C# programming

C# programming

• 2.
   Name thelanguage... Func<intlist, intlist> Sort = xs => xs.Case( () => xs, (head,tail) => (Sort(tail.Where(x => x < head))) .Concat (Single(head)) .Concat (Sort(tail.Where(x => x >= head))) ); C# 3.0 type inference append higher-order function parameterized type of functions recursion filter lambda expression
• 3.
   C# 3.0has many features well-known to functional programmers ◦ Parameterized types and polymorphic functions (generics) ◦ First-class functions (delegates) ◦ Lightweight lambda expressions & closure conversion ◦ Type inference (for locals and lambdas) ◦ Streams (iterators) ◦ A library of higher-order functions for collections & iterators ◦ And even: GADTs (polymorphic inheritance)  This talk: is it serious competition for ML and Haskell? ◦ (Note: Java 5 has many but not all of the above features)
• 4.
   C# 1.0: ◦First-class functions (delegates), created only from named methods. Environment=object, code=method.  C# 2.0: ◦ Parameterized types and polymorphic methods (generics) ◦ Anonymous methods: creation of delegate objects from code bodies, closure-converted by C# compiler ◦ Iterators: stream abstraction, like generators from Clu  C# 3.0: ◦ Lambda expressions: lightweight syntax for anonymous methods whose bodies are expressions ◦ Type inference for locals and lambdas ◦ (Also, not discussed: expression trees for lambdas)
• 5.
   Essentially namedfunction types e.g. delegate bool IntPred(int x);  Delegate objects capture a method code pointer together with an object reference e.g. class Point { int x; int y; bool Above(int ybound) { return y >= ybound; } } Point point; IntPred predicate = new IntPred(point.Above);  Compare (environment, code pointer) closure in a functional language.
• 6.
   Types (classes,interfaces, structs and delegates) can be parameterized on other types e.g. delegate R Func<A,R>(A arg); class List<T> { ... } class Dict<K,D> { ... }  Methods (instance and static) can be parameterized on types e.g. static void Sort<T>(T[] arr); static void Swap<T>(ref T x, ref T y); class List<T> { List<Pair<T,U>> Zip<U>(List<U> other) ..  Very few restrictions: ◦ Parameterization over primitive types, reference types, structs ◦ Types preserved at runtime, in spirit of the .NET object model
• 7.
  1. Polymorphic recursione.g. static void Foo<T>(List<T> xs) { …Foo<List<List<T>>>(…)… } 2. First-class polymorphism (System F) e.g. interface Sorter { void Sort<T>(T[] arr); } class QuickSort : Sorter { … } class MergeSort : Sorter { … } 3. GADTs e.g. abstract class Expr<T> { T Eval(); } class Lit : Expr<int> { int Eval() { … } } class PairExpr<A,B> : Expr<Pair<A,B>> { Expr<A> e1; Expr<B> e2; Pair<A,B> Eval() { … } } Also possible in Java 5
• 8.
   Delegates areclumsy: programmer has to name the function and “closure-convert” by hand  So C# 2.0 introduced anonymous methods ◦ No name ◦ Compiler does closure-conversion, creating a class and object that captures the environment e.g. bool b = xs.Exists(delegate(int x) { return x>y; }); Local y is free in body of anonymous method
• 9.
   Like Java,C# provides interfaces that abstract the ability to enumerate a collection: interface IEnumerable<T> { IEnumerator<T> GetEnumerator(); } interface IEnumerator<T> { T Current { get; } bool MoveNext(); }  To “consume” an enumerable collection, we can use the foreach construct: foreach (int x in xs) { Console.WriteLine(x); }  But in C# 1.0, implementing the “producer” side was error-prone (must implement Current and MoveNext methods)
• 10.
   C# 2.0introduces iterators, easing task of implementing IEnumerable e.g. static IEnumerable<int> UpAndDown(int bottom, int top) { for (int i = bottom; i < top; i++) { yield return i; } for (int j = top; j >= bottom; j--) { yield return j; } }  Iterators can mimic functional-style streams. They can be infinite: static IEnumerable<int> Evens() { for (int i = 0; true; i += 2) { yield return i; } }  The System.Query library provides higher-order functions on IEnumerable<T> for map, filter, fold, append, drop, take, etc. static IEnumerable<T> Drop(IEnumerable<T> xs, int n) { foreach(T x in xs) { if (n>0) n--; else yield return x; }}
• 11.
   Anonymous methodsare just a little too heavy compared with lambdas in Haskell or ML: compare delegate (int x, int y) { return x*x + y*y; } (x,y) -> x*x + y*y fn (x,y) => x*x + y*y  C# 3.0 introduces lambda expressions with a lighter syntax, inference (sometimes) of argument types, and expression bodies: (x,y) => x*x + y*y  Language specification simply defines lambdas by translation to anonymous methods.
• 12.
   Introduction ofgenerics in C# 2.0, and absence of type aliases, leads to typefull programs! Dict<string,Func<int,Set<int>>> d = new Dict<string,Func<int,Set<int>>>(); Func<int,int,int> f = delegate (int x, int y) { return x*x + y*y; }  C# 3.0 supports a modicum of type inference for local variables and lambda arguments: var d = new Dict<string,Func<int,Set<int>>>(); Func<int,int,int> f = (x,y) => x*x + y*y;
• 13.
   Generalized AlgebraicData Types permit constructors to return different instantiations of the defined type  Canonical example is well-typed expressions e.g. datatype Expr a with Lit : int → Expr int | PairExpr : Expr a → Expr b → Expr (a £ b) | Fst : Expr (a £ b) → Expr a …  In C#, we can render this using “polymorphic inheritance”: abstract class Expr<a> class Lit : Expr<int> { int val; … } class PairExpr<a,b> : Expr<Pair<a,b>> { Expr<a> e1; Expr<b> e2; … } class Fst<a,b> : Expr<a> { Expr<Pair<a,b>> e; … }  Demo: strongly-typed printf
• 14.
   C# iscompiled to IL, an Intermediate Language that is executed on the .NET Common Language Runtime  The CLR has direct support for many of the features described here ◦ Delegates are special classes with fast calling convention ◦ Generics (parametric polymorphism) is implemented by just-in- time specialization so no boxing is required ◦ Closure conversion is done by the C# compiler, which shares environments between closures where possible
• 15.
  1. Take yourfavourite functional pearl 2. Render it in C# 3.0  Here, Hutton & Meijer’s monadic parser combinators. Demo.
• 16.
   It’s functionalprogramming bolted onto a determinedly imperative object-oriented language ◦ Quite nicely done, but C# 3.0 shows its history ◦ The additional features in C# 3.0 were driven by the LINQ project (Language INtegrated Query)  Contrast Scala, which started with (almost) a clean slate: ◦ Object-oriented programming (new design) + functional programming (new design)  Many features remain the preserve of functional languages ◦ Datatypes & pattern matching ◦ Higher-kinded types, existentials, sophisticated modules ◦ Unification/constraint-based type inference ◦ True laziness
• 17.
   Why? Clue:r-values vs l-values. Arguably, the right design: static void While(VoidFunc<bool> condition, VoidFunc action) { … } int x = 1; While(() => x < 10, () => { x=2*x; }); var funs = new Func<int,int>[5]; // Array of functions of type int→int for (int i = 0; i<5; i++) { funs[i] = j => i+j; // To position index i, assign λj. i+j } Console.WriteLine(funs[1](2));  Guess the output Result is “7”!
• 18.
   Iterator combinatorscan be defined purely using foreach and yield. X Head<X>(IEnumerable<X> xs) { foreach (X x in xs) { return x; } } IEnumerable<X> Tail<X>(IEnumerable<X> xs) { bool head = true; foreach (X x in xs) { if (head) head = false; else yield return x; } }  But performance implications are surprising: IEnumerable<int> xs; for (int i = 0; i < n; i++) { xs = Tail(xs); } int v = Head(xs); Cost is O(n2 )!
• 19.
   Closure creationand application are relatively cheap operations ◦ But almost no optimizations are performed. Contrast ML/Haskell uncurrying, arity-raising, flow analysis, etc.  Iterators are not lazy streams ◦ No memoizing of results ◦ Chaining of IEnumerable wrappers can lead to worsening of asymptotic complexity ◦ Though there’s nothing to prevent the programmer implementing a proper streams library, as in ML