1 / 40

Lecture 3: Of Lists and Slippery Slopes

Lecture 3: Of Lists and Slippery Slopes. CS655: Programming Languages University of Virginia. David Evans http://www.cs.virginia.edu/~evans. Menu. Language Features and the Inevitability of Language Decay Lists Higher Order Procedures. Recap: Last Time. Primitives

candy
Download Presentation

Lecture 3: Of Lists and Slippery Slopes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 3: Of Lists and Slippery Slopes CS655: Programming Languages University of Virginia David Evans http://www.cs.virginia.edu/~evans CS 655: Lecture 3

  2. Menu • Language Features and the Inevitability of Language Decay • Lists • Higher Order Procedures CS 655: Lecture 3

  3. Recap: Last Time • Primitives • Numerals, Booleans, Functions • Means of Combination • Application • lambda for making procedures • Special forms: if • Means of Abstraction • define for giving expressions names CS 655: Lecture 3

  4. What Else? • We can probably write all possible programs with just this (but no one has proven it yet, Challenge #1) • But, it will be easier to write and understand them, and make them perform efficiently if we have more stuff in the language • Another Means of Abstraction – compound data CS 655: Lecture 3

  5. Lists • LISP = LISt Processing • Could we define pair functions using Mini-Scheme? (make-pair 3 4) pair <3, 4> (first (make-pair 3 4)) 3 (second (make-pair 3 4)) 4 CS 655: Lecture 3

  6. Defining Pair (define make-pair (lambda (x) (lambda (y) (+ x (* 10000 y))) (define (pair-first p) (if (> p 10000) p (pair-first (+ p -10000))))) (define (pair-second p) (mod p 10000)) Only works for pair elements from 0 – 10000. But could do something more complicated to make it work for all numbers. CS 655: Lecture 3

  7. Too Painful...Provide new Primitives • We can always make particular programs easier to write by adding primitives to our language • This is a very slippery slope! • C++: spec is 680 pages, 223 unresolved issues, 50 operators with 16 precedence levels, etc. CS 655: Lecture 3

  8. The Slippery Slope • Everything you add to a language makes it harder to implement, harder to reason about, harder to understand programs in, harder to learn, more expensive to buy manuals for, etc... • Everything you add to a language interacts with everything else in complex and often unexpected ways CS 655: Lecture 3

  9. Java on the Slope • Java in 1994: “Write once, run anywhere” • Language spec and tutorial and HotJava description: ~40 pages • Java in 2001: “Write once, test everywhere” • Java Standard (J2SE), Java Enterprise (J2EE), Java Micro (J2ME),PersonalJava, EmbeddedJava, JavaCard, JavaPhone, JavaTV, etc. (all from Sun) • J2EE Spec Book: 800 pages, 7 authors • J2SE Language Spec: 544 pages • AP Computer Science (transitioning to Java by 2003?) – needs 35 points to define Java subset CS 655: Lecture 3

  10. Java is on the slope now, about to crash! (Duke suicide picture by Gary McGraw.) CS 655: Lecture 3

  11. Feature-itis • Every possible new feature makes some program better • Every programmer will clamor for their favorite feature • There is no cure: Mistakes never go away, they just get “deprecated” CS 655: Lecture 3

  12. Committees  Feature-itis • Languages designed and/or evolved by committees* and marketing departments (not passionate creators) quickly become morasses of complexity • * Unless the committee has at least 3 eventual Turing award winners • Algol 60: Backus, McCarthy, Perlis • Algol 68: Dijkstra, Hoare, McCarthy, Wirth CS 655: Lecture 3

  13. “A final hint: listen carefully to what language users say they really want, until you have an understanding of what they really want. Then find some way of achieving the latter at a small fraction of the cost of the former.” C. A. R. Hoare, Hints on Programming Language Design, 1973. CS 655: Lecture 3

  14. So... When programmers say they want pairs, lists, binary trees, red-black trees, hash tables, network streams, 3D graphics, complex numbers, etc. we should give them a way to glue two values together. CS 655: Lecture 3

  15. Enough (for now) on language complexity...Next: how do we provide that glue CS 655: Lecture 3

  16. List Primitives in Scheme cons: ,   List (“construct”) ,   ( . ) Procedure that takes something of any type, something of any type, and returns something a List. (cons 3 4) (3 . 4) (cons 3 (cons 4 5) (3 . (4 . 5)) shown as (3 4 . 5) CS 655: Lecture 3

  17. cdr, cdr, sdr • car – ( . )   Takes a cons pair and returns the first element. Contents of the Address part of the Register • cdr – ( . )   Takes a cons pair and returns the second element. Contents of the Decrement part of the Register • They sdr called cdr “tail”, and car, “head” but historical names too entrenched in LISP to change. CS 655: Lecture 3

  18. Examples (car (cons 3 4)) 3 (cdr (cons 3 4)) 4 (cdr (cons 3 (cons 4 5))) (4 . 5) (car (cdr (cons 3 (cons 4 5)))) 4 CS 655: Lecture 3

  19. Lists • ‘() is a special constant known as nil. • We call zero or more cons’s ending with ‘() a “List” (nil is a list). (cons 3 ‘()) (3) CS 655: Lecture 3

  20. Creating Lists • list is short for using a lot of cons’s (list) () (list 1 2 3 4) = (cons 1 (cons 2 (cons 3 (cons 4 ‘()) (1 2 3 4) • ‘ (quote) is even shorter (but has more special meanings) (quote (1 2 3)) (1 2 3) ‘(1 2 3) (1 2 3) CS 655: Lecture 3

  21. Examples (cdr (cons 3 4)) 4 (cdr (list 3 4)) (4) (cdr ‘(3 4)) (4) (car ‘((3 4) (5 4 6) 7)) (3 4) CS 655: Lecture 3

  22. List Predicates: null? null?:   boolean (not really a type in Scheme) #f is applied to something that is not nil. (null? (cdr (cons 3 4))) #f Scheme 7.5 gives (), means the same thing. (null? (cdr (list 3))) #t CS 655: Lecture 3

  23. List Predicates: list? list?:   boolean #f if applied to something that is not a list. (list? (cons 3 4)) #f (list? (cons 3 ‘()) #t CS 655: Lecture 3

  24. Manipulating Cons Cells set-car!: ( . ),  Takes a cons cell and value of any type. Replaces first element of cons with value. (define pair1 (cons 1 2)) (1 . 2) (set-car! pair1 3) no value pair1 (3 . 2) CS 655: Lecture 3

  25. Manipulating Cons Cells set-cdr!: ( . ),  Takes a cons cell and value of any type. Replaces second element of cons with value. pair1 (3 . 2) (set-cdr! pair1 3) no value pair1 (3 . 3) CS 655: Lecture 3

  26. Can we make an infinite list? (define little '(1)) little little (1) (set-cdr! little little) Unspecified return value little (1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1... CS 655: Lecture 3

  27. Bang! • set-car! and set-cdr! change state • All Scheme (primitive) procedures that change state end with ! (pronounced “bang”) • Without them, Scheme is a pure functional language. CS 655: Lecture 3

  28. Functions • Mathematical Abstraction: same inputs always give same outputs +, , max, <, etc. are all functions • If f is a function and x contains only functions and literals, we can always replace (+ (fx) (f x)) with (define result (f x)) (+ result result)) CS 655: Lecture 3

  29. Referential Transparency • This is called referenential transparency • It doesn’t matter what order or how often you do things if everything is a function • Once you have procedures that change state, it does matter, and language implementers much be much more careful! • Much more on this later in the course... CS 655: Lecture 3

  30. Forget the last 7 slides • For the next two+ weeks, we will concentrate only on the purely functional subset of Scheme • Adding set-car! and set-cdr! is an example of how a small language change has a big impact • You should not use any non-functional things in PS1. CS 655: Lecture 3

  31. Higher Order Procedures • Last time – procedure that returns procedure (define (make-adder x) (lambda (y) (+ x y))) • Now – procedures that use procedures are parameters CS 655: Lecture 3

  32. Maximum of a List (define (max x y) (if (> x y) x y))) (define (max-worker arg val) (if (null? arg) val (max-worker (cdr arg) (max (car arg) val)))) (define (max-list arg) (max-worker arg 0)) CS 655: Lecture 3

  33. Sum of a List (define (sum-worker arg val) (if (null? arg) val (sum-worker (cdr arg) (+ (car arg) val)))) (define (sum-list arg) (sum-worker arg 0)) CS 655: Lecture 3

  34. Can we abstract this? (define (max-worker arg val) (if (null? arg) val (max-worker (cdr arg) (max (car arg) val)))) (define (sum-worker arg val) (if (null? arg) val (sum-worker (cdr arg) (+ (car arg) val)))) CS 655: Lecture 3

  35. Sure... (define (hard-worker arg val func) (if (null? arg) val (hard-worker (cdr arg) (func (car arg) val) func))) (define (max-list arg) (hard-worker arg 0 max)) (define (sum-list arg) (hard-worker arg 0 +)) CS 655: Lecture 3

  36. Average (define (average-list arg) (/ (sum-list arg) (hard-worker arg 0 (lambda (x y) (+ y 1))))) length CS 655: Lecture 3

  37. Even harder worker... (define (harder-worker arg end-val func) (if (null? arg) end-val (func (car arg) (harder-worker (cdr arg) end-val func)))) (harder-worker '(1 2 3) '() (lambda (el ls) (cons el ls))) (1 2 3) CS 655: Lecture 3

  38. More examples... (harder-worker ‘(1 2 3) ‘() (lambda (el ls) (append ls (list el)))) (3 2 1) (harder-worker ‘(1 2 3) 0 (lambda (x y) (+ x y))) 6 CS 655: Lecture 3

  39. Even more convoluted/powerful example ((harder-worker '(1 2 4) (lambda (x y) (* x y)) (lambda (el f) (lambda (x y) (f el (f x y))))) 1 2) 2048 = 211 CS 655: Lecture 3

  40. Charge • Problem Set 1 due Thursday • Office hours, Monday 3-5 • Not on the problem set, but do for fun: • Obfuscated Scheme contest: try to come up with a convoluted use of higher-order procedures for your problem set partner to figure out. • Next time: Metalinguistic Abstraction (Ch 4) CS 655: Lecture 3

More Related