COMP313A Functional Programming (1)

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COMP313AFunctional

Programming (1)

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Main Differences with Imperative Languages

• Say more about what is computed as opposed to how

• Pure functional languages have no – variables– assignments – other side effects

• Means no loops

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Differences…

• Imperative languages have an implicit state (state of variables) that is modified (side effect).

• Sequencing is important to gain precise, deterministic control over the state

• assignment statement needed to change the binding for a particular variable (alter the implicit state)

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• Imperative factorial

n:=x;a:=1;while n > 0 dobegin

a:= a * n;n := n-1;

end;

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Differences

• Declarative languages have no implicit state

• Program with expressions• The state is carried around explicitly

– e.g. the formal parameter n in factorial

• Looping is accomplished with recursion rather than sequencing

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Haskellfac ::Int -> Intfac n | n == 0 = 1 | n > 0 = fac(n-1) *

n | otherwise = 0

Scheme (define fac (lambda (n) (if (= n 0) 1 (* n (fac (- n 1))))))

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Advantages

• similar to traditional mathematics• referential transparency makes

programs more readable• higher order functions give great

flexibility• concise programs

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Referential Transparency

• the value of a function depends only on the values of its parameters

for example Haskell

…x + x…where x = f a

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Evolution of Functional languages

Lambda Calculus• Alonzo Church• Captures the essence of functional

programming• Formalism for expressing computation by

functions• Lambda abstraction

• In Scheme

• In Haskell

(x. + 1 x)

(lambda(x) (+ 1 x)))

add1 :: Int -> Intadd1 x = 1 + x

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Lambda Calculus…

• application of expressions

• reduction rule that substitutes 2 for x in the lambda (beta reduction)

(x. + 1 x) 2

(x. + 1 x) 2 (+ 1 2) 3

(x. * x x) (+ 2 3) i.e. (+ 2 3) / x)

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Lambda Calculus…

• (x.E) – variable x is bound by the lambda– the scope of the binding is the expression E

• (x. + y x)– (x. + y x)2 (+ y 2)

• (x. + ((y. ((x. * x y) 2)) x) y)

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Lambda Calculus…

• Each lambda abstraction binds only one variable

• Need to bind each variable with its own lambda.

(x. (y. + x y))

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Lisp

• McCarthy late 1950s• Used Lambda Calculus for

anonymous functions• List processing language• First attempt built on top of FORTRAN

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LISP…• McCarthy’s main contributions were

1. the conditional expression and its use in writing recursive functions

Scheme (define fac (lambda (n) (if (= n 0) 1 (if (> n 0) (* n (fac (- n 1))) 0))))

Scheme(define fac2 (lambda (n) (cond ((= n 0) 1) ((> n 0) (* n (fac2 (- n 1)))) (else 0))))

Haskellfac ::Int -> Intfac n | n == 0 = 1 | n > 0 = fac(n-1) * n | otherwise = 0

Haskellfac :: Int -> Intfac n = if n == 0 then 1 else if n > 0 then fac(n-1) * n else 0

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2.the use of lists and higher order operations over lists such as mapcar

Lisp…

Scheme(mymap + ’(3 4 6) ’(5 7 8))

Haskellmymap :: (Int -> Int-> Int) -> [Int] -> [Int] ->[Int]mymap f [] [] = []mymap f (x:xs) (y:ys) = f x y : mymap f xs ys

add :: Int -> Int -> Intadd x y = x + y

Scheme(define mymap (lambda (f a b) (cond ((and (null? a) (null? b)) '()) (else (cons (f (first a) (first b)) (mymap f (rest a) (rest b)))))))

Haskellmymap add [3, 4, 6] [5, 7, 8]

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Lisp

3. cons cell and garbage collection of unused cons cells

Scheme (cons ’1 ’(3 4 5 7))

(1 3 4 5 7)

Haskellcons :: Int -> [Int] -> [Int]cons x xs = x:xs

Scheme(define mymap (lambda (f a b) (cond ((and (null? a) (null? b)) '()) (else (cons (f (first a) (first b)) (mymap f (first a) (first b)))))))

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Lisp…

4. use of S-expressions to represent both program and data

An expression is an atom or a listBut a list can hold anything…

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Scheme (cons ’1 ’(3 4 5 7))

(1 3 4 5 7)


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ISWIM

• Peter Landin – mid 1960s• If You See What I Mean

Landon wrote “…can be looked upon as an attempt to deliver Lisp from its eponymous commitment to lists, its reputation for hand-to-mouth storage allocation, the hardware dependent flavour of its pedagogy, its heavy bracketing, and its compromises with tradition”

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Iswim…

• Contributions– Infix syntax– let and where clauses, including a notion of

simultaneous and mutually recursive definitions

– the off side rule based on indentation• layout used to specify beginning and end of

definitions

– Emphasis on generality • small but expressive core language

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let in Scheme

• let* - the bindings are performed sequentially

(let* ((x 2) (y 3))

(* x y))

(let* ((x 2) (y 3))

(let* ((x 7) (z (+ x y)))

(* z x)))

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let in Scheme

(let ((x 2) (y 3)) ?

(* x y))

• let - the bindings are performed in parallel, i.e. the initial values are computed before any of the variables are bound

(let ((x 2) (y 3)) (let ((x 7) (z (+ x y))) ?

(* z x)))

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letrec in Scheme

• letrec – all the bindings are in effect while their initial values are being computed, allows mutually recursive definitions

(letrec ((even? (lambda (n) (if (zero? n) #t (odd? (- n 1))))) (odd? (lambda (n) (if (zero? n) #f (even? (- n 1)))))) (even? 88))

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ML

• Gordon et al 1979• Served as the command language for a proof

generating system called LCF– LCF reasoned about recursive functions

• Comprised a deductive calculus and an interactive programming language – Meta Language (ML)

• Has notion of references much like locations in memory

• I/O – side effects• But encourages functional style of programming

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ML

• Type System– it is strongly and statically typed– uses type inference to determine the type of

every expression– allows polymorphic functions

• Has user defined ADTs

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SASL, KRC, and Miranda

• Used guards • Higher Order Functions• Lazy Evaluation• Currying

SASLfac n = 1, n = 0 = n * fac (n-1), n>0

Haskellfac n | n ==0 = 1 | n >0 = n * ( fac(n-1)

add x y = + x y

add 1

switch :: Int -> a -> a -> aswitch n x y| n > 0 = x| otherwise = y

multiplyC :: Int -> Int -> IntVersusmultiplyUC :: (Int, Int) -> Int

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SASL, KRC and Miranda

• KRC introduced list comprehension

• Miranda borrowed strong data typing and user defined ADTs from ML

comprehension :: [Int] -> [Int]comprehension ex = [2 * n | n <- ex]

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COMP313AFunctional

Programming (1)

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LISP…• McCarthy’s main contributions were

1. the conditional expression and its use in writing recursive functions

Scheme (define fac (lambda (n) (if (= n 0) 1 (if (> n 0) (* n (fac (- n 1))) 0))))

Scheme(define fac2 (lambda (n) (cond ((= n 0) 1) ((> n 0) (* n (fac2 (- n 1)))) (else 0))))

Haskellfac ::Int -> Intfac n | n == 0 = 1 | n > 0 = fac(n-1) * n | otherwise = 0

Haskellfac :: Int -> Intfac n = if n == 0 then 1 else if n > 0 then fac(n-1) * n else 0

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2.the use of lists and higher order operations over lists such as mapcar

Lisp…

Scheme(mymap + ’(3 4 6) ’(5 7 8))

Haskellmymap :: (Int -> Int-> Int) -> [Int] -> [Int] ->[Int]mymap f [] [] = []mymap f (x:xs) (y:ys) = f x y : mymap f xs ys

add :: Int -> Int -> Intadd x y = x + y

Scheme(define mymap (lambda (f a b) (cond ((and (null? a) (null? b)) '()) (else (cons (f (first a) (first b)) (mymap f (rest a) (rest b)))))))

Haskellmymap add [3, 4, 6] [5, 7, 8]

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Lisp

3. cons cell and garbage collection of unused cons cells

Scheme (cons ’1 ’(3 4 5 7))

(1 3 4 5 7)

Haskellcons :: Int -> [Int] -> [Int]cons x xs = x:xs


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Lisp…

4. use of S-expressions to represent both program and data

An expression is an atom or a listBut a list can hold anything…

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Scheme (cons ’1 ’(3 4 5 7))

(1 3 4 5 7)


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ISWIM

• Peter Landin – mid 1960s• If You See What I Mean

Landon wrote “…can be looked upon as an attempt to deliver Lisp from its eponymous commitment to lists, its reputation for hand-to-mouth storage allocation, the hardware dependent flavour of its pedagogy, its heavy bracketing, and its compromises with tradition”

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Iswim…

• Contributions– Infix syntax– let and where clauses, including a notion of

simultaneous and mutually recursive definitions

– the off side rule based on indentation• layout used to specify beginning and end of

definitions

– Emphasis on generality • small but expressive core language

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let in Scheme

• let* - the bindings are performed sequentially

(let* ((x 2) (y 3))

(* x y))

(let* ((x 2) (y 3))

(let* ((x 7) (z (+ x y)))

(* z x)))

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let in Scheme

(let ((x 2) (y 3)) ?

(* x y))

• let - the bindings are performed in parallel, i.e. the initial values are computed before any of the variables are bound

(let ((x 2) (y 3)) (let ((x 7) (z (+ x y))) ?

(* z x)))

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letrec in Scheme

• letrec – all the bindings are in effect while their initial values are being computed, allows mutually recursive definitions

(letrec ((even? (lambda (n) (if (zero? n) #t (odd? (- n 1))))) (odd? (lambda (n) (if (zero? n) #f (even? (- n 1)))))) (even? 88))

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• Emacs was/is written in LISP

• Very popular in AI research

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ML

• Gordon et al 1979• Served as the command language for a proof

generating system called LCF– LCF reasoned about recursive functions

• Comprised a deductive calculus and an interactive programming language – Meta Language (ML)

• Has notion of references much like locations in memory

• I/O – side effects• But encourages functional style of programming

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ML

• Type System– it is strongly and statically typed– uses type inference to determine the type of

every expression– allows polymorphic functions

• Has user defined ADTs

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SASL, KRC, and Miranda

• Used guards • Higher Order Functions• Lazy Evaluation• Currying

SASLfac n = 1, n = 0 = n * fac (n-1), n>0

Haskellfac n | n ==0 = 1 | n >0 = n * fac(n-1)

add x y = + x y

silly_add x y = x

add 4 (3 * a)

switch :: Int -> a -> a -> aswitch n x y| n > 0 = x| otherwise = y

multiplyC :: Int -> Int -> Int VersusmultiplyUC :: (Int, Int) -> Int

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SASL, KRC and Miranda

• KRC introduced list comprehension

• Miranda borrowed strong data typing and user defined ADTs from ML

comp_example :: [Int] -> [Int]comp_example ex = [2 * n | n <- ex]

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The Move to Haskell

• Lots of functional languages in late 1970’s and 1980’s

• Tower of Babel• Among these was Hope

– strongly typed– polymorphism but explicit type declarations

as part of all function definitions– simple module facility– user-defined concrete data types with

pattern matching

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The Move to Haskell

• 1987 – considered lack of common language was hampering the adoption of functional languages

• Haskell was born– higher order functions– lazy evaluation– static polymorphic typing– user-defined datatypes– pattern matching– list comprehensions

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Haskell…

• as well– module facility– well defined I/O system– rich set of primitive data types

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Higher Order Functions

• Functions as first class values– stored as data structures, passed as

arguments, returned as results

• Function is the primary abstraction mechanism– increase the use of this abstraction

• Higher order functions are the “guts” of functional programming

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Higher Order FunctionsComputations over lists

• Mapping– add 5 to every element of a list. – add the corresponding elements of 2 lists

• Filtering– Selecting the elements with a certain property

• Folding – Combine the items in a list in some way

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List Comprehension

Double all the elements in a list

Using primitive recursiondoubleAll :: [Int] -> [Int]doubleAll [] = []doubleAll x:xs = 2 * x : doubleAll xs

Using list comprehensiondoubleAll :: [Int] -> [Int]doubleAll xs :: [ 2 * x | x <- xs ]

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Primitive Recursionversus

General Recursion

sum :: [Int] -> Intsum [] = 0sum (x:xs) = x + sum xs

qsort :: [Int] -> [Int]qsort [] = []qsort (x : xs) = qsort [ y | y<-xs , y<=x] ++ [x] ++ qsort [ y | y <- xs ,

y>x]

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COMP313AFunctional

Programming (3)

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Higher Order Functions

• Functions as first class values– stored as data structures, passed as

arguments, returned as results

• Function is the primary abstraction mechanism– increase the use of this abstraction

• Higher order functions are the “guts” of functional programming

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Higher Order FunctionsComputations over lists

• Mapping– add 5 to every element of a list. – add the corresponding elements of 2 lists

• Filtering– Selecting the elements with a certain property

• Folding – Combine the items in a list in some way

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List Comprehension

Double all the elements in a list

Using primitive recursiondoubleAll :: [Int] -> [Int]doubleAll [] = []doubleAll x:xs = 2 * x : doubleAll xs

Using list comprehensiondoubleAll :: [Int] -> [Int]doubleAll xs = [ 2 * x | x <- xs ]

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Primitive Recursionversus

General Recursion

sum :: [Int] -> Intsum [] = 0sum (x:xs) = x + sum xs

qsort :: [Int] -> [Int]qsort [] = []qsort (x : xs) = qsort [ y | y<-xs , y<=x] ++ [x] ++ qsort [ y | y <- xs ,

y>x]

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Some Haskell Syntax

Write a function to calculate the maximum of three numbers

i.e. maxThree :: Int -> Int -> Int

maxThree x y z | x >= y && x >= z = x | y >= z = y | otherwise = z

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Write a function to calculate the average of three numbers

averageThree :: Int-> Int -> Int -> Float

averageThree x y z = (x + y + z) / 3

funny x = x + x peculiar y = y

funny x = x + xpeculiar y = y

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Tuples and Lists

Examples of lists

[345, 67, 34, 9] [‘a’, ‘h’, ‘z’] [“Fred”, “foo”]

Examples of tuples

(“Fred”, 471) (“apples”, 3.41) (“baseball_bat”, “aluminium” 60.0)

Strings are lists of char i.e. [Char]

[‘f’, ‘r’, ‘e’, ‘d’] = “fred”

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Types

type album = (String, String, Int)

type collection = [album]

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Some more examplespattern matching

Write a function which will extract all the even numbers from a list

Lets first create an isEven predicate

isEven n = (n ‘mod’ 2 == 0)

evenList [] = []evenList ex = [n | n <- ex , isEven n]

evenList2 [] = []evenList2 (x:xs) |isEven x = x : evenList2 xs |otherwise = evenList2 xs

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Write a function listpairs which returns a list of the pairsof corresponding elements in two lists

listpairs :: [a] -> [b] -> [(a,b)]

>>listpairs [3, 4, 5] [6, 7, 8]>> [(3,6), (4, 7), (5,8)]

Would this work?

listpairs xs ys = [(m,n) | m <- xs, n <- ys]

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COMP313AFunctional

Programming (4)

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Lecture Outline

• A little bit of revision• Higher Order Functions

– Functions as arguments– Functions as values

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isEven n = (mod n 2 == 0)



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List Comprehension

Given a function isDigit

isDigit :: Char -> Bool

which returns True if a character is a digit, write a function digits which will find all the digits in a string

digits str = [ aChar | aChar <- , ]

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Map

double x = 2 * x

doubleAll xs = map double xs

doubleAll [4, 5, 6] [8, 10, 12]

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Implementing Map using List Comprehension

map :: (a -> b) -> [a] -> [b]

map f xs = [ f x | x <- xs ]

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Functions as Argumentsfold

> foldr1 (+) [4, 5, 6]> 15

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foldr1 (non empty list)

foldr1 :: (a -> a -> a) -> [a] -> afoldr1 f [x] = xfoldr1 f (x:xs) = f x (foldr f xs)

What does this tell us abut the characteristics of function “f”

Produces an error when given an empty list How could we define foldr to work with an empty list

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foldr f s [] = sfoldr f s (x:xs) = f x (foldr f s xs)

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1 More Higher Order FunctionFilter


> filter isEven [2, 4, 6, 7, 1]>??

isEven returns a predicate (returns a Bool)

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Implementing Filter using list comprehension

filter p xs = [x | x <- xs, p x]

p returns a Bool

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Some exercises

• Write functions to

(1)Return the list consisting of the squares of the integers in a list, ns.

(2)Return the sum of squares of items in a list

(3)Check whether all the items of a list are greater than zero using filter

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Functions as values

• functions as data• function composition

sqr (succ 5) concat (map bracketedWithoutVowels xs)

concatenates a list of lists into a single list flattens a list

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Functions as valuesFunction Composition (.)

(sqr . succ) 5• The output of one function becomes the input of

another

g f

f.ga a b c c

(.) :: (b -> c) -> (a ->b) -> (a -> c)

type of f type of g type of (f.g)

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f . g x as opposed to (f . g) x

Function application binds more tightly than composition

e.g not . not True

or

succ .pred 5

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Functions as values and results

twice fun = ( fun . fun )

• fun is a function • The result is fun composed with itself

For this to work fun has to have …..and twice :: ( ) -> ( )

> twice succ 2 > 4

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Expressions Defining Functions

addnum :: Int -> (Int -> Int)addnum n = addN where addN m = n + m

When addnum 10 say is called returns a function addN which adds 10 to m

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Main> addNum 4 59

Main> addNum 4

ERROR - Cannot find "show" function for:*** Expression : addNum 4*** Of type : Integer -> Integer

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test :: Int -> Int -> Int

test x y | x >= y = f y | otherwise = 4 where f = addnum 4

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COMP313AFunctional

Programming (4)

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Lecture Outline

• A little bit of revision• Higher Order Functions

– Functions as arguments– Functions as values

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List Comprehension

Given a function isDigit

isDigit :: Char -> Bool

which returns True if a character is a digit, write a function digits which will find all the digits in a string

digits str = [ aChar | aChar <- , ]

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Map

double x = 2 * x

doubleAll xs = map double xs

doubleAll [4, 5, 6] [8, 10, 12]

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Implementing Map using List Comprehension

map :: (a -> b) -> [a] -> [b]

map f xs = [ f x | x <- xs ]

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Functions as Argumentsfold

> foldr1 (+) [4, 5, 6]> 15

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foldr1 (non empty list)

foldr1 :: (a -> a -> a) -> [a] -> afoldr1 f [x] = xfoldr1 f (x:xs) = f x (foldr f xs)

What does this tell us abut the characteristics of function “f”

Produces an error when given an empty list How could we define foldr to work with an empty list

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foldr f s [] = sfoldr f s (x:xs) = f x (foldr f s xs)

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1 More Higher Order FunctionFilter


> filter isEven [2, 4, 6, 7, 1]>??

isEven returns a predicate (returns a Bool)

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Implementing Filter using list comprehension

filter p xs = [x | x <- xs, p x]

p returns a Bool

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Some exercises

• Write functions to

(1)Return the list consisting of the squares of the integers in a list, ns.

(2)Return the sum of squares of items in a list

(3)Check whether all the items of a list are greater than zero using filter

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Functions as values

• functions as data• function composition

sqr (succ 5) concat (map bracketedWithoutVowels xs)

concatenates a list of lists into a single list flattens a list

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Functions as valuesFunction Composition (.)

(sqr . succ) 5• The output of one function becomes the input of

another

g f

f.ga a b c c

(.) :: (b -> c) -> (a ->b) -> (a -> c)

type of f type of g type of (f.g)

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f . g x as opposed to (f . g) x

Function application binds more tightly than composition

e.g not . not True

or

succ .pred 5

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Functions as values and results

twice fun = ( fun . fun )

• fun is a function • The result is fun composed with itself

For this to work fun has to have …..and twice :: ( ) -> ( )

> twice succ 2 > 4

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addnum :: Int -> (Int -> Int)addnum n = addN where addN m = n + m


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Main> addNum 4 59

Main> addNum 4

ERROR - Cannot find "show" function for:*** Expression : addNum 4*** Of type : Integer -> Integer

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test :: Int -> Int -> Int

test x y | x >= y = f y | otherwise = 4 where f = addnum 4

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COMP313A Programming Languages

Functional Programming (5)

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Lecture Outline

• Higher order functions• Functions as arguments• Some recapping and

exercises• Some more functions as

data and results

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addnum :: Int -> (Int -> Int)addnum n = addN where -- local definition addN m = n + m


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test :: Int -> Int -> Inttest x y | x >= y = somefun f y | otherwise = 4 where f = addnum 4

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Lambda in Haskell

\m -> n + m

addNum n = (\m -> n + m)

Write a function “test n” that returns a function which tests if some argument is <= to n. Use a lambda.

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Partial Application of Functions

add :: Int -> Int -> Intadd x y = x+y 4

5

4

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Partial Application of Functions…

add4 :: [Int] -> [Int]add4 xs = map (add 4) xs

add4 :: ([Int] -> [Int])add4 = map (add 4)

How would we use partial applications of functions to get the same result as the “addNum n” example?

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Types of partial applications

The type of function is t1 -> t2 -> … tn -> t

and it is applied to arguments

e1 :: t1, e2 :: t2 … , ek :: tk

if k <= n (partial application) then cancel the ones that match t1 – tk

leaving

tk+1 -> tk+2 -> … -> tn

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Types of Partial Function Application

add :: Int -> Int -> Int

add 2 :: Int ->Intadd 2 3 :: Int

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Operator Sections

(*2) (2*)(>2) (6:) (\2)

filter (>0) . map (+1) Find operator sections sec1 and sec2 so thatmap sec1. filter sec2 Has the same effect as filter (>0) . map (+1)

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Partial Application of Functionsand

Operator Sections

elem :: Char -> [Char] -> Bool

elem ch whitespace

where whitespace is the string “ \t\n”

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Partial Application of Functionsand Operator Sections

i.e. whitespace = “ \t\n”

The problem with partial application of function is that the argument of interest may not always be the first argument

So

member xs x = elem x xs

and

member whitespace

Alternatively we can use a lambda function\ch -> elem ch whitespace

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Partial Application of Functionsand

Operator Sections

To filter all non-whitespace characters from a string

filter (not . member whitespace)

filter (\ch -> not (elem ch whitespace))

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Write a recursive function to extract a word from a string

whitespace = “ \n\t”

getword :: String -> Stringgetword [ ] = [ ]getword (x : xs) | --how do we know we have a word | -- otherwise build the word - - recursively

getword “the quick brown”

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Can write something more general - pass the “test” as an argument

getUntil :: (a -> Bool) -> [a] -> [a]getUntil p [ ] = [ ]getUntil p (x:xs) | p x = [ ] | otherwise = x : getUntil p xs

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Okay but now how do we get a word

getWord xs = getUntil p xs where - - local definition p x = member whitespace x

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But we don’t really need the local definition

We can use our partial function instead

getWord xs = getUntil p xs where - - local definition p x = member whitespace x

getWord xs = getUntil (member whitespace) xs

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Finally

The last word

getWord = getUntil (member whitespace)

---get characters until a whitespace is found

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Currying and Uncurrying

• functions of two or more arguments take arguments in sequence, one at a time.– this is the curried form. – named after Haskell Curry

• Uncurried version puts the arguments into a pair

addUC :: (Int, Int) -> Int addUC (x,y) = (x + y)

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curried versus uncurried

• Curried has neater notation• Curried permits partial application• Can easily convert from one to the other

curry f (x y) = f x y

uncurry f x y = f (x y)

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Type Checking in HaskellMonomorphic Type Checking

• Expressionsliteral, variable, constant, function applied to some arguments

• type checking function application– what do we need to consider

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f e

e must have type s

the result has type t

f must have a function type t

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ord ‘c’ +Int 3Int

ord ‘c’ +Int False

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Type Checking Function Definitions

fib :: Int ->Intfib n | n == 0 = 0 | n == 1 = 1 | n >1 = fib (n-2) + fib (n-1)

•Each of the guards must be of type ?

•The value returned in each clause must be of type ?

•The pattern n must be consistent with type argument ?

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COMP313A Programming Languages

Logic Programming (1)

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Lecture Outline

• Conceptual foundations of Logic Programming

• The Basics of Logic Programming– Predicate Calculus

• A little bit of logic programming– Prolog

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Conceptual Foundations

• What versus how– specification versus implementation

• Declarative Programming– Programmer declares the logical properties that

describe the property to be solved– From this a solution is inferred– Inference engine

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An example

Searching for an element in a listSearching for an element in a list

Predicate is_in(x,L) true whenever element x is in the list Predicate is_in(x,L) true whenever element x is in the list L.L.

For all elements x and lists L: is_in(x,L) For all elements x and lists L: is_in(x,L) IFFIFF

L = [x]L = [x] oror L = L1 L = L1 .. L2 and L2 and (is_in (x,L1) or is_in(x, L2))(is_in (x,L1) or is_in(x, L2))

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Example continuedImplementation

• Need to know how to split a list into right and left sublists

• How to order the elements stored in the list

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A solution in C++int binary_search(const int val, const int size, const int int binary_search(const int val, const int size, const int array[]){array[]){int high, low, mid;int high, low, mid;if size <= 0{if size <= 0{ return (-1);return (-1);}}high = size; high = size; low = 0;low = 0;for(;;) {for(;;) { mid = (high + low) / 2;mid = (high + low) / 2; if (mid = low){if (mid = low){ return (val != array[low]) ?-1:mid;return (val != array[low]) ?-1:mid;}}if (val < array[mid]) {if (val < array[mid]) { high = mid;high = mid;}}else if (val > array[mid]) {else if (val > array[mid]) { low = mid;low = mid;}}else return midelse return mid}}}}}}

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A Declarative Solution

Given an element x and a list L, to prove that x is in L, Given an element x and a list L, to prove that x is in L, proceed as follows: proceed as follows: (1)(1) Prove that L is [x]Prove that L is [x]

(2)(2) Otherwise split L into L1 Otherwise split L into L1 .. L2 and prove one of the following L2 and prove one of the following(2.1) x is in L1, or(2.1) x is in L1, or(2.2) x is in L2(2.2) x is in L2

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A sorting example

A predicate A predicate sort(X,Y)sort(X,Y)

Sort(X,Y) Sort(X,Y) is true if the nonempty list Y is the sorted is true if the nonempty list Y is the sorted version of Xversion of X

Use two auxiliary predicates: permutation(X,Y) and Use two auxiliary predicates: permutation(X,Y) and is_sorted(Y)is_sorted(Y)

For all integer lists X,Y: sort(X,Y) iffFor all integer lists X,Y: sort(X,Y) iff permutation(X,Y) and sorted(Y)permutation(X,Y) and sorted(Y)

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Logic and Logic Programming

• First-order predicate calculus– Logic statements

ExamplesJohn is a man. man(John).John is a human. human(John).Tristan is the son of Margaret. son(Margaret,Tristan).A horse is a mammal. loathes(Margaret, Heavy_Metal).0 is a natural number . natural(0).

Mammals have four legs and no arms or two legs and two arms.

For all X, mammal (x) -> legs(x,4) and arms(x,0) or legs(x,2) and arms(x,2).

Humans have two legs and two arms.For all X, human(x) -> legs(x,2) and arms(x,2).If x is a natural number then so is the successor of x.For all x, natural(x) -> natural(successor(x)).

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First-Order Predicate Calculus

• Constants• Predicates• Functions• Variables that stand for as yet unamed

quantities• Atomic sentences• Connectives construct more complex sentences• Quantifiers• Punctuation• Arguments to predicates can only be terms –

variables, constants and functions

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First-Order Predicate Calculus cont…

• Quanitifiers– Universal, existential

• Express properties of entire collections of objects• Universal quantifiers make statements about

every object, x

A cat is a mammal

x Cat(x) Mammal(x)

Cat(Spot) Mammal(Spot) Cat(Rebecca) Mammal(Rebecca) Cat(Felix) Mammal(Felix) Cat(Richard) Mammal(Richard) Cat(John) Mammal(John) ……

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• Existential Quantifiers make statements about Existential Quantifiers make statements about some objects, some objects, xx

Spot has a sister who is a catSpot has a sister who is a cat

x Sister(x, Spot) x Sister(x, Spot) Cat(x) Cat(x)

(Sister(Spot, Spot) (Sister(Spot, Spot) Cat(Spot)) Cat(Spot)) (Sister(Rebecca, Spot) (Sister(Rebecca, Spot) Cat(Rebecca)) Cat(Rebecca)) (Sister(Felix, Spot) (Sister(Felix, Spot) Cat(Felix)) Cat(Felix)) (Sister(Richard, Spot) (Sister(Richard, Spot) Cat(Richard)) Cat(Richard)) (Sister(John, Spot) (Sister(John, Spot) Cat(John)) Cat(John)) ……

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• Connections between and • NegationNegation

Everyone dislikes rugbyEveryone dislikes rugby Noone likes rugbyNoone likes rugby

x x Likes (x, rugby) Likes (x, rugby) x Likes(x, rugby)x Likes(x, rugby)

Everyone likes icecream Everyone likes icecream Noone dislikes icecreamNoone dislikes icecream

x Likes (x, icecream) x Likes (x, icecream) x x Likes(x, icecream)Likes(x, icecream)

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• is a conjunction over the universe of objects

• Is a disjunction over the universe of objects

x P x P

x P x P

x P x P

x P x P


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De Morgan’s Laws

PQ (PQ)

PQ) PQ PQ PQ)

PQ)PQ

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Using First-Order Predicate Calculus

1. Marcus was a man

2. Marcus was a Pompeian

3. All Pompeians were Romans

4. Caesar was a ruler

5. All Romans were either loyal to Caesar or hated him

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6. Everyone is loyal to someone

7. People only try to assassinate rulers they are not loyal to

8. Marcus tried to assassinate Caesar

Was Marcus loyal to Caesar?

Prove loyalto(Marcus, Caesar)

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• Turn the following sentences into formulae in first order predicate logic

• John likes all kinds of food• Apples are food• Chicken is food• Anything anyone eats and isn’t killed by is food• Bill eats peanuts and is still alive• Sue eats everything Bill eats

• Prove that John likes peanuts using backward chaining

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A little bit of Prolog

• Objects and relations between objects• Facts and rules

parent(pam, bob). parent(tom,bob).parent(tom, liz). parent(bob, ann).parent(bob, pat). parent(pat, jim).

? parent(bob, pat).? parent(bob, liz).? parent(bob, ben).? parent(bob, X).? parent(X, Y).

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Prolog

grandparent (X,Y) :- parent(X, Z), parent(Z, Y).

For all X and Y X is the grandparent of Y if X is a parent of Z and Z is a parent of Y

sister (X,Y) :- parent(Z, X), parent(Z, Y), female(X)

For all X and Y X is the sister of Y if Z is the parent of both X and Y and X is a female

COMP313A Functional Programming (1)

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