libvigraimpex-dev 1.9.0+dfsg-10+b2 / usr / include / vigra / mathutil.hxx

/************************************************************************/
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/*               Copyright 1998-2011 by Ullrich Koethe                  */
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#ifndef VIGRA_MATHUTIL_HXX
#define VIGRA_MATHUTIL_HXX

#ifdef _MSC_VER
# pragma warning (disable: 4996) // hypot/_hypot confusion
#endif

#include <cmath>
#include <cstdlib>
#include <complex>
#include "config.hxx"
#include "error.hxx"
#include "tuple.hxx"
#include "sized_int.hxx"
#include "numerictraits.hxx"
#include "algorithm.hxx"

/*! \page MathConstants Mathematical Constants

    <TT>M_PI, M_SQRT2 etc.</TT>

    <b>\#include</b> \<vigra/mathutil.hxx\>

    Since mathematical constants such as <TT>M_PI</TT> and <TT>M_SQRT2</TT> 
    are not officially standardized, we provide definitions here for those 
    compilers that don't support them.

    \code
    #ifndef M_PI
    #    define M_PI     3.14159265358979323846
    #endif

    #ifndef M_SQRT2
    #    define M_2_PI   0.63661977236758134308
    #endif

    #ifndef M_PI_2
    #    define M_PI_2   1.57079632679489661923
    #endif

    #ifndef M_PI_4
    #    define M_PI_4   0.78539816339744830962
    #endif

    #ifndef M_SQRT2
    #    define M_SQRT2  1.41421356237309504880
    #endif

    #ifndef M_EULER_GAMMA
    #    define M_EULER_GAMMA  0.5772156649015329
    #endif
    \endcode
*/
#ifndef M_PI
#    define M_PI     3.14159265358979323846
#endif

#ifndef M_2_PI
#    define M_2_PI   0.63661977236758134308
#endif

#ifndef M_PI_2
#    define M_PI_2   1.57079632679489661923
#endif

#ifndef M_PI_4
#    define M_PI_4   0.78539816339744830962
#endif

#ifndef M_SQRT2
#    define M_SQRT2  1.41421356237309504880
#endif

#ifndef M_EULER_GAMMA
#    define M_EULER_GAMMA  0.5772156649015329
#endif

namespace vigra {

/** \addtogroup MathFunctions Mathematical Functions

    Useful mathematical functions and functors.
*/
//@{
// import functions into namespace vigra which VIGRA is going to overload

using VIGRA_CSTD::pow;  
using VIGRA_CSTD::floor;  
using VIGRA_CSTD::ceil;  
using VIGRA_CSTD::exp;  

// import abs(float), abs(double), abs(long double) from <cmath>
//        abs(int), abs(long), abs(long long) from <cstdlib>
//        abs(std::complex<T>) from <complex>
using std::abs;  

// define the missing variants of abs() to avoid 'ambiguous overload'
// errors in template functions
#define VIGRA_DEFINE_UNSIGNED_ABS(T) \
    inline T abs(T t) { return t; }

VIGRA_DEFINE_UNSIGNED_ABS(bool)
VIGRA_DEFINE_UNSIGNED_ABS(unsigned char)
VIGRA_DEFINE_UNSIGNED_ABS(unsigned short)
VIGRA_DEFINE_UNSIGNED_ABS(unsigned int)
VIGRA_DEFINE_UNSIGNED_ABS(unsigned long)
VIGRA_DEFINE_UNSIGNED_ABS(unsigned long long)

#undef VIGRA_DEFINE_UNSIGNED_ABS

#define VIGRA_DEFINE_MISSING_ABS(T) \
    inline T abs(T t) { return t < 0 ? static_cast<T>(-t) : t; }

VIGRA_DEFINE_MISSING_ABS(signed char)
VIGRA_DEFINE_MISSING_ABS(signed short)

#if defined(_MSC_VER) && _MSC_VER < 1600
VIGRA_DEFINE_MISSING_ABS(signed long long)
#endif

#undef VIGRA_DEFINE_MISSING_ABS

// scalar dot is needed for generic functions that should work with
// scalars and vectors alike

#define VIGRA_DEFINE_SCALAR_DOT(T) \
    inline NumericTraits<T>::Promote dot(T l, T r) { return l*r; }

VIGRA_DEFINE_SCALAR_DOT(unsigned char)
VIGRA_DEFINE_SCALAR_DOT(unsigned short)
VIGRA_DEFINE_SCALAR_DOT(unsigned int)
VIGRA_DEFINE_SCALAR_DOT(unsigned long)
VIGRA_DEFINE_SCALAR_DOT(unsigned long long)
VIGRA_DEFINE_SCALAR_DOT(signed char)
VIGRA_DEFINE_SCALAR_DOT(signed short)
VIGRA_DEFINE_SCALAR_DOT(signed int)
VIGRA_DEFINE_SCALAR_DOT(signed long)
VIGRA_DEFINE_SCALAR_DOT(signed long long)
VIGRA_DEFINE_SCALAR_DOT(float)
VIGRA_DEFINE_SCALAR_DOT(double)
VIGRA_DEFINE_SCALAR_DOT(long double)

#undef VIGRA_DEFINE_SCALAR_DOT

using std::pow;

// support 'double' exponents for all floating point versions of pow()

inline float pow(float v, double e)
{
    return std::pow(v, (float)e);
}

inline long double pow(long double v, double e)
{
    return std::pow(v, (long double)e);
}

    /*! The rounding function.

        Defined for all floating point types. Rounds towards the nearest integer 
        such that <tt>abs(round(t)) == round(abs(t))</tt> for all <tt>t</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
#ifdef DOXYGEN // only for documentation
REAL round(REAL v);
#endif

inline float round(float t)
{
     return t >= 0.0
                ? floor(t + 0.5f)
                : ceil(t - 0.5f);
}

inline double round(double t)
{
     return t >= 0.0
                ? floor(t + 0.5)
                : ceil(t - 0.5);
}

inline long double round(long double t)
{
     return t >= 0.0
                ? floor(t + 0.5)
                : ceil(t - 0.5);
}


    /*! Round and cast to integer.

        Rounds to the nearest integer like round(), but casts the result to 
        <tt>int</tt> (this will be faster and is usually needed anyway).

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline int roundi(double t)
{
     return t >= 0.0
                ? int(t + 0.5)
                : int(t - 0.5);
}

    /*! Round up to the nearest power of 2.

        Efficient algorithm for finding the smallest power of 2 which is not smaller than \a x
        (function clp2() from Henry Warren: "Hacker's Delight", Addison-Wesley, 2003,
         see http://www.hackersdelight.org/).
        If \a x > 2^31, the function will return 0 because integer arithmetic is defined modulo 2^32.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline UInt32 ceilPower2(UInt32 x) 
{
    if(x == 0) return 0;
    
    x = x - 1;
    x = x | (x >> 1);
    x = x | (x >> 2);
    x = x | (x >> 4);
    x = x | (x >> 8);
    x = x | (x >>16);
    return x + 1;
} 
    
    /*! Round down to the nearest power of 2.

        Efficient algorithm for finding the largest power of 2 which is not greater than \a x
        (function flp2() from Henry Warren: "Hacker's Delight", Addison-Wesley, 2003,
         see http://www.hackersdelight.org/).

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline UInt32 floorPower2(UInt32 x) 
{ 
    x = x | (x >> 1);
    x = x | (x >> 2);
    x = x | (x >> 4);
    x = x | (x >> 8);
    x = x | (x >>16);
    return x - (x >> 1);
}

namespace detail {

template <class T>
class IntLog2
{
  public:
    static Int32 table[64];
};

template <class T>
Int32 IntLog2<T>::table[64] = {
         -1,  0,  -1,  15,  -1,  1,  28,  -1,  16,  -1,  -1,  -1,  2,  21,  
         29,  -1,  -1,  -1,  19,  17,  10,  -1,  12,  -1,  -1,  3,  -1,  6,  
         -1,  22,  30,  -1,  14,  -1,  27,  -1,  -1,  -1,  20,  -1,  18,  9,  
         11,  -1,  5,  -1,  -1,  13,  26,  -1,  -1,  8,  -1,  4,  -1,  25,  
         -1,  7,  24,  -1,  23,  -1,  31,  -1};

} // namespace detail

    /*! Compute the base-2 logarithm of an integer.

        Returns the position of the left-most 1-bit in the given number \a x, or
        -1 if \a x == 0. That is,
        
        \code
        assert(k >= 0 && k < 32 && log2i(1 << k) == k);
        \endcode
        
        The function uses Robert Harley's algorithm to determine the number of leading zeros
        in \a x (algorithm nlz10() at http://www.hackersdelight.org/). But note that the functions
        \ref floorPower2() or \ref ceilPower2() are more efficient and should be preferred when possible.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline Int32 log2i(UInt32 x) 
{ 
    // Propagate leftmost 1-bit to the right.
    x = x | (x >> 1);
    x = x | (x >> 2);
    x = x | (x >> 4);
    x = x | (x >> 8);
    x = x | (x >>16);
    x = x*0x06EB14F9; // Multiplier is 7*255**3. 
    return detail::IntLog2<Int32>::table[x >> 26];
}

    /*! The square function.

        <tt>sq(x) = x*x</tt> is needed so often that it makes sense to define it as a function.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T>
inline 
typename NumericTraits<T>::Promote sq(T t)
{
    return t*t;
}

namespace detail {

template <class V, unsigned>
struct cond_mult
{
    static V call(const V & x, const V & y) { return x * y; }
};
template <class V>
struct cond_mult<V, 0>
{
    static V call(const V &, const V & y) { return y; }
};

template <class V, unsigned n>
struct power_static
{
    static V call(const V & x)
    {
        return n / 2
            ? cond_mult<V, n & 1>::call(x, power_static<V, n / 2>::call(x * x))
            : n & 1 ? x : V();
    }
};
template <class V>
struct power_static<V, 0>
{
    static V call(const V & x)
    {
        return V(1);
    }
};

} // namespace detail

    /*! Exponentiation to a positive integer power by squaring.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <unsigned n, class V>
inline V power(const V & x)
{
    return detail::power_static<V, n>::call(x);
}
//doxygen_overloaded_function(template <unsigned n, class V> power(const V & x))

namespace detail {

template <class T>
class IntSquareRoot
{
  public:
    static UInt32 sqq_table[];
    static UInt32 exec(UInt32 v);
};

template <class T>
UInt32 IntSquareRoot<T>::sqq_table[] = {
           0,  16,  22,  27,  32,  35,  39,  42,  45,  48,  50,  53,  55,  57,
          59,  61,  64,  65,  67,  69,  71,  73,  75,  76,  78,  80,  81,  83,
          84,  86,  87,  89,  90,  91,  93,  94,  96,  97,  98,  99, 101, 102,
         103, 104, 106, 107, 108, 109, 110, 112, 113, 114, 115, 116, 117, 118,
         119, 120, 121, 122, 123, 124, 125, 126, 128, 128, 129, 130, 131, 132,
         133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 144, 145,
         146, 147, 148, 149, 150, 150, 151, 152, 153, 154, 155, 155, 156, 157,
         158, 159, 160, 160, 161, 162, 163, 163, 164, 165, 166, 167, 167, 168,
         169, 170, 170, 171, 172, 173, 173, 174, 175, 176, 176, 177, 178, 178,
         179, 180, 181, 181, 182, 183, 183, 184, 185, 185, 186, 187, 187, 188,
         189, 189, 190, 191, 192, 192, 193, 193, 194, 195, 195, 196, 197, 197,
         198, 199, 199, 200, 201, 201, 202, 203, 203, 204, 204, 205, 206, 206,
         207, 208, 208, 209, 209, 210, 211, 211, 212, 212, 213, 214, 214, 215,
         215, 216, 217, 217, 218, 218, 219, 219, 220, 221, 221, 222, 222, 223,
         224, 224, 225, 225, 226, 226, 227, 227, 228, 229, 229, 230, 230, 231,
         231, 232, 232, 233, 234, 234, 235, 235, 236, 236, 237, 237, 238, 238,
         239, 240, 240, 241, 241, 242, 242, 243, 243, 244, 244, 245, 245, 246,
         246, 247, 247, 248, 248, 249, 249, 250, 250, 251, 251, 252, 252, 253,
         253, 254, 254, 255
};

template <class T>
UInt32 IntSquareRoot<T>::exec(UInt32 x) 
{
    UInt32 xn;
    if (x >= 0x10000)
        if (x >= 0x1000000)
            if (x >= 0x10000000)
                if (x >= 0x40000000) {
                    if (x >= (UInt32)65535*(UInt32)65535)
                        return 65535;
                    xn = sqq_table[x>>24] << 8;
                } else
                    xn = sqq_table[x>>22] << 7;
            else
                if (x >= 0x4000000)
                    xn = sqq_table[x>>20] << 6;
                else
                    xn = sqq_table[x>>18] << 5;
        else {
            if (x >= 0x100000)
                if (x >= 0x400000)
                    xn = sqq_table[x>>16] << 4;
                else
                    xn = sqq_table[x>>14] << 3;
            else
                if (x >= 0x40000)
                    xn = sqq_table[x>>12] << 2;
                else
                    xn = sqq_table[x>>10] << 1;

            goto nr1;
        }
    else
        if (x >= 0x100) {
            if (x >= 0x1000)
                if (x >= 0x4000)
                    xn = (sqq_table[x>>8] >> 0) + 1;
                else
                    xn = (sqq_table[x>>6] >> 1) + 1;
            else
                if (x >= 0x400)
                    xn = (sqq_table[x>>4] >> 2) + 1;
                else
                    xn = (sqq_table[x>>2] >> 3) + 1;

            goto adj;
        } else
            return sqq_table[x] >> 4;

    /* Run two iterations of the standard convergence formula */

    xn = (xn + 1 + x / xn) / 2;
  nr1:
    xn = (xn + 1 + x / xn) / 2;
  adj:

    if (xn * xn > x) /* Correct rounding if necessary */
        xn--;

    return xn;
}

} // namespace detail

using VIGRA_CSTD::sqrt;

    /*! Signed integer square root.
    
        Useful for fast fixed-point computations.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline Int32 sqrti(Int32 v)
{
    if(v < 0)
        throw std::domain_error("sqrti(Int32): negative argument.");
    return (Int32)detail::IntSquareRoot<UInt32>::exec((UInt32)v);
}

    /*! Unsigned integer square root.

        Useful for fast fixed-point computations.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline UInt32 sqrti(UInt32 v)
{
    return detail::IntSquareRoot<UInt32>::exec(v);
}

#ifdef VIGRA_NO_HYPOT
    /*! Compute the Euclidean distance (length of the hypotenuse of a right-angled triangle).

        The  hypot()  function  returns  the  sqrt(a*a  +  b*b).
        It is implemented in a way that minimizes round-off error.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double hypot(double a, double b) 
{ 
    double absa = VIGRA_CSTD::fabs(a), absb = VIGRA_CSTD::fabs(b);
    if (absa > absb) 
        return absa * VIGRA_CSTD::sqrt(1.0 + sq(absb/absa)); 
    else 
        return absb == 0.0
                   ? 0.0
                   : absb * VIGRA_CSTD::sqrt(1.0 + sq(absa/absb)); 
}

#else

using ::hypot;

#endif

    /*! The sign function.

        Returns 1, 0, or -1 depending on the sign of \a t, but with the same type as \a t.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T>
inline T sign(T t) 
{ 
    return t > NumericTraits<T>::zero()
               ? NumericTraits<T>::one()
               : t < NumericTraits<T>::zero()
                    ? -NumericTraits<T>::one()
                    : NumericTraits<T>::zero();
}

    /*! The integer sign function.

        Returns 1, 0, or -1 depending on the sign of \a t, converted to int.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T>
inline int signi(T t) 
{ 
    return t > NumericTraits<T>::zero()
               ? 1
               : t < NumericTraits<T>::zero()
                    ? -1
                    : 0;
}

    /*! The binary sign function.

        Transfers the sign of \a t2 to \a t1.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T1, class T2>
inline T1 sign(T1 t1, T2 t2) 
{ 
    return t2 >= NumericTraits<T2>::zero()
               ? abs(t1)
               : -abs(t1);
}


#ifdef DOXYGEN // only for documentation
    /*! Check if an integer is even.

        Defined for all integral types.
    */
bool even(int t);

    /*! Check if an integer is odd.

        Defined for all integral types.
    */
bool odd(int t);

#endif

#define VIGRA_DEFINE_ODD_EVEN(T) \
    inline bool even(T t) { return (t&1) == 0; } \
    inline bool odd(T t)  { return (t&1) == 1; }

VIGRA_DEFINE_ODD_EVEN(char)
VIGRA_DEFINE_ODD_EVEN(short)
VIGRA_DEFINE_ODD_EVEN(int)
VIGRA_DEFINE_ODD_EVEN(long)
VIGRA_DEFINE_ODD_EVEN(long long)
VIGRA_DEFINE_ODD_EVEN(unsigned char)
VIGRA_DEFINE_ODD_EVEN(unsigned short)
VIGRA_DEFINE_ODD_EVEN(unsigned int)
VIGRA_DEFINE_ODD_EVEN(unsigned long)
VIGRA_DEFINE_ODD_EVEN(unsigned long long)

#undef VIGRA_DEFINE_ODD_EVEN

#define VIGRA_DEFINE_NORM(T) \
    inline NormTraits<T>::SquaredNormType squaredNorm(T t) { return sq(t); } \
    inline NormTraits<T>::NormType norm(T t) { return abs(t); }

VIGRA_DEFINE_NORM(bool)
VIGRA_DEFINE_NORM(signed char)
VIGRA_DEFINE_NORM(unsigned char)
VIGRA_DEFINE_NORM(short)
VIGRA_DEFINE_NORM(unsigned short)
VIGRA_DEFINE_NORM(int)
VIGRA_DEFINE_NORM(unsigned int)
VIGRA_DEFINE_NORM(long)
VIGRA_DEFINE_NORM(unsigned long)
VIGRA_DEFINE_NORM(long long)
VIGRA_DEFINE_NORM(unsigned long long)
VIGRA_DEFINE_NORM(float)
VIGRA_DEFINE_NORM(double)
VIGRA_DEFINE_NORM(long double)

#undef VIGRA_DEFINE_NORM

template <class T>
inline typename NormTraits<std::complex<T> >::SquaredNormType
squaredNorm(std::complex<T> const & t)
{
    return sq(t.real()) + sq(t.imag());
}

#ifdef DOXYGEN // only for documentation
    /*! The squared norm of a numerical object.

        For scalar types: equals <tt>vigra::sq(t)</tt><br>.
        For vectorial types: equals <tt>vigra::dot(t, t)</tt><br>.
        For complex types: equals <tt>vigra::sq(t.real()) + vigra::sq(t.imag())</tt><br>.
        For matrix types: results in the squared Frobenius norm (sum of squares of the matrix elements).
    */
NormTraits<T>::SquaredNormType squaredNorm(T const & t);

#endif

    /*! The norm of a numerical object.

        For scalar types: implemented as <tt>abs(t)</tt><br>
        otherwise: implemented as <tt>sqrt(squaredNorm(t))</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T>
inline typename NormTraits<T>::NormType 
norm(T const & t)
{
    typedef typename NormTraits<T>::SquaredNormType SNT;
    return sqrt(static_cast<typename SquareRootTraits<SNT>::SquareRootArgument>(squaredNorm(t)));
}

    /*! Compute the eigenvalues of a 2x2 real symmetric matrix.
    
        This uses the analytical eigenvalue formula 
        \f[
           \lambda_{1,2} = \frac{1}{2}\left(a_{00} + a_{11} \pm \sqrt{(a_{00} - a_{11})^2 + 4 a_{01}^2}\right)
        \f]

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T>
void symmetric2x2Eigenvalues(T a00, T a01, T a11, T * r0, T * r1)
{
    double d  = hypot(a00 - a11, 2.0*a01);
    *r0 = static_cast<T>(0.5*(a00 + a11 + d));
    *r1 = static_cast<T>(0.5*(a00 + a11 - d));
    if(*r0 < *r1)
        std::swap(*r0, *r1);
}

    /*! Compute the eigenvalues of a 3x3 real symmetric matrix.
    
        This uses a numerically stable version of the analytical eigenvalue formula according to
        <p>
        David Eberly: <a href="http://www.geometrictools.com/Documentation/EigenSymmetric3x3.pdf">
        <em>"Eigensystems for 3 × 3 Symmetric Matrices (Revisited)"</em></a>, Geometric Tools Documentation, 2006


        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T>
void symmetric3x3Eigenvalues(T a00, T a01, T a02, T a11, T a12, T a22,
                             T * r0, T * r1, T * r2)
{
    static double inv3 = 1.0 / 3.0, root3 = std::sqrt(3.0);
    
    double c0 = a00*a11*a22 + 2.0*a01*a02*a12 - a00*a12*a12 - a11*a02*a02 - a22*a01*a01;
    double c1 = a00*a11 - a01*a01 + a00*a22 - a02*a02 + a11*a22 - a12*a12;
    double c2 = a00 + a11 + a22;
    double c2Div3 = c2*inv3;
    double aDiv3 = (c1 - c2*c2Div3)*inv3;
    if (aDiv3 > 0.0) 
        aDiv3 = 0.0;
    double mbDiv2 = 0.5*(c0 + c2Div3*(2.0*c2Div3*c2Div3 - c1));
    double q = mbDiv2*mbDiv2 + aDiv3*aDiv3*aDiv3;
    if (q > 0.0) 
        q = 0.0;
    double magnitude = std::sqrt(-aDiv3);
    double angle = std::atan2(std::sqrt(-q),mbDiv2)*inv3;
    double cs = std::cos(angle);
    double sn = std::sin(angle);
    *r0 = static_cast<T>(c2Div3 + 2.0*magnitude*cs);
    *r1 = static_cast<T>(c2Div3 - magnitude*(cs + root3*sn));
    *r2 = static_cast<T>(c2Div3 - magnitude*(cs - root3*sn));
    if(*r0 < *r1)
        std::swap(*r0, *r1);
    if(*r0 < *r2)
        std::swap(*r0, *r2);
    if(*r1 < *r2)
        std::swap(*r1, *r2);
}

namespace detail {

template <class T>
T ellipticRD(T x, T y, T z)
{
    double f = 1.0, s = 0.0, X, Y, Z, m;
    for(;;)
    {
        m = (x + y + 3.0*z) / 5.0;
        X = 1.0 - x/m;
        Y = 1.0 - y/m;
        Z = 1.0 - z/m;
        if(std::max(std::max(VIGRA_CSTD::fabs(X), VIGRA_CSTD::fabs(Y)), VIGRA_CSTD::fabs(Z)) < 0.01)
            break;
        double l = VIGRA_CSTD::sqrt(x*y) + VIGRA_CSTD::sqrt(x*z) + VIGRA_CSTD::sqrt(y*z);
        s += f / (VIGRA_CSTD::sqrt(z)*(z + l));
        f /= 4.0;
        x = (x + l)/4.0;
        y = (y + l)/4.0;
        z = (z + l)/4.0;
    }
    double a = X*Y;
    double b = sq(Z);
    double c = a - b;
    double d = a - 6.0*b;
    double e = d + 2.0*c;
    return 3.0*s + f*(1.0+d*(-3.0/14.0+d*9.0/88.0-Z*e*4.5/26.0)
                      +Z*(e/6.0+Z*(-c*9.0/22.0+a*Z*3.0/26.0))) / VIGRA_CSTD::pow(m,1.5);
}

template <class T>
T ellipticRF(T x, T y, T z)
{
    double X, Y, Z, m;
    for(;;)
    {
        m = (x + y + z) / 3.0;
        X = 1.0 - x/m;
        Y = 1.0 - y/m;
        Z = 1.0 - z/m;
        if(std::max(std::max(VIGRA_CSTD::fabs(X), VIGRA_CSTD::fabs(Y)), VIGRA_CSTD::fabs(Z)) < 0.01)
            break;
        double l = VIGRA_CSTD::sqrt(x*y) + VIGRA_CSTD::sqrt(x*z) + VIGRA_CSTD::sqrt(y*z);
        x = (x + l)/4.0;
        y = (y + l)/4.0;
        z = (z + l)/4.0;
    }
    double d = X*Y - sq(Z);
    double p = X*Y*Z;
    return (1.0 - d/10.0 + p/14.0 + sq(d)/24.0 - d*p*3.0/44.0) / VIGRA_CSTD::sqrt(m);
}

} // namespace detail

    /*! The incomplete elliptic integral of the first kind.

        Computes
        
        \f[
            \mbox{F}(x, k) = \int_0^x \frac{1}{\sqrt{1 - k^2 \sin(t)^2}} dt
        \f]
        
        according to the algorithm given in Press et al. "Numerical Recipes". 

        Note: In some libraries (e.g. Mathematica), the second parameter of the elliptic integral
        functions must be k^2 rather than k. Check the documentation when results disagree!

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double ellipticIntegralF(double x, double k)
{
    double c2 = sq(VIGRA_CSTD::cos(x));
    double s = VIGRA_CSTD::sin(x);
    return s*detail::ellipticRF(c2, 1.0 - sq(k*s), 1.0);
}

    /*! The incomplete elliptic integral of the second kind.

        Computes
        
        \f[
            \mbox{E}(x, k) = \int_0^x \sqrt{1 - k^2 \sin(t)^2} dt
        \f]
        
        according to the algorithm given in Press et al. "Numerical Recipes". The
        complete elliptic integral of the second kind is simply <tt>ellipticIntegralE(M_PI/2, k)</TT>.
        
        Note: In some libraries (e.g. Mathematica), the second parameter of the elliptic integral
        functions must be k^2 rather than k. Check the documentation when results disagree!

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double ellipticIntegralE(double x, double k)
{
    double c2 = sq(VIGRA_CSTD::cos(x));
    double s = VIGRA_CSTD::sin(x);
    k = sq(k*s);
    return s*(detail::ellipticRF(c2, 1.0-k, 1.0) - k/3.0*detail::ellipticRD(c2, 1.0-k, 1.0));
}

#ifdef _MSC_VER

namespace detail {

template <class T>
double erfImpl(T x)
{
    double t = 1.0/(1.0+0.5*VIGRA_CSTD::fabs(x));
    double ans = t*VIGRA_CSTD::exp(-x*x-1.26551223+t*(1.00002368+t*(0.37409196+
                                    t*(0.09678418+t*(-0.18628806+t*(0.27886807+
                                    t*(-1.13520398+t*(1.48851587+t*(-0.82215223+
                                    t*0.17087277)))))))));
    if (x >= 0.0)
        return 1.0 - ans;
    else
        return ans - 1.0;
}

} // namespace detail 

    /*! The error function.

        If <tt>erf()</tt> is not provided in the C standard math library (as it should according to the
        new C99 standard ?), VIGRA implements <tt>erf()</tt> as an approximation of the error 
        function
        
        \f[
            \mbox{erf}(x) = \int_0^x e^{-t^2} dt
        \f]
        
        according to the formula given in Press et al. "Numerical Recipes".

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double erf(double x)
{
    return detail::erfImpl(x);
}

#else

using ::erf;

#endif

namespace detail {

template <class T>
double noncentralChi2CDFApprox(unsigned int degreesOfFreedom, T noncentrality, T arg)
{
    double a = degreesOfFreedom + noncentrality;
    double b = (a + noncentrality) / sq(a);
    double t = (VIGRA_CSTD::pow((double)arg / a, 1.0/3.0) - (1.0 - 2.0 / 9.0 * b)) / VIGRA_CSTD::sqrt(2.0 / 9.0 * b);
    return 0.5*(1.0 + erf(t/VIGRA_CSTD::sqrt(2.0)));
}

template <class T>
void noncentralChi2OneIteration(T arg, T & lans, T & dans, T & pans, unsigned int & j)
{
    double tol = -50.0;
    if(lans < tol)
    {
        lans = lans + VIGRA_CSTD::log(arg / j);
        dans = VIGRA_CSTD::exp(lans);
    }
    else
    {
        dans = dans * arg / j;
    }
    pans = pans - dans;
    j += 2;
}

template <class T>
std::pair<double, double> noncentralChi2CDF(unsigned int degreesOfFreedom, T noncentrality, T arg, T eps)
{
    vigra_precondition(noncentrality >= 0.0 && arg >= 0.0 && eps > 0.0,
        "noncentralChi2P(): parameters must be positive.");
    if (arg == 0.0 && degreesOfFreedom > 0)
        return std::make_pair(0.0, 0.0);

    // Determine initial values
    double b1 = 0.5 * noncentrality,
           ao = VIGRA_CSTD::exp(-b1),
           eps2 = eps / ao,
           lnrtpi2 = 0.22579135264473,
           probability, density, lans, dans, pans, sum, am, hold;
    unsigned int maxit = 500,
        i, m;
    if(degreesOfFreedom % 2)
    {
        i = 1;
        lans = -0.5 * (arg + VIGRA_CSTD::log(arg)) - lnrtpi2;
        dans = VIGRA_CSTD::exp(lans);
        pans = erf(VIGRA_CSTD::sqrt(arg/2.0));
    }
    else
    {
        i = 2;
        lans = -0.5 * arg;
        dans = VIGRA_CSTD::exp(lans);
        pans = 1.0 - dans;
    }
    
    // Evaluate first term
    if(degreesOfFreedom == 0)
    {
        m = 1;
        degreesOfFreedom = 2;
        am = b1;
        sum = 1.0 / ao - 1.0 - am;
        density = am * dans;
        probability = 1.0 + am * pans;
    }
    else
    {
        m = 0;
        degreesOfFreedom = degreesOfFreedom - 1;
        am = 1.0;
        sum = 1.0 / ao - 1.0;
        while(i < degreesOfFreedom)
            detail::noncentralChi2OneIteration(arg, lans, dans, pans, i);
        degreesOfFreedom = degreesOfFreedom + 1;
        density = dans;
        probability = pans;
    }
    // Evaluate successive terms of the expansion
    for(++m; m<maxit; ++m)
    {
        am = b1 * am / m;
        detail::noncentralChi2OneIteration(arg, lans, dans, pans, degreesOfFreedom);
        sum = sum - am;
        density = density + am * dans;
        hold = am * pans;
        probability = probability + hold;
        if((pans * sum < eps2) && (hold < eps2))
            break; // converged
    }
    if(m == maxit)
        vigra_fail("noncentralChi2P(): no convergence.");
    return std::make_pair(0.5 * ao * density, std::min(1.0, std::max(0.0, ao * probability)));
}

} // namespace detail

    /*! Chi square distribution. 

        Computes the density of a chi square distribution with \a degreesOfFreedom 
        and tolerance \a accuracy at the given argument \a arg
        by calling <tt>noncentralChi2(degreesOfFreedom, 0.0, arg, accuracy)</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double chi2(unsigned int degreesOfFreedom, double arg, double accuracy = 1e-7)
{
    return detail::noncentralChi2CDF(degreesOfFreedom, 0.0, arg, accuracy).first;
}

    /*! Cumulative chi square distribution. 

        Computes the cumulative density of a chi square distribution with \a degreesOfFreedom 
        and tolerance \a accuracy at the given argument \a arg, i.e. the probability that
        a random number drawn from the distribution is below \a arg
        by calling <tt>noncentralChi2CDF(degreesOfFreedom, 0.0, arg, accuracy)</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double chi2CDF(unsigned int degreesOfFreedom, double arg, double accuracy = 1e-7)
{
    return detail::noncentralChi2CDF(degreesOfFreedom, 0.0, arg, accuracy).second;
}

    /*! Non-central chi square distribution. 

        Computes the density of a non-central chi square distribution with \a degreesOfFreedom, 
        noncentrality parameter \a noncentrality and tolerance \a accuracy at the given argument 
        \a arg. It uses Algorithm AS 231 from Appl. Statist. (1987) Vol.36, No.3 (code ported from 
        http://lib.stat.cmu.edu/apstat/231). The algorithm has linear complexity in the number of
        degrees of freedom.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double noncentralChi2(unsigned int degreesOfFreedom, 
              double noncentrality, double arg, double accuracy = 1e-7)
{
    return detail::noncentralChi2CDF(degreesOfFreedom, noncentrality, arg, accuracy).first;
}

    /*! Cumulative non-central chi square distribution. 

        Computes the cumulative density of a chi square distribution with \a degreesOfFreedom, 
        noncentrality parameter \a noncentrality and tolerance \a accuracy at the given argument 
        \a arg, i.e. the probability that a random number drawn from the distribution is below \a arg
        It uses Algorithm AS 231 from Appl. Statist. (1987) Vol.36, No.3 (code ported from 
        http://lib.stat.cmu.edu/apstat/231). The algorithm has linear complexity in the number of
        degrees of freedom (see noncentralChi2CDFApprox() for a constant-time algorithm).

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double noncentralChi2CDF(unsigned int degreesOfFreedom, 
              double noncentrality, double arg, double accuracy = 1e-7)
{
    return detail::noncentralChi2CDF(degreesOfFreedom, noncentrality, arg, accuracy).second;
}

    /*! Cumulative non-central chi square distribution (approximate). 

        Computes approximate values of the cumulative density of a chi square distribution with \a degreesOfFreedom, 
        and noncentrality parameter \a noncentrality at the given argument 
        \a arg, i.e. the probability that a random number drawn from the distribution is below \a arg
        It uses the approximate transform into a normal distribution due to Wilson and Hilferty 
        (see Abramovitz, Stegun: "Handbook of Mathematical Functions", formula 26.3.32). 
        The algorithm's running time is independent of the inputs, i.e. is should be used
        when noncentralChi2CDF() is too slow, and approximate values are sufficient. The accuracy is only 
        about 0.1 for few degrees of freedom, but reaches about 0.001 above dof = 5.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double noncentralChi2CDFApprox(unsigned int degreesOfFreedom, double noncentrality, double arg)
{
    return detail::noncentralChi2CDFApprox(degreesOfFreedom, noncentrality, arg);
}

namespace detail  {

// computes (l+m)! / (l-m)!
// l and m must be positive
template <class T>
T facLM(T l, T m)
{
    T tmp = NumericTraits<T>::one();
    for(T f = l-m+1; f <= l+m; ++f)
        tmp *= f;
    return tmp;
}

} // namespace detail

    /*! Associated Legendre polynomial. 

        Computes the value of the associated Legendre polynomial of order <tt>l, m</tt> 
        for argument <tt>x</tt>. <tt>x</tt> must be in the range <tt>[-1.0, 1.0]</tt>, 
        otherwise an exception is thrown. The standard Legendre polynomials are the 
        special case <tt>m == 0</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class REAL>
REAL legendre(unsigned int l, int m, REAL x)
{
    vigra_precondition(abs(x) <= 1.0, "legendre(): x must be in [-1.0, 1.0].");
    if (m < 0)
    {
        m = -m;
        REAL s = odd(m)
                   ? -1.0
                   :  1.0;
        return legendre(l,m,x) * s / detail::facLM<REAL>(l,m);
    }
    REAL result = 1.0;
    if (m > 0)
    {
        REAL r = std::sqrt( (1.0-x) * (1.0+x) );
        REAL f = 1.0;
        for (int i=1; i<=m; i++)
        {
            result *= (-f) * r;
            f += 2.0;
        }
    }
    if((int)l == m) 
        return result;

    REAL result_1 = x * (2.0 * m + 1.0) * result;
    if((int)l == m+1) 
        return result_1;
    REAL other = 0.0;
    for(unsigned int i = m+2; i <= l; ++i)
    {
        other = ( (2.0*i-1.0) * x * result_1 - (i+m-1.0)*result) / (i-m);
        result = result_1;
        result_1 = other;
    }
    return other;
}

    /*! Legendre polynomial. 

        Computes the value of the Legendre polynomial of order <tt>l</tt> for argument <tt>x</tt>.
        <tt>x</tt> must be in the range <tt>[-1.0, 1.0]</tt>, otherwise an exception is thrown.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class REAL>
REAL legendre(unsigned int l, REAL x)
{
    return legendre(l, 0, x);
}

    /*! sin(pi*x). 

        Essentially calls <tt>std::sin(M_PI*x)</tt> but uses a more accurate implementation
        to make sure that <tt>sin_pi(1.0) == 0.0</tt> (which does not hold for
        <tt>std::sin(M_PI)</tt> due to round-off error), and <tt>sin_pi(0.5) == 1.0</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class REAL>
REAL sin_pi(REAL x)
{
    if(x < 0.0)
        return -sin_pi(-x);
    if(x < 0.5)
        return std::sin(M_PI * x);

    bool invert = false;
    if(x < 1.0)
    {
        invert = true;
        x = -x;
    }

    REAL rem = std::floor(x);
    if(odd((int)rem))
        invert = !invert;
    rem = x - rem;
    if(rem > 0.5)
        rem = 1.0 - rem;
    if(rem == 0.5)
        rem = NumericTraits<REAL>::one();
    else
        rem = std::sin(M_PI * rem);
    return invert 
              ? -rem 
              : rem;
}

    /*! cos(pi*x). 

        Essentially calls <tt>std::cos(M_PI*x)</tt> but uses a more accurate implementation
        to make sure that <tt>cos_pi(1.0) == -1.0</tt> and <tt>cos_pi(0.5) == 0.0</tt>.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class REAL>
REAL cos_pi(REAL x)
{
    return sin_pi(x+0.5);
}

namespace detail {

template <class REAL>
REAL gammaImpl(REAL x)
{
    int i, k, m, ix = (int)x;
    double ga = 0.0, gr = 0.0, r = 0.0, z = 0.0;

    static double g[] = {
        1.0,
        0.5772156649015329,
       -0.6558780715202538,
       -0.420026350340952e-1,
        0.1665386113822915,
       -0.421977345555443e-1,
       -0.9621971527877e-2,
        0.7218943246663e-2,
       -0.11651675918591e-2,
       -0.2152416741149e-3,
        0.1280502823882e-3,
       -0.201348547807e-4,
       -0.12504934821e-5,
        0.1133027232e-5,
       -0.2056338417e-6,
        0.6116095e-8,
        0.50020075e-8,
       -0.11812746e-8,
        0.1043427e-9,
        0.77823e-11,
       -0.36968e-11,
        0.51e-12,
       -0.206e-13,
       -0.54e-14,
        0.14e-14};

    vigra_precondition(x <= 171.0,
        "gamma(): argument cannot exceed 171.0.");

    if (x == ix) 
    {
        if (ix > 0) 
        {
            ga = 1.0;               // use factorial
            for (i=2; i<ix; ++i) 
            {
               ga *= i;
            }
        }
        else
        {
            vigra_precondition(false,
                 "gamma(): gamma function is undefined for 0 and negative integers.");
        }
     }
     else 
     {
        if (abs(x) > 1.0) 
        {
            z = abs(x);
            m = (int)z;
            r = 1.0;
            for (k=1; k<=m; ++k) 
            {
                r *= (z-k);
            }
            z -= m;
        }
        else
        {
            z = x;
        }
        gr = g[24];
        for (k=23; k>=0; --k) 
        {
            gr = gr*z+g[k];
        }
        ga = 1.0/(gr*z);
        if (abs(x) > 1.0) 
        {
            ga *= r;
            if (x < 0.0) 
            {
                ga = -M_PI/(x*ga*sin_pi(x));
            }
        }
    }
    return ga;
}

/*
 * the following code is derived from lgamma_r() by Sun
 * 
 * ====================================================
 * Copyright (C) 1993 by Sun Microsystems, Inc. All rights reserved.
 *
 * Developed at SunPro, a Sun Microsystems, Inc. business.
 * Permission to use, copy, modify, and distribute this
 * software is freely granted, provided that this notice 
 * is preserved.
 * ====================================================
 *
 */
template <class REAL>
REAL loggammaImpl(REAL x)
{
    vigra_precondition(x > 0.0,
        "loggamma(): argument must be positive.");
    
    vigra_precondition(x <= 1.0e307,
        "loggamma(): argument must not exceed 1e307.");

    double res;
    
    if (x < 4.2351647362715017e-22)
    {
        res = -std::log(x);
    }
    else if ((x == 2.0) || (x == 1.0))
    {
        res = 0.0;
    }
    else if (x < 2.0)
    {
        static const double a[] =  { 7.72156649015328655494e-02,
                               3.22467033424113591611e-01,
                               6.73523010531292681824e-02,
                               2.05808084325167332806e-02,
                               7.38555086081402883957e-03,
                               2.89051383673415629091e-03,
                               1.19270763183362067845e-03,
                               5.10069792153511336608e-04,
                               2.20862790713908385557e-04,
                               1.08011567247583939954e-04,
                               2.52144565451257326939e-05,
                               4.48640949618915160150e-05 };
        static const double t[] = { 4.83836122723810047042e-01,
                              -1.47587722994593911752e-01,
                               6.46249402391333854778e-02,
                              -3.27885410759859649565e-02,
                               1.79706750811820387126e-02,
                              -1.03142241298341437450e-02,
                               6.10053870246291332635e-03,
                              -3.68452016781138256760e-03,
                               2.25964780900612472250e-03,
                              -1.40346469989232843813e-03,
                               8.81081882437654011382e-04,
                              -5.38595305356740546715e-04,
                               3.15632070903625950361e-04,
                              -3.12754168375120860518e-04,
                               3.35529192635519073543e-04 };
        static const double u[] = { -7.72156649015328655494e-02,
                               6.32827064025093366517e-01,
                               1.45492250137234768737e+00,
                               9.77717527963372745603e-01,
                               2.28963728064692451092e-01,
                               1.33810918536787660377e-02 };
        static const double v[] = { 0.0,
                               2.45597793713041134822e+00,
                               2.12848976379893395361e+00,
                               7.69285150456672783825e-01,
                               1.04222645593369134254e-01,
                               3.21709242282423911810e-03 };
        static const double tc  =  1.46163214496836224576e+00;
        static const double tf  = -1.21486290535849611461e-01;
        static const double tt  = -3.63867699703950536541e-18;
        if (x <= 0.9)
        {
            res = -std::log(x);
            if (x >= 0.7316)
            {
                double y = 1.0-x;
                double z = y*y;
                double p1 = a[0]+z*(a[2]+z*(a[4]+z*(a[6]+z*(a[8]+z*a[10]))));
                double p2 = z*(a[1]+z*(a[3]+z*(a[5]+z*(a[7]+z*(a[9]+z*a[11])))));
                double p  = y*p1+p2;
                res  += (p-0.5*y);
            }
            else if (x >= 0.23164)
            {
                double y = x-(tc-1.0);
                double z = y*y;
                double w = z*y;
                double p1 = t[0]+w*(t[3]+w*(t[6]+w*(t[9] +w*t[12])));
                double p2 = t[1]+w*(t[4]+w*(t[7]+w*(t[10]+w*t[13])));
                double p3 = t[2]+w*(t[5]+w*(t[8]+w*(t[11]+w*t[14])));
                double p  = z*p1-(tt-w*(p2+y*p3));
                res += (tf + p);
            }
            else
            {
                double y = x;
                double p1 = y*(u[0]+y*(u[1]+y*(u[2]+y*(u[3]+y*(u[4]+y*u[5])))));
                double p2 = 1.0+y*(v[1]+y*(v[2]+y*(v[3]+y*(v[4]+y*v[5]))));
                res += (-0.5*y + p1/p2);
            }
        }
        else
        {
            res = 0.0;
            if (x >= 1.7316)
            {
                double y = 2.0-x;
                double z = y*y;
                double p1 = a[0]+z*(a[2]+z*(a[4]+z*(a[6]+z*(a[8]+z*a[10]))));
                double p2 = z*(a[1]+z*(a[3]+z*(a[5]+z*(a[7]+z*(a[9]+z*a[11])))));
                double p  = y*p1+p2;
                res  += (p-0.5*y);
            }
            else if(x >= 1.23164)
            {
                double y = x-tc;
                double z = y*y;
                double w = z*y;
                double p1 = t[0]+w*(t[3]+w*(t[6]+w*(t[9] +w*t[12])));
                double p2 = t[1]+w*(t[4]+w*(t[7]+w*(t[10]+w*t[13])));
                double p3 = t[2]+w*(t[5]+w*(t[8]+w*(t[11]+w*t[14])));
                double p  = z*p1-(tt-w*(p2+y*p3));
                res += (tf + p);
            }
            else
            {
                double y = x-1.0;
                double p1 = y*(u[0]+y*(u[1]+y*(u[2]+y*(u[3]+y*(u[4]+y*u[5])))));
                double p2 = 1.0+y*(v[1]+y*(v[2]+y*(v[3]+y*(v[4]+y*v[5]))));
                res += (-0.5*y + p1/p2);
            }
        }
    }
    else if(x < 8.0)
    {
        static const double s[] = { -7.72156649015328655494e-02,
                               2.14982415960608852501e-01,
                               3.25778796408930981787e-01,
                               1.46350472652464452805e-01,
                               2.66422703033638609560e-02,
                               1.84028451407337715652e-03,
                               3.19475326584100867617e-05 };
        static const double r[] = { 0.0,
                               1.39200533467621045958e+00,
                               7.21935547567138069525e-01,
                               1.71933865632803078993e-01,
                               1.86459191715652901344e-02,
                               7.77942496381893596434e-04,
                               7.32668430744625636189e-06 };
        double i = std::floor(x);
        double y = x-i;
        double p = y*(s[0]+y*(s[1]+y*(s[2]+y*(s[3]+y*(s[4]+y*(s[5]+y*s[6]))))));
        double q = 1.0+y*(r[1]+y*(r[2]+y*(r[3]+y*(r[4]+y*(r[5]+y*r[6])))));
        res = 0.5*y+p/q;
        double z = 1.0;
        while (i > 2.0)
        {
            --i;
            z *= (y+i);
        }
        res += std::log(z);
    }
    else if (x < 2.8823037615171174e+17)
    {
        static const double w[] = { 4.18938533204672725052e-01,
                               8.33333333333329678849e-02,
                              -2.77777777728775536470e-03,
                               7.93650558643019558500e-04,
                              -5.95187557450339963135e-04,
                               8.36339918996282139126e-04,
                              -1.63092934096575273989e-03 };
        double t = std::log(x);
        double z = 1.0/x;
        double y = z*z;
        double yy = w[0]+z*(w[1]+y*(w[2]+y*(w[3]+y*(w[4]+y*(w[5]+y*w[6])))));
        res = (x-0.5)*(t-1.0)+yy;
    }
    else
    {
        res =  x*(std::log(x) - 1.0);
    }
    
    return res;
}


} // namespace detail

    /*! The gamma function.

        This function implements the algorithm from<br>
        Zhang and Jin: "Computation of Special Functions", John Wiley and Sons, 1996.
        
        The argument must be <= 171.0 and cannot be zero or a negative integer. An
        exception is thrown when these conditions are violated.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double gamma(double x)
{
    return detail::gammaImpl(x);
}

    /*! The natural logarithm of the gamma function.

        This function is based on a free implementation by Sun Microsystems, Inc., see
        <a href="http://www.sourceware.org/cgi-bin/cvsweb.cgi/~checkout~/src/newlib/libm/mathfp/er_lgamma.c?rev=1.6&content-type=text/plain&cvsroot=src">sourceware.org</a> archive. It can be removed once all compilers support the new C99
        math functions.
        
        The argument must be positive and < 1e30. An exception is thrown when these conditions are violated.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
inline double loggamma(double x)
{
    return detail::loggammaImpl(x);
}


namespace detail {

// both f1 and f2 are unsigned here
template<class FPT>
inline
FPT safeFloatDivision( FPT f1, FPT f2 )
{
    return  f2 < NumericTraits<FPT>::one() && f1 > f2 * NumericTraits<FPT>::max()
                ? NumericTraits<FPT>::max() 
                : (f2 > NumericTraits<FPT>::one() && f1 < f2 * NumericTraits<FPT>::smallestPositive()) || 
                   f1 == NumericTraits<FPT>::zero()
                     ? NumericTraits<FPT>::zero() 
                     : f1/f2;
}

} // namespace detail
    
    /*! Tolerance based floating-point comparison.

        Check whether two floating point numbers are equal within the given tolerance.
        This is useful because floating point numbers that should be equal in theory are
        rarely exactly equal in practice. If the tolerance \a epsilon is not given,
        twice the machine epsilon is used.

        <b>\#include</b> \<vigra/mathutil.hxx\><br>
        Namespace: vigra
    */
template <class T1, class T2>
bool 
closeAtTolerance(T1 l, T2 r, typename PromoteTraits<T1, T2>::Promote epsilon)
{
    typedef typename PromoteTraits<T1, T2>::Promote T;
    if(l == 0.0)
        return VIGRA_CSTD::fabs(r) <= epsilon;
    if(r == 0.0)
        return VIGRA_CSTD::fabs(l) <= epsilon;
    T diff = VIGRA_CSTD::fabs( l - r );
    T d1   = detail::safeFloatDivision<T>( diff, VIGRA_CSTD::fabs( r ) );
    T d2   = detail::safeFloatDivision<T>( diff, VIGRA_CSTD::fabs( l ) );

    return (d1 <= epsilon && d2 <= epsilon);
}

template <class T1, class T2>
inline bool closeAtTolerance(T1 l, T2 r)
{
    typedef typename PromoteTraits<T1, T2>::Promote T;
    return closeAtTolerance(l, r, T(2.0) * NumericTraits<T>::epsilon());
}

//@}

} // namespace vigra

#endif /* VIGRA_MATHUTIL_HXX */