That was the perfect resource, thank you Tim Holy! Here's a question about a specific situation:
Suppose I have a type that has two String variables, but at construction, these might not be the same type of Strings (e.g. one might be ASCIIString, the other SubString{ASCIIString}). Which parametrization is better: type Foo{T <: String, U <: String} a::T b::U end or type Foo{T <: String} a::T b::String end On Thursday, March 5, 2015 at 2:59:40 PM UTC-6, Tim Holy wrote: > > Extensive discussion here: > > http://docs.julialang.org/en/release-0.3/manual/faq/#how-do-abstract-or-ambiguous-fields-in-types-interact-with-the-compiler > > > --Tim > > On Thursday, March 05, 2015 12:50:59 PM Benjamin Deonovic wrote: > > This has been very helpful > > > > @Ivar Nesje > > > > Can you explain the difference between your two examples of type A? I > think > > that is where most of my confusion comes from. > > > > On Thursday, March 5, 2015 at 12:24:12 PM UTC-6, Ivar Nesje wrote: > > > 1. Make sure that your code is correct for the inputs you allow. > There > > > is no need to accept BigFloat (nor Float16) if you end up > converting to > > > Float64 for the calculation anyway (the user of your code can do > that > > > himself). If you don't care enough about different precisions to > even > > > think > > > about how it will affect your program, I think it is better to add > a > > > TODO > > > comment in the code/documentation, so that others that care might > > > submit > > > the required changes in a PR. > > > 2. Testing your algorithm with random Float16 and BigInt will > > > sometimes raise new issues that affects Float64, but is much harder > to > > > find > > > there. There is definitely value in thinking about how different > makes > > > a > > > difference (or why it doesn't). > > > > > > Usually you shouldn't use abstract types in a type definition, but > rather > > > make a parametric type. This is for performance, because the current > Julia > > > runtime is very slow if it can't statically infer the types of the > members > > > of a type. See that > > > > > > type A{T<:FloatingPoint} > > > > > > member::T > > > > > > end > > > > > > is usually much better than > > > > > > type A > > > > > > member::FloatingPoint > > > > > > end > > > > > > Regards > > > Ivar > > > > > > torsdag 5. mars 2015 18.27.38 UTC+1 skrev Simon Danisch følgende: > > >> I think it's a good idea to have things parametric and type stable. > So > > >> I'd vote for T <: FloatingPoint. > > >> Like this, the type you call a function with can be propagated down > to > > >> all other functions and no conversions are needed. > > >> As you said, this gets difficult as some people have Float64 hard > coded > > >> all over the place. It's understandable as John pointed out. > > >> But for someone like me who works with GPU's which depending on the > > >> graphics card perform up to 30 times faster with Float32, this is > quite > > >> annoying as I always need to convert©. > > >> > > >> Am Donnerstag, 5. März 2015 17:55:40 UTC+1 schrieb Benjamin Deonovic: > > >>> Moving a post from julia issues to here since it is more > appropriate: > > >>> https://github.com/JuliaLang/julia/issues/10408 > > >>> > > >>> If I am making a function or composite type that involves floating > point > > >>> numbers, should I enforce those numbers to be Float64 or > FloatingPoint? > > >>> I thought it should be FloatingPoint so that the function/type will > > >>> work with any kind of floating point number. However, several julia > > >>> packages enforce Float64 (e.g. Distributions package Multinomial > > >>> distribution) and so I run into problems and have to put in a lot of > > >>> converts in my code to Float64. Am I doing this wrong? I'm quite new > to > > >>> julia > > >>> > > >>> > > >>> I don't have any intention to use non Float64 floatingpoints > numbers, > > >>> I'm just trying to write good code. I saw a lot of examples where > > >>> people recommended to to use Integer rather than Int64 or String > rather > > >>> than ASCIIString, etc. I'm just trying to be consistent. I'm fine > just > > >>> using Float64 if that is the appropriate approach here. > >