AnderGray
commented
5 years ago On the points:
-RandomVariable: Entirely agree, we should just use the objects from Distributions.jl. It's very extensive, and if we find that there are any distributions that we need, we can just create a new class of type Sampleable{Univariate}
-Parameter: I think this depends. If we want to give a name or a description property to the Parameter, this should be done. However, this could also be done when the parameter is given to the Input as an argument in the constructor. I think I prefer option 2, as it means that native Julia variables can be given to the input object before making a parameter object. Maybe we can allow for both.
-Function: I'm not exactly sure that we should make a Function class. Julia natively allows for functions to be passed as arguments to other functions, and we should make use of this if we can.
I think creating a common subtype is quite difficult, since only abstract types can be inherited. So we'd have to say that both RandomVariableSet and Parameter are subtypes of whatever the abstract supertype of the univariate distributions are.
I think a good alternative however is that we restrict the arguments to Input to be Union{<:RandomVariableSet, <:Sampleable{Univariate}, <:Parameter}
FriesischScott
Apart from
RandomVariableSetwhat other inputs do we need?RandomVariable: No need to wrap the objects fromDistributions.jl.Parameter: This could potentially just be any subtype ofNumberand just be filled into theDataFrameFunction: If we do this, it should probably just take a simplelambdafunction that evaluates one variable and adds it to theDataFrame. Alternatively this could be omitted and people would need to just include it in their model/solver. Might be nice though for intermediate values or something you might want in your end results.Should we have a common
typefor everything that can be an input? This type could be sub-typed together with what we need fromDistributions.jl.