ML (Meta Language) a brief introduction

1. ML (Meta Language)a brief introduction Fun max (x,y) = if x > y then x else y; Alex Proctor and Brian Lee For CSCI 431 at the University of North Carolina, Asheville

2. An Overview of ML • Description • ML is a high level functional programming language similar to LISP, Prolog, and Hope. • ML is further differentiated by a strong and static type system, type inference, and garbage collection. • ML was the first language to include statically checked polymorphic typing. • History • ML was created as a metalanguage for the Edinburgh LCF proof assistant in 1973. • It has since developed into its own language, hence the acronymn ML no longer is a relevant descriptor of the language.

3. ML: An Example Program • ML Computation: Calculation of Expressions • ML is a robust functional language that combines many of the features of other programming languages we have studied in this course. • The unit of evaluation in ML is the expression. Every expression has a type, possibly a value, and may or may not cause an effect. • ML also limits defined functions to only take a single parameter, however, this parameter may be a tuple, or a ‘unit’ (the empty set). • ML is typically run in an interpretive manner. • (ML expression): fun average (x, y) = (x +y) / 2.0; • (User input): -average (5, 7); • (Resulting values): - val it = 6 : int

4. ML As a Functional Language: ML is more Powerful! • ML is Powerful: • ML has many of the attributes that you would expect in a functional language like Scheme or LISP, such as: • Computation by expression rather than execution of instruction • Declarative programming abstraction • Lambda calculus function methodology • Yet ML incorporates other features not usually found in functional languages, such as: • Exception Handling • Garbage Collection • User defined Abstract Data Types • Strong and type handling and type inference

5. ML As a Functional Language: Further Details • ML exibits referential transparency • Variable assignment is a ‘once only’ operation. • Variable values do not change once assigned. • Referential transparency greatly increases the ability of a program to be quantitatively analyzed, used as a proof, and debugged. • ML variables are statically scoped • ML’s variables are entirely local to a called function • Variables are bound at run-time. Compiling is not often used. • Global variables don’t exist. Neither do instances of an object as might be found in other languages supporting object abstractions. • ML can handle many of the more interesting data types • Lists, Real Numbers, User defined ADTs

6. ML Functions: Extending the Functional Languages • ML functions may be of the highest order • ML uses aggressive typeinference methods • ML has few standard data types, but all are extensible • Even Functions may define other Functions!, as in this example: Fun try (a, x) = a x; > val try = fn : (‘a -> ‘b) * ‘a -> ‘b Try (hd, [1,2,3]; >val it = 1 : int Try (t1, [1,2,3]); > val it = [2, 3] : int list • Such higher order functions have great utility and are part of the attraction of a Functional language.

7. ML Functions: Extending the Functional Languages • ML supports Currying and Mapping: • Higher order functions may even embed arguments into the definition of a new function.This is called ‘Currying’. • For Example: • Fun incr x y = x + y; • Val incr = fn : int -> int -> int • Incr 5 3; • Vali it = 8 : int • (incr 5) 3; • Val it = 8 : int • Val incr5 = incr 5; • Val incr5 = fn : int -> int • Incr5 3; • Val it = 8 : int

8. ML Polymorphism: • ML broke programming ground with strong polymorphism: • ML uses the term polymorphism to refer to the attribute of a function to handle more than one type of parameter. List is a good example. It works with any list of any type of elements. • The list functions are similar to Scheme, only cleaner. • The ML conception of polymorphism is different than the idea of method overloading that the term polymorphism can also signify. We do not mean merely having two or more functions with the same name. • ML functions are polymorphic if they contain only polymorphic functions and operators. For example: • Fun revPair (x, y) = (y, x)

9. ML Functions: Local Variables are ‘Let’ into a Function • ML allows local variables in a function. • The syntax for local variables follows the pattern : • Let………..in…………end • As we see in the following example: Fun circleData (radius) = Let Val pi = 3.1415926536 Val circumference = two * pi * radius; Fun area radius = pi * radius * radius In (circumference, area (radius)) End;

10. ML Flow Control: The Function Stack • Flow Control is an Interpretive Process • ML parses each line as an ML expression. • ML can use files and libraries, much like scheme. • ML performs I./O with simple grace, for example : fun copyFile (inputFileName, outputFileName) = Let Val inFile = openIn inputFileName; Val outFile = openOut outputFileName In ( output (outFile, input inFile); flushOut outFile; closeOut outFile ) End; • ML has been ported to many graphical windowing environments, such as the unix/linux x-windowing system.

11. ML Handles Exceptions • ML supports user-defined error flow control • Such exception handling makes ML an attractive alternative to other Functional languages where this is not possible

12. Conclusion: ML is a little known giant • ML should be better known and used: • ML is highly orthogonal. Most functions that work with one type of parameter will work with any parameter. ML has good internal type checking mechanisms which are applied to every evaluated expression consistently. • Because ML allows for recursion, nested constructs, list functions, and polymorphism, it is considered a highly abstracted language, capable of implementing the most complex of programming tasks with the most ease for the programmer.

13. Conclusion: ML is a popular outcast • ML in their words: • “ML has made significant inroads into the computer science educational and research communities. The exposure of the type mechanism at the source language level is a feature not available in other widely used languages. However, commercial applications of ML programs are few, and so far it remains mostly a vehicle for computer science research and educational use.” (That old book). • “…This may be because there are not enough…” • Gentle intro to ML: readers comments