LISP, an acronym for list processing, is a programming language that was designed for easy manipulation of data strings. It is a commonly used language for artificial intelligence (AI) programming.
Here is a recursive function to check if a list contains an element:
(defun contains (element list)
(cond ((null list) nil)
((equal element (car list)) t)
(t (contains element (cdr list)))))
To check the guest list:
(contains 'robocop guest-list)
This function:
1. Base case: If list is empty, element is not contained - return nil
2. Check if element equals car of list - if so, return t
3. Otherwise, recursively call contains on element and cdr of list
So it will recursively traverse the list until it finds a match or reaches empty list.
The document provides an introduction to the Lisp programming language. It begins with an overview of Lisp and discusses its key features: it is a list processing language where lists are the basic data structure; it is functional in nature; and it uses interpretation rather than compilation. The document then covers Lisp basics like data types, evaluation rules, defining functions, conditional statements, loops, and input/output operations. It also introduces some common Lisp functions and techniques like car, cdr, cons, append, cond, do, dotimes, and dolist.
The document discusses syntax analysis and parsing. It defines a syntax analyzer as creating the syntactic structure of a source program in the form of a parse tree. A syntax analyzer, also called a parser, checks if a program satisfies the rules of a context-free grammar and produces the parse tree if it does, or error messages otherwise. It describes top-down and bottom-up parsing methods and how parsers use grammars to analyze syntax.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It was closely tied to early AI research and used in systems like SHRDLU. Lisp allows programs to treat code as data through homoiconicity and features like lambdas, conses, and list processing functions make it good for symbolic and functional programming.
This document provides an overview of the LISP (List Processing) programming language. It discusses how LISP was commonly used for artificial intelligence programming due to its ability to modify programs dynamically. The document then describes various LISP dialects and the invention of LISP by John McCarthy. It also summarizes key LISP features like being machine-independent, providing object-oriented programming and advanced data types. The document concludes by explaining functions, predicates, conditionals, recursion, arrays, property lists, mapping functions and lambda expressions in LISP.
Theory of automata and formal languageRabia Khalid
The document discusses theory of automata and formal languages. It defines key concepts like abstract machines, automata, alphabets, strings, words, languages and provides examples to describe them. Abstract machines are theoretical models of computer systems used to analyze how they work. Automata are self-operating machines that follow predetermined sequences of operations. Alphabets are sets of symbols, strings are concatenations of symbols, and words are strings belonging to a language. Languages can be defined descriptively or recursively and examples are given to illustrate different ways of defining languages.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
Here are the steps to construct an NFA-ε equivalent to the given regular expressions:
1. ε + a b (a+b)*
- States: q0, q1, q2
- Transitions: q0 -> q1 on ε, q0 -> q2 on a, q1 -> q1 on a/b, q1 -> q2 on b
- Start state: q0
- Final states: q2
2. (0+1)* (00+11)
- States: q0, q1, q2, q3
- Transitions: q0 -> q0 on 0/1, q0 -> q1 on 0/1
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
Formal Languages and Automata Theory Unit 1Srimatre K
The document describes the minimization of a deterministic finite automaton (DFA) using the equivalence theorem. It involves partitioning the states into sets where states in the same set are indistinguishable based on their transitions. The initial partition P0 separates final and non-final states. Subsequent partitions P1, P2, etc. further split sets if states within are distinguishable. The process stops when the partition no longer changes, resulting in the minimized DFA with states merged within each final set. An example application of the steps to a 6-state DFA is also provided.
This document provides an overview of the Lisp programming language. It begins with some notable quotes about Lisp praising its power and importance. It then covers the basic syntax of Lisp including its use of prefix notation, basic data types like integers and booleans, and variables. It demonstrates how to print, use conditional statements like IF and COND, and describes lists as the core data structure in Lisp.
LR parsing allows parsers to see the entire right-hand side of a rule and perform lookahead, allowing it to handle a wider range of grammars than LL parsing. The document provides an example of LR parsing a simple expression grammar. It demonstrates the steps of building the LR parsing items, closure, and goto functions to generate the LR parsing table from the grammar. LR parsing tables contain the states, symbols, and parsing actions (reduce, shift, accept).
Context-free languages can be described using context-free grammars, which are recursive rules that generate strings in a language. An example grammar is presented that generates strings of 1s and 0s separated by # symbols. Context-free grammars consist of variables, terminals, rules with variables on the left-hand side replacing with strings on the right-hand side, and a starting variable. Context-free languages can be recognized by pushdown automata using an extra stack. Regular languages are a subset of context-free languages. Context-free languages have closure properties including union, concatenation, and homomorphism. Derivation trees can represent grammar derivations and Backus-Naur form is a notation for compactly representing
The document discusses namespaces in .NET. Namespaces help organize classes and interfaces logically and avoid naming conflicts. Namespaces use dot notation and can be defined using the namespace keyword. Assemblies contain namespaces and provide execution context and versioning. Private assemblies are used within one application while public assemblies in the global assembly cache can be used across applications. The compiler compiles to CIL and produces metadata. The runtime loads assemblies and the JIT compiler converts CIL to native code for the CPU.
This document discusses derivations and languages in context-free grammars. It covers recursive inference versus derivation to determine if a string belongs to a language. It also discusses leftmost and rightmost derivations, derivation trees, sentential forms, and left and right recursive grammars. Several example problems are provided to demonstrate derivations, parse trees, and generating strings from given grammars.
The document discusses the theory of computation topics of undecidability, recursive and non-recursive languages. It defines recursive, recursively enumerable (RE), and non-RE languages, and provides examples. Recursive languages are decidable by a Turing machine halting for all inputs. RE languages are decidable for strings in the language but a Turing machine may not halt on strings not in the language. Non-RE languages have no Turing machine to enumerate them. The document also discusses Turing machine encodings, universal Turing machines, and reductions between decision problems.
n this presentation, Manoj K. has talked about “Regular Expression”. Here he has explained how Regular Expressions are used. He has covered all of the codes and what they are used for. The goal is to teach you how to use regular expressions once and for all.
----------------------------------------------------------
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Also since {a} is regular, {a}* is a regular language which is the set of strings consisting of a's such as , a, aa, aaa, aaaa etc. Note also that *, which is the set of strings consisting of a's and b's, is a regular language because {a, b} is regular. Regular expressions are used to denote regular languages.
This document provides an overview of automata and formal languages. It defines key terms like automata, finite automata, deterministic finite automaton (DFA), non-deterministic finite automaton (NDFA), and grammars. It also explains the differences between DFAs and NDFAs, and between Mealy and Moore machines. Formal definitions of these concepts are given using common notation like tuples.
The document discusses the key concepts of object-oriented programming (OOP) in C++, including objects, classes, abstraction, encapsulation, inheritance, polymorphism, overloading, and exception handling. Objects are instances of classes that contain data members and member functions. Classes define the blueprint for objects and allow data and functions to be bundled together. Abstraction hides unnecessary details and focuses on essential information. Encapsulation binds data and functions together within a class. Inheritance allows code reuse through deriving a new class from an existing class. Polymorphism and overloading allow functions to operate on different data types. Exception handling manages errors at runtime.
This document provides an introduction to algorithms and their design and analysis. It discusses what algorithms are, their key characteristics, and the steps to develop an algorithm to solve a problem. These steps include defining the problem, developing a model, specifying and designing the algorithm, checking correctness, analyzing efficiency, implementing, testing, and documenting. Common algorithm design techniques like top-down design and recursion are explained. Factors that impact algorithm efficiency like use of loops, initial conditions, invariants, and termination conditions are covered. Finally, common control structures for algorithms like if/else, loops, and branching are defined.
This document provides an introduction to the Java programming language. It discusses Java's evolution and history from 1991 to present. It also covers Java fundamentals including data types, operators, decision making and looping constructs, classes and objects, arrays and strings. The document is intended as an overview of the major topics and features in Java.
Planning involves representing an initial state, possible actions, and a goal state. A planning agent uses a knowledge base to select action sequences that transform the initial state into a goal state. STRIPS is a common planning representation that uses predicates to describe states and logical operators to represent actions and their effects. A STRIPS planning problem specifies the initial state, goal conditions, and set of operators. A solution is a sequence of ground operator instances that produces the goal state from the initial state.
In this session we will go over the fundamentals of functional programming and see how functional programming can help make our code more reusable, stable, scalable and fun.
Greedy algorithms make locally optimal choices at each step to try to find a global optimum. They choose the best option available at each state. For the travelling salesman problem, the greedy approach is to always choose the nearest unvisited city from the current city to build the route. While greedy algorithms are simple to implement, they do not always find the true optimal solution, especially for large complex problems, as they only consider the best choice at each step rather than the overall route.
This document discusses Noam Chomsky's hierarchy of formal languages. It introduces Chomsky's classification of formal languages from Type-0 to Type-3 based on the type of grammar that generates them. Type-0 languages are the most powerful, being generated by unrestricted grammars and equivalent to Turing machines. Type-3 languages are the simplest, being generated by regular grammars and equivalent to finite state automata. Examples are provided for each language type along with the computing models that recognize them, such as pushdown automata for context-free Type-2 languages.
Designing A Syntax Based Retrieval System03Avelin Huo
The document proposes a syntax-based text retrieval system to support grammatical querying of tagged corpora for language learners and teachers. It describes building an index of part-of-speech tagged n-grams from a corpus, with a filter to select discriminative index terms. A regular expression query is rewritten and its positions in the index are used to find candidate matching text units efficiently. An evaluation compares the proposed index to a complete index in terms of size and query performance.
Lecture: Regular Expressions and Regular LanguagesMarina Santini
This document provides an introduction to regular expressions and regular languages. It defines the key operations used in regular expressions: union, concatenation, and Kleene star. It explains how regular expressions can be converted into finite state automata and vice versa. Examples of regular expressions are provided. The document also defines regular languages as those languages that can be accepted by a deterministic finite automaton. It introduces the pumping lemma as a way to determine if a language is not regular. Finally, it includes some practical activities for readers to practice converting regular expressions to automata and writing regular expressions.
Formal Languages and Automata Theory Unit 1Srimatre K
The document describes the minimization of a deterministic finite automaton (DFA) using the equivalence theorem. It involves partitioning the states into sets where states in the same set are indistinguishable based on their transitions. The initial partition P0 separates final and non-final states. Subsequent partitions P1, P2, etc. further split sets if states within are distinguishable. The process stops when the partition no longer changes, resulting in the minimized DFA with states merged within each final set. An example application of the steps to a 6-state DFA is also provided.
This document provides an overview of the Lisp programming language. It begins with some notable quotes about Lisp praising its power and importance. It then covers the basic syntax of Lisp including its use of prefix notation, basic data types like integers and booleans, and variables. It demonstrates how to print, use conditional statements like IF and COND, and describes lists as the core data structure in Lisp.
LR parsing allows parsers to see the entire right-hand side of a rule and perform lookahead, allowing it to handle a wider range of grammars than LL parsing. The document provides an example of LR parsing a simple expression grammar. It demonstrates the steps of building the LR parsing items, closure, and goto functions to generate the LR parsing table from the grammar. LR parsing tables contain the states, symbols, and parsing actions (reduce, shift, accept).
Context-free languages can be described using context-free grammars, which are recursive rules that generate strings in a language. An example grammar is presented that generates strings of 1s and 0s separated by # symbols. Context-free grammars consist of variables, terminals, rules with variables on the left-hand side replacing with strings on the right-hand side, and a starting variable. Context-free languages can be recognized by pushdown automata using an extra stack. Regular languages are a subset of context-free languages. Context-free languages have closure properties including union, concatenation, and homomorphism. Derivation trees can represent grammar derivations and Backus-Naur form is a notation for compactly representing
The document discusses namespaces in .NET. Namespaces help organize classes and interfaces logically and avoid naming conflicts. Namespaces use dot notation and can be defined using the namespace keyword. Assemblies contain namespaces and provide execution context and versioning. Private assemblies are used within one application while public assemblies in the global assembly cache can be used across applications. The compiler compiles to CIL and produces metadata. The runtime loads assemblies and the JIT compiler converts CIL to native code for the CPU.
This document discusses derivations and languages in context-free grammars. It covers recursive inference versus derivation to determine if a string belongs to a language. It also discusses leftmost and rightmost derivations, derivation trees, sentential forms, and left and right recursive grammars. Several example problems are provided to demonstrate derivations, parse trees, and generating strings from given grammars.
The document discusses the theory of computation topics of undecidability, recursive and non-recursive languages. It defines recursive, recursively enumerable (RE), and non-RE languages, and provides examples. Recursive languages are decidable by a Turing machine halting for all inputs. RE languages are decidable for strings in the language but a Turing machine may not halt on strings not in the language. Non-RE languages have no Turing machine to enumerate them. The document also discusses Turing machine encodings, universal Turing machines, and reductions between decision problems.
n this presentation, Manoj K. has talked about “Regular Expression”. Here he has explained how Regular Expressions are used. He has covered all of the codes and what they are used for. The goal is to teach you how to use regular expressions once and for all.
----------------------------------------------------------
Get Socialistic
Our website: http://valuebound.com/
LinkedIn: http://bit.ly/2eKgdux
Facebook: https://www.facebook.com/valuebound/
Twitter: https://twitter.com/valuebound
Also since {a} is regular, {a}* is a regular language which is the set of strings consisting of a's such as , a, aa, aaa, aaaa etc. Note also that *, which is the set of strings consisting of a's and b's, is a regular language because {a, b} is regular. Regular expressions are used to denote regular languages.
This document provides an overview of automata and formal languages. It defines key terms like automata, finite automata, deterministic finite automaton (DFA), non-deterministic finite automaton (NDFA), and grammars. It also explains the differences between DFAs and NDFAs, and between Mealy and Moore machines. Formal definitions of these concepts are given using common notation like tuples.
The document discusses the key concepts of object-oriented programming (OOP) in C++, including objects, classes, abstraction, encapsulation, inheritance, polymorphism, overloading, and exception handling. Objects are instances of classes that contain data members and member functions. Classes define the blueprint for objects and allow data and functions to be bundled together. Abstraction hides unnecessary details and focuses on essential information. Encapsulation binds data and functions together within a class. Inheritance allows code reuse through deriving a new class from an existing class. Polymorphism and overloading allow functions to operate on different data types. Exception handling manages errors at runtime.
This document provides an introduction to algorithms and their design and analysis. It discusses what algorithms are, their key characteristics, and the steps to develop an algorithm to solve a problem. These steps include defining the problem, developing a model, specifying and designing the algorithm, checking correctness, analyzing efficiency, implementing, testing, and documenting. Common algorithm design techniques like top-down design and recursion are explained. Factors that impact algorithm efficiency like use of loops, initial conditions, invariants, and termination conditions are covered. Finally, common control structures for algorithms like if/else, loops, and branching are defined.
This document provides an introduction to the Java programming language. It discusses Java's evolution and history from 1991 to present. It also covers Java fundamentals including data types, operators, decision making and looping constructs, classes and objects, arrays and strings. The document is intended as an overview of the major topics and features in Java.
Planning involves representing an initial state, possible actions, and a goal state. A planning agent uses a knowledge base to select action sequences that transform the initial state into a goal state. STRIPS is a common planning representation that uses predicates to describe states and logical operators to represent actions and their effects. A STRIPS planning problem specifies the initial state, goal conditions, and set of operators. A solution is a sequence of ground operator instances that produces the goal state from the initial state.
In this session we will go over the fundamentals of functional programming and see how functional programming can help make our code more reusable, stable, scalable and fun.
Greedy algorithms make locally optimal choices at each step to try to find a global optimum. They choose the best option available at each state. For the travelling salesman problem, the greedy approach is to always choose the nearest unvisited city from the current city to build the route. While greedy algorithms are simple to implement, they do not always find the true optimal solution, especially for large complex problems, as they only consider the best choice at each step rather than the overall route.
This document discusses Noam Chomsky's hierarchy of formal languages. It introduces Chomsky's classification of formal languages from Type-0 to Type-3 based on the type of grammar that generates them. Type-0 languages are the most powerful, being generated by unrestricted grammars and equivalent to Turing machines. Type-3 languages are the simplest, being generated by regular grammars and equivalent to finite state automata. Examples are provided for each language type along with the computing models that recognize them, such as pushdown automata for context-free Type-2 languages.
Designing A Syntax Based Retrieval System03Avelin Huo
The document proposes a syntax-based text retrieval system to support grammatical querying of tagged corpora for language learners and teachers. It describes building an index of part-of-speech tagged n-grams from a corpus, with a filter to select discriminative index terms. A regular expression query is rewritten and its positions in the index are used to find candidate matching text units efficiently. An evaluation compares the proposed index to a complete index in terms of size and query performance.
This document provides an overview of the LISP programming language. It discusses LISP syntax, data types, functions, predicates, conditionals, variables, and other core concepts. Some key points:
- LISP uses prefix notation and parentheses extensively. Functions are applied using (func arg1 arg2).
- Core data types include numbers, symbols, strings, and lists. Lists are a fundamental data structure and there are many functions for manipulating them.
- Functions are defined using defun and can take parameters. Predicates test conditions and return t or nil.
- Conditionals include cond, if, when, and case. Assignment is done with setq.
-
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It has been widely used in artificial intelligence and remains a popular language for prototyping due to its simple syntax and ability to treat code as data.
This document provides a brief introduction to the Lisp programming language. It discusses Lisp's history from its origins in 1958 to modern implementations like Common Lisp and Scheme. It also covers Lisp's support for functional, imperative, and object-oriented paradigms. A key feature of Lisp is its use of s-expressions as both code and data, which enables powerful macros to transform and generate code at compile time.
Scala supports first-class functions that can be passed as arguments to other functions or stored in variables. Functions can be defined as local functions or anonymously as function literals. Function literals allow functions to be defined without a name. Closures are function values that capture free variables from their enclosing scope at runtime. This allows functions to access variables in the scope where they were defined even if that scope no longer exists.
Scala supports first-class functions that can be passed as arguments to other functions or stored in variables. Functions can be defined as method members or as anonymous function literals. Function literals allow defining unnamed functions inline. Functions in Scala support closures, where functions can close over variables in the enclosing scope. This allows functions to access variables that are not passed in as arguments. The document discusses various ways to define functions in Scala including local functions, function literals, partially applied functions, and repeated parameters.
Scala supports first-class functions that can be passed as arguments to other functions or stored in variables. Functions can be defined as local functions or anonymously as function literals. Function literals allow functions to be defined without a name. Closures are function values that capture free variables from their enclosing scope at runtime. This allows functions to access variables in the scope where they were defined even if that scope no longer exists.
This document provides an overview of the Lisp programming language. It discusses key features of Lisp including its invention in 1958, machine independence, dynamic updating, and wide data types. The document also covers Lisp syntax, data types, variables, constants, operators, decision making, arrays, loops, text editors, and common uses of Lisp like Emacs. Overall, the document serves as a high-level introduction to the concepts and capabilities of the Lisp programming language.
This document provides an introduction to lambda calculus through a series of lectures:
- It outlines the syntax and evaluation of lambda calculus, including function creation, application, and substitution.
- Common data types like booleans, pairs, and natural numbers can be encoded in lambda calculus by representing them as functions.
- Despite its simplicity, lambda calculus is Turing complete and can encode complex computations through recursive functions and encodings of data types.
Lisp and Scheme are dialects of the Lisp programming language. Scheme is favored for teaching due to its simplicity. Key features of Lisp include S-expressions as the universal data type, functional programming style with first-class functions, and uniform representation of code and data. Functions are defined using DEFINE and special forms like IF and COND control program flow. Common list operations include functions like CAR, CDR, CONS, LIST, and REVERSE.
This document provides an overview of a lecture on functional programming in Scala. It covers the following topics:
1. A recap of functional programming principles like functions as first-class values and no side effects.
2. An introduction to the Haskell programming language including its syntax for defining functions.
3. How functions are defined in Scala and how they are objects at runtime.
4. Examples of defining the factorial function recursively in Haskell and Scala, and making it tail recursive.
5. Concepts of first-class functions, currying, partial application, and an example of implementing looping in Scala using these techniques.
This document discusses the relational model and relational database concepts. It covers domains and relations, relational keys like primary keys, candidate keys, foreign keys and their rules. It also discusses relational operators, relational algebra, relational calculus, and the SQL language. Key types like alternate keys, candidate keys, compound keys, primary keys, superkeys, and foreign keys are defined. Relational algebra operations like selection, projection, renaming, union, intersection, difference, cartesian product, and join are explained. Tuple relational calculus and domain relational calculus are introduced. Examples of queries using relational algebra and calculus are provided. Components of SQL like DDL, DML, DCL are listed
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Nomad is a workload orchestrator designed by HashiCorp to deploy and manage containers and non-containerized applications across on-premises and cloud environments. It is a single binary that schedules applications and services on a cluster of machines and is highly scalable and performant. Nomad is known for its simplicity and flexibility, offering developers and operators a unified workflow to deploy applications. Nomad supports containerized, virtualized, and standalone applications, and its workload support includes Docker, Windows, QEMU, and Java. It integrates seamlessly with other HashiCorp tools like Consul for service discovery and Vault for secrets management, providing a full-stack solution for infrastructure management.
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Managing State & HTTP Requests In Ionic.Knoldus Inc.
Ionic is a complete open-source SDK for hybrid mobile app development created by Max Lynch, Ben Sperry, and Adam Bradley of Drifty Co. in 2013.The original version was released in 2013 and built on top of AngularJS and Apache Cordova. However, the latest release was re-built as a set of Web Components using StencilJS, allowing the user to choose any user interface framework, such as Angular, React or Vue.js. It also allows the use of Ionic components with no user interface framework at all.[4] Ionic provides tools and services for developing hybrid mobile, desktop, and progressive web apps based on modern web development technologies and practices, using Web technologies like CSS, HTML5, and Sass. In particular, mobile apps can be built with these Web technologies and then distributed through native app stores to be installed on devices by utilizing Cordova or Capacitor.
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2. Agenda
● Why LISP
● What is LISP
● Timeline of Lisp dialects
● Fundamentals of LISP
● Expression Evaluation in LISP
● Defining Variables
● Evaluating Combinations
● Procedure Definitions
● The Substitution Model for Procedure Application
● Conditional Expressions
● Linear Recursion and Iteration
3. Why LISP
The Key motivations behind Learning LISP are as follow:-
● Some of the features of Scala are inherited from LISP.
● Scala is Hybrid Programming Language. It consist features of both
Functional and Object Oriented Programming Language.
● We most of us know the Object Oriented programming Language like JAVA,
C++ etc.
● Undersanding the Basics of LISP can help us understand the other
functional programming languages like clojure.
4. What is LISP
● LISP, an acronym for list processing, is a programming language that was
designed for easy manipulation of data strings.
● Developed in 1959 by John McCarthy,
● It is a commonly used language for artificial intelligence (AI) programming.
● It is one of the oldest programming languages still in relatively wide use.
● All program code is written as s-expressions, or parenthesized lists.
6. What is LISP
● LISP, an acronym for list processing, is a programming language that was
designed for easy manipulation of data strings.
● Developed in 1959 by John McCarthy,
● It is a commonly used language for artificial intelligence (AI) programming.
● It is one of the oldest programming languages still in relatively wide use.
● All program code is written as s-expressions, or parenthesized lists.
7. Fundamentals of LISP
Every powerful language has three mechanisms for accomplishing this :-
● Primitive expressions, which represent the simplest entities the language
is concerned with,
● Means of combination, by which compound elements are built from
simpler ones, and
● Means of abstraction, by which compound elements can be named and
manipulated as units.
8. Expression Evaluation in LISP
Expressions representing numbers may be combined with an expression representing a
primitive procedure (such as + or *) to form a compound expression that represents the
application of the procedure to those numbers. For example:-
(+ 137 349)
486
(- 1000 334)
666
(* 5 99)
495
(/ 10 5)
2
9. Expression Evaluation in LISP
Expressions such as these, formed by delimiting a list of expressions within
parentheses in order to denote procedure application, are called combinations.
● The leftmost element in the list is called the operator, and the other elements are
called operands.
● The value of a combination is obtained by applying the procedure specified by the
operator to the arguments that are the values of the operands.
● The convention of placing the operator to the left of the operands is known as
prefix notation.
● No ambiguity can arise, because the operator is always the leftmost element and
the entire combination is delimited by the parentheses.
10. Defining Variables
Define is LISP language's simplest means of abstraction, for it allows us to
use simple names to refer to the results of compound operation.
(define pi 3.14159)
(define radius 10)
(* pi (* radius radius))
314.159
(define circumference (* 2 pi radius))
Circumference
62.8318
11. Evaluating Combinations
To evaluate a combination, do the following:
● 1. Evaluate the subexpressions of the combination.
● 2. Apply the procedure that is the
value of the leftmost subexpression
(the operator) to the arguments
that are the values of the other
subexpressions (the operands).
(* (+ 2 (* 4 6))
(+ 3 5 7))
12. Evaluating Combinations
Each combination is represented by a node with branches corresponding to the
operator and the operands of the combination stemming from it.
The terminal nodes (that is, nodes with no branches stemming from them)
represent either operators or numbers. Viewing evaluation in terms of the tree,
we can imagine that the values of the operands percolate upward, starting from
the terminal nodes and then combining at higher and higher levels.
the ``percolate values upward'' form of the evaluation rule is an example of a
general kind of process known as tree accumulation.
13. Procedure Definitions
A much more powerful abstraction technique by which a compound operation can
be given a name and then referred to as a unit.
(define (square x) (* x x))
We can understand this in the following way:
(define (square x) (* x x))
square something, multiply it by itself.
We have here a compound procedure, which has been given the name square. The
procedure represents the operation of multiplying something by itself. The thing to
be multiplied is given a local name, x, which plays the same role that a pronoun
plays in natural language. Evaluating the definition creates this compound
procedure and associates it with the name square
14. The Substitution Model for Procedure Application
There Exists 2 type of Orders to Evaluate any Procedure in LISP.
● Normal Order
● Applicative Order
● Normal Order :- the interpreter first evaluates the operator and operands
and then applies the resulting procedure to the resulting arguments
15. Normal Order
Normal Order Example :-
(sum-of-squares (+ 5 1) (* 5 2))
(+ (square 6) (square 10 ) )
If we use the definition of square, this reduces to
(+ (* 6 6) (* 10 10))
which reduces by multiplication to
(+ 36 100)
and finally to
136
16. Applicative Order
● Applicative Order :- An alternative evaluation model would not evaluate the operands until their
values were needed. Instead it would first substitute operand expressions for parameters until it
obtained an expression involving only primitive operators, and would then perform the evaluation.
(sum-of-squares (+ 5 1) (* 5 2))
(+ (square (+ 5 1)) (square (* 5 2)) )
(+ (* (+ 5 1) (+ 5 1)) (* (* 5 2) (* 5 2)))
followed by the reductions
(+ (* 6 6) (* 10 10))
(+ 36 100)
136
17. Conditional Expressions
Most of the time situation arises when we need to perform some operation based on
some condition.
For Example :- Finding the Absolute value.
Condition will be :-
● IF x > 0 return x
IF x = 0 return 0
IF x < 0 return -x
This construct is called a case analysis, and there is a special form in Lisp for notating such
a case analysis. It is called cond (which stands for ‘‘conditional’’), and it is used as follows:
● (define (abs x)
(cond ((> x 0) x)
((= x 0) 0)
((< x 0) (- x))))
18. Linear Recursion and Iteration
● Recursion:- the repeated application of a recursive procedure or definition.
● Iteration:- the repetition of a process.
Example:- Finding the Factorial of some number.This can easily implemented in 2
ways .
● Using Recusrion
● Using Iteration.
19. Linear Recursion
● Factorial Using the Recursion in LISP
(define (factorial n)
(if (= n 1)
1
(* n (factorial (- n 1)))))
● Here, Time Complexity is O(x)
● Space Complexity is O(x)