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Lexical Analysis, Regular Expressions & Finite State Machines.

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Presentation on theme: "Lexical Analysis, Regular Expressions & Finite State Machines."— Presentation transcript:

1 Lexical Analysis, Regular Expressions & Finite State Machines

2 Processing English Consider the following two sentences Hi, I am 22 years old. I come from Alabama. 22 come Alabama I, old from am. Hi years I. Are they both correct? How do you know? Same words, numbers and punctuation What did you do first? 1.Find words, numbers and punctuation 2.Then, check order (grammar rules)

3 Finding Words and Numbers How did you find words, numbers and punctuation? You have a definition of what each is, or looks like For example, what is a number? a word? Although your are a bit more agile, the process was: 1.Start with first character 2.If letter, assume word; if digit, assume number 3.Scan left to right 1 character at a time, until punctuation mark (space, comma, etc.) 4.Recognize word or number 5.If no more characters, done; otherwise return to 1

4 Processing Code How do you process the following? What are the main parts in which to break the input? void quote() { print( "To iterate is human, to recurse divine." + " - L. Peter Deutsch" ); } Schemes: childOf(X,Y) marriedTo(X,Y) Facts: marriedTo('Zed','Bea'). marriedTo('Jack','Jill'). childOf('Jill','Zed'). childOf('Sue','Jack'). Rules: childOf(X,Y) :- childOf(X,Z), marriedTo(Y,Z). marriedTo(X,Y) :- marriedTo(Y,X). Queries: marriedTo('Bea','Zed')? childOf('Jill','Bea')? def addABC(x): s = “ABC” return x + s addABC(input(“String: ”))

5 Example def addABC ( x ) : s = “ABC” return x + s addABC ( input ( “String: ” ) )

6 What are the Parts? They are called TOKENS Process similar to English processing Lexical Analysis Input: A program in some language Output: A list of tokens (type, value, location)

7 Example Revisited Sample Input:Sample Output: def addABC(x): s = “ABC” return x + s addABC(input(“String: ”)) (FUNDEF,”def”,1) (ID,”addABC”,1) (LEFT_PAREN,”(”,1) (ID,”x”,1) (RIGHT_PAREN,”)”,1) (COLON,”:”,1) (ID,”s”,2) (ASSIGN,”=”,2) (STRING,”’ABC’”,2) (FUNRET,”return”,3) (ID,”x”,3) (OPERATOR,”+”,3) (ID,”s”,3) (ID,”addABC”,4) (LEFT_PAREN,”(”,4) …

8 Program Compilation Lexical Analysis is first step of process Program Compiler Code Lexical Analyzer Program Parser Tokens Code Generator Internal DataCode Keywords String literals Variables … Error messages Syntax AnalysisOr Interpreter (Executed directly)

9 Token Specification Regular Expressions Pattern description for strings Concatenation: abc -> “abc” Boolean OR: ab|ac -> “ab”, “ac” Kleene closure: ab * -> “a”, “ab”, “abbb”, etc. Optional: ab?c -> “ac”, “abc” One or more: ab + -> “ab”, “abbb” Group using () (a|b)c -> “ac”, “bc” (a|b) * c -> “c”, “ac”, “bc”, “bac”, “abaaabbbabbaaaaac”, etc.

10 RegEx Extensions Exactly n: a 3 b + -> “aaab”, “aaabb”, … [A-Z] = A|B|…|Z [ABC] = A|B|C [~aA] = any character but “a” or “A” \ = escape character (e.g., \* -> “*”) Whitespace characters \s, \t, \n, \v

11 Token Recognition Finite State Machine A DFSM is a 5-tuple (Σ,S,s 0,δ,F) Σ: finite, non-empty set of symbols (input alphabet) S: finite, non-empty set of states s 0 : member of S designated as start state δ: state-transition function δ: S x Σ -> S F: subset of S (final states, may be empty)

12 FSM & RegEx abc a(b|c) ab* (a(b?c)) + abc Note the special double-circle designation of a final/accepting state. a a a b b b a c c c

13 Finite State Transducer Extended FSM: Γ: finite, non-empty set of symbols (output alphabet) δ: state-transition function δ: S x Σ -> S x Γ FST consumes input symbols and emits output symbols Lexical analyzer consume raw characters emit tokens

14 CS 236 Coolness Factor! Design our own language Subset of Datalog (LP-like) Build an interpreter for our language Lexical Analyzer (Project 1) Parser (Project 2) Interpreter (Projects 3 and 4) Optimization (Project 5)

15 Designing a Language Define the tokens Elements of the language, punctuation, etc. For example, what are they in C++? Recognize the tokens (lexical analysis) Define the grammar Forms of correct sentences For example, what are they in C++? Recognize the grammar (parsing) Interpret and execute the program C++ is a bit too complicated for us…

16 Varied World Views fct personlist siblings(person x) { return x’s siblings } fct int square(int x) { return x * x } fct boolean succeeds(person x) { if studies(x) return T else return F } fct boolean sibling(person x, person y) { if y is x’s sibling return T else return F } fct boolean square(int x, int y) { if y == x * x return T else return F } fct boolean succeeds(person x) { if studies(x) return T else return F } Look up table or oracle No concerns with efficiency

17 Logic Programming Assume: all functions are Boolean Compute using facts and rules Facts are the known true values of the functions Rules express relations among functions Example: studies(x), succeeds(x) Facts: studies(Matt), studies(Jenny) Rule: succeeds(x) :- studies(x) Closed-world Assumption

18 Logic Programming Computing is like issuing queries First check if it can be answered with facts Second check if rules can be applied Examples studies(Alex)? NO (neither facts nor rules to establish it) studies(Matt)? YES (there is fact about that) succeeds(Jenny)? YES (no fact, but a rule that if Jenny studies then she succeeds and a fact that Jenny studies)

19 Functions of Several Arguments Examples loves(x,y), parent(x,y), inclass(x,y) loves(x,y) :- married(x,y) Computing parent(Christophe, Samuel)? Yes, if there is a fact that matches parent(Christophe, X)? Yes, if there is a value of X that would cause it to match a fact – return value of X loves(X, Y)? Yes, if there are values of X and Y that would make this true, either by matching a fact or via rules (e.g., married(Christophe, Isabelle)) – return values of X and Y

20 When We Are Done Sample Program:Sample Execution: Schemes: snap(S,N,A,P) csg(C,S,G) cn(C,N) ncg(N,C,G) Facts: snap('12345','C. Brown','12 Apple St.',' '). snap('22222','P. Patty','56 Grape Blvd',' '). snap('33333','Snoopy','12 Apple St.',' '). csg('CS101','12345','A'). csg('CS101','22222','B'). csg('CS101','33333','C'). csg('EE200','12345','B+'). csg('EE200','22222','B'). Rules: cn(C,N) :- snap(S,N,A,P),csg(C,S,G). ncg(N,C,G) :- snap(S,N,A,P),csg(C,S,G). Queries: cn('CS101',Name)? ncg('Snoopy',Course,Grade)? cn('CS101',Name)? Yes(3) Name='C. Brown' Name='P. Patty' Name='Snoopy' ncg('Snoopy',Course,Grade)? Yes(1) Course='CS101', Grade='C' Demo…

21 Project 1: Lexical Analyzer Sample Input:Sample Output: Queries: IsInRoomAtDH('Snoopy',R,'M',H) #SchemesFactsRules. (QUERIES,"Queries",1) (COLON,":",1) (ID,"IsInRoomAtDH",2) (LEFT_PAREN,"(",2) (STRING,"'Snoopy'",2) (COMMA,",",2) (ID,"R",2) (COMMA,",",2) (STRING,"'M'",2) (COMMA,",",2) (ID,"H",2) (RIGHT_PAREN,")",2) (COMMENT,"#SchemesFactsRules",3) (PERIOD,".",4) Total Tokens = 14 Define and find the tokens

22 Basic FST for Project 1 and )> ‘ ‘ : string : … white space ident. - | | | eof error Special check for Keywords (Schemes, Facts, Rules, Queries) or :- or keywd. start :- error or

23 Implementing a FST State in Variable state = START; input = readChar(); while (state != ACCEPT) { if (state == START) { if (input == QUOTE) { input = readChar(); state = STRING; } else if (input ==...) {... other kinds of tokens... } } else if (state == STRING) { if (input == QUOTE) { input = readChar(); state = ACCEPT; } else { input = readChar(); state = STRING; } State in Position in Code input = readChar(); // begin in START state if (input == QUOTE) { input = readChar(); // now in STRING state while (input != QUOTE) { input = readChar(); // stay in STRING state } input = readChar(); // now in ACCEPT state } else if (input ==...) {... other kinds of tokens... }


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