Specifically, I noticed that the language of regular expressions itself isn't regular. So, I can't use a regular expression to parse a given regular expression. I need to use a parser since the language of the regular expression itself is context free.
Is there any way regular expressions can be represented in a way that the resulting string can be parsed using a regular expression?
Note: My question isn't about whether there is a regexp to match the current syntax of regexes, but whether there exists a "representation" for regular expressions as we know it today (maybe not a neat as what we know them as today) that can be parsed using regular expressions. Also, please could someone remove the dup since it isn't a dup. I'm asking something completely different. I already know that the current language of regular expressions isn't regular (it is how I started my original question).
Depending on what you mean by "represent", the answer is "yes" or "no":
If you want a language that (homomorphically) maps 1:1 to the usual basic regular expression language, the answer is no, because a regular language cannot be isomorphic to a non-regular language, and the standard regular expression language is non-regular. This is because the syntax requires matching opening and closing parentheses of arbitrary depth.
If "represent" only means another method of specifying regular languages, the answer is yes, and right now I can think of at least three ways to achieve this:
The "dumbest" and easiest way is to define some surjective mapping f : ℕ -> RegEx from the natural numbers onto the set of all valid standard regular expressions. You can define the natural numbers using the regular expression 0|1[01]*, and the regular language denoted by a (string representing the) natural number n is the regular language denoted by f(n).
Of course, the meaning attached to a natural number would not be obvious to a human reader at all, so this "regular expression language" would be utterly useless.
As parentheses are the only non-regular part in simple regular expressions, the easiest human-interpretable method would be to extend the standard simple regular expression syntax to allow dangling parentheses and defining semantics for dangling parentheses.
The obvious choice would be to ignore non-matching opening parentheses and interpreting non-matching closing parentheses as matching the beginning of the regex. This essentially amounts to implicitly inserting as many opening parentheses at the beginning and as many closing parentheses at the end of the regex as necessary. Additionally, (* would have to be interpreted as repetition of the empty string. If I didn't miss anything, this definition should turn any string into a "regular expression" with a specified meaning, so .* defines this "regular expression language".
This variant even has the same abstract syntax as standard regular expressions.
Another variant would be to specify the NFA that recognizes the language directly using a regular language, e.g.: ([a-z]+,([^,]|\\,|\\\\)+,[a-z]+\$?;)*.
The idea is that [a-z]+ is used as a label for states, and the expression is a list of transition triples (s, c, t) from source state s to target state t consuming character c, and a $ indicating accepting transitions (cf. note below). In c, backslashes are used to escape commas or backslashes - I assumed that you use the same alphabet for standard regular expressions, but of course you can replace the middle component with any other regular language of symbols denotating characters of any alphabet you wish.
The first source state mentioned is the (single) initial state. An empty expression defines the empty language.
Above, I wrote "accepting transition", not "accepting state" because that would make the regex above a bit more complex. You can interpret a triple containing a $ as two transitions, namely one transition consuming c from s to a new, unique state, and an ε-transition from that state to t. This should allow any NFA to be represented, by replacing each transition to an accepting state with a $ triple and each transition to a non-accepting state with a non-$ triple.
One note that might make the "yes" part look more intuitive: Assembly languages are regular, and those are even Turing-complete, so it would be unexpected if it wasn't possible to specify "mere" regular languages using a regular language.
The answer is probably NO.
As you have pointed out, set of all possible regular expressions itself is not a regular set. Any TRUE regular expression (not those extended) can be converted into finite automata (FA). If regular expression can be represented in a form that can be parsed by itself, then FA can be parsed by regular expression as well.
But that's not possible as far as I know. RE itself can be reduced into three basic operation(According to the Dragon Book):
concatenation: e.g. ab
alternation: e.g. a|b
kleen closure: e.g. a*
The kleen closure can match infinite number of characters, but it cannot know how many characters to match.
Just think such case: you want to match 3 consecutive as. Then the corresponding regular expression is /aaa/. But what if you want match 4, 5, 6... as? Parser with only one RE cannot know the exact number of as. So it fails to give the right matching to arbitrary expressions. However, the RE parser has to match infinite different forms of REs. According to your expression, a regular expression cannot match all the possibilities.
Well, the only difference of a RE parser is that it does not need a tokenizer.(probably that's why RE is used in lexical analysis) Every character in RE is a token (excluding those escape charcters). But to parse RE, whatever it is converted,one has to face up with NFA/DFA/TREE... all equivalent structures that cannot be parsed by RE itself.
I was trying to write a pattern which doesn't contain binary string (let's assume 101). I know that such expressions cannot be written using Regular Expression considering http://en.wikipedia.org/wiki/Regular_language.
I tried writing the pattern for the above problem using Regular Expression though and it seems to be working.
\b(?!101)\w+\b
What I wanted to ask is that can a regular expression be written for my problem and why? And if yes, then is my regular expression correct?
To match a whole string that doesn't contain 101:
^(?!.*101).*$
Look-ahead are indeed an easy way to check a condition on a string through regex, but your regex will only match alphanumeric words that do not start with 101.
You wrote
I know that such expressions cannot be written using Regular
Expression considering http://en.wikipedia.org/wiki/Regular_language.
In that Wikipedia article, you seem to have missed the
Note that the "regular expression" features provided with many
programming languages are augmented with features that make them
capable of recognizing languages that can not be expressed by the
formal regular expressions (as formally defined below).
The negative lookahead construct is such a feature.
I am not sure how it is called: negation, complementary or inversion. The concept is this. For example having alphabet "ab"
R = 'a'
!R = the regexp that matche everyhting exept what R matches
In this simple example it should be soemthing like
!R = 'b*|[ab][ab]+'
How is such a regexp called? I remeber from my studies that there is a way to calculate that, but it is something complicated and generally too hard to make by hand. Is there a nice online tool (or regular software) to do that?
jbo5112's answer gives good practical help. However, on the theoretical side: a regular expression corresponds to a regular language, so the term you're looking for is complementation.
To complement a regex:
Convert into the equivalent NFA. This is a well-known and defined process.
Convert the NFA to a DFA via the powerset construction
Complement the DFA by making accept states not accept and vice versa.
Convert the DFA to a regular expression.
You now have the complement of the original regular expression!
If all you're doing is searching, then some software/languages for regular expressions have a way to negate the match built in. For example, with grep you can use a '-v' option to get lines that don't match and the SQL variants I've seen allow you to use a 'not' qualifier to negate the match.
Another option that some/most/all regex dialects support is to use "negative lookahead". You may have to look up your specific syntax, but it's an interesting tool that is well worth reading about. Generally it's something like this: if R='<regex>', then Negative_of_R='(?!<regex>)'. Unfortunately, it can vary with the peculiarities of your language (e.g. vim uses \(<regex>\)\#!).
A word of caution: If you're not careful, a negated regular expression will match more than you expect. If you have the text This doesn't match 'mystring'. and search for (?!mystring), then it will match everything except the 'm' in mystring.
Why repeated strings such as
[wcw|w is a string of a's and b's]
cannot be denoted by regular expressions?
pls. give me detailed answer as i m new to lexical analysis.
Thanks ...
Regular expressions in their original form describe regular languages/grammars. Those cannot contain nested structures as those languages can be described by a simple finite state machine. Simplified you can picture that as if each word of the language grows strictly from left to right (or right to left), where repeating structures have to be explicitly defined and are static.
What this means is, that no information whatsoever from previous states can be carried over to later states (a few characters further in the input). So if you have your symbol w you can't specify that the input must have exactly the same string w later in the sequence. Similarly you can't ensure that each opening paranthesis needs a closin paren as well (so regular expressions themselves are not even a regular language and thus cannot be described by regular expressions :-)).
In theoretical computer science we worked with a very restricted set of regex operators, basically only consisting of sequence, alternative (|) and repetition (*), everything else can be described with those operations.
However, usually regex engines allow grouping of certain sub-patterns into matches which can then be referenced or extracted later. Some engines even allow to use such a backreference in the search expression string itself, thereby allowing the expression to describe more than just a regular language. If I remember correctly such use of backreferences can even yield languages that are not context-free.
Additional pointers:
This StackOverflow question
Wikipedia
It can be, you just can't assure that it's the same string of "a"s and "b"s because there's no way to retain the information acquired in traversing the first half for use in traversing the second.
Lets say that I have 10,000 regexes and one string and I want to find out if the string matches any of them and get all the matches.
The trivial way to do it would be to just query the string one by one against all regexes. Is there a faster,more efficient way to do it?
EDIT:
I have tried substituting it with DFA's (lex)
The problem here is that it would only give you one single pattern. If I have a string "hello" and patterns "[H|h]ello" and ".{0,20}ello", DFA will only match one of them, but I want both of them to hit.
This is the way lexers work.
The regular expressions are converted into a single non deterministic automata (NFA) and possibily transformed in a deterministic automata (DFA).
The resulting automaton will try to match all the regular expressions at once and will succeed on one of them.
There are many tools that can help you here, they are called "lexer generator" and there are solutions that work with most of the languages.
You don't say which language are you using. For C programmers I would suggest to have a look at the re2c tool. Of course the traditional (f)lex is always an option.
I've come across a similar problem in the past. I used a solution similar to the one suggested by akdom.
I was lucky in that my regular expressions usually had some substring that must appear in every string it matches. I was able to extract these substrings using a simple parser and index them in an FSA using the Aho-Corasick algorithms. The index was then used to quickly eliminate all the regular expressions that trivially don't match a given string, leaving only a few regular expressions to check.
I released the code under the LGPL as a Python/C module. See esmre on Google code hosting.
We had to do this on a product I worked on once. The answer was to compile all your regexes together into a Deterministic Finite State Machine (also known as a deterministic finite automaton or DFA). The DFA could then be walked character by character over your string and would fire a "match" event whenever one of the expressions matched.
Advantages are it runs fast (each character is compared only once) and does not get any slower if you add more expressions.
Disadvantages are that it requires a huge data table for the automaton, and there are many types of regular expressions that are not supported (for instance, back-references).
The one we used was hand-coded by a C++ template nut in our company at the time, so unfortunately I don't have any FOSS solutions to point you toward. But if you google regex or regular expression with "DFA" you'll find stuff that will point you in the right direction.
Martin Sulzmann Has done quite a bit of work in this field.
He has a HackageDB project explained breifly here which use partial derivatives seems to be tailor made for this.
The language used is Haskell and thus will be very hard to translate to a non functional language if that is the desire (I would think translation to many other FP languages would still be quite hard).
The code is not based on converting to a series of automata and then combining them, instead it is based on symbolic manipulation of the regexes themselves.
Also the code is very much experimental and Martin is no longer a professor but is in 'gainful employment'(1) so may be uninterested/unable to supply any help or input.
this is a joke - I like professors, the less the smart ones try to work the more chance I have of getting paid!
10,000 regexen eh? Eric Wendelin's suggestion of a hierarchy seems to be a good idea. Have you thought of reducing the enormity of these regexen to something like a tree structure?
As a simple example: All regexen requiring a number could branch off of one regex checking for such, all regexen not requiring one down another branch. In this fashion you could reduce the number of actual comparisons down to a path along the tree instead of doing every single comparison in 10,000.
This would require decomposing the regexen provided into genres, each genre having a shared test which would rule them out if it fails. In this way you could theoretically reduce the number of actual comparisons dramatically.
If you had to do this at run time you could parse through your given regular expressions and "file" them into either predefined genres (easiest to do) or comparative genres generated at that moment (not as easy to do).
Your example of comparing "hello" to "[H|h]ello" and ".{0,20}ello" won't really be helped by this solution. A simple case where this could be useful would be: if you had 1000 tests that would only return true if "ello" exists somewhere in the string and your test string is "goodbye;" you would only have to do the one test on "ello" and know that the 1000 tests requiring it won't work, and because of this, you won't have to do them.
If you're thinking in terms of "10,000 regexes" you need to shift your though processes. If nothing else, think in terms of "10,000 target strings to match". Then look for non-regex methods built to deal with "boatloads of target strings" situations, like Aho-Corasick machines. Frankly, though, it seems like somethings gone off the rails much earlier in the process than which machine to use, since 10,000 target strings sounds a lot more like a database lookup than a string match.
Aho-Corasick was the answer for me.
I had 2000 categories of things that each had lists of patterns to match against. String length averaged about 100,000 characters.
Main Caveat: The patters to match were all language patters not regex patterns e.g. 'cat' vs r'\w+'.
I was using python and so used https://pypi.python.org/pypi/pyahocorasick/.
import ahocorasick
A = ahocorasick.Automaton()
patterns = [
[['cat','dog'],'mammals'],
[['bass','tuna','trout'],'fish'],
[['toad','crocodile'],'amphibians'],
]
for row in patterns:
vals = row[0]
for val in vals:
A.add_word(val, (row[1], val))
A.make_automaton()
_string = 'tom loves lions tigers cats and bass'
def test():
vals = []
for item in A.iter(_string):
vals.append(item)
return vals
Running %timeit test() on my 2000 categories with about 2-3 traces per category and a _string length of about 100,000 got me 2.09 ms vs 631 ms doing sequential re.search() 315x faster!.
You'd need to have some way of determining if a given regex was "additive" compared to another one. Creating a regex "hierarchy" of sorts allowing you to determine that all regexs of a certain branch did not match
You could combine them in groups of maybe 20.
(?=(regex1)?)(?=(regex2)?)(?=(regex3)?)...(?=(regex20)?)
As long as each regex has zero (or at least the same number of) capture groups, you can look at what what captured to see which pattern(s) matched.
If regex1 matched, capture group 1 would have it's matched text. If not, it would be undefined/None/null/...
If you're using real regular expressions (the ones that correspond to regular languages from formal language theory, and not some Perl-like non-regular thing), then you're in luck, because regular languages are closed under union. In most regex languages, pipe (|) is union. So you should be able to construct a string (representing the regular expression you want) as follows:
(r1)|(r2)|(r3)|...|(r10000)
where parentheses are for grouping, not matching. Anything that matches this regular expression matches at least one of your original regular expressions.
I would recommend using Intel's Hyperscan if all you need is to know which regular expressions match. It is built for this purpose. If the actions you need to take are more sophisticated, you can also use ragel. Although it produces a single DFA and can result in many states, and consequently a very large executable program. Hyperscan takes a hybrid NFA/DFA/custom approach to matching that handles large numbers of expressions well.
I'd say that it's a job for a real parser. A midpoint might be a Parsing Expression Grammar (PEG). It's a higher-level abstraction of pattern matching, one feature is that you can define a whole grammar instead of a single pattern. There are some high-performance implementations that work by compiling your grammar into a bytecode and running it in a specialized VM.
disclaimer: the only one i know is LPEG, a library for Lua, and it wasn't easy (for me) to grasp the base concepts.
I'd almost suggest writing an "inside-out" regex engine - one where the 'target' was the regex, and the 'term' was the string.
However, it seems that your solution of trying each one iteratively is going to be far easier.
You could compile the regex into a hybrid DFA/Bucchi automata where each time the BA enters an accept state you flag which regex rule "hit".
Bucchi is a bit of overkill for this, but modifying the way your DFA works could do the trick.
I use Ragel with a leaving action:
action hello {...}
action ello {...}
action ello2 {...}
main := /[Hh]ello/ % hello |
/.+ello/ % ello |
any{0,20} "ello" % ello2 ;
The string "hello" would call the code in the action hello block, then in the action ello block and lastly in the action ello2 block.
Their regular expressions are quite limited and the machine language is preferred instead, the braces from your example only work with the more general language.
Try combining them into one big regex?
I think that the short answer is that yes, there is a way to do this, and that it is well known to computer science, and that I can't remember what it is.
The short answer is that you might find that your regex interpreter already deals with all of these efficiently when |'d together, or you might find one that does. If not, it's time for you to google string-matching and searching algorithms.
The fastest way to do it seems to be something like this (code is C#):
public static List<Regex> FindAllMatches(string s, List<Regex> regexes)
{
List<Regex> matches = new List<Regex>();
foreach (Regex r in regexes)
{
if (r.IsMatch(string))
{
matches.Add(r);
}
}
return matches;
}
Oh, you meant the fastest code? i don't know then....