# CPS 506 Comparative Programming Languages. Programming Language Paradigm

Size: px
Start display at page:

Transcription

1 CPS 506 Comparative Programming Languages Functional Programming Language Paradigm

2 Topics Introduction Mathematical Functions Fundamentals of Functional Programming Languages The First Functional Programming Language: LISP Introduction to Scheme COMMON LISP ML Haskell Applications of Functional Languages Comparison of Functional and Imperative Languages 2

3 Introduction Programming Paradigms (con t) Functional Collection of mathematical functions One input (domain) and one result (range) Functions interacts using Composition Conditionals Recursion Examples Lisp, Scheme, ML, Haskell 3

4 Introduction The design of the imperative i languages is based directly on the von Neumann architecture Efficiency i is the primary concern, rather than the suitability of the language for software development The design of the functional languages is based on mathematical functions A solid theoretical basis that is also closer to the user, but relatively unconcerned with the architecture of the machines on which programs will run 4

5 Mathematical Functions A mathematical function is a mapping of members of one set, called the domain set, to another set, called the range set A lambda expression specifies the parameter(s) and the mapping of a function in the following form λ(x) x * x * x for the function cube (x) = x * x * x 5

6 Lambda Expressions Lambda expressions describe nameless functions Lambda expressions are applied to parameter(s) by placing the parameter(s) after the expression e.g., (λ(x) x * x * x)(2) which evaluates to 8 6

7 Functional Forms A higher-order h function, or functional form, is one that either takes functions as parameters or yields a function as its result, or both 7

8 Function Composition A functional form that takes two functions as parameters and yields a function whose value is the first actual parameter function applied to the application of the second Form: h f g which means h (x) f ( g ( x)) For f (x) x + 2and 2 g (x) 3 * x, h f g yields (3 * x)+ 2 8

9 Apply-to-all A functional form that takes a single function as a parameter and yields a list of values obtained by applying the given function to each element of a list of parameters Form: α For h (x) x * x α( h, (2, 3, 4)) yields (4, 9, 16) 9

10 Fundamentals of Functional Programming Languages The objective of the design of a FPL is to mimic m mathematical functions to the greatest extent possible The basic process of computation is fundamentally different in a FPL than in an imperative language In an imperative language, operations are done and the results are stored in variables for later use Management of variables is a constant t concern and source of complexity for imperative programming In an FPL, variables are not necessary, as is the case in mathematics 10

11 Fundamentals of Functional Programming Languages - continued Referential Transparency - In an FPL, the evaluation of a function always produces the same result given the same parameters Tail Recursion Writing recursive functions that can be automatically converted to iteration 11

12 LISP Data Types and Structures Data object types: originally only atoms and lists List form: parenthesized collections of sublists and/or atoms e.g., (A B (C D) E) Originally, LISP was a typeless language LISP lists are stored internally as singlelinked lists 12

13 LISP Interpretation Lambda notation is used to specify functions and function definitions. Function applications and data have the same form. e.g., If the list (A B C) is interpreted as data it is a simple list of three atoms, A, B, and C If it is interpreted as a function application, it means that the function named A is applied to the two parameters, B and C The first LISP interpreter appeared only as a demonstration of the universality of the computational capabilities of the notation 13

14 Origins of Scheme A mid-1970s dialect of LISP, designed d to be a cleaner, more modern, and simpler version than the contemporary dialects of LISP Uses only static scoping Functions are first-class entities They can be the values of expressions and elements of lists They can be assigned to variables and passed as parameters 14

15 Evaluation Parameters are evaluated, in no particular order The values of the parameters are substituted into the function body The function body is evaluated The value of the last expression in the body is the value of the function 15

16 Primitive Functions Arithmetic: +, -, *, /, ABS, SQRT, REMAINDER, MIN, MAX e.g., (+ 5 2) yields 7 QUOTE - takes one parameter; returns the parameter without evaluation QUOTE is required because the Scheme interpreter, named EVAL, always evaluates parameters to function applications before applying the function. QUOTE is used to avoid parameter evaluation when it is not appropriate QUOTE can be abbreviated with the apostrophe prefix operator '(A B) is equivalent to (QUOTE (A B)) 16

17 Function Definition: LAMBDA Lambda Expressions Form is based on λ notation e.g., (LAMBDA (x) (* x x) x is called a bound variable Lambda expressions can be applied e.g., ((LAMBDA (x) (* x x)) 7) 17

18 Special Form Function: DEFINE A Function for Constructing Functions DEFINE -Two forms: 1. To bind a symbol to an expression e.g., (DEFINE pi ) Example use: (DEFINE two_pi (* 2 pi)) 2. To bind names to lambda expressions e.g., (DEFINE (square x) (* x x)) Example use: (square 5) - The evaluation process for DEFINE is different! The first parameter is never evaluated. The second parameter is evaluated and bound to the first parameter. 18

19 Output Functions (DISPLAY expression) (NEWLINE) 19

20 Numeric Predicate Functions #T is true and #F is false (sometimes () is used for false) =, <>, >, <, >=, <= EVEN?, ODD?, ZERO?, NEGATIVE? 20

21 Control Flow: IF Selection- the special form, IF (IF predicate then_exp exp else_exp) exp) e.g., (IF (<> count 0) (/ sum count) 0) 21

22 Control Flow: COND Multiple Selection - the special form, COND General form: (COND (predicate_1 expr {expr}) (predicate_1 expr {expr})... (predicate_1 expr {expr}) (ELSE expr {expr})) Returns the value of the last expression in the first pair whose predicate evaluates to true 22

23 Example of COND (DEFINE (compare x y) (COND ((> x y) x is greater than y ) ((< x y) y is greater than x ) (ELSE x and y are equal ) ) ) 23

24 List Functions: CONS and LIST CONS takes two parameters, the first of which can be either an atom or a list and the second of which is a list; returns a new list that includes the first parameter as its first element and the second parameter as the remainder of its result e.g., (CONS 'A '(B C)) returns (A B C) LIST takes any number of parameters; returns a list with the parameters as elements 24

25 List Functions: CAR and CDR CAR takes a list parameter; returns the first element of that list e.g., (CAR '(A B C)) yields A (CAR '((A B) C D)) yields (A B) CDR takes a list parameter; returns the list after removing its first element e.g., (CDR '(A B C)) yields (B C) (CDR '((A B) C D)) yields (C D) 25

26 Predicate Function: EQ? EQ? takes two symbolic parameters; it returns #T if both parameters are atoms and the two are the same; otherwise #F e.g., (EQ? 'A 'A) yields #T (EQ? 'A 'B) yields #F Note that if EQ? is called with list parameters, the result is not reliable Also EQ? does not work for numeric atoms 26

27 Predicate Functions: LIST? and NULL? LIST? takes one parameter; it returns #T if the parameter is a list; otherwise #F NULL? takes one parameter; it returns #T if the parameter is the empty list; otherwise #F Note that NULL? returns #T if the parameter is() 27

28 Example Scheme Function: member member takes an atom and a simple list; returns #T if the atom is in the list; #F otherwise DEFINE (member atm lis) (COND )) ((NULL? lis) #F) ((EQ? atm (CAR lis)) #T) ((ELSE (member atm (CDR lis))) 28

29 Example Scheme Function: equalsimp equalsimp takes two simple lists as parameters; returns #T if the two simple lists are equal; #F otherwise (DEFINE (equalsimp lis1 lis2) (COND )) ((NULL? lis1) (NULL? lis2)) ((NULL? lis2) #F) ((EQ? (CAR lis1) (CAR lis2)) (ELSE #F) (equalsimp(cdr lis1)(cdr lis2))) 29

30 Example Scheme Function: equal equal takes two general lists as parameters; returns #T if the two lists are equal; #F otherwise (DEFINE (equal lis1 lis2) )) (COND ((NOT (LIST? lis1))(eq? lis1 lis2)) ((NOT (LIST? lis2)) #F) ((NULL? lis1) (NULL? lis2)) ((NULL? lis2) #F) ((equal (CAR lis1) (CAR lis2)) (equal (CDR lis1) (CDR lis2))) (ELSE #F) 30

31 Example Scheme Function: append append takes two lists as parameters; returns the first parameter list with the elements of the second parameter list appended d at the end (DEFINE (append lis1 lis2) (COND ((NULL? lis1) lis2) (ELSE (CONS (CAR lis1) (append (CDR lis1) lis2))) )) 31

32 Example Scheme Function: LET General form: (LET ( (name_1 expression_1) (name_2 expression_2)... (name_n expression_n)) body ) Evaluate all expressions, then bind the values to the names; evaluate the body 32

33 LET Example (DEFINE (quadratic_roots a b c) (LET ( (root_part_over_2a (/ (SQRT (- (* b b) (* 4 a c)))(* 2 a))) (minus_b_over_2a (/ (- 0 b) (* 2 a))) (DISPLAY (+ minus_ b_ over_ 2a root_p part_ over_ 2a)) (NEWLINE) (DISPLAY (- minus_b_over_2a root_part_over_2a)) )) 33

34 Tail Recursion in Scheme Definition: A function is tail recursive if its recursive call is the last operation in the function A tail recursive function can be automatically converted by a compiler to use iteration, making it faster Scheme language definition requires that Scheme language systems convert all tail recursive functions to use iteration 34

35 Tail Recursion in Scheme - continued Example of rewriting a function to make it tail recursive, using helper a function Original: (DEFINE (factorial n) (IF (= n 0) 1 (* n (factorial (- n 1))) )) Tail recursive: (DEFINE (facthelper n factpartial) (IF (= n 0) factpartial facthelper((- n 1) (* n factpartial))) )) (DEFINE (factorial n) (facthelper n 1)) 35

36 Scheme Functional Forms Composition The previous examples have used it (CDR (CDR '(A B C))) returns (C) Apply to All - one form in Scheme is mapcar Applies the given function to all elements of the given list; (DEFINE (mapcar fun lis) (COND ((NULL? lis) ()) (ELSE (CONS (fun (CAR lis)) (mapcar fun (CDR lis)))) )) 36

37 Functions That Build Code It is possible in Scheme to define a function that builds Scheme code and requests its interpretation This is possible because the interpreter is a user-available function, EVAL 37

38 Adding a List of Numbers ((DEFINE (adder lis) (COND ((NULL? lis) 0) (ELSE (EVAL (CONS '+ lis))) )) The parameter is a list of numbers to be added; adder inserts a + operator and evaluates the resulting list Use CONS to insert the atom + into the list of numbers. Be sure that + is quoted to prevent evaluation Submit the new list to EVAL for evaluation 38

39 guess Example (DEFINE (guess list1 list2) (COND ((NULL? list1) ()) ((member (CAR list1) list2) (CONS (CAR list1) (guess (CDR list1) list2))) (ELSE (guess (CDR list1) list2)) )) 39

40 COMMON LISP A combination i of many of the features of the popular dialects of LISP around in the early 1980s A large and complex language--the opposite of Scheme Features include: records arrays complex numbers character strings powerful I/O capabilities packages with access control iterative control statements 40

41 ML A static-scoped functional language with syntax that is closer to Pascal than to LISP Uses type declarations, but also does type inferencing to determine the types of undeclared variables It is strongly typed (whereas Scheme is essentially typeless) and has no type coercions Includes exception handling and a module facility for implementing i abstract data types Includes lists and list operations 41

42 ML Specifics Function declaration form: fun name (parameters) = body; e.g., fun cube (x : int) = x * x * x; -The type could be attached to return value, as in fun cube (x) : int = x * x * x; - With no type specified, it would default to int (the default for numeric values) - User-defined overloaded functions are not allowed, so if we wanted a cube function for real parameters, it would need to have a different name - There are no type coercions in ML 42

43 ML Specifics (continued) ML selection if expression then then_expression else else_expression where the first expression must evaluate to a Boolean value Pattern matching is used to allow a function to operate on different parameter forms fun fact(0) = 1 fact(n : int) : int = n * fact(n 1) 43

44 ML Specifics (continued) Lists Literal lists are specified in brackets [3, 5, 7] [] is the empty list CONS is the binary infix operator, :: 4 :: [3, 5, 7], which evaluates to [4, 3, 5, 7] CAR is the unary operator hd CDR is the unary operator tl fun length([]) = 0 length(h :: t) = 1 + length(t); fun append([], lis2) = lis2 append(h :: t, lis2) = h :: append(t, lis2); 44

45 ML Specifics (continued) The val statement binds a name to a value (similar to DEFINE in Scheme) val distance = time * speed; - As is the case with DEFINE, val is nothing like an assignment statement in an imperative language 45

46 Haskell Similar to ML (syntax, static scoped, strongly typed, type inferencing, pattern matching) Different from ML Polymorphic functions Non-strict semantics Strict: All actual parameters to be fully evaluated Non-strict More efficient Lazy Evaluation 46

47 Haskell Syntax differences from ML fact 0 = 1 fact n = n * fact (n 1) fib 0 = 1 fib 1 = 1 fib (n + 2) = fib (n + 1) + fib n 47

48 Function Definitions with Different Parameter Ranges fact n n == 0 = 1 n > 0 = n * fact(n 1) sub n n < 10 = 0 n > 100 = 2 otherwise = 1 square x = x * x - Works for any numeric type of x (Polymorphic) 48

49 Lists List notation: ti Put elements in brackets, e.g. directions = ["north, "south", "east", "west"] Length: # e.g., #directions is 4 Arithmetic series with the.. operator e.g., [2, 4..10] is [2, 4, 6, 8, 10] Catenation is with ++ e.g., [1, 3] ++ [5, 7] results in [1, 3, 5, 7] CONS, CAR, CDR via the colon operator (as in Prolog) e.g., 1:[3, 5, 7] results in [1, 3, 5, 7] 49

50 Factorial Revisited product [] = 1 product (a:x) = a * product x fact n = product [1..n] 50

51 List Comprehension Set notation [ body qualifiers ] List of the squares of the first 20 positive integers: [n * n n [1..20]] All of the factors of its given parameter: factors n = [i i [1..n div 2], n mod i == 0] 51

52 Quicksort sort [] = [] sort (a:x) = sort [b b x; b <= a] ++ [a] ++ sort [b b x; b > a] 52

53 Lazy Evaluation A language is strict if it requires all actual parameters to be fully evaluated A language is nonstrict if it does not have the strict requirement Nonstrict languages are more efficient and allow some interesting capabilities infinite lists Lazy evaluation - Only compute those values that are necessary Positive numbers 53

54 Lazy Evaluation positives = [0..] evens = [2, 4..] squares = [n * n n <- [0..]] Determining if 16 is a square number member squares 16 54

55 Member Revisited The member function could be written as: member [] b = False member(a:x) b=(a == b) member x b However, this would only work if the parameter to squares was a perfect square; if not, it will keep generating them forever. The following version will always work: member2 (m:x) n m < n = member2 x n m == n = True otherwise = False 55

56 Applications of Functional Languages APL is used for throw-away programs LISP is used for artificial intelligence Knowledge representation Machine learning Natural language processing Modeling of speech and vision Scheme is used to teach introductory programming at some universities 56

57 Comparing Functional and Imperative Imperative Languages: Efficient execution Complex semantics s Complex syntax Languages Concurrency is programmer designed d Functional Languages: Simple semantics Simple syntax Inefficient execution Programs can automatically be made concurrent 57

58 Summary Functional programming languages use function application, conditional expressions, recursion, and functional forms to control program execution instead of imperative features such as variables and assignments LISP began as a purely functional language and later included imperative features Scheme is a relatively simple dialect of LISP that uses static scoping exclusively COMMON LISP is a large LISP-based language ML is a static-scoped and strongly typed functional language which includes type inference, exception handling, and a variety of data structures and abstract data types Haskell is a lazy functional language supporting infinite lists and set comprehension. Purely functional languages have advantages over imperative alternatives, but their lower efficiency on existing machine architectures has prevented them from enjoying widespread use 58

### Example Scheme Function: equal

ICOM 4036 Programming Languages Functional Programming Languages Mathematical Functions Fundamentals of Functional Programming Languages The First Functional Programming Language: LISP Introduction to

### Chapter 15. Functional Programming Languages ISBN

Chapter 15 Functional Programming Languages ISBN 0-321-49362-1 Chapter 15 Topics Introduction Mathematical Functions Fundamentals of Functional Programming Languages The First Functional Programming Language:

### Chapter 15. Functional Programming Languages

Chapter 15 Functional Programming Languages Chapter 15 Topics Introduction Mathematical Functions Fundamentals of Functional Programming Languages The First Functional Programming Language: Lisp Introduction

### 15. Functional Programming

15. Functional Programming 15.1 Introduction The design of the imperative languages is based directly on the von Neumann architecture Efficiency is the primary concern, rather than the suitability of the

### Chapter 15 Functional Programming Languages

Chapter 15 Functional Programming Languages Fundamentals of Functional Programming Languages Introduction to Scheme A programming paradigm treats computation as the evaluation of mathematical functions.

### Functional Programming. Big Picture. Design of Programming Languages

Functional Programming Big Picture What we ve learned so far: Imperative Programming Languages Variables, binding, scoping, reference environment, etc What s next: Functional Programming Languages Semantics

### Concepts of Programming Languages

Concepts of Programming Languages Lecture 15 - Functional Programming Patrick Donnelly Montana State University Spring 2014 Patrick Donnelly (Montana State University) Concepts of Programming Languages

### Functional Programming Languages (FPL)

Functional Programming Languages (FPL) 1. Definitions... 2 2. Applications... 2 3. Examples... 3 4. FPL Characteristics:... 3 5. Lambda calculus (LC)... 4 6. Functions in FPLs... 7 7. Modern functional

### Organization of Programming Languages CS3200/5200N. Lecture 11

Organization of Programming Languages CS3200/5200N Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu Functional vs. Imperative The design of the imperative languages

### 11/6/17. Functional programming. FP Foundations, Scheme (2) LISP Data Types. LISP Data Types. LISP Data Types. Scheme. LISP: John McCarthy 1958 MIT

Functional programming FP Foundations, Scheme (2 In Text: Chapter 15 LISP: John McCarthy 1958 MIT List Processing => Symbolic Manipulation First functional programming language Every version after the

### COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen

COP4020 Programming Languages Functional Programming Prof. Robert van Engelen Overview What is functional programming? Historical origins of functional programming Functional programming today Concepts

### Functional Programming Languages (FPL)

Functional Programming Languages (FPL) 1. Definitions... 3 2. Applications... 3 3. Examples... 4 4. FPL Characteristics:... 5 5. Lambda calculus (LC)... 6 5.1. LC expressions forms... 6 5.2. Semantic of

### LECTURE 16. Functional Programming

LECTURE 16 Functional Programming WHAT IS FUNCTIONAL PROGRAMMING? Functional programming defines the outputs of a program as a mathematical function of the inputs. Functional programming is a declarative

### FP Foundations, Scheme

FP Foundations, Scheme In Text: Chapter 15 1 Functional Programming -- Prelude We have been discussing imperative languages C/C++, Java, Fortran, Pascal etc. are imperative languages Imperative languages

### CS 314 Principles of Programming Languages

CS 314 Principles of Programming Languages Lecture 16: Functional Programming Zheng (Eddy Zhang Rutgers University April 2, 2018 Review: Computation Paradigms Functional: Composition of operations on data.

### Imperative languages

Imperative languages Von Neumann model: store with addressable locations machine code: effect achieved by changing contents of store locations instructions executed in sequence, flow of control altered

### Functional Programming. Pure Functional Programming

Functional Programming Pure Functional Programming Computation is largely performed by applying functions to values. The value of an expression depends only on the values of its sub-expressions (if any).

### CSC312 Principles of Programming Languages : Functional Programming Language. Copyright 2006 The McGraw-Hill Companies, Inc.

CSC312 Principles of Programming Languages : Functional Programming Language Overview of Functional Languages They emerged in the 1960 s with Lisp Functional programming mirrors mathematical functions:

### Functional Programming

Functional Programming CS331 Chapter 14 Functional Programming Original functional language is LISP LISt Processing The list is the fundamental data structure Developed by John McCarthy in the 60 s Used

### Scheme Tutorial. Introduction. The Structure of Scheme Programs. Syntax

Scheme Tutorial Introduction Scheme is an imperative language with a functional core. The functional core is based on the lambda calculus. In this chapter only the functional core and some simple I/O is

### Summer 2017 Discussion 10: July 25, Introduction. 2 Primitives and Define

CS 6A Scheme Summer 207 Discussion 0: July 25, 207 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

### Functional Programming. Pure Functional Languages

Functional Programming Pure functional PLs S-expressions cons, car, cdr Defining functions read-eval-print loop of Lisp interpreter Examples of recursive functions Shallow, deep Equality testing 1 Pure

### Functional Programming. Pure Functional Languages

Functional Programming Pure functional PLs S-expressions cons, car, cdr Defining functions read-eval-print loop of Lisp interpreter Examples of recursive functions Shallow, deep Equality testing 1 Pure

### Fall 2018 Discussion 8: October 24, 2018 Solutions. 1 Introduction. 2 Primitives

CS 6A Scheme Fall 208 Discussion 8: October 24, 208 Solutions Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write

### Programming Systems in Artificial Intelligence Functional Programming

Click to add Text Programming Systems in Artificial Intelligence Functional Programming Siegfried Nijssen 8/03/16 Discover thediscover world at the Leiden world University at Leiden University Overview

### It is better to have 100 functions operate one one data structure, than 10 functions on 10 data structures. A. Perlis

Chapter 14 Functional Programming Programming Languages 2nd edition Tucker and Noonan It is better to have 100 functions operate one one data structure, than 10 functions on 10 data structures. A. Perlis

### Functional Languages. Hwansoo Han

Functional Languages Hwansoo Han Historical Origins Imperative and functional models Alan Turing, Alonzo Church, Stephen Kleene, Emil Post, etc. ~1930s Different formalizations of the notion of an algorithm

### (Func&onal (Programming (in (Scheme)))) Jianguo Lu

(Func&onal (Programming (in (Scheme)))) Jianguo Lu 1 Programming paradigms Func&onal No assignment statement No side effect Use recursion Logic OOP AOP 2 What is func&onal programming It is NOT what you

### Haskell 98 in short! CPSC 449 Principles of Programming Languages

Haskell 98 in short! n Syntax and type inferencing similar to ML! n Strongly typed! n Allows for pattern matching in definitions! n Uses lazy evaluation" F definition of infinite lists possible! n Has

### An introduction to Scheme

An introduction to Scheme Introduction A powerful programming language is more than just a means for instructing a computer to perform tasks. The language also serves as a framework within which we organize

### Scheme. Functional Programming. Lambda Calculus. CSC 4101: Programming Languages 1. Textbook, Sections , 13.7

Scheme Textbook, Sections 13.1 13.3, 13.7 1 Functional Programming Based on mathematical functions Take argument, return value Only function call, no assignment Functions are first-class values E.g., functions

### Programming Languages

Programming Languages Tevfik Koşar Lecture - XIII March 2 nd, 2006 1 Roadmap Functional Languages Lambda Calculus Intro to Scheme Basics Functions Bindings Equality Testing Searching 2 1 Functional Languages

### CSC 533: Programming Languages. Spring 2015

CSC 533: Programming Languages Spring 2015 Functional programming LISP & Scheme S-expressions: atoms, lists functional expressions, evaluation, define primitive functions: arithmetic, predicate, symbolic,

### 4/19/2018. Chapter 11 :: Functional Languages

Chapter 11 :: Functional Languages Programming Language Pragmatics Michael L. Scott Historical Origins The imperative and functional models grew out of work undertaken by Alan Turing, Alonzo Church, Stephen

### Lambda Calculus see notes on Lambda Calculus

Lambda Calculus see notes on Lambda Calculus Shakil M. Khan adapted from Gunnar Gotshalks recap so far: Lisp data structures basic Lisp programming bound/free variables, scope of variables Lisp symbols,

### Functional programming with Common Lisp

Functional programming with Common Lisp Dr. C. Constantinides Department of Computer Science and Software Engineering Concordia University Montreal, Canada August 11, 2016 1 / 81 Expressions and functions

### Principles of Programming Languages Topic: Functional Programming Professor L. Thorne McCarty Spring 2003

Principles of Programming Languages Topic: Functional Programming Professor L. Thorne McCarty Spring 2003 CS 314, LS, LTM: Functional Programming 1 Scheme A program is an expression to be evaluated (in

### Documentation for LISP in BASIC

Documentation for LISP in BASIC The software and the documentation are both Copyright 2008 Arthur Nunes-Harwitt LISP in BASIC is a LISP interpreter for a Scheme-like dialect of LISP, which happens to have

### A Brief Introduction to Scheme (I)

A Brief Introduction to Scheme (I) Philip W. L. Fong pwlfong@cs.uregina.ca Department of Computer Science University of Regina Regina, Saskatchewan, Canada Scheme Scheme I p.1/44 Scheme: Feature Set A

### SCHEME 7. 1 Introduction. 2 Primitives COMPUTER SCIENCE 61A. October 29, 2015

SCHEME 7 COMPUTER SCIENCE 61A October 29, 2015 1 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

### Programming Languages

Programming Languages Lambda Calculus and Scheme CSCI-GA.2110-003 Fall 2011 λ-calculus invented by Alonzo Church in 1932 as a model of computation basis for functional languages (e.g., Lisp, Scheme, ML,

### SCHEME 8. 1 Introduction. 2 Primitives COMPUTER SCIENCE 61A. March 23, 2017

SCHEME 8 COMPUTER SCIENCE 61A March 2, 2017 1 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs,

### SOFTWARE ARCHITECTURE 6. LISP

1 SOFTWARE ARCHITECTURE 6. LISP Tatsuya Hagino hagino@sfc.keio.ac.jp slides URL https://vu5.sfc.keio.ac.jp/sa/ 2 Compiler vs Interpreter Compiler Translate programs into machine languages Compilers are

### Fall 2017 Discussion 7: October 25, 2017 Solutions. 1 Introduction. 2 Primitives

CS 6A Scheme Fall 207 Discussion 7: October 25, 207 Solutions Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write

### Chapter 11 :: Functional Languages

Chapter 11 :: Functional Languages Programming Language Pragmatics Michael L. Scott Copyright 2016 Elsevier 1 Chapter11_Functional_Languages_4e - Tue November 21, 2017 Historical Origins The imperative

### A First Look at ML. Chapter Five Modern Programming Languages, 2nd ed. 1

A First Look at ML Chapter Five Modern Programming Languages, 2nd ed. 1 ML Meta Language One of the more popular functional languages (which, admittedly, isn t saying much) Edinburgh, 1974, Robin Milner

### Processadors de Llenguatge II. Functional Paradigm. Pratt A.7 Robert Harper s SML tutorial (Sec II)

Processadors de Llenguatge II Functional Paradigm Pratt A.7 Robert Harper s SML tutorial (Sec II) Rafael Ramirez Dep Tecnologia Universitat Pompeu Fabra Paradigm Shift Imperative Paradigm State Machine

### CS 314 Principles of Programming Languages

CS 314 Principles of Programming Languages Lecture 15: Review and Functional Programming Zheng (Eddy) Zhang Rutgers University March 19, 2018 Class Information Midterm exam forum open in Sakai. HW4 and

### 9/21/17. Outline. Expression Evaluation and Control Flow. Arithmetic Expressions. Operators. Operators. Notation & Placement

Outline Expression Evaluation and Control Flow In Text: Chapter 6 Notation Operator evaluation order Operand evaluation order Overloaded operators Type conversions Short-circuit evaluation of conditions

### Scheme Quick Reference

Scheme Quick Reference COSC 18 Winter 2003 February 10, 2003 1 Introduction This document is a quick reference guide to common features of the Scheme language. It is by no means intended to be a complete

### User-defined Functions. Conditional Expressions in Scheme

User-defined Functions The list (lambda (args (body s to a function with (args as its argument list and (body as the function body. No quotes are needed for (args or (body. (lambda (x (+ x 1 s to the increment

### Chapter 1. Fundamentals of Higher Order Programming

Chapter 1 Fundamentals of Higher Order Programming 1 The Elements of Programming Any powerful language features: so does Scheme primitive data procedures combinations abstraction We will see that Scheme

### Introduction to Functional Programming

Introduction to Functional Programming Xiao Jia xjia@cs.sjtu.edu.cn Summer 2013 Scheme Appeared in 1975 Designed by Guy L. Steele Gerald Jay Sussman Influenced by Lisp, ALGOL Influenced Common Lisp, Haskell,

### Scheme Quick Reference

Scheme Quick Reference COSC 18 Fall 2003 This document is a quick reference guide to common features of the Scheme language. It is not intended to be a complete language reference, but it gives terse summaries

### Functional Programming. Another representative from the Declarative paradigm

Functional Programming Another representative from the Declarative paradigm 1 Variables in Imperative Languages A variable in an imperative programming language can be regarded as an abstraction of the

### SML A F unctional Functional Language Language Lecture 19

SML A Functional Language Lecture 19 Introduction to SML SML is a functional programming language and acronym for Standard d Meta Language. SML has basic data objects as expressions, functions and list

### 11/6/17. Outline. FP Foundations, Scheme. Imperative Languages. Functional Programming. Mathematical Foundations. Mathematical Foundations

Outline FP Foundations, Scheme In Text: Chapter 15 Mathematical foundations Functional programming λ-calculus LISP Scheme 2 Imperative Languages We have been discussing imperative languages C/C++, Java,

### Fundamentals of Artificial Intelligence COMP221: Functional Programming in Scheme (and LISP)

Fundamentals of Artificial Intelligence COMP221: Functional Programming in Scheme (and LISP) Prof. Dekai Wu Department of Computer Science and Engineering The Hong Kong University of Science and Technology

### CSc 372. Comparative Programming Languages. 2 : Functional Programming. Department of Computer Science University of Arizona

1/37 CSc 372 Comparative Programming Languages 2 : Functional Programming Department of Computer Science University of Arizona collberg@gmail.com Copyright c 2013 Christian Collberg 2/37 Programming Paradigms

### 1.3. Conditional expressions To express case distinctions like

Introduction Much of the theory developed in the underlying course Logic II can be implemented in a proof assistant. In the present setting this is interesting, since we can then machine extract from a

### F28PL1 Programming Languages. Lecture 11: Standard ML 1

F28PL1 Programming Languages Lecture 11: Standard ML 1 Imperative languages digital computers are concrete realisations of von Neumann machines stored program memory associations between addresses and

### Spring 2018 Discussion 7: March 21, Introduction. 2 Primitives

CS 61A Scheme Spring 2018 Discussion 7: March 21, 2018 1 Introduction In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme

### Principles of Programming Languages COMP251: Functional Programming in Scheme (and LISP)

Principles of Programming Languages COMP251: Functional Programming in Scheme (and LISP) Prof. Dekai Wu Department of Computer Science and Engineering The Hong Kong University of Science and Technology

### Note that pcall can be implemented using futures. That is, instead of. we can use

Note that pcall can be implemented using futures. That is, instead of (pcall F X Y Z) we can use ((future F) (future X) (future Y) (future Z)) In fact the latter version is actually more parallel execution

### Principles of Programming Languages

Ben-Gurion University of the Negev Faculty of Natural Science Department of Computer Science Mira Balaban Lecture Notes April 9, 2013 Many thanks to Tamar Pinhas, Azzam Maraee, Ami Hauptman, Eran Tomer,

### Scheme in Scheme: The Metacircular Evaluator Eval and Apply

Scheme in Scheme: The Metacircular Evaluator Eval and Apply CS21b: Structure and Interpretation of Computer Programs Brandeis University Spring Term, 2015 The metacircular evaluator is A rendition of Scheme,

### Introductory Scheme. Revision 1

Introductory Scheme Revision 1 Joseph W. Lavinus and James D. Arthur (lavinus@cs.vt.edu and arthur@cs.vt.edu) Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg,

### Introduction to Typed Racket. The plan: Racket Crash Course Typed Racket and PL Racket Differences with the text Some PL Racket Examples

Introduction to Typed Racket The plan: Racket Crash Course Typed Racket and PL Racket Differences with the text Some PL Racket Examples Getting started Find a machine with DrRacket installed (e.g. the

### Functional Programming in Scheme

Cristian Giumale / Lecture Notes 1 Functional Programming in Scheme Functional programming languages - or applicative languages - follow closely the Lambda calculus model. Their strengths and weaknesses

### Imperative, OO and Functional Languages A C program is

Imperative, OO and Functional Languages A C program is a web of assignment statements, interconnected by control constructs which describe the time sequence in which they are to be executed. In Java programming,

Functional Programming and Haskell Tim Dawborn University of Sydney, Australia School of Information Technologies Tim Dawborn Functional Programming and Haskell 1/22 What are Programming Paradigms? A programming

### CIS24 Project #3. Student Name: Chun Chung Cheung Course Section: SA Date: 4/28/2003 Professor: Kopec. Subject: Functional Programming Language (ML)

CIS24 Project #3 Student Name: Chun Chung Cheung Course Section: SA Date: 4/28/2003 Professor: Kopec Subject: Functional Programming Language (ML) 1 Introduction ML Programming Language Functional programming

### Introduction to Functional Programming in Racket. CS 550 Programming Languages Jeremy Johnson

Introduction to Functional Programming in Racket CS 550 Programming Languages Jeremy Johnson 1 Objective To introduce functional programming in racket Programs are functions and their semantics involve

### CSCE 314 TAMU Fall CSCE 314: Programming Languages Dr. Flemming Andersen. Haskell Functions

1 CSCE 314: Programming Languages Dr. Flemming Andersen Haskell Functions 2 Outline Defining Functions List Comprehensions Recursion 3 Conditional Expressions As in most programming languages, functions

### Announcements. The current topic: Scheme. Review: BST functions. Review: Representing trees in Scheme. Reminder: Lab 2 is due on Monday at 10:30 am.

The current topic: Scheme! Introduction! Object-oriented programming: Python Functional programming: Scheme! Introduction! Numeric operators, REPL, quotes, functions, conditionals! Function examples, helper

### Programming Language Pragmatics

Chapter 10 :: Functional Languages Programming Language Pragmatics Michael L. Scott Historical Origins The imperative and functional models grew out of work undertaken Alan Turing, Alonzo Church, Stephen

### 1. true / false By a compiler we mean a program that translates to code that will run natively on some machine.

1. true / false By a compiler we mean a program that translates to code that will run natively on some machine. 2. true / false ML can be compiled. 3. true / false FORTRAN can reasonably be considered

### Functional Languages. CSE 307 Principles of Programming Languages Stony Brook University

Functional Languages CSE 307 Principles of Programming Languages Stony Brook University http://www.cs.stonybrook.edu/~cse307 1 Historical Origins 2 The imperative and functional models grew out of work

### Programming Languages Third Edition. Chapter 9 Control I Expressions and Statements

Programming Languages Third Edition Chapter 9 Control I Expressions and Statements Objectives Understand expressions Understand conditional statements and guards Understand loops and variation on WHILE

### Concepts of programming languages

Concepts of programming languages Lecture 7 Wouter Swierstra 1 Last time Relating evaluation and types How to handle variable binding in embedded languages? 2 DSLs: approaches A stand-alone DSL typically

### n n Try tutorial on front page to get started! n spring13/ n Stack Overflow!

Announcements n Rainbow grades: HW1-6, Quiz1-5, Exam1 n Still grading: HW7, Quiz6, Exam2 Intro to Haskell n HW8 due today n HW9, Haskell, out tonight, due Nov. 16 th n Individual assignment n Start early!

### Functional Programming Lecture 1: Introduction

Functional Programming Lecture 1: Introduction Viliam Lisý Artificial Intelligence Center Department of Computer Science FEE, Czech Technical University in Prague viliam.lisy@fel.cvut.cz Acknowledgements

### A Brief Introduction to Scheme (II)

A Brief Introduction to Scheme (II) Philip W. L. Fong pwlfong@cs.uregina.ca Department of Computer Science University of Regina Regina, Saskatchewan, Canada Lists Scheme II p.1/29 Lists Aggregate data

### Functional Programming

Functional Programming COMS W4115 Prof. Stephen A. Edwards Spring 2003 Columbia University Department of Computer Science Original version by Prof. Simon Parsons Functional vs. Imperative Imperative programming

### Streams, Delayed Evaluation and a Normal Order Interpreter. CS 550 Programming Languages Jeremy Johnson

Streams, Delayed Evaluation and a Normal Order Interpreter CS 550 Programming Languages Jeremy Johnson 1 Theme This lecture discusses the stream model of computation and an efficient method of implementation

### Modern Programming Languages. Lecture LISP Programming Language An Introduction

Modern Programming Languages Lecture 18-21 LISP Programming Language An Introduction 72 Functional Programming Paradigm and LISP Functional programming is a style of programming that emphasizes the evaluation

### Introduction to Scheme

How do you describe them Introduction to Scheme Gul Agha CS 421 Fall 2006 A language is described by specifying its syntax and semantics Syntax: The rules for writing programs. We will use Context Free

### Principles of Programming Languages 2017W, Functional Programming

Principles of Programming Languages 2017W, Functional Programming Assignment 3: Lisp Machine (16 points) Lisp is a language based on the lambda calculus with strict execution semantics and dynamic typing.

### Functional Programming. Functional Programming

2014-06-27 Functional Programming Functional Programming 2014-06-27 Functional Programming 1 Imperative versus Functional Languages attributes of imperative languages: variables, destructive assignment,

### CS 61A Interpreters, Tail Calls, Macros, Streams, Iterators. Spring 2019 Guerrilla Section 5: April 20, Interpreters.

CS 61A Spring 2019 Guerrilla Section 5: April 20, 2019 1 Interpreters 1.1 Determine the number of calls to scheme eval and the number of calls to scheme apply for the following expressions. > (+ 1 2) 3

### Plan (next 4 weeks) 1. Fast forward. 2. Rewind. 3. Slow motion. Rapid introduction to what s in OCaml. Go over the pieces individually

Plan (next 4 weeks) 1. Fast forward Rapid introduction to what s in OCaml 2. Rewind 3. Slow motion Go over the pieces individually History, Variants Meta Language Designed by Robin Milner @ Edinburgh Language

### Streams and Evalutation Strategies

Data and Program Structure Streams and Evalutation Strategies Lecture V Ahmed Rezine Linköpings Universitet TDDA69, VT 2014 Lecture 2: Class descriptions - message passing ( define ( make-account balance

### SCHEME The Scheme Interpreter. 2 Primitives COMPUTER SCIENCE 61A. October 29th, 2012

SCHEME COMPUTER SCIENCE 6A October 29th, 202 In the next part of the course, we will be working with the Scheme programming language. In addition to learning how to write Scheme programs, we will eventually

### CSc 520 Principles of Programming Languages

CSc 520 Principles of Programming Languages 3: Scheme Introduction Christian Collberg collberg@cs.arizona.edu Department of Computer Science University of Arizona Copyright c 2005 Christian Collberg [1]

### Functional programming in LISP

Programming Languages Week 4 Functional programming in LISP College of Information Science and Engineering Ritsumeikan University review of part 3 enumeration of dictionaries you receive a sequence of

### Functional Programming

Functional Programming Björn B. Brandenburg The University of North Carolina at Chapel Hill Based in part on slides and notes by S. Olivier, A. Block, N. Fisher, F. Hernandez-Campos, and D. Stotts. Brief