A Brief Introduction to Common Lisp

This is a keynote that I made when I was studying at Mid Sweden University about one and half year ago. In a project course, every student was invited to give a presentation about what kind of tool or language been used for the project as the course’s first discussion session. This keynote is thus the one (but not the original one) I’d used to give a brief introduction to Common Lisp, which is used to build a simple paper recommendation system for the course.

Transcript

1. A Brief Introduction to Common Lisp David Gu macdavid313@gmail.com
2. A Brief History Originally specified in 1958, Lisp is the second-oldest high- level programming language in widespread use today; only Fortran is older (by one year). Lisp stands for LISt Processing (while Fortran stands for FORmula TRANslator). 1958 ~ 1980: Various dialects and systems, MacLisp, InterLisp, and Lisp Machines.
3. A Brief History 1975 ~ 1980: Scheme, the first dialect choosing lexical scope, developed in MIT. 1980 ~ 1990: Common Lisp, an industry-level language, published in ANSI standard. 1990 ~ Now: Various implementation for Common Lisp and Scheme; More 3rd party libraries; A new successful dialect Clojure.
4. First Impression / / C C o d e i n t f a c t o r i a l ( i n t n ) { i f ( n = = 0 ) r e t u r n 1 ; e l s e r e t u r n n * f a c t o r i a l ( n - 1 ) ; } ; ; C o m m o n L i s p C o d e ( d e f u n f a c t o r i a l ( n ) ( i f ( = n 0 ) 1 ( * n ( f a c t o r i a l ( - n 1 ) ) ) ) )
5. First Impression: Evolution / / C c o d e , u s i n g t a i l r e c u r s i o n i n t f a c t o r i a l _ h e l p e r ( i n t r e s u l t , i n t c o u n t ) { i f ( c o u n t = = 0 ) r e t u r n r e s u l t ; e l s e r e t u r n f a c t o r i a l _ h e l p e r ( r e s u l t * c o u n t , c o u n t - 1 ) ; } i n t f a c t o r i a l ( i n t n ) { r e t u r n f a c t o r i a l _ h e l p e r ( 1 , n ) ; }
6. First Impression: Evolution ; ; C o m m o n L i s p c o d e , u s i n g t a i l r e c u r s i o n ( d e f u n f a c t o r i a l ( n ) ( d e c l a r e ( o p t i m i z e ( s p e e d 3 ) ) ) ( l a b e l s ( ( i t e r ( r e s u l t c o u n t ) ( i f ( = c o u n t 0 ) r e s u l t ( i t e r ( * r e s u l t c o u n t ) ( 1 - c o u n t ) ) ) ) ) ( i t e r 1 n ) ) )
7. First Impression: Final 'Product' ; ; ; ; c a n e v e n u s e a f u n c t i o n c a l l e d ‘ d i s a s s e m b l e ’ ; ; ; ; t o c h e c k t h e a s s e m b l e c o d e . ( d e f u n f a c t o r i a l ( n ) ( d e c l a r e ( o p t i m i z e ( s p e e d 3 ) ) ) ( l a b e l s ( ( i t e r ( r e s u l t c o u n t ) ( d e c l a r e ( t y p e f i x n u m r e s u l t c o u n t ) ) ( i f ( = c o u n t 0 ) r e s u l t ( i t e r ( t h e f i x n u m ( * r e s u l t c o u n t ) ) ( t h e f i x n u m ( - c o u n t 1 ) ) ) ) ) ) ( i t e r 1 n ) ) )
8. Multi-Paradigms: Functional Lambda(λ) ( ( l a m b d a ( x y ) ( + x y ) ) 1 2 ) = > 3 Map ( m a p ' l i s t # ' ( l a m b d a ( x ) ( 1 + x ) ) ( l i s t 0 1 2 3 ) ) = > ( 1 2 3 4 )
9. Multi-Paradigms: Functional Filter = > ( 1 3 5 ) Fold(foldr in ML) ( r e d u c e # ' + ( l i s t 1 2 3 4 5 ) ) = > 1 5 Curried Function, Lazy Evaluation, and more...
10. Multi-Paradigms: Imperative Common Lisp does provide imperative operators like: ( s e t f x 1 0 ) ⇔ x : = 1 0 And for functional functions, Common Lisp also provides their ‘destructive’ version: map ⇔ map-into filter (remove-if-not) ⇔ delete-if-not And even more: goto, for, while...
11. Multi-Paradigms: OOP Common Lisp has its own implementation for object oriented programming, which is called CLOS. Unlike Java or C++, Common Lisp uses an approach called Generic Function instead of Message Passing. Basically, all the methods belongs to generic function instead of a specific class.
12. Multi-Paradigms: OOP For example, if there's a human class and there's a method called s p e a k , then I create an instance of human called 'me': In message passing style: m e . s p e a k ( " H e l l o " ) In generic function style: s p e a k ( m e , " H e l l o " )
13. Multi-Paradigms: OOP ; ; ; C L O S E x a m p l e ( d e f c l a s s h u m a n ( ) ( ( n a m e : i n i t f o r m " A n o n y m o u s " : i n i t a r g : n a m e : a c c e s s o r n a m e ) ) ) ( d e f c l a s s m a n ( h u m a n ) ( ) ) ( d e f c l a s s w o m a n ( h u m a n ) ( ) ) ( d e f g e n e r i c s a y - l o v e ( h u m a n ) ( : d o c u m e n t a t i o n " A h u m a n s a y s : ' I l o v e y o u ! ' " ) ) ( d e f m e t h o d s a y - l o v e ( ( o b j h u m a n ) ) ( f o r m a t t " ~ a s a y s : ' I l o v e y o u ! ' ~ % " ( n a m e o b j ) ) ) ( d e f m e t h o d s a y - l o v e : a f t e r ( ( o b j m a n ) ) ( f o r m a t t " I n ~ a ' s m i n d : H m m . . . W h y s h o u l d I s a y t h a t ? ~ % " ( d e f v a r r e m e o ( m a k e - i n s t a n c e ' m a n : n a m e " R o m e o " ) ) ( d e f v a r j u l i e t ( m a k e - i n s t a n c e ' w o m a n : n a m e " J u l i e t " ) )
14. Multi-Paradigms: OOP A 'men' will have different behavior. C L - U S E R > ( s a y - l o v e j u l i e t ) J u l i e t s a y s : ' I l o v e y o u ! ' N I L C L - U S E R > ( s a y - l o v e r e m e o ) R o m e o s a y s : ' I l o v e y o u ! ' I n R o m e o ' s m i n d : H m m . . . W h y s h o u l d I s a y t h a t ? N I L It’s called method combination.
15. What Makes Lisp Special? 1. S-Expression 2. Macro
16. S-Expression In Common Lisp, a s-exp would be like: s-exp : (op s-exp1 s-exp2 ...) op: a function | a macro | a special form And interestingly, a s-exp could be ‘made’ like this: ( c o n s 1 2 ) = > ( 1 . 2 ) ( c o n s 1 ( c o n s 2 n i l ) ) = > ( 1 . ( 2 . n i l ) ) = > ( 1 2 ) ( c o n s ' + ( c o n s 1 ( c o n s 2 n i l ) ) ) = > ( + 1 2 ) ( e v a l ( c o n s ' + ( c o n s 1 ( c o n s 2 n i l ) ) ) ) = > 3
17. S-Expression A s-exp looks like a linked list but actually a tree. ( c a r ( l i s t ' + 1 2 ) ) = > ' + ( c d r ( l i s t ' + 1 2 ) ) = > ( 1 2 ) Writing s-expressions is actually writing the Abstract Syntax Tree(AST).
18. S-Expression: Benefits There will be no lexical analysis because all the codes already are the AST. There will be no need to worry about the Operator Precedence. Very convenient to represent data structures like trees and graphs.
19. Macro: Why Lisp is Called Programmable Programming Language? Unlike functions, macros will be expanded first before evaluated; After expanding finished, the whole s-expression generated by macro will be evaluated; So a macro is actually a function that transforms arguments to s-expressions.
20. Macro: A Simple Example In this case, it acts like a inline function. C L - U S E R > ( d e f u n a d d ( x y ) ( + x y ) ) A D D C L - U S E R > ( d e f m a c r o a d d - 1 ( x y ) ` ( + , x , y ) ) A D D - 1 C L - U S E R > ( a d d 1 0 2 0 ) 3 0 C L - U S E R > ( a d d - 1 1 0 2 0 ) 3 0 C L - U S E R > ( m a c r o e x p a n d ' ( a d d - 1 1 0 " s t r i n g " ) ) ( + 1 0 " s t r i n g " ) T
21. Macro: A Real (but a little silly) Example Macros can do something that a function can never do. ( d e f m a c r o d e l a y ( t h i n g ) ; ; r e t u r n a t h u n k ` ( l a m b d a ( ) , t h i n g ) ) ( d e f u n f o r c e ( t h u n k ) ( f u n c a l l t h u n k ) ) ( d e f m a c r o m y - i f ( t e s t t h e n e l s e ) ` ( b l o c k n i l ( a n d , t e s t ( r e t u r n , t h e n ) ) , e l s e ) ) ( d e f u n f a c t o r i a l ( n ) ( m y - i f ( = n 0 ) ; ; t e s t 1 ; ; t h e n ( * n ( f a c t o r i a l ( - n 1 ) ) ) ) ) ; ; e l s e
22. Macro: A Real Example Macros can do something that a function can never do. C L - U S E R > ( d e l a y ( l o o p ) ) # < F U N C T I O N ( L A M B D A ( ) ) { 1 0 0 3 A F 4 E B B } > C L - U S E R > ( m a c r o e x p a n d ' ( d e l a y ( l o o p ) ) ) # ' ( L A M B D A ( ) ( L O O P ) ) T C L - U S E R > ( f o r c e ( d e l a y ( + 1 2 ) ) ) 3 C L - U S E R > ( f a c t o r i a l 2 0 ) 2 4 3 2 9 0 2 0 0 8 1 7 6 6 4 0 0 0 0 C L - U S E R > ( m a c r o e x p a n d ' ( m y - i f ( = n 0 ) ; t e s t 1 ; t h e n ( * n ( f a c t o r i a l ( - n 1 ) ) ) ) ) ; e l s e ( B L O C K N I L ( A N D ( = N 0 ) ( R E T U R N 1 ) ) ( * N ( F A C T O R I A L ( - N 1 ) ) ) ) T
23. Macro: Define New Syntax ; ; ; ; d e f i n e n e w s y n t a x b y m a c r o ( d e f m a c r o w h i l e ( t e s t & b o d y b o d y ) ` ( d o ( ) ( ( n o t , t e s t ) ) , @ b o d y ) ) ( d e f m a c r o f o r ( v a r s t a r t e n d & b o d y b o d y ) ( l e t ( ( g e n d ( g e n s y m ) ) ) ` ( d o ( ( , v a r , s t a r t ( 1 + , v a r ) ) ( , g e n d , e n d ) ) ( ( > , v a r , g e n d ) ) , @ b o d y ) ) ) C L - U S E R > ( l e t ( ( x 5 ) ) ( w h i l e ( > x 0 ) ( f o r m a t t " ~ d " x ) ( d e c f x ) ) ) 5 4 3 2 1 N I L C L - U S E R > ( f o r i 1 5 ( f o r m a t t " ~ d " i ) ) 1 2 3 4 5 N I L
24. Macro: Even Let Evaluation Happens During 'Compiling' Time As mentioned before, a macro will be expanded before evaluated, even before compiled. C L - U S E R > ( d e f m a c r o a v g ( & r e s t n u m b e r s ) ` ( / ( + , @ n u m b e r s ) , ( l e n g t h n u m b e r s ) ) ) A V G C L - U S E R > ( a v g 1 2 3 4 5 6 7 8 9 1 0 ) 1 1 / 2 C L - U S E R > ( m a c r o e x p a n d ' ( a v g 1 2 3 4 5 6 7 8 9 1 0 ) ) ( / ( + 1 2 3 4 5 6 7 8 9 1 0 ) 1 0 ) T 'Counting how many numbers' happens before run time.
25. Lisp: An ‘Edge’ of Programming Languages
26. Thanks for Watching! We toast the Lisp programmer who pens his thoughts within nests of parentheses. -- Alan J. Perlis <SICP>'s foreword