dolfin team mailing list archive

[Anders Logg <logg@xxxxxxxxx>]

On Fri, May 08, 2009 at 09:36:26AM +0200, Martin Sandve Alnæs wrote:
> On Fri, May 8, 2009 at 9:17 AM, Anders Logg <logg@xxxxxxxxx> wrote:
> > On Fri, May 08, 2009 at 09:06:16AM +0200, Johan Hake wrote:
> >> On Friday 08 May 2009 08:49:59 Garth N. Wells wrote:
> >> > Anders Logg wrote:
> >> > > On Fri, May 08, 2009 at 08:12:35AM +0200, Johan Hake wrote:
> >> > >> On Thursday 07 May 2009 23:16:54 Anders Logg wrote:
> >> > >>> On Thu, May 07, 2009 at 11:05:49PM +0200, Johan Hake wrote:
> >> > >>>> On Thursday 07 May 2009 18:54:04 Anders Logg wrote:
> >> > >>>>> I've added some of the requested features to the parameter system,
> >> > >>>>> some pushed and some sitting here in a local repository. But the
> >> > >>>>> current design makes it a pain to add new features. A single change
> >> > >>>>> will make it necessary to add a function in at least 5 different
> >> > >>>>> classes.
> >> > >>>>>
> >> > >>>>> So I'm thinking of reimplementing and simplifying the parameter
> >> > >>>>> system. I think I know how to make it simpler.
> >> > >>>>>
> >> > >>>>> But before I do that, does anyone have opinions on the
> >> > >>>>> design/implementation? Is there any third-party library that we
> >> > >>>>> could/should use (maybe something in boost)?
> >> > >>>>
> >> > >>>> It would be nice to have something that easely could be transferable
> >> > >>>> to Python.
> >> > >>>>
> >> > >>>> Having a base class let say Parameterized and then let all inherit
> >> > >>>> this to be able to define parameters will not work well for the
> >> > >>>> shared_ptr interface we have. We have problems with the Variable
> >> > >>>> class, which does not work for the derived shared_ptr classes e.g.
> >> > >>>> Function. I would rather have classes that have a parameter rather
> >> > >>>> than beeing.
> >> > >>>
> >> > >>> How would that work? Inheritance now provides get/set functions for
> >> > >>> subclasses making it possible to do
> >> > >>>
> >> > >>>   solver.set("tolerance", 0.1);
> >> > >>
> >> > >> Not sure what you ask for here. I know of Parametrized and I agree that
> >> > >> the above syntax is nice. But I prefer to keep the parameters in its own
> >> > >> object and just operate on that. These can then be collected into one
> >> > >> "dict/map" and then form the parameters of an application. This is also
> >> > >> easier to wrap to python.
> >> > >>
> >> > >> The shared_ptr argument might not be so relevant as the potential
> >> > >> parametrized classes may not be declared as shared_ptr classes in the
> >> > >> swig interface anyway. However if that will be the case we must declare
> >> > >> Parametrized as a shared_ptr class in swig and then we must declare all
> >> > >> Parametrized sub classes as shared_ptr...
> >> > >>
> >> > >>>> Also by defining a parameter(list/dict) class which can be accessed as
> >> > >>>> a dict let us make the transition to python smoother.
> >> > >>>>
> >> > >>>>    ParameterDict p = solver.default_params();
> >> > >>>>    p["abs_tol"] = 1e-9;
> >> > >>>
> >> > >>> It would need to be
> >> > >>>
> >> > >>>     ParameterDict& p = solver.default_params();
> >> > >>
> >> > >> Sure :P
> >> > >>
> >> > >>> and I'd suggest naming it Parameters:
> >> > >>>
> >> > >>>     Parameters& p = solver.parameters();
> >> > >>
> >> > >> Fine.
> >> > >>
> >> > >>>> By defining some templated check classes we could controll the
> >> > >>>> assignment. In the Solver:
> >> > >>>>    ...
> >> > >>>>    ParameterDict& default_params(){
> >> > >>>>       if (!_par)
> >> > >>>>       {
> >> > >>>>          _par = new ParameterDict();
> >> > >>>>          _par->add_param("abs_tol",new RangeCheck<double>(1e-15,0,1));
> >> > >>>>          vector<string> * allowed_prec = new Vector<string>();
> >> > >>>>          allowed_prec->push_back("ilu");
> >> > >>>>          allowed_prec->push_back("amg");
> >> > >>>>          allowed_prec->push_back("jacobi");
> >> > >>>>          _par->add_param("prec",new
> >> > >>>> OptionCheck<string>("ilu"),allowed_prec));
> >> > >>>> _par->add_param("nonsense","jada"); // No checks
> >> > >>>>       }
> >> > >>>>    }
> >> > >>>>
> >> > >>>> Well, I admit that the above code is not beautiful, and others can
> >> > >>>> probably make it cleaner and spot errors. The point is that RangeCheck
> >> > >>>> and OptionCheck can be derived from a ParCheck class that overloads
> >> > >>>> the operator=(). This will just call a private set function which is
> >> > >>>> defined in the derived classes, and which do the check.
> >> > >>>
> >> > >>> I think we can also solve this without excessive templating... ;-)
> >> > >>
> >> > >> Good!
> >> > >>
> >> > >>>> The to and from file can be implemented in the ParameterDict body. The
> >> > >>>> checks do not have to be written or read, as a ParameterDict can only
> >> > >>>> read in allready predefined parameters, and the check will be done
> >> > >>>> when the file is read.
> >> > >>>>
> >> > >>>> The option parser ability can also be implemented in ParameterDict
> >> > >>>> using boost or other libraries, based on the registered parameters.
> >> > >>>>
> >> > >>>> I have implemented something like this in Python, and the above is a
> >> > >>>> try to scetch something similare in c++.
> >> > >>>
> >> > >>> What exactly is needed from the Python side? I think I can make a
> >> > >>> fairly simple implementation of this in C++ using a minimal amount of
> >> > >>> templates with simple syntax.
> >> > >>
> >> > >> Using operator[] to get and set parameters can straightforwardly be
> >> > >> mapped to python, and we can then also implement the map/dict protocol
> >> > >> on top of that. Other get and set methods can also be used, however set
> >> > >> is a built in type in Python and not a good alternative.
> >> > >>
> >> > >>> Is the main difference that instead of inheriting Parametrized, a
> >> > >>> subclass needs to implement a method named parameters() which returns
> >> > >>> the parameter "dictionary"?
> >> > >>
> >> > >> Yes.
> >> > >
> >> > > ok, I'll try this. I'll add a sketch of a new class using as much of
> >> > > po as seems reasonable and then you could have a look before I proceed.
> >> >
> >> > Will there be just one parameter dictionary, or will objects have their
> >> > own? I'm thinking of cases like when a program uses two Krylov solvers
> >> > but may use different tolerances for each one.
> >>
> >> You mean one parameter dictionary per class or one per instance? I have the
> >> same distinction in a Python application. Some places I need one per instance
> >> and other places it is more convinient to have one per class.
> >
> > One per instance. But there could be a default Parameter database for
> > "Krylov solver" which is used if an option is not set for a specific
> > instance.
> 
> 
> Suggestions:
> 
> const Parameters & a = FooBarType::default_parameters();
> Parameters & b = foobar.parameters();
> 
> Parameters p = b.diff(a); // parameters in b that differs from a
> 
> p.disp();
> file << p.format();
> 
> Parameters par;
> par["beta"] = 1.0;
> foobar.set_parameters(par);
> 
> 
> I prefer the global/class defaults to be immutable.
> (Global state is _always_ evil!)
> Otherwise combining applications becomes a real mess.
> Application-wide defaults can still be handled manually
> using set_parameters.

I mostly agree. I'll keep this in memory and see what comes out of it.
Once we have the first iteration in place, it will be easier to
discuss the details.

-- 
Anders

Attachment: signature.asc
Description: Digital signature