1. Scheme Tutorial

2. Goals Combine several simple ideas into one compound idea to obtain complex ideas Bring two ideas together to obtain relations Seperate ideas from all other ideas that accompany them in real existence to obtain general ideas (this is called abstraction) by John Locke, An Essay Concerning Human Understanding (1690)

3. Features of LISP Recursive Functions of Symbolic Expressions and Their Computation by Machine ( John McCarthy, 1960 ) LISP stands for LISt Processing An interpreted language (not efficient) Second oldest language (after FORTRAN) Designed to solve problems in the form of symbolic differentiation & integration of algebraic expressions

4. Features of LISP LISP’s ability –to represent procedures as data –to manipulate programs as data LISP is currently a family of dialects (share most of the original features) Scheme is a dialect of LISP (small yet powerful)

5. Characteristics of SCHEME Supports functional programming - but not on an exclusive basis Functions are first class data objects Uses static binding of free names in procedures and functions Types are checked and handled at run time - no static type checking Parameters are evaluated before being passed - no lazyness

6. Elements of Language Primitive expressions – simple expressions Means of combination – compound expressions Means of abstraction – compound objects can be named and manipulated as units

7. Scheme Expressions - 1 > 395 395 > (+ 137 349) 486 > (/ 10 6) 1.66667

8. Scheme Expressions - 2 Prefix Notation take arbitrary number of arguments > (+ 21 35 12 7) 75 > (* 25 4 12) 1200

9. Scheme Expressions - 3 Prefix Notation allow combinations to be nested > (+ (* 3 5) (- 10 6) ) 19 > (* (+ 3 (- (+ 2 1) 5) (/ 4 2)) (* 3 2)) Read – Eval – Print loop

10. Scheme Pretty-Printing > (+ (* 3 (+ (* 2 4) (+ 3 5))) (+ (- 10 7) 6)) (+ (* 3 (+ (* 2 4) (+ 3 5))) (+ (- 10 7) 6)) > (load “e1.scm”)

11. Scheme Naming > (define size 2) > size 2 > (* 5 size) 10 > (load “e2.scm”)

12. Compound Procedures > (define (square x) (* x x)) > (square 3) 9 (define ( ) ) > (load “e3.scm”) Substitution model for procedure application

13. Conditional Expressions > (define (abs x) (cond ((> x 0) x) ((= x 0) 0) ((< x 0) (- x)))) > (define (abs x) (cond ((< x 0) (- x)) (else x)))

14. Conditional Expressions (cond ( ) ( ) : ( )) p is predicate either true (non-nil) or false (nil) e is consequent expression returns the value of it

15. Conditional Expressions > (define (abs x) (if (< x 0) (- x) x)) (if )

16. Logical Operators Primitive operators :, = Logical operators : and, or, not > (define (>= x y) (or (> x y) (= x y))) Example: another definition of (>= x y) Example: 5 < x < 10

17. Procedures different than mathematical functions  x = the y such that y  0 and y 2 = x > (define (sqrt x) (the y (and (>= y 0) (= (square y) x)))) Mathematics – declarative (what is) Programs – imperative (how to) Square Roots by Newton’s Method > (load “e4.scm”) -  2 = 1.4142...

18. Square Roots by Newton’s Method break the problem into subproblems how to tell whether a guess is good enough how to improve a guess how to calculate the average of two numbers etc. each of the above tasks is a procedure

19. Square Roots by Newton’s Method sqrt sqrt-iter good-enough? squareabs improve average > (load “e4.scm”)

20. Procedures – Black Box Abstractions (define (square x) (* x x)) (define (square x) (exp (+ (log x) (log x))))

21. Procedural Abstractions (define (square x) (* x x)) (define (square y) (* y y)) (define (square variable) (* variable variable)) parameter names that are local to the procedure bound variables – change throughout the procedure does not change the meaning of the procedure free variables – if a variable is not bound in the proc (define (good-enough? guess x) (< (abs (- (square guess) x)).001))

22. Internal Definitions Encapsulation – hiding details > (load “e4.scm”) Nesting definitions – block structure > (load “e5.scm”) Lexical scoping – not necessary to pass x explicitly to internal procedures, so x becomes free variable in the internal definitions > (load “e6.scm”)

23. Procedures Procedure : a pattern for the local evolution of a computational process. At each step, the next state of the process is computed from its current state according to the rules of interpreting procedures.

24. Linear Recursion (factorial 6) (* 6 (factorial 5)) (* 6 (* 5 (factorial 4))) (* 6 (* 5 (* 4 (factorial 3)))) (* 6 (* 5 (* 4 (* 3 (factorial 2))))) (* 6 (* 5 (* 4 (* 3 (* 2 (factorial 1)))))) (* 6 (* 5 (* 4 (* 3 (* 2 1))))) (* 6 (* 5 (* 4 (* 3 2)))) (* 6 (* 5 (* 4 6))) (* 6 (* 5 24)) (* 6 120) 720 process does grow and shrink

25. Linear Recursion n! = n * (n-1) * (n-2)... 2 * 1 n! = n * (n-1)! n! = n * ( (n-1) * (n-2)! ) n! = n * (... ( (n-1) * (n-2) *... * 1! ) )... ) (define (factorial n) (if (= n 1) 1 (* n (factorial (- n 1)))))

26. Linear Iteration (factorial 6) (fact-iter 1 1 6) (fact-iter 1 2 6) (fact-iter 2 3 6) (fact-iter 6 4 6) (fact-iter 24 5 6) (fact-iter 120 6 6) (fact-iter 720 7 6) 720 process does not grow and shrink

27. Linear Iteration Product, counter = 1 do while counter < n product = counter * product counter = counter + 1 (define (factorial n) (fact-iter 1 1 n)) (define (fact-iter product counter max-count) (if (> counter max-count) product (fact-iter (* counter product) (+ counter 1) max-count)))

28. Tree Recursion Fibonacci numbers : 0, 1, 1, 2, 3, 5, 8, 13, 21,... 0 if n = 0 Fib( n ) = 1 if n = 1 Fib(n-1) + Fib(n-2) otherwise (define (fib n) (cond ((= n 0) 0) ((= n 1) 1) (else (+ (fib (- n 1)) (fib (- n 2))))))

29. Tree Recursion Fib(5) Fib(4)Fib(3) Fib(2) Fib(1) Fib(2)Fib(1) Fib(0) Fib(1)Fib(0)Fib(1)Fib(0)

30. Tree Recursion For Fibonacci numbers, Use linear iteration instead of tree recursion (define (fib n) (fib-iter 1 0 n)) (define (fib-iter a b count) (if (= count 0) b (fib-iter (+ a b) a (- count 1))))

31. Exponentiation – linear recursion (define (expt b n) (if (= n 0) 1 (* b (expt b (- n 1)))))

32. Exponentiation – linear iteration (define (expt b n) (exp-iter b n 1)) (define (exp-iter b counter product) (if (= counter 0) product (exp-iter b (- counter 1) (* b product))))

33. Exponentiation – fast method (define (fast-exp b n) (cond ( (= n 0) 1) ( (even? n) (square (fast-exp b (/ n 2)))) (else (* b (fast-exp b (- n 1)))))) (define (even? n) (= (remainder n 2) 0)) b n = (b n/2 ) 2 if n is even b n = b * b n-1 if n is odd

34. Greatest Common Divisor (define (gcd a b) (if (= b 0) a (gcd b (remainder a b)))) GCD( a, b ) is defined to be the largest integer that evenly divides both a and b. GCD( a, b ) = GCD( b, r ) where r is the remainder of a / b. GCD(206, 40)=GCD(40,6)=GCD(6, 4)=GCD(4, 2)=GCD(2, 0)=2

35. Higher-Order Procedures Build abstractions by assigning names to common patterns (define (cube x) (* x x x)) Procedures that manipulate data i.e. accept data as argument and return data What about procedures that manipulate procedures i.e. accept procedures as argument and return procedures Higher-Order Procedures

36. Procedures as Parameters (define (sum-cubes a b) (if (> a b) 0 (+ (cube a) (sum-cubes (+ a 1) b)))) (define (sum-integers a b) (if (> a b) 0 (+ a (sum-integers (+ a 1) b))))

37. Procedures as Parameters (define ( a b) (if (> a b) 0 (+ ( a) ( ( a) b))))

38. Procedures as Parameters (define (sum term a next b) (if (> a b) 0 (+ (term a) (sum term (next a) next b)))) (define (sum-cubes a b) (define (inc x) (+ x 1)) (sum cube a inc b))

39. Procedures using Lambda Lambda – define anonymous (lambda (x) (+ x 1)) (lambda ( ) ) (define (sum-cubes a b) (sum (lambda (x) (* x x x)) a (lambda (x) (+ x 1)) b))

40. Procedures using Lambda Lambda – define anonymous (define (plus4 x) (+ x 4)) (define plus4 (lambda (x) (+ x 4))

41. Lambda Calculus A lambda expression describes a "nameless" function Specifies both the parameter(s) and the mapping Consider this function: cube (x) = x * x * x Corresponding lambda expr: (x) x * x * x Can be "applied" to parameter(s) by placing the parameter(s) after the expression ( (x) x * x * x)(3) The above application evaluates to 27

42. Lambda Calculus Based on notation (lambda ( l ) (car (car l ))) l is called a "bound variable"; think of it as a formal parameter name Lambda expressions can be applied ((lambda ( l ) (car (car l ))) '((a b) c d))

43. Lambda Examples > (define x 6) > (lambda (x) (+ x 1)) > (define inc (lambda (x) (+ x 1))) > (define same (lambda (x) (x))) > (if (even? x) inc same) > ((if (even? x) inc same) 5) 6

44. Lambda Examples > ((lambda(x) (+ x 1)) 3) 4 > (define fu-lst (list (lambda (x) (+ x 1)) (lambda (x) (* x 5)))) > fu-lst (# # ) > ((second fu-lst) 6) 30

45. Internal Definitions Internal definitions: the special form, LET (let ( (x ‘(a b c)) (y ‘(d e f)) ) (cons x y)) * Introduces a list of local names (use define for top- level entities, but use let for internal definitions) * Each name is given a value

46. Using Let to Define Local Variables f(x,y) = x(1 + xy) 2 + y(1 – y) + (1 + xy)(1 – y) a = 1 + xy b = 1 – y f(x,y) = xa 2 + yb + ab

47. Using Let to Define Local Variables (define (f x y) (define a (+ 1 (* x y))) (define b (– 1 y)) (+ (* x (square a)) (* y b) (* a b)))

48. Using Let to Define Local Variables (define (f x y) (let ((a (+ 1 (* x y))) (b (– 1 y))) (+ (* x (square a)) (* y b) (* a b)))

49. Using Let to Define Local Variables (define (f x y) ((lambda (a b) (+ (* x (square a)) (* y b) (* a b))) (+ 1 (* x y)) (– 1 y)))

50. Using Let to Define Local Variables (let (( ) ( ) : ( )) )

51. Procedures as Returned Values The derivative of x 3 is 3x 2 Procedure: derivative Argument: a function Return value: another function Derivative Procedure If f is a function and dx is some number, then Df of f is the function whose value at any number x is given (limit of dx) by f(x + dx) – f(x) D f(x) = ---------------------- dx

52. Procedures as Returned Values (define (deriv f dx) (lambda (x) (/ (- (f (+ x dx)) (f x)) (dx))) > ((deriv cube.001) 5) 75.015

53. Pairs Compund Structure called Pair constructor procedure “cons ” extractor procedure “car ” extractor procedure “cdr ” (cadr ) = (car (cdr )) (cons 1 2) 1 2 Box & Pointer Representation

54. Pairs (continued) (cons (cons 1 2) (cons 3 4)) 12 34 (cons (cons 1 (cons 2 3)) 4) 123 4

55. Hierarchical Data Pairs enable us to represent hierarchical data hierarchical data – data made up of parts Data structures such as sequences and trees

56. Data Structures Lists (list... ) is equal to (cons (cons (cons... (cons nil))...) List Operations – append, delete, list, search, nth, len Sets > (load “e8.scm”) Trees > (load “e9.scm”)

57. Symbols and Quote (define a 1) (define b 2) (list a b)  (1 2) (list ‘a ‘b)  (a b) (car ‘(a b c))  a (cdr ‘(a b c))  (b c)

58. Data Abstraction from Primitive Data to Compund Data Real numbers – Rational numbers Operations on primitive data : +, -, *, / Operations on compound data : +rat, -rat, *rat, /rat Generic operators for all numbers : add, sub, mul, div

59. Rational Numbers (define (make-rat n d) (cons n d)) (define (numer x) (car x)) (define (denom x) (cdr x)) (define (+rat x y) (make-rat (+ (* (numer x) (denom y)) (* (denom x) (numer y)) (* (denom x) (denom y))))

60. Use of Complex Numbers Operations on compound data : +rat, -rat, *rat, /rat +c -c *c /c Complex arithmetic package Rectangular representationPolar representation List structure and primitive machine arithmetic

61. Use of Numbers Generic operators for all numbers : add, sub, mul, div add sub mul div Generic arithmetic package List structure and primitive machine arithmetic +rat –rat *rat /rat+c –c *c /c+ – * / RectangularPolar Complex arithmetic Real arithmetic Rational arithmetic

62. Complex Arithmetic - 1 z = x + i y where i 2 = -1 Real coordinate is x Imaginary coordinate is y (define (make-rect x y) (cons x y)) (define (real-part z) (car z)) (define (imag-part z) (cdr z)) Im Re r y x A z = x + i y = r e iA

63. Complex Arithmetic - 2 z = x + i y = r e iA Magnitude is r Angle is A (define (make-polar r a) (cons (* r (cos a)) (* r (sin a)))) (define (magnitude z) (sqrt (+ (square (car z)) (square (cdr z))))) (define (angle z) (atan (cdr z) (car z)))

64. Complex Arithmetic - 3 (define (+c z1 z2) (make-rect (+ (real-part z1) (real-part z2)) (+ (imag-part z1) (imag-part z2)))) (define (*c z1 z2) (make-polar (* (magnitude z1) (magnitude z2)) (+ (angle z1) (angle z2))))

65. Complex Arithmetic - 4 We may choose to implement complex numbers in polar form instead of rectangular form. (define (make-polar r a) (cons r a)) (define (make-rect x y) (cons (sqrt (+ (square x) (square y))) (atan y x))) The discipline of data abstraction ensures that the implementation of complex-number operators is independent of which representation we choose.

66. Manifest Types A data object that has a type that can be recognized and tested is said to have manifest type. (define (attach-type type contents) (cons type contents)) For complex numbers, we have two types rectangular & polar

67. Manifest Types (define (type datum) (if (not (atom? datum)) (car datum) (error “Bad typed datum – Type ” datum))) (define (contents datum) (if (not (atom? datum)) (cdr datum) (error “Bad typed datum – Contents ” datum)))

68. Complex Numbers (define (make-rect x y) (attach-type ‘rect (cons x y))) (define (make-polar r a) (attach-type ‘polar (cons r a))) (define (rect? z) (eq? (type z) ‘rect)) (define (polar? z) (eq? (type z) ‘polar))

69. Complex Numbers (define (real-part z) (cond ( (rect? z) (real-part-rect (contents z))) ( (polar? z) (real-part-polar (contents z))))) (define (imag-part z) (cond ( (rect? z) (imag-part-rect (contents z))) ( (polar? z) (imag-part-polar (contents z)))))

70. Complex Numbers (define (real-part-rect z) (car z)) (define (imag-part-rect z) (cdr z))

71. Let Expressions (let ((a 4) (b -3)) (let ((a-squared (* a a)) (b-squared (* b b))) (+ a-squared b-squared))) 25

72. Let Expressions (let ((x 1)) (let ((x (+ x 1))) (+ x x))) 4

73. Let Expressions Shadowing may be avoided by choosing different names for variables. The expression above could be rewritten so that the variable bound by the inner let is new-x. (let ((x 1)) (let ((new-x (+ x 1))) (+ new-x new-x))) 4

74. Lambda Expressions ((lambda (x) (+ x x)) (* 3 4)) ⇒ 24 Because procedures are objects, we can establish a procedure as the value of a variable and use the procedure more than once. (let ((double (lambda (x) (+ x x)))) (list (double (* 3 4)) (double (/ 99 11)) (double (- 2 7)))) ⇒ (24 18 -10)

75. Lambda Expressions (let ((double-any (lambda (f x) (f x x)))) (list (double-any + 13) (double-any cons 'a))) ⇒ (26 (a. a))

76. Lambda Expressions (define double-any (lambda (f x) (f x x))) The variable double-any now has the same status as cons or the name of any other primitive procedure. We can use double- any as if it were a primitive procedure. (double-any + 10) ⇒ 20 (double-any cons 'a) ⇒ (a. a)

77. Lambda Expressions (map abs '(1 -2 3 -4 5 -6)) ⇒ (1 2 3 4 5 6) (map cons '(a b c) '(1 2 3)) ⇒ ((a. 1) (b. 2) (c. 3)) (map (lambda (x) (* x x)) '(1 -3 -5 7)) ⇒ (1 9 25 49)

78. Lambda Expressions (define map1 (lambda (p ls) (if (null? ls) '() (cons (p (car ls)) (map1 p (cdr ls)))))) (map1 abs '(1 -2 3 -4 5 -6)) ⇒ (1 2 3 4 5 6)

79. Lambda Expressions (let ((x 'a)) (let ((f (lambda (y) (list x y)))) (f 'b))) ⇒ (a b) The occurrence of x within the lambda expression refers to the x outside the lambda that is bound by the outer let expression. The variable x is said to occur free in the lambda expression or to be a free variable of the lambda expression. The variable y does not occur free in the lambda expression since it is bound by the lambda expression.

80. Free & Bound Variables > (occurs-free? ’x ’x) #t > (occurs-free? ’x ’y) #f > (occurs-free? ’x ’(lambda (x) (x y))) #f > (occurs-free? ’x ’(lambda (y) (x y))) #t > (occurs-free? ’x ’((lambda (x) x) (x y))) #t > (occurs-free? ’x ’(lambda (y) (lambda (z) (x (y z))))) #t

81. Free & Bound Variables We can summarize these cases in the rules: If the expression e is a variable, then the variable x occurs free in e if and only if x is the same as e. If the expression e is of the form (lambda (y) e), then the variable x occurs free in e if and only if y is different from x and x occurs free in e. If the expression e is of the form (e1 e2), then x occurs free in e if and only if it occurs free in e1 or e2. Here, we use “or” to mean inclusive or, meaning that this includes the possibility that x occurs free in both e1 and e2. We will generally use “or” in this sense.

82. Free & Bound Variables (define occurs-free? (lambda (var exp) (cond ((symbol? exp) (eqv? var exp)) ((eqv? (car exp) ’lambda) (and (not (eqv? var (car (cadr exp)))) (occurs-free? var (caddr exp)))) (else (or (occurs-free? var (car exp)) (occurs-free? var (cadr exp)))))))