Introduction to Haskell
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CSCI 2041: Lazy Evaluation
CSCI 2041: Lazy Evaluation Chris Kauffman Last Updated: Wed Dec 5 12:32:32 CST 2018 1 Logistics Reading Lab13: Lazy/Streams I Module Lazy on lazy Covers basics of delayed evaluation computation I Module Stream on streams A5: Calculon Lambdas/Closures I Arithmetic language Briefly discuss these as they interpreter pertain Calculon I 2X credit for assignment I 5 Required Problems 100pts Goals I 5 Option Problems 50pts I Eager Evaluation I Milestone due Wed 12/5 I Lazy Evaluation I Final submit Tue 12/11 I Streams 2 Evaluation Strategies Eager Evaluation Lazy Evaluation I Most languages employ I An alternative is lazy eager evaluation evaluation I Execute instructions as I Execute instructions only as control reaches associated expression results are needed code (call by need) I Corresponds closely to I Higher-level idea with actual machine execution advantages and disadvantages I In pure computations, evaluation strategy doesn’t matter: will produce the same results I With side-effects, when code is run matter, particular for I/O which may see different printing orders 3 Exercise: Side-Effects and Evaluation Strategy Most common place to see differences between Eager/Lazy eval is when functions are called I Eager eval: eval argument expressions, call functions with results I Lazy eval: call function with un-evaluated expressions, eval as results are needed Consider the following expression let print_it expr = printf "Printing it\n"; printf "%d\n" expr; ;; print_it (begin printf "Evaluating\n"; 5; end);; Predict results and output for both Eager and Lazy Eval strategies 4 Answers: Side-Effects and Evaluation Strategy let print_it expr = printf "Printing it\n"; printf "%d\n" expr; ;; print_it (begin printf "Evaluating\n"; 5; end);; Evaluation > ocamlc eager_v_lazy.ml > ./a.out Eager Eval # ocaml’s default Evaluating Printing it 5 Lazy Eval Printing it Evaluating 5 5 OCaml and explicit lazy Computations I OCaml’s default model is eager evaluation BUT. -
GHC Reading Guide
GHC Reading Guide - Exploring entrances and mental models to the source code - Takenobu T. Rev. 0.01.1 WIP NOTE: - This is not an official document by the ghc development team. - Please refer to the official documents in detail. - Don't forget “semantics”. It's very important. - This is written for ghc 9.0. Contents Introduction 1. Compiler - Compilation pipeline - Each pipeline stages - Intermediate language syntax - Call graph 2. Runtime system 3. Core libraries Appendix References Introduction Introduction Official resources are here GHC source repository : The GHC Commentary (for developers) : https://gitlab.haskell.org/ghc/ghc https://gitlab.haskell.org/ghc/ghc/-/wikis/commentary GHC Documentation (for users) : * master HEAD https://ghc.gitlab.haskell.org/ghc/doc/ * latest major release https://downloads.haskell.org/~ghc/latest/docs/html/ * version specified https://downloads.haskell.org/~ghc/9.0.1/docs/html/ The User's Guide Core Libraries GHC API Introduction The GHC = Compiler + Runtime System (RTS) + Core Libraries Haskell source (.hs) GHC compiler RuntimeSystem Core Libraries object (.o) (libHsRts.o) (GHC.Base, ...) Linker Executable binary including the RTS (* static link case) Introduction Each division is located in the GHC source tree GHC source repository : https://gitlab.haskell.org/ghc/ghc compiler/ ... compiler sources rts/ ... runtime system sources libraries/ ... core library sources ghc/ ... compiler main includes/ ... include files testsuite/ ... test suites nofib/ ... performance tests mk/ ... build system hadrian/ ... hadrian build system docs/ ... documents : : 1. Compiler 1. Compiler Compilation pipeline 1. compiler The GHC compiler Haskell language GHC compiler Assembly language (native or llvm) 1. compiler GHC compiler comprises pipeline stages GHC compiler Haskell language Parser Renamer Type checker Desugarer Core to Core Core to STG STG to Cmm Assembly language Cmm to Asm (native or llvm) 1. -
Control Flow
Control Flow COMS W4115 Prof. Stephen A. Edwards Spring 2002 Columbia University Department of Computer Science Control Flow “Time is Nature’s way of preventing everything from happening at once.” Scott identifies seven manifestations of this: 1. Sequencing foo(); bar(); 2. Selection if (a) foo(); 3. Iteration while (i<10) foo(i); 4. Procedures foo(10,20); 5. Recursion foo(int i) { foo(i-1); } 6. Concurrency foo() jj bar() 7. Nondeterminism do a -> foo(); [] b -> bar(); Ordering Within Expressions What code does a compiler generate for a = b + c + d; Most likely something like tmp = b + c; a = tmp + d; (Assumes left-to-right evaluation of expressions.) Order of Evaluation Why would you care? Expression evaluation can have side-effects. Floating-point numbers don’t behave like numbers. Side-effects int x = 0; int foo() { x += 5; return x; } int a = foo() + x + foo(); What’s the final value of a? Side-effects int x = 0; int foo() { x += 5; return x; } int a = foo() + x + foo(); GCC sets a=25. Sun’s C compiler gave a=20. C says expression evaluation order is implementation-dependent. Side-effects Java prescribes left-to-right evaluation. class Foo { static int x; static int foo() { x += 5; return x; } public static void main(String args[]) { int a = foo() + x + foo(); System.out.println(a); } } Always prints 20. Number Behavior Basic number axioms: a + x = a if and only if x = 0 Additive identity (a + b) + c = a + (b + c) Associative a(b + c) = ab + ac Distributive Misbehaving Floating-Point Numbers 1e20 + 1e-20 = 1e20 1e-20 1e20 (1 + 9e-7) + 9e-7 6= 1 + (9e-7 + 9e-7) 9e-7 1, so it is discarded, however, 1.8e-6 is large enough 1:00001(1:000001 − 1) 6= 1:00001 · 1:000001 − 1:00001 · 1 1:00001 · 1:000001 = 1:00001100001 requires too much intermediate precision. -
210 CHAPTER 7. NAMES and BINDING Chapter 8
210 CHAPTER 7. NAMES AND BINDING Chapter 8 Expressions and Evaluation Overview This chapter introduces the concept of the programming environment and the role of expressions in a program. Programs are executed in an environment which is provided by the operating system or the translator. An editor, linker, file system, and compiler form the environment in which the programmer can enter and run programs. Interac- tive language systems, such as APL, FORTH, Prolog, and Smalltalk among others, are embedded in subsystems which replace the operating system in forming the program- development environment. The top-level control structure for these subsystems is the Read-Evaluate-Write cycle. The order of computation within a program is controlled in one of three basic ways: nesting, sequencing, or signaling other processes through the shared environment or streams which are managed by the operating system. Within a program, an expression is a nest of function calls or operators that returns a value. Binary operators written between their arguments are used in infix syntax. Unary and binary operators written before a single argument or a pair of arguments are used in prefix syntax. In postfix syntax, operators (unary or binary) follow their arguments. Parse trees can be developed from expressions that include infix, prefix and postfix operators. Rules for precedence, associativity, and parenthesization determine which operands belong to which operators. The rules that define order of evaluation for elements of a function call are as follows: • Inside-out: Evaluate every argument before beginning to evaluate the function. 211 212 CHAPTER 8. EXPRESSIONS AND EVALUATION • Outside-in: Start evaluating the function body. -
Exam 1 Review: Solutions Capture the Dragons
CSCI 1101B: Introduction to Computer Science Instructor: Prof. Harmon Exam 1 Review: Solutions Capture the Dragons Boolean Expressions 1. Of the following, which are Boolean expressions? (a)2+3 Not Boolean (b)2>3 Boolean (c) num >= 2 Boolean (d) num1 = num2 Not Boolean 2. If x = 3, and x <= y evaluates to True, what values can y have? y >= 3 3. Simplify each of the following expressions: (a) my_num or True Depends on what the value of my_num is. See rules as in #5. (b) False and "Mary" or True True (c) False and ("Mary" or True) False 4. Is x =< y the same as x <= y? No. Python only recognizes the latter. The former is invalid syntax. 5. If x = 1 and y = 2, explain the following results: (a) >>> x and y # = 2 (b) >>> x or y # = 1 Python uses lazy evaluation. • The expression x and y first evaluates x. If x is equivalent to False (or empty/zero), its value is returned. Otherwise, y is evaluated and the resulting value is returned. • The expression x or y first evaluates x. If x is equivalent to True (or non-empty/nonzero), its value is returned. Otherwise, y is evaluated and the resulting value is returned. 1 CSCI 1101B: Introduction to Computer Science Exam 1 Review: Solutions Conditionals 1. What is indentation, and how is it related to the concept of a code block? Indentation is the placement of text (to the left or right). Python uses indenta- tion to associate code into groups, or blocks. 2. What is the difference between a chained conditional and a nested conditional? Chained => if, elif, else Nested => conditionals exist (are "nested") inside each other 3. -
Haskell-Like S-Expression-Based Language Designed for an IDE
Department of Computing Imperial College London MEng Individual Project Haskell-Like S-Expression-Based Language Designed for an IDE Author: Supervisor: Michal Srb Prof. Susan Eisenbach June 2015 Abstract The state of the programmers’ toolbox is abysmal. Although substantial effort is put into the development of powerful integrated development environments (IDEs), their features often lack capabilities desired by programmers and target primarily classical object oriented languages. This report documents the results of designing a modern programming language with its IDE in mind. We introduce a new statically typed functional language with strong metaprogramming capabilities, targeting JavaScript, the most popular runtime of today; and its accompanying browser-based IDE. We demonstrate the advantages resulting from designing both the language and its IDE at the same time and evaluate the resulting environment by employing it to solve a variety of nontrivial programming tasks. Our results demonstrate that programmers can greatly benefit from the combined application of modern approaches to programming tools. I would like to express my sincere gratitude to Susan, Sophia and Tristan for their invaluable feedback on this project, my family, my parents Michal and Jana and my grandmothers Hana and Jaroslava for supporting me throughout my studies, and to all my friends, especially to Harry whom I met at the interview day and seem to not be able to get rid of ever since. ii Contents Abstract i Contents iii 1 Introduction 1 1.1 Objectives ........................................ 2 1.2 Challenges ........................................ 3 1.3 Contributions ...................................... 4 2 State of the Art 6 2.1 Languages ........................................ 6 2.1.1 Haskell .................................... -
Top Functional Programming Languages Based on Sentiment Analysis 2021 11
POWERED BY: TOP FUNCTIONAL PROGRAMMING LANGUAGES BASED ON SENTIMENT ANALYSIS 2021 Functional Programming helps companies build software that is scalable, and less prone to bugs, which means that software is more reliable and future-proof. It gives developers the opportunity to write code that is clean, elegant, and powerful. Functional Programming is used in demanding industries like eCommerce or streaming services in companies such as Zalando, Netflix, or Airbnb. Developers that work with Functional Programming languages are among the highest paid in the business. I personally fell in love with Functional Programming in Scala, and that’s why Scalac was born. I wanted to encourage both companies, and developers to expect more from their applications, and Scala was the perfect answer, especially for Big Data, Blockchain, and FinTech solutions. I’m glad that my marketing and tech team picked this topic, to prepare the report that is focused on sentiment - because that is what really drives people. All of us want to build effective applications that will help businesses succeed - but still... We want to have some fun along the way, and I believe that the Functional Programming paradigm gives developers exactly that - fun, and a chance to clearly express themselves solving complex challenges in an elegant code. LUKASZ KUCZERA, CEO AT SCALAC 01 Table of contents Introduction 03 What Is Functional Programming? 04 Big Data and the WHY behind the idea of functional programming. 04 Functional Programming Languages Ranking 05 Methodology 06 Brand24 -
CS1101S Studio Session Week 11: Stream
Welcome CS1101S Studio Session Week 11: Stream Niu Yunpeng [email protected] October 30, 2018 Niu Yunpeng CS1101S Studio Week 11 October 30, 2018 1 / 27 Overview 1 Lazy evaluation Computational model Function application 2 Stream Delayed evaluation Stream programming Niu Yunpeng CS1101S Studio Week 11 October 30, 2018 2 / 27 Lazy Evaluation Computational model Computational model is a useful guideline for us to understand how the interpreter works. Computational model may vary depending on programming language and the runtime system used. In CS1101S, we introduce two computational models: substitution model and environment model. What to expect In the coming weeks, these two models will still be valid. Niu Yunpeng CS1101S Studio Week 11 October 30, 2018 3 / 27 Lazy Evaluation Substitution model For stateless programming only: Evaluate all actual arguments; Replace all formal parameters with their actual arguments; Apply each statement in the function body (and get the return value); Repeat the first 3 steps until done. Niu Yunpeng CS1101S Studio Week 11 October 30, 2018 4 / 27 Lazy Evaluation Environment model For stateful programming: Each frame contains a series of bindings of names and values. The value of a variable depends on its environment, a sequence of frames up to the global frame. Each function call will create a new frame and extend its enclosing environment. Niu Yunpeng CS1101S Studio Week 11 October 30, 2018 5 / 27 Lazy Evaluation Function in JavaScript Function in JavaScript is a first-class citizen (object). They have a call method. The call method is triggered when this function is applied. Function application When a function is applied, “this” is prepanded to the list of parameters. -
A Formal Methodology for Deriving Purely Functional Programs from Z Specifications Via the Intermediate Specification Language Funz
Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1995 A Formal Methodology for Deriving Purely Functional Programs From Z Specifications via the Intermediate Specification Language FunZ. Linda Bostwick Sherrell Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Sherrell, Linda Bostwick, "A Formal Methodology for Deriving Purely Functional Programs From Z Specifications via the Intermediate Specification Language FunZ." (1995). LSU Historical Dissertations and Theses. 5981. https://digitalcommons.lsu.edu/gradschool_disstheses/5981 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. H ie quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandardm argins, and improper alignment can adversely affect reproduction. In the unlikely, event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. -
The Logic of Demand in Haskell
Under consideration for publication in J. Functional Programming 1 The Logic of Demand in Haskell WILLIAM L. HARRISON Department of Computer Science University of Missouri Columbia, Missouri RICHARD B. KIEBURTZ Pacific Software Research Center OGI School of Science & Engineering Oregon Health & Science University Abstract Haskell is a functional programming language whose evaluation is lazy by default. However, Haskell also provides pattern matching facilities which add a modicum of eagerness to its otherwise lazy default evaluation. This mixed or “non-strict” semantics can be quite difficult to reason with. This paper introduces a programming logic, P-logic, which neatly formalizes the mixed evaluation in Haskell pattern-matching as a logic, thereby simplifying the task of specifying and verifying Haskell programs. In P-logic, aspects of demand are reflected or represented within both the predicate language and its model theory, allowing for expressive and comprehensible program verification. 1 Introduction Although Haskell is known as a lazy functional language because of its default eval- uation strategy, it contains a number of language constructs that force exceptions to that strategy. Among these features are pattern-matching, data type strictness annotations and the seq primitive. The semantics of pattern-matching are further enriched by irrefutable pattern annotations, which may be embedded within pat- terns. The interaction between Haskell’s default lazy evaluation and its pattern- matching is surprisingly complicated. Although it offers the programmer a facility for fine control of demand (Harrison et al., 2002), it is perhaps the aspect of the Haskell language least well understood by its community of users. In this paper, we characterize the control of demand first in a denotational semantics and then in a verification logic called “P-logic”. -
Monads for Functional Programming
Monads for functional programming Philip Wadler, University of Glasgow? Department of Computing Science, University of Glasgow, G12 8QQ, Scotland ([email protected]) Abstract. The use of monads to structure functional programs is de- scribed. Monads provide a convenient framework for simulating effects found in other languages, such as global state, exception handling, out- put, or non-determinism. Three case studies are looked at in detail: how monads ease the modification of a simple evaluator; how monads act as the basis of a datatype of arrays subject to in-place update; and how monads can be used to build parsers. 1 Introduction Shall I be pure or impure? The functional programming community divides into two camps. Pure lan- guages, such as Miranda0 and Haskell, are lambda calculus pure and simple. Impure languages, such as Scheme and Standard ML, augment lambda calculus with a number of possible effects, such as assignment, exceptions, or continu- ations. Pure languages are easier to reason about and may benefit from lazy evaluation, while impure languages offer efficiency benefits and sometimes make possible a more compact mode of expression. Recent advances in theoretical computing science, notably in the areas of type theory and category theory, have suggested new approaches that may integrate the benefits of the pure and impure schools. These notes describe one, the use of monads to integrate impure effects into pure functional languages. The concept of a monad, which arises from category theory, has been applied by Moggi to structure the denotational semantics of programming languages [13, 14]. The same technique can be applied to structure functional programs [21, 23]. -
The Scala Programming Language
The Scala Programming Language Mooly Sagiv Slides taken from Martin Odersky (EPFL) Donna Malayeri (CMU) Hila Peleg (TAU) Modern Functional Programming • Higher order • Modules • Pattern matching • Statically typed with type inference • Two viable alternatives • Haskel • Pure lazy evaluation and higher order programming leads to Concise programming • Support for domain specific languages • I/O Monads • Type classes • OCaml • Encapsulated side-effects via references Then Why aren’t FP adapted? • Education • Lack of OO support • Subtyping increases the complexity of type inference • Programmers seeks control on the exact implementation • Imperative programming is natural in certain situations Why Scala? (Coming from OCaml) • Runs on the JVM/.NET • Can use any Java code in Scala • Combines functional and imperative programming in a smooth way • Effective library • Inheritance • General modularity mechanisms The Java Programming Language • Designed by Sun 1991-95 • Statically typed and type safe • Clean and Powerful libraries • Clean references and arrays • Object Oriented with single inheritance • Interfaces with multiple inhertitence • Portable with JVM • Effective JIT compilers • Support for concurrency • Useful for Internet Java Critique • Downcasting reduces the effectiveness of static type checking • Many of the interesting errors caught at runtime • Still better than C, C++ • Huge code blowouts • Hard to define domain specific knowledge • A lot of boilerplate code • Sometimes OO stands in our way • Generics only partially helps Why