(int(l) | int(r));
}

namespace detail {

template <class T, class C1, class C2, class C3, class C4>
void
prepareDataImpl(const MultiArrayView<2, T, C1> & A, 
               MultiArrayView<2, T, C2> & res, MultiArrayView<2, T, C3> & offset, MultiArrayView<2, T, C4> & scaling, 
               DataPreparationGoals goals)
{
    MultiArrayIndex m = rowCount(A);
    MultiArrayIndex n = columnCount(A);
    vigra_precondition(A.shape() == res.shape() && 
                       n == columnCount(offset) && 1 == rowCount(offset) &&
                       n == columnCount(scaling) && 1 == rowCount(scaling),
                       "prepareDataImpl(): Shape mismatch between input and output.");

    if(!goals)
    {
        res = A;
        offset.init(NumericTraits<T>::zero());
        scaling.init(NumericTraits<T>::one());
        return;
    }
    
    bool zeroMean = (goals & ZeroMean) != 0;
    bool unitVariance = (goals & UnitVariance) != 0;
    bool unitNorm = (goals & UnitNorm) != 0;
    bool unitSum = (goals & UnitSum) != 0;

    if(unitSum)
    {
        vigra_precondition(goals == UnitSum,
             "prepareData(): Unit sum is not compatible with any other data preparation goal.");
             
        transformMultiArray(srcMultiArrayRange(A), destMultiArrayRange(scaling), FindSum<T>());
        
        offset.init(NumericTraits<T>::zero());
        
        for(MultiArrayIndex k=0; k<n; ++k)
        {
            if(scaling(0, k) != NumericTraits<T>::zero())
            {
                scaling(0, k) = NumericTraits<T>::one() / scaling(0, k);
                columnVector(res, k) = columnVector(A, k) * scaling(0, k);
            }
            else
            {
                scaling(0, k) = NumericTraits<T>::one();
            }
        }
        
        return;     
    }

    vigra_precondition(!(unitVariance && unitNorm),
        "prepareData(): Unit variance and unit norm cannot be achieved at the same time.");

    Matrix<T> mean(1, n), sumOfSquaredDifferences(1, n);
    detail::columnStatisticsImpl(A, mean, sumOfSquaredDifferences);
    
    for(MultiArrayIndex k=0; k<n; ++k)
    {
        T stdDev = std::sqrt(sumOfSquaredDifferences(0, k) / T(m-1));
        if(closeAtTolerance(stdDev / mean(0,k), NumericTraits<T>::zero()))
            stdDev = NumericTraits<T>::zero();
        if(zeroMean && stdDev > NumericTraits<T>::zero()) 
        {
            columnVector(res, k) = columnVector(A, k) - mean(0,k);
            offset(0, k) = mean(0, k);
            mean(0, k) = NumericTraits<T>::zero();
        }
        else 
        {
            columnVector(res, k) = columnVector(A, k);
            offset(0, k) = NumericTraits<T>::zero();
        }
        
        T norm = mean(0,k) == NumericTraits<T>::zero()
                  ? std::sqrt(sumOfSquaredDifferences(0, k))
                  : std::sqrt(sumOfSquaredDifferences(0, k) + T(m) * sq(mean(0,k)));
        if(unitNorm && norm > NumericTraits<T>::zero())
        {
            columnVector(res, k) /= norm;
            scaling(0, k) = NumericTraits<T>::one() / norm;
        }
        else if(unitVariance && stdDev > NumericTraits<T>::zero())
        {
            columnVector(res, k) /= stdDev;
            scaling(0, k) = NumericTraits<T>::one() / stdDev;
        }
        else
        {
            scaling(0, k) = NumericTraits<T>::one();
        }
    }
}

} // namespace detail

    /** \brief Standardize the columns of a matrix according to given <tt>DataPreparationGoals</tt>.
    
    For every column of the matrix \a A, this function computes mean, 
    standard deviation, and norm. It then applies a linear transformation to the values of 
    the column according to these statistics and the given <tt>DataPreparationGoals</tt>.
    The result is returned in matrix \a res which must have the same size as \a A.
    Optionally, the transformation applied can also be returned in the matrices \a offset
    and \a scaling (see below for an example how these matrices can be used to standardize
    more data according to the same transformation).
    
    The following <tt>DataPreparationGoals</tt> are supported:
    
    <DL>
    <DT><tt>ZeroMean</tt><DD> Subtract the column mean form every column if the values in the column are not constant. 
                              Do nothing in a constant column.
    <DT><tt>UnitSum</tt><DD> Scale the columns so that the their sum is one if the sum was initially non-zero. 
                              Do nothing in a zero-sum column.
    <DT><tt>UnitVariance</tt><DD> Divide by the column standard deviation if the values in the column are not constant. 
                              Do nothing in a constant column.
    <DT><tt>UnitNorm</tt><DD> Divide by the column norm if it is non-zero.
    <DT><tt>ZeroMean | UnitVariance</tt><DD> First subtract the mean and then divide by the standard deviation, unless the 
                                             column is constant (in which case the column remains unchanged).
    <DT><tt>ZeroMean | UnitNorm</tt><DD> If the column is non-constant, subtract the mean. Then divide by the norm
                                         of the result if the norm is non-zero.
    </DL>

    <b> Declarations:</b>

    Standardize the matrix and return the parameters of the linear transformation.
    The matrices \a offset and \a scaling must be row vectors with as many columns as \a A.
    \code
    namespace vigra { namespace linalg {
        template <class T, class C1, class C2, class C3, class C4>
        void
        prepareColumns(MultiArrayView<2, T, C1> const & A, 
                       MultiArrayView<2, T, C2> & res, 
                       MultiArrayView<2, T, C3> & offset, 
                       MultiArrayView<2, T, C4> & scaling, 
                       DataPreparationGoals goals = ZeroMean | UnitVariance);
    } }
    \endcode

    Only standardize the matrix.
    \code
    namespace vigra { namespace linalg {
        template <class T, class C1, class C2>
        void
        prepareColumns(MultiArrayView<2, T, C1> const & A, 
                       MultiArrayView<2, T, C2> & res, 
                       DataPreparationGoals goals = ZeroMean | UnitVariance);
    } }
    \endcode

    <b> Usage:</b>

    <b>\#include</b> \<vigra/matrix.hxx\> or<br>
    <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
    Namespaces: vigra and vigra::linalg

    \code
    Matrix A(rows, columns);
    .. // fill A
    Matrix standardizedA(rows, columns), offset(1, columns), scaling(1, columns);

    prepareColumns(A, standardizedA, offset, scaling, ZeroMean | UnitNorm);
    
    // use offset and scaling to prepare additional data according to the same transformation
    Matrix newData(nrows, columns);
    
    Matrix standardizedNewData = (newData - repeatMatrix(offset, nrows, 1)) * pointWise(repeatMatrix(scaling, nrows, 1));

    \endcode
    */
doxygen_overloaded_function(template <...> void prepareColumns)

template <class T, class C1, class C2, class C3, class C4>
inline void
prepareColumns(MultiArrayView<2, T, C1> const & A, 
               MultiArrayView<2, T, C2> & res, MultiArrayView<2, T, C3> & offset, MultiArrayView<2, T, C4> & scaling, 
               DataPreparationGoals goals = ZeroMean | UnitVariance)
{
    detail::prepareDataImpl(A, res, offset, scaling, goals);
}

template <class T, class C1, class C2>
inline void
prepareColumns(MultiArrayView<2, T, C1> const & A, MultiArrayView<2, T, C2> & res, 
               DataPreparationGoals goals = ZeroMean | UnitVariance)
{
    Matrix<T> offset(1, columnCount(A)), scaling(1, columnCount(A));
    detail::prepareDataImpl(A, res, offset, scaling, goals);
}

    /** \brief Standardize the rows of a matrix according to given <tt>DataPreparationGoals</tt>.
    
    This algorithm works in the same way as \ref prepareColumns() (see there for detailed
    documentation), but is applied to the rows of the matrix \a A instead. Accordingly, the
    matrices holding the parameters of the linear transformation must be column vectors
    with as many rows as \a A.

    <b> Declarations:</b>

    Standardize the matrix and return the parameters of the linear transformation.
    The matrices \a offset and \a scaling must be column vectors
    with as many rows as \a A.
    
    \code
    namespace vigra { namespace linalg {
        template <class T, class C1, class C2, class C3, class C4>
        void
        prepareRows(MultiArrayView<2, T, C1> const & A, 
                    MultiArrayView<2, T, C2> & res, 
                    MultiArrayView<2, T, C3> & offset, 
                    MultiArrayView<2, T, C4> & scaling, 
                    DataPreparationGoals goals = ZeroMean | UnitVariance)´;
    } }
    \endcode

    Only standardize the matrix.
    \code
    namespace vigra { namespace linalg {
        template <class T, class C1, class C2>
        void
        prepareRows(MultiArrayView<2, T, C1> const & A, 
                    MultiArrayView<2, T, C2> & res, 
                    DataPreparationGoals goals = ZeroMean | UnitVariance);
    } }
    \endcode

    <b> Usage:</b>

    <b>\#include</b> \<vigra/matrix.hxx\> or<br>
    <b>\#include</b> \<vigra/linear_algebra.hxx\><br>
    Namespaces: vigra and vigra::linalg

    \code
    Matrix A(rows, columns);
    .. // fill A
    Matrix standardizedA(rows, columns), offset(rows, 1), scaling(rows, 1);

    prepareRows(A, standardizedA, offset, scaling, ZeroMean | UnitNorm);
    
    // use offset and scaling to prepare additional data according to the same transformation
    Matrix newData(rows, ncolumns);
    
    Matrix standardizedNewData = (newData - repeatMatrix(offset, 1, ncolumns)) * pointWise(repeatMatrix(scaling, 1, ncolumns));

    \endcode
*/
doxygen_overloaded_function(template <...> void prepareRows)

template <class T, class C1, class C2, class C3, class C4>
inline void
prepareRows(MultiArrayView<2, T, C1> const & A, 
            MultiArrayView<2, T, C2> & res, MultiArrayView<2, T, C3> & offset, MultiArrayView<2, T, C4> & scaling, 
            DataPreparationGoals goals = ZeroMean | UnitVariance)
{
    MultiArrayView<2, T, StridedArrayTag> tr = transpose(res), to = transpose(offset), ts = transpose(scaling);
    detail::prepareDataImpl(transpose(A), tr, to, ts, goals);
}

template <class T, class C1, class C2>
inline void
prepareRows(MultiArrayView<2, T, C1> const & A, MultiArrayView<2, T, C2> & res, 
            DataPreparationGoals goals = ZeroMean | UnitVariance)
{
    MultiArrayView<2, T, StridedArrayTag> tr = transpose(res);
    Matrix<T> offset(1, rowCount(A)), scaling(1, rowCount(A));
    detail::prepareDataImpl(transpose(A), tr, offset, scaling, goals);
}

//@}

} // namespace linalg

using linalg::columnStatistics;
using linalg::prepareColumns;
using linalg::rowStatistics;
using linalg::prepareRows;
using linalg::ZeroMean;
using linalg::UnitVariance;
using linalg::UnitNorm;
using linalg::UnitSum;

}  // namespace vigra



#endif // VIGRA_MATRIX_HXX
