ML (Meta Language) a brief introduction

1
ML (Meta Language)a brief introduction
Fun max (x,y) if x gt 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
• MLs variables are entirely local to a called
  function
• Variables are bound at run-time. Compiling is not
  often used.
• Global variables dont 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 type inference 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
• gt val try fn (a -gt b) a -gt b
• Try (hd, 1,2,3
• gtval it 1 int
• Try (t1, 1,2,3)
• gt 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 -gt int -gt int
• Incr 5 3
• Vali it 8 int
• (incr 5) 3
• Val it 8 int
• Val incr5 incr 5
• Val incr5 fn int -gt 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..inend
• 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