C++11 random number distributions are not consiste

Question

I'm looking for a set of portable distributions for the standard C++11 engines like `std::mt19937' (see http://en.cppreference.com/w/cpp/numeric/random).

The engine implementations perform consistently (i.e. same sequence generated on different platforms – tested with Clang and MSVC), but the distributions seem to be implemented differently on the different platforms.

So, even though the engines produce the same sequence, it seems that a distribution (for example, std::normal_distribution<double>) does not use the same number of samples (i.e. produces different results) on the different platforms, which is not acceptable in my case.

Is there maybe a 3rd party lib I can use that follows the C++11 random templates, but that will deliver consistent values across popular platforms (Looking at support across GCC, MSVC and Clang/llvm).

Options I have looked at so far are:

• Boost.random (a bit heavy, but worthwhile since it matches the c++11 counterparts quite well)
• Cloning from libstd++ (also worthwhile and probably portable, but pulling out specific functions might not be straightforward)
• Creating my own C++11-like random distributions

I need uniform, normal, poison and Rayleigh.

Answer 1

I have created my own C++11 distributions:

template <typename T>
class UniformRealDistribution
{
 public:
    typedef T result_type;

 public:
    UniformRealDistribution(T _a = 0.0, T _b = 1.0)
        :m_a(_a),
         m_b(_b)
    {}

    void reset() {}

    template <class Generator>
    T operator()(Generator &_g)
    {
        double dScale = (m_b - m_a) / ((T)(_g.max() - _g.min()) + (T)1); 
        return (_g() - _g.min()) * dScale  + m_a;
    }

    T a() const {return m_a;}
    T b() const {return m_b;}

 protected:
    T       m_a;
    T       m_b;
};

template <typename T>
class NormalDistribution
{
 public:
    typedef T result_type;

 public:
    NormalDistribution(T _mean = 0.0, T _stddev = 1.0)
        :m_mean(_mean),
         m_stddev(_stddev)
    {}

    void reset()
    {
        m_distU1.reset();
    }

    template <class Generator>
    T operator()(Generator &_g)
    {
        // Use Box-Muller algorithm
        const double pi = 3.14159265358979323846264338327950288419716939937511;
        double u1 = m_distU1(_g);
        double u2 = m_distU1(_g);
        double r = sqrt(-2.0 * log(u1));
        return m_mean + m_stddev * r * sin(2.0 * pi * u2);
    }

    T mean() const {return m_mean;}
    T stddev() const {return m_stddev;}

protected:
    T                           m_mean;
    T                           m_stddev;
    UniformRealDistribution<T>  m_distU1;
};

The uniform distribution seems to deliver good results and the normal distribution delivers very good results:

100000 values -> 68.159% within 1 sigma; 95.437% within 2 sigma; 99.747% within 3 sigma

The normal distribution uses the Box-Muller method, which according to what I have read so far, is not the fastest method, but it runs more that fast enough for my application.

Both the uniform and normal distributions should work with any C++11 engine (tested with std::mt19937) and provides the same sequence on all platforms, which is exactly what I wanted.