Lecture 7 Data Abstraction Pairs and Lists Sections

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Lecture 7 Data Abstraction. Pairs and Lists. (Sections 2. 1. 1 – 2. 2.

Lecture 7 Data Abstraction. Pairs and Lists. (Sections 2. 1. 1 – 2. 2. 1) 7 מבוא מורחב שיעור 1

Procedural abstraction • Publish: name, number and type of arguments (and conditions they must

Procedural abstraction • Publish: name, number and type of arguments (and conditions they must satisfy) type of procedure’s return value • Guarantee: the behavior of the procedure Interface Implementation • Hide: local variables and procedures, way of implementation, internal details, etc. Export only what is needed. 7 מבוא מורחב שיעור 2

Data-object abstraction • Publish: constructors, selectors • Guarantee: the behavior Interface Implementation • Hide:

Data-object abstraction • Publish: constructors, selectors • Guarantee: the behavior Interface Implementation • Hide: local variables and procedures, way of implementation, internal details, etc. Export only what is needed. 7 מבוא מורחב שיעור 3

An example: Rational numbers We would like to represent rational numbers. A rational number

An example: Rational numbers We would like to represent rational numbers. A rational number is a quotient a/b of two integers. Constructor: (make-rat a b) Selectors: (numer r) (denom r) Guarantee: (numer (make-rat a b)) = a (denom (make-rat a b)) = b 7 מבוא מורחב שיעור 4

An example: Rational numbers We would like to represent rational numbers. A rational number

An example: Rational numbers We would like to represent rational numbers. A rational number is a quotient a/b of two integers. Constructor: (make-rat a b) Selectors: (numer r) (denom r) A better Guarantee: (numer (make-rat a b)) = (denom (make-rat a b)) a b A weaker condition, but still sufficient! 7 מבוא מורחב שיעור 5

We can now use the constructors and selectors to implement operations on rational numbers:

We can now use the constructors and selectors to implement operations on rational numbers: (add-rat x y) (sub-rat x y) (mul-rat x y) (div-rat x y) (equal-rat? x y) (print-rat x) A form of wishful thinking: we don’t know how make -rat numer and denom are implemented, but we use them. 7 מבוא מורחב שיעור 6

Implementing the operations (define (add-rat x y) ; n 1/d 1 + n 2/d

Implementing the operations (define (add-rat x y) ; n 1/d 1 + n 2/d 2 = (n 1. d 2 + n 2. d 1) / (d 1. d 2) (make-rat (+ (* (numer x) (denom y)) (* (numer y) (denom x))) (* (denom x) (denom y)))) (define (sub-rat x y) … (define (mul-rat x y) (make-rat (* (numer x) (numer y)) (* (denom x) (denom y)))) (define (div-rat x y) (make-rat (* (numer x) (denom y)) (* (denom x) (numer y)))) (define (equal-rat? x y) (= (* (numer x) (denom y)) (* (numer y) (denom x)))) 7 מבוא מורחב שיעור 7

Using the rational package (define (print-rat x) (newline) (display (numer x)) (display ”/”) (display

Using the rational package (define (print-rat x) (newline) (display (numer x)) (display ”/”) (display (denom x))) (define one-half (make-rat 1 2)) (print-rat one-half) 1/2 (define one-third (make-rat 1 3)) (print-rat (add-rat one-half one-third)) 5/6 (print-rat (add-rat one-third)) 6/9 7 מבוא מורחב שיעור 8

Abstraction barriers Programs that use rational numbers in problem domain add-rat sub-rat mul-rat… rational

Abstraction barriers Programs that use rational numbers in problem domain add-rat sub-rat mul-rat… rational numbers as numerators and denumerators make-rat numer denom 7 מבוא מורחב שיעור 9

Gluing things together We still have to implement numer, denom, and make-rat We need

Gluing things together We still have to implement numer, denom, and make-rat We need a way to glue things together… A pair: (define x (cons 1 2)) (car x) 1 (cdr x) 2 7 מבוא מורחב שיעור 10

Pair: A primitive data type. Constructor: (cons a b) Selectors: (car p) (cdr p)

Pair: A primitive data type. Constructor: (cons a b) Selectors: (car p) (cdr p) Guarantee: (car (cons a b)) = a (cdr (cons a b)) = b Abstraction barrier: We say nothing about the representation or implementation of pairs. 7 מבוא מורחב שיעור 11

Pairs (define x (cons 1 2)) (define y (cons 3 4)) (define z (cons

Pairs (define x (cons 1 2)) (define y (cons 3 4)) (define z (cons x y)) (car z)) 1 ; (caar z) (car (cdr z)) 3 ; (cadr z) 7 מבוא מורחב שיעור 12

Implementing make-rat, numer, denom (define (make-rat n d) (cons n d)) (define (numer x)

Implementing make-rat, numer, denom (define (make-rat n d) (cons n d)) (define (numer x) (car x)) (define (denom x) (cdr x)) 7 מבוא מורחב שיעור 13

Abstraction barriers Programs that use rational numbers in problem domain add-rat sub-rat mul-rat. .

Abstraction barriers Programs that use rational numbers in problem domain add-rat sub-rat mul-rat. . . rational numbers as numerators and denumerators make-rat numer denom rational numbers as pairs cons car cdr 7 מבוא מורחב שיעור 14

Alternative implementation for add-rat (define (add-rat x y) (cons (+ (* (car x) (cdr

Alternative implementation for add-rat (define (add-rat x y) (cons (+ (* (car x) (cdr y)) (* (car y) (cdr x))) (* (cdr x) (cdr y)))) Abstraction Violation If we bypass an abstraction barrier, changes to one level may affect many levels above it. Maintenance becomes more difficult. 7 מבוא מורחב שיעור 15

Rationals - Alternative Implementation In our current implementation we keep 10000/20000 as such and

Rationals - Alternative Implementation In our current implementation we keep 10000/20000 as such and not as 1/2. This: • Makes the computation more expensive. • Prints out clumsy results. A solution: change the constructor (define (make-rat a b) (let ((g (gcd a b))) (cons (/ a g) (/ b g)))) No other changes are required! 7 מבוא מורחב שיעור 16

Reducing to lowest terms, another way (define (make-rat n d) (cons n d)) (define

Reducing to lowest terms, another way (define (make-rat n d) (cons n d)) (define (numer x) (let ((g (gcd (car x) (cdr x)))) (/ (car x) g))) (define (denom x) (let ((g (gcd (car x) (cdr x)))) (/ (cdr x) g))) 7 מבוא מורחב שיעור 17

How can we implement pairs? (first solution) (define (cons x y) (lambda (f) (f

How can we implement pairs? (first solution) (define (cons x y) (lambda (f) (f x y))) (define (car z) (z (lambda (x y) x))) (define (cdr z) (z (lambda (x y) y))) 7 מבוא מורחב שיעור 18

How can we implement pairs? (first solution, cont’) Name > (define p (cons 1

How can we implement pairs? (first solution, cont’) Name > (define p (cons 1 2)) Value (lambda(f) (f 1 2)) p > (car p) ( (lambda(f) (f 1 2)) (lambda (x y) x)) ( (lambda(x y) x) 1 2 ) (define (cons x y) (lambda (f) (f x y))) >1 (define (car z) (z (lambda (x y) x))) (define (cdr z) (z (lambda (x y) y))) 7 מבוא מורחב שיעור 19

How can we implement pairs? (Second solution: message passing) (define (cons x (lambda (m)

How can we implement pairs? (Second solution: message passing) (define (cons x (lambda (m) (cond ((= m (else y) 0) x) 1) y) (error "Argument not 0 or 1 -- CONS" m)))))) (define (car z) (z 0)) (define (cdr z) (z 1)) 7 מבוא מורחב שיעור 20

Implementing pairs (second solution, cont’) > (define p (cons 3 4)) Name p >

Implementing pairs (second solution, cont’) > (define p (cons 3 4)) Name p > (car p) Value (lambda(m) (cond ((= m 0) 3) ((= m 1) 4) (else. . ))) ((lambda(m) (cond. . )) 0) (cond ((= 0 0) 3) ((= 0 1) 4) (else >3 (define (cons x y) (lambda (m). . . ))) (cond ((= m 0) x) ((= m 1) y) (else. . . ))) (define (car z) (z 0)) (define (cdr z) (z 1)) 7 מבוא מורחב שיעור 21

Implementation of Pairs The way it is really done • Scheme provides an implementation

Implementation of Pairs The way it is really done • Scheme provides an implementation of pairs, so we do not need to use these “clever” implementations. • The natural implementation is by using storage. • The two solutions we presented show that the distinction between storage and computation is not always clear. • Sometimes we can trade data for computation. • The solutions we showed have their own significance: • The first is used to show that lambda calculus can simulate other models of computation (theoretical importance). • The second – message passing – is the basis for Object Oriented Programming. We will return to it later. 7 מבוא מורחב שיעור 22

Box and Pointer Diagram (define a (cons 1 2)) a 2 1 A pair

Box and Pointer Diagram (define a (cons 1 2)) a 2 1 A pair can be implemented directly using two “pointers”. Originally on IBM 704: (car a) Contents of Address part of Register (cdr a) Contents of Decrement part of Register 7 מבוא מורחב שיעור 23

Box and pointer diagrams (cont. ) (cons 1 (cons 2 3)) 4) 4 3

Box and pointer diagrams (cont. ) (cons 1 (cons 2 3)) 4) 4 3 1 2 7 מבוא מורחב שיעור 24

Compound Data A closure property: The result obtained by creating a compound data structure

Compound Data A closure property: The result obtained by creating a compound data structure can itself be treated as a primitive object and thus be input to the creation of another compound object. Pairs have the closure property: We can pairs, pairs of pairs etc. 3 2 (cons 1 2) 3) 1 7 מבוא מורחב שיעור 25

The empty list (a. k. a. null or nill) Lists (cons 1 (cons 3

The empty list (a. k. a. null or nill) Lists (cons 1 (cons 3 (cons 2 ’() ))) 1 3 2 Syntactic sugar: (list 1 3 2) 7 מבוא מורחב שיעור 26

Formal Definition of a List A list is either • ’() -- The empty

Formal Definition of a List A list is either • ’() -- The empty list • A pair whose cdr is a list. Lists are closed under the operations cons and cdr: • If lst is a non-empty list, then (cdr lst) is a list. • If lst is a list and x is arbitrary, then (cons x lst) is a list. 7 מבוא מורחב שיעור 27

Lists (list <x 1> <x 2>. . . <xn>) is syntactic sugar for (cons

Lists (list <x 1> <x 2>. . . <xn>) is syntactic sugar for (cons <x 1> (cons <x 2> ( … (cons <xn> ’() )))) … <x 1> <x 2> <xn> 7 מבוא מורחב שיעור 28

Lists (examples) The following expressions all result in the same structure: (cons 3 (list

Lists (examples) The following expressions all result in the same structure: (cons 3 (list 1 2)) (cons 3 (cons 1 (cons 2 ’() ))) (list 3 1 2) and similarly the following 1 3 2 (cdr (list 1 2 3)) (cdr (cons 1 (cons 2 (cons 3 ’() )))) (cons 2 (cons 3 ’() )) (list 2 3) 2 7 מבוא מורחב שיעור 3 29

More Elaborate Lists (list 1 2 3 4) Prints as (1 2 3 4)

More Elaborate Lists (list 1 2 3 4) Prints as (1 2 3 4) 1 2 3 4 (cons (list 1 2) (list 3 4)) Prints as ((1 2) 3 4) 3 1 2 4 (list 1 2) (list 3 4)) Prints as ((1 2) (3 4)) 7 מבוא מורחב שיעור 3 4 31

Yet More Examples Ø (define p (cons 1 2)) Øp (1. 2) p 1

Yet More Examples Ø (define p (cons 1 2)) Øp (1. 2) p 1 Ø (define p 1 (cons 3 p) 3 Ø p 1 (3 1. 2) p 1 2 Ø (define p 2 (list p p)) Ø p 2 ( (1. 2) ) p 2 7 מבוא מורחב שיעור 32

The Predicate Null? null? : anytype -> boolean (null? <z>) #t if <z> evaluates

The Predicate Null? null? : anytype -> boolean (null? <z>) #t if <z> evaluates to empty list #f otherwise (null? 2) #f (null? (list 1)) #f (null? (cdr (list 1))) #t ’()) #t (null? null) #t (null? 7 מבוא מורחב שיעור 33

The Predicate Pair? pair? : anytype -> boolean (pair? <z>) #t if <z> evaluates

The Predicate Pair? pair? : anytype -> boolean (pair? <z>) #t if <z> evaluates to a pair #f otherwise. (pair? (cons 1 2)) #t (pair? (cons 1 2))) #t (pair? (list 1)) #t (pair? ’()) #f (pair? 3) #f (pair? ) #f 7 מבוא מורחב שיעור 34

The Predicate Atom? atom? : anytype -> boolean (define (atom? z) (and (not (pair?

The Predicate Atom? atom? : anytype -> boolean (define (atom? z) (and (not (pair? z)) (not (null? z)))) (define (square x) (* x x)) (atom? square) #t (atom? 3) #t (atom? (cons 1 2)) #f 7 מבוא מורחב שיעור 35

More examples Ø(define digits (list 1 2 3 4 5 6 7 8 9))

More examples Ø(define digits (list 1 2 3 4 5 6 7 8 9)) Ø(define digits 1 (cons 0 digits)) Ødigits 1 ? 1 2 3 4 5 6 7 8 9) (0 Ø(define l (list 0 digits)) Øl ? (1 2 3 4 5 6 7 8 9)) (0 7 מבוא מורחב שיעור 36

The procedure length Ø(define digits (list 1 2 3 4 5 6 7 8

The procedure length Ø(define digits (list 1 2 3 4 5 6 7 8 9)) Ø(length digits) 9 Ø(define l null) Ø(length l) 0 Ø(define l (cons 1 l)) Ø(length l) 1 (define (length l) (if (null? l) 0 (+ 1 (length (cdr l))))) 7 מבוא מורחב שיעור 37