Skip to main content

C# for Haskell and ML Programmers

An interesting question was posed to /r/Haskell today: is there a quick intro to C# from Haskell programmers?

Well there are plenty of explanations of C#'s basic syntax, classes, structs, etc., but nothing that specifically addresses the functional mindset a Haskell programmer would be starting with, so I wrote a reply providing links to the various familiar concepts from functional programming found in C#, and described various caveats that might be surprising to a Haskell user. I'll reproduce the post here for posterity:

I think there's a reasonable C# subset for functional programming, so if you stick to that you should be able to pick it up relatively quickly. Read up on:

  • LINQ -- you can use the query comprehension syntax, or the regular first-class function syntax. The former is sugar for the latter.
  • Tuples
  • Lambdas and delegates (delegates are first-class functions)
  • Parametric polymorphism is known as generics. You can place generic parameters on methods/functions, and type declarations.
  • The standard System.Func* and System.Action* delegates. These are delegates with generic parameters. Most first-class functions you deal with will be one of these two classes of delegates.
  • Type constraints

Caveats:

  • lambdas and delegates are nominally typed, so you can't implicitly or explicitly coerce a delegate of one type into a delegate of a compatible signature, ie. System.Predicate<int> is signature compatible with System.Func<int, bool>, but they are not interconvertible without doing some magic like I do in my Sasa.Func.Coerce library function.
  • methods and delegates are multiparameter, not curried like in OCaml and Haskell, thus leading to all the Func* and Action* overloads.
  • The "void" return type is not a type, so it can't be used as a generic argument. Hence the need for all the Action* delegate types distinct from the Func* delegate types. Action* differ only in the fact that they return void.
  • generic parameters on methods are strictly more general than generic parameters on delegates (which are types). Method generics support first-class polymorphism, while type declaration generics do not.
  • classes are always implicitly option types, ie. they are nullable, while struct types always have a "valid" value, ie. are not nullable. Struct types are then useful for eliminating null reference exceptions in programs, as long the default struct value is meaningful.

Comments

Popular posts from this blog

Easy Reverse Mode Automatic Differentiation in C#

Continuing from my last post on implementing forward-mode automatic differentiation (AD) using C# operator overloading , this is just a quick follow-up showing how easy reverse mode is to achieve, and why it's important. Why Reverse Mode Automatic Differentiation? As explained in the last post, the vector representation of forward-mode AD can compute the derivatives of all parameter simultaneously, but it does so with considerable space cost: each operation creates a vector computing the derivative of each parameter. So N parameters with M operations would allocation O(N*M) space. It turns out, this is unnecessary! Reverse mode AD allocates only O(N+M) space to compute the derivatives of N parameters across M operations. In general, forward mode AD is best suited to differentiating functions of type: R → R N That is, functions of 1 parameter that compute multiple outputs. Reverse mode AD is suited to the dual scenario: R N → R That is, functions of many parameters t...

async.h - asynchronous, stackless subroutines in C

The async/await idiom is becoming increasingly popular. The first widely used language to include it was C#, and it has now spread into JavaScript and Rust. Now C/C++ programmers don't have to feel left out, because async.h is a header-only library that brings async/await to C! Features: It's 100% portable C. It requires very little state (2 bytes). It's not dependent on an OS. It's a bit simpler to understand than protothreads because the async state is caller-saved rather than callee-saved. #include "async.h" struct async pt; struct timer timer; async example(struct async *pt) { async_begin(pt); while(1) { if(initiate_io()) { timer_start(&timer); await(io_completed() || timer_expired(&timer)); read_data(); } } async_end; } This library is basically a modified version of the idioms found in the Protothreads library by Adam Dunkels, so it's not truly ground bre...

Easy Automatic Differentiation in C#

I've recently been researching optimization and automatic differentiation (AD) , and decided to take a crack at distilling its essence in C#. Note that automatic differentiation (AD) is different than numerical differentiation . Math.NET already provides excellent support for numerical differentiation . C# doesn't seem to have many options for automatic differentiation, consisting mainly of an F# library with an interop layer, or paid libraries . Neither of these are suitable for learning how AD works. So here's a simple C# implementation of AD that relies on only two things: C#'s operator overloading, and arrays to represent the derivatives, which I think makes it pretty easy to understand. It's not particularly efficient, but it's simple! See the "Optimizations" section at the end if you want a very efficient specialization of this technique. What is Automatic Differentiation? Simply put, automatic differentiation is a technique for calcu...