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CSE3302 Programming Languages (n-n-n-notes)

CSE3302 Programming Languages (n-n-n-notes). Summer 2003 Dr. Carter Tiernan. Functional Programming. List Processing - LISP Complex interrelationships among data Recursion in conjunction with conditional expressions Primitive list-handling subroutines Applicative language. LISP.

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CSE3302 Programming Languages (n-n-n-notes)

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  1. CSE3302Programming Languages(n-n-n-notes) Summer 2003 Dr. Carter Tiernan Programming Languages

  2. Functional Programming • List Processing - LISP • Complex interrelationships among data • Recursion in conjunction with conditional expressions • Primitive list-handling subroutines • Applicative language Programming Languages

  3. LISP • Function applications • Prefix (Polish) notation : flexibility • Fully parenthesized : no precedence rules • Symbolic data • Lists of symbols called atoms • List is ONLY data structure in LISP Programming Languages

  4. Lists • S-expressions • Function applications are evaluated • Quoted lists are treated as data • Programs and data represented the same way • Convenient for one program to generate and call for execution of another • Simple to write program manipulators Programming Languages

  5. LISP Execution • Usually interpreted and often interactive • Functions • Pure : compute a value only • eq, plus, difference, etc. • Pseudo : have side effects on computer state • set, defun, etc. Programming Languages

  6. Data Structures • Constructor : list • Primitives : atoms • Numeric (integer and floating point) • Many ops provided: arithmetic, increment, decrement, max, min, relational, predicates • Nonnumeric character strings • Limited ops: comparisons for equality • nil (false, empty set) • null is the test op Programming Languages

  7. Constructor • List • Surrounded by parentheses • Separated by blanks • Zero, one or more elements • Can contain lists • () equivalent to nil; called empty or null list Programming Languages

  8. Accessing Parts of a List • Car • Selects the first element of a list • Cdr • Returns all of a list except its first element • Car and Cdr are selectors • Car and Cdr can be combined to access interior parts of a list: ex. caadr Programming Languages

  9. Building a list • Cons • Creates a list from a first element and a list • Is the inverse of car and cdr • Example: • (car ‘(to be or not)) = to • (cdr ‘(to be or not)) = (be or not) • (cons ‘to ‘(be or not)) = (to be or not) Programming Languages

  10. Info Representation • Property lists • (p1 v1 p2 v2 … pn vn) where pn is the nth property and vn is the value of that property • P-lists give flexibility • Easy to define a function to get properties Programming Languages

  11. Info Representation • Association lists • ((a1 v1) (a2 v2) … (an vn)) • Handles properties which are flags or properties with multiple values Programming Languages

  12. Recursive list construction • Append two lists together • Identify simplest cases • (append ‘() L) = L • (append L ‘()) = L • Reduce other cases to the simplest cases • (append L M) = (cons (car L) (append (cdr L) M) • (defun append (L M) (if (null L)) M (cons (car L) (append (cdr L) M) )) Programming Languages

  13. Atoms • Created by mentioning them • Have properties and relations (p-list) • Print name / pname • putprop adds a property to an atom • apval denotes the binding of an atom to a value • set binds an atom • symbol-plist shows property list of an atom Programming Languages

  14. List representation • Linked list • Cells in the list have a right and a left • Right part points to next cell in list • Left part points to value of the cell • Data and programs both represented the same way • Program linked list called an expression tree Programming Languages

  15. List Primitives • Efficient • Car returns a left part • Cdr returns a right part • Cons requires storage allocation for new cell • Car, Cdr, and Cons do not change values • Sublists can be shared • Pseudo-functions alter lists • rplaca, rplacd • Have same drawbacks as aliasing Programming Languages

  16. Conditional expression • Cond • Mimics mathematical notation • Logical ops are defined in terms of the conditional • And & Or • Operands are evaluated sequentially • Conditional interpretation vs. strict Programming Languages

  17. LISP on omega • LISP interpreter on both omega and gamma • Invoke by typing ‘lisp’ • Exit by typing ‘(quit)’ • CMU Common Lisp • Info at www.cons.org/cmucl/ Programming Languages

  18. Iteration • Performed by recursion • Reduction • Perform some op on every element of a list • Uses a binary function to reduce a list to a single value • Mapping • Apply a function to every element of a list • Returns a list of the results • Filtering • Forms a sublist containing all elements that satisfy some property Programming Languages

  19. Functional arguments • Abstracting out pattern • Mapcar • Filter • Reduce • Suppress details • Simplify combination Programming Languages

  20. Recursive Interpreters • Arranged by cases • Atoms • Numeric • Nonnumeric • Quote • Conditional • Functions Programming Languages

  21. Interpreters • Primitive ops performed explicitly • User-defined functions • Evaluate parameters • Bind formals to actuals • Add bindings to environment • Evaluate function in environment Programming Languages

  22. Environment of evaluation • Arguments going to (apply f x a)(p.380) • f the function - lambda expression • x the parameters - bound formals • a the environment - existing environment with the addition of the bound formals • Universal function Programming Languages

  23. Static scoping • Closure to indicate environment of definition - function keyword • Instruction part (ip) - program, lambda expr. • Environment part (ep) - definition environment • (closure ip ep) • User-defined functions can be • Dynamically scoped lambda expressions • Statically scoped function closures Programming Languages

  24. Incompatible scope rules • Options • Adopt uniform static scoping • Use default static scoping but allow “special” dynamic scoping Programming Languages

  25. Storage reclamation • Explicit erasure • Work for programmers • Security problems (dangling pointers) • Automatic reclamation • Reference counts • Garbage collection Programming Languages

  26. Reference counts • Accessible cells are referenced • Reference count keeps track of how many other cells reference the current one • Count must be incremented and decremented correctly • Cells with zero count are added to the free list Programming Languages

  27. Reference counts • Cycles defeat reference counts • Cyclic reference boosts the count of each member plus one member has an outside reference. • If outside reference goes away, cycle still has references internally but no access from anywhere else in program • Allowing cycles is an open issue Programming Languages

  28. Garbage Collection • When storage becomes low, system starts up garbage collection • Mark and sweep process • Starting at roots mark all cells that can be reached recursively moving from each cell to its children • When all are marked, sweep through memory from top, free unmarked cells and reset flags on marked cells Programming Languages

  29. Garbage Collection • Wreaks havoc on execution time • Non uniform and unpredictable • Approaches to more efficiency include • Continuous garbage collection • Parallel Programming Languages

  30. LISP • AI - represent and manipulate complex interrelationships among symbolic data • Suited to ill-specified problems • Easy to manipulate LISP programs in LISP Programming Languages

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