on Sun Nov 18 2007, StephQ wrote:

I am currently working on a mathematical library for the simulation of stochastic processes. I am a researcher in Statistics, hence my limited skills in c++ programming :)

Let me introduce the framework briefly. The part of the library I am interested with is very similar to the boost random library. I need to implement it because I need to extend some functionality and I am not confident in modifying the boost random library to extend its behaviour (too complicated for me :P).

We have (lazy syntax):

PseudoRng<TImpl> //A particular pseduo random number generator QuasiRng<TImpl> // A particualr quasi random number generator GaussianDistribution // A particular statistical distribution

Sampler<GaussianDistribution> // The interface (for other components of the library) of a sampler of random numbers distributed as a GaussianDistribution

Then I consider:

template < class TGenerator, class TDistribution, typename TDistribution::variate_t (TDistribution::*algorithm)( const TGenerator& ) const = &TDistribution::sample_default >

class VariateSampler : public Sampler<TDistribution> { ... }

Here TGenerator can be PseudoRng<TImpl> or QuasiRng<TImpl>, TDistribution can be GaussianDistribution and the pointer to member function algorithm select the algorithm used to sample.

While constructing VariateSampler I would like to: 1) Automatically deduce the type of TGenerator and TDistribution

From what information and by what rules?

2) Automatically select a different default for algorithm, for particular TDistribution (like GaussianDistribution,PoissonDistribution....) depending on the fact that TGenerator is of type PseudoRng<TImpl> or QuasiRng<Timpl>. So for every TDistribution two different defaults for algorithm.

I think that boost parameter should solve my problem, but I am quite confused about how to implement it, expecially the second point. Could you help me with the basic steps I need to consider?

Because Boost.Parameter is geared towards template type parameters, it might be easier to approach this problem by starting with a different interface for VariateSampler. Let's say: template < class TGenerator, class TDistribution, class Algorithm

struct _VariateSampler : Sampler<TDistribution> { }; where Algorithm is required to be a function (object) callable with TDistribution and TGenerator arguments. You can easily map it onto the default you've specified using: template struct default_algorithm { typename TDistribution::variate_t operator()(TDistribution const& d, TGenerator const& g) const { return d->sample_default(g); } }; Default selection might be done through a simple metafunction: template <class T> struct is_PseudoRng; template <class U> struct is_PseudoRng : boost::mpl::true_ {}; ... mpl::if_, default1, default2>::type ... HTH, -- Dave Abrahams Boost Consulting http://www.boost-consulting.com