I didn't get the answer to this anywhere. What is the runtime complexity of a Regex match and substitution?
Edit: I work in python. But would like to know in general about most popular languages/tools (java, perl, sed).
From a purely theoretical stance:
The implementation I am familiar with would be to build a Deterministic Finite Automaton to recognize the regex. This is done in O(2^m), m being the size of the regex, using a standard algorithm. Once this is built, running a string through it is linear in the length of the string - O(n), n being string length. A replacement on a match found in the string should be constant time.
So overall, I suppose O(2^m + n).
Other theoretical info of possible interest.
For clarity, assume the standard definition for a regular expression
http://en.wikipedia.org/wiki/Regular_language
from the formal language theory. Practically, this means that the only building
material are alphabet symbols, operators of concatenation, alternation and
Kleene closure, along with the unit and zero constants (which appear for
group-theoretic reasons). Generally it's a good idea not to overload this term
despite the everyday practice in scripting languages which leads to
ambiguities.
There is an NFA construction that solves the matching problem for a regular
expression r and an input text t in O(|r| |t|) time and O(|r|) space, where
|-| is the length function. This algorithm was further improved by Myers
http://doi.acm.org/10.1145/128749.128755
to the time and space complexity O(|r| |t| / log |t|) by using automaton node listings and the Four Russians paradigm. This paradigm seems to be named after four Russian guys who wrote a groundbreaking paper which is not
online. However, the paradigm is illustrated in these computational biology
lecture notes
http://lyle.smu.edu/~saad/courses/cse8354/lectures/lecture5.pdf
I find it hilarious to name a paradigm by the number and
the nationality of authors instead of their last names.
The matching problem for regular expressions with added backreferences is
NP-complete, which was proven by Aho
http://portal.acm.org/citation.cfm?id=114877
by a reduction from the vertex-cover problem which is a classical NP-complete problem.
To match regular expressions with backreferences deterministically we could
employ backtracking (not unlike the Perl regex engine) to keep track of the
possible subwords of the input text t that can be assigned to the variables in
r. There are only O(|t|^2) subwords that can be assigned to any one variable
in r. If there are n variables in r, then there are O(|t|^2n) possible
assignments. Once an assignment of substrings to variables is fixed, the
problem reduces to the plain regular expression matching. Therefore the
worst-case complexity for matching regular expressions with backreferences is
O(|t|^2n).
Note however, regular expressions with backreferences are not yet
full-featured regexen.
Take, for example, the "don't care" symbol apart from any other
operators. There are several polynomial algorithms deciding whether a set of
patterns matches an input text. For example, Kucherov and Rusinowitch
http://dx.doi.org/10.1007/3-540-60044-2_46
define a pattern as a word w_1#w_2#...#w_n where each w_i is a word (not a regular expression) and "#" is a variable length "don't care" symbol not contained in either of w_i. They derive an O((|t| + |P|) log |P|) algorithm for matching a set of patterns P against an input text t, where |t| is the length of the text, and |P| is the length of all the words in P.
It would be interesting to know how these complexity measures combine and what
is the complexity measure of the matching problem for regular expressions with
backreferences, "don't care" and other interesting features of practical
regular expressions.
Alas, I haven't said a word about Python... :)
Depends on what you define by regex. If you allow operators of concatenation, alternative and Kleene-star, the time can actually be O(m*n+m), where m is size of a regex and n is length of the string. You do it by constructing a NFA (that is linear in m), and then simulating it by maintaining the set of states you're in and updating that (in O(m)) for every letter of input.
Things that make regex parsing difficult:
parentheses and backreferences: capturing is still OK with the aforementioned algorithm, although it would get the complexity higher, so it might be infeasable. Backreferences raise the recognition power of the regex, and its difficulty is well
positive look-ahead: is just another name for intersection, which raises the complexity of the aforementioned algorithm to O(m^2+n)
negative look-ahead: a disaster for constructing the automaton (O(2^m), possibly PSPACE-complete). But should still be possible to tackle with the dynamic algorithm in something like O(n^2*m)
Note that with a concrete implementation, things might get better or worse. As a rule of thumb, simple features should be fast enough, and unambiguous (eg. not like a*a*) regexes are better.
To delve into theprise's answer, for the construction of the automaton, O(2^m) is the worst case, though it really depends on the form of the regular expression (for a very simple one that matches a word, it's in O(m), using for example the Knuth-Morris-Pratt algorithm).
Depends on the implementation. What language/library/class? There may be a best case, but it would be very specific to the number of features in the implementation.
You can trade space for speed by building a nondeterministic finite automaton instead of a DFA. This can be traversed in linear time. Of course, in the worst case this could need O(2^m) space. I'd expect the tradeoff to be worth it.
If you're after matching and substitution, that implies grouping and backreferences.
Here is a perl example where grouping and backreferences can be used to solve an NP complete problem:
http://perl.plover.com/NPC/NPC-3SAT.html
This (coupled with a few other theoretical tidbits) means that using regular expressions for matching and substitution is NP-complete.
Note that this is different from the formal definition of a regular expression - which don't have the notion of grouping - and match in polynomial time as described by the other answers.
In python's re library, even if a regex is compiled, the complexity can still be exponential (in string length) in some cases, as it is not built on DFA. Some references here, here or here.
Related
What is the complexity with respect to the string length that takes to perform a regular expression comparison on a string?
The answer depends on what exactly you mean by "regular expressions." Classic regexes can be compiled into Deterministic Finite Automata that can match a string of length N in O(N) time. Certain extensions to the regex language change that for the worse.
You may find the following document of interest: Regular Expression Matching Can Be Simple And Fast.
unbounded - you can create a regular expression that never terminates, on an empty input string.
If you use normal (TCS:no backreference, concatenation,alternation,Kleene star) regexp and regexp is already compiled then it's O(n).
If you're looking for tight asymptotic bounds on RegEx (without respect to the expression itself), then there isn't one. As Alex points out, you can create a regex that is O(1) or a regex that is Omega(infinity). As a purely mathematical algorithm, a regular expression engine would be far too complicated to perform any sort of formal asymptotic analysis (aside from the fact that such analysis would be basically worthless).
The growth rate of a particular expression (since that, really, constitutes an algorithm, anyway) would be far more meaningful, though not necessarily any easier to analyze.
There are some features in modern regex engines which allow you to match languages that couldn't be matched without that feature. For example the following regex using back references matches the language of all strings that consist of a word that repeats itself: (.+)\1. This language is not regular and can't be matched by a regex that does not use back references.
Does lookaround also affect which languages can be matched by a regular expression? I.e. are there any languages that can be matched using lookaround that couldn't be matched otherwise? If so, is this true for all flavors of lookaround (negative or positive lookahead or lookbehind) or just for some of them?
The answer to the question you ask, which is whether a larger class of languages than the regular languages can be recognised with regular expressions augmented by lookaround, is no.
A proof is relatively straightforward, but an algorithm to translate a regular expression containing lookarounds into one without is messy.
First: note that you can always negate a regular expression (over a finite alphabet). Given a finite state automaton that recognises the language generated by the expression, you can simply exchange all the accepting states for non-accepting states to get an FSA that recognises exactly the negation of that language, for which there are a family of equivalent regular expressions.
Second: because regular languages (and hence regular expressions) are closed under negation they are also closed under intersection since A intersect B = neg ( neg(A) union neg(B)) by de Morgan's laws. In other words given two regular expressions, you can find another regular expression that matches both.
This allows you to simulate lookaround expressions. For example u(?=v)w matches only expressions that will match uv and uw.
For negative lookahead you need the regular expression equivalent of the set theoretic A\B, which is just A intersect (neg B) or equivalently neg (neg(A) union B). Thus for any regular expressions r and s you can find a regular expression r-s which matches those expressions that match r which do not match s. In negative lookahead terms: u(?!v)w matches only those expressions which match uw - uv.
There are two reasons why lookaround is useful.
First, because the negation of a regular expression can result in something much less tidy. For example q(?!u)=q($|[^u]).
Second, regular expressions do more than match expressions, they also consume characters from a string - or at least that's how we like to think about them. For example in python I care about the .start() and .end(), thus of course:
>>> re.search('q($|[^u])', 'Iraq!').end()
5
>>> re.search('q(?!u)', 'Iraq!').end()
4
Third, and I think this is a pretty important reason, negation of regular expressions does not lift nicely over concatenation. neg(a)neg(b) is not the same thing as neg(ab), which means that you cannot translate a lookaround out of the context in which you find it - you have to process the whole string. I guess that makes it unpleasant for people to work with and breaks people's intuitions about regular expressions.
I hope I have answered your theoretical question (its late at night, so forgive me if I am unclear). I agree with a commentator who said that this does have practical applications. I met very much the same problem when trying to scrape some very complicated web pages.
EDIT
My apologies for not being clearer: I do not believe you can give a proof of regularity of regular expressions + lookarounds by structural induction, my u(?!v)w example was meant to be just that, an example, and an easy one at that. The reason a structural induction won't work is because lookarounds behave in a non-compositional way - the point I was trying to make about negations above. I suspect any direct formal proof is going to have lots of messy details. I have tried to think of an easy way to show it but cannot come up with one off the top of my head.
To illustrate using Josh's first example of ^([^a]|(?=..b))*$ this is equivalent to a 7 state DFSA with all states accepting:
A - (a) -> B - (a) -> C --- (a) --------> D
Λ | \ |
| (not a) \ (b)
| | \ |
| v \ v
(b) E - (a) -> F \-(not(a)--> G
| <- (b) - / |
| | |
| (not a) |
| | |
| v |
\--------- H <-------------------(b)-----/
The regular expression for state A alone looks like:
^(a([^a](ab)*[^a]|a(ab|[^a])*b)b)*$
In other words any regular expression you are going to get by eliminating lookarounds will in general be much longer and much messier.
To respond to Josh's comment - yes I do think the most direct way to prove the equivalence is via the FSA. What makes this messier is that the usual way to construct an FSA is via a non-deterministic machine - its much easier to express u|v as simply the machine constructed from machines for u and v with an epsilon transition to the two of them. Of course this is equivalent to a deterministic machine, but at the risk of exponential blow-up of states. Whereas negation is much easier to do via a deterministic machine.
The general proof will involve taking the cartesian product of two machines and selecting those states you wish to retain at each point you want to insert a lookaround. The example above illustrates what I mean to some extent.
My apologies for not supplying a construction.
FURTHER EDIT:
I have found a blog post which describes an algorithm for generating a DFA out of a regular expression augmented with lookarounds. Its neat because the author extends the idea of an NFA-e with "tagged epsilon transitions" in the obvious way, and then explains how to convert such an automaton into a DFA.
I thought something like that would be a way to do it, but I'm pleased that someone has written it up. It was beyond me to come up with something so neat.
As the other answers claim, lookarounds don't add any extra power to regular expressions.
I think we can show this using the following:
One Pebble 2-NFA (see the Introduction section of the paper which refers to it).
The 1-pebble 2NFA does not deal with nested lookaheads, but, we can use a variant of multi-pebble 2NFAs (see section below).
Introduction
A 2-NFA is a non deterministic finite automaton which has the ability to move either left or right on it's input.
A one pebble machine is where the machine can place a pebble on the input tape (i.e. mark a specific input symbol with a pebble) and do possibly different transitions based on whether there is a pebble at the current input position or not.
It is known the One Pebble 2-NFA has the same power as a regular DFA.
Non-nested Lookaheads
The basic idea is as follows:
The 2NFA allows us to backtrack (or 'front track') by moving forward or backward in the input tape. So for a lookahead we can do the match for the lookahead regular expression and then backtrack what we have consumed, in matching the lookahead expression. In order to know exactly when to stop backtracking, we use the pebble! We drop the pebble before we enter the dfa for the lookahead to mark the spot where the backtracking needs to stop.
Thus at the end of running our string through the pebble 2NFA, we know whether we matched the lookahead expression or not and the input left (i.e. what is left to be consumed) is exactly what is required to match the remaining.
So for a lookahead of the form u(?=v)w
We have the DFAs for u, v and w.
From the accepting state (yes, we can assume there is only one) of DFA for u, we make an e-transition to the start state of v, marking the input with a pebble.
From an accepting state for v, we e-transtion to a state which keeps moving the input left, till it finds a pebble, and then transitions to start state of w.
From a rejecting state of v, we e-transition to a state which keeps moving left until it finds the pebble, and transtions to the accepting state of u (i.e where we left off).
The proof used for regular NFAs to show r1 | r2, or r* etc, carry over for these one pebble 2nfas. See http://www.coli.uni-saarland.de/projects/milca/courses/coal/html/node41.html#regularlanguages.sec.regexptofsa for more info on how the component machines are put together to give the bigger machine for the r* expression etc.
The reason why the above proofs for r* etc work is that the backtracking ensures that the input pointer is always at the right spot, when we enter the component nfas for repetition. Also, if a pebble is in use, then it is being processed by one of the lookahead component machines. Since there are no transitions from lookahead machine to lookahead machine without completely backtracking and getting back the pebble, a one pebble machine is all that is needed.
For eg consider ([^a] | a(?=...b))*
and the string abbb.
We have abbb which goes through the peb2nfa for a(?=...b), at the end of which we are at the state: (bbb, matched) (i.e in input bbb is remaining, and it has matched 'a' followed by '..b'). Now because of the *, we go back to the beginning (see the construction in the link above), and enter the dfa for [^a]. Match b, go back to beginning, enter [^a] again two times, and then accept.
Dealing with Nested Lookaheads
To handle nested lookaheads we can use a restricted version of k-pebble 2NFA as defined here: Complexity Results for Two-Way and Multi-Pebble Automata and their Logics (see Definition 4.1 and Theorem 4.2).
In general, 2 pebble automata can accept non-regular sets, but with the following restrictions, k-pebble automata can be shown to be regular (Theorem 4.2 in above paper).
If the pebbles are P_1, P_2, ..., P_K
P_{i+1} may not be placed unless P_i is already on the tape and P_{i} may not be picked up unless P_{i+1} is not on the tape. Basically the pebbles need to be used in a LIFO fashion.
Between the time P_{i+1} is placed and the time that either P_{i} is picked up or P_{i+2} is placed, the automaton can traverse only the subword located between the current location of P_{i} and the end of the input word that lies in the direction of P_{i+1}. Moreover, in this sub-word, the automaton can act only as a 1-pebble automaton with Pebble P_{i+1}. In particular it is not allowed to lift up, place or even sense the presence of another pebble.
So if v is a nested lookahead expression of depth k, then (?=v) is a nested lookahead expression of depth k+1. When we enter a lookahead machine within, we know exactly how many pebbles have to have been placed so far and so can exactly determine which pebble to place and when we exit that machine, we know which pebble to lift. All machines at depth t are entered by placing pebble t and exited (i.e. we return to processing of a depth t-1 machine) by removing pebble t. Any run of the complete machine looks like a recursive dfs call of a tree and the above two restrictions of the multi-pebble machine can be catered to.
Now when you combine expressions, for rr1, since you concat, the pebble numbers of r1 must be incremented by the depth of r. For r* and r|r1 the pebble numbering remains the same.
Thus any expression with lookaheads can be converted to an equivalent multi-pebble machine with the above restrictions in pebble placement and so is regular.
Conclusion
This basically addresses the drawback in Francis's original proof: being able to prevent the lookahead expressions from consuming anything which are required for future matches.
Since Lookbehinds are just finite string (not really regexs) we can deal with them first, and then deal with the lookaheads.
Sorry for the incomplete writeup, but a complete proof would involve drawing a lot of figures.
It looks right to me, but I will be glad to know of any mistakes (which I seem to be fond of :-)).
I agree with the other posts that lookaround is regular (meaning that it does not add any fundamental capability to regular expressions), but I have an argument for it that is simpler IMO than the other ones I have seen.
I will show that lookaround is regular by providing a DFA construction. A language is regular if and only if it has a DFA that recognizes it. Note that Perl doesn't actually use DFAs internally (see this paper for details: http://swtch.com/~rsc/regexp/regexp1.html) but we construct a DFA for purposes of the proof.
The traditional way of constructing a DFA for a regular expression is to first build an NFA using Thompson's Algorithm. Given two regular expressions fragments r1 and r2, Thompson's Algorithm provides constructions for concatenation (r1r2), alternation (r1|r2), and repetition (r1*) of regular expressions. This allows you to build a NFA bit by bit that recognizes the original regular expression. See the paper above for more details.
To show that positive and negative lookahead are regular, I will provide a construction for concatenation of a regular expression u with positive or negative lookahead: (?=v) or (?!v). Only concatenation requires special treatment; the usual alternation and repetition constructions work fine.
The construction is for both u(?=v) and u(?!v) is:
In other words, connect every final state of the existing NFA for u to both an accept state and to an NFA for v, but modified as follows. The function f(v) is defined as:
Let aa(v) be a function on an NFA v that changes every accept state into an "anti-accept state". An anti-accept state is defined to be a state that causes the match to fail if any path through the NFA ends in this state for a given string s, even if a different path through v for s ends in an accept state.
Let loop(v) be a function on an NFA v that adds a self-transition on any accept state. In other words, once a path leads to an accept state, that path can stay in the accept state forever no matter what input follows.
For negative lookahead, f(v) = aa(loop(v)).
For positive lookahead, f(v) = aa(neg(v)).
To provide an intuitive example for why this works, I will use the regex (b|a(?:.b))+, which is a slightly simplified version of the regex I proposed in the comments of Francis's proof. If we use my construction along with the traditional Thompson constructions, we end up with:
The es are epsilon transitions (transitions that can be taken without consuming any input) and the anti-accept states are labeled with an X. In the left half of the graph you see the representation of (a|b)+: any a or b puts the graph in an accept state, but also allows a transition back to the begin state so we can do it again. But note that every time we match an a we also enter the right half of the graph, where we are in anti-accept states until we match "any" followed by a b.
This is not a traditional NFA because traditional NFAs don't have anti-accept states. However we can use the traditional NFA->DFA algorithm to convert this into a traditional DFA. The algorithm works like usual, where we simulate multiple runs of the NFA by making our DFA states correspond to subsets of the NFA states we could possibly be in. The one twist is that we slightly augment the rule for deciding if a DFA state is an accept (final) state or not. In the traditional algorithm a DFA state is an accept state if any of the NFA states was an accept state. We modify this to say that a DFA state is an accept state if and only if:
= 1 NFA states is an accept state, and
0 NFA states are anti-accept states.
This algorithm will give us a DFA that recognizes the regular expression with lookahead. Ergo, lookahead is regular. Note that lookbehind requires a separate proof.
I have a feeling that there are two distinct questions being asked here:
Are Regex engines that encorporate "lookaround" more
powerful than Regex engines that don't?
Does "lookaround"
empower a Regex engine with the ability to parse languages that are
more complex than those generated from a Chomsky Type 3 - Regular grammar?
The answer to the first question in a practical sense is yes. Lookaround will give a Regex engine that
uses this feature fundamentally more power than one that doesn't. This is because
it provides a richer set of "anchors" for the matching process.
Lookaround lets you define an entire Regex as a possible anchor point (zero width assertion). You can
get a pretty good overview of the power of this feature here.
Lookaround, although powerful, does not lift the Regex engine beyond the theoretical
limits placed on it by a Type 3 Grammar. For example, you will never be able to reliably
parse a language based on a Context Free - Type 2 Grammar using a Regex engine
equipped with lookaround. Regex engines are limited to the power of a Finite State Automation
and this fundamentally restricts the expressiveness of any language they can parse to the level of a Type 3 Grammar. No matter
how many "tricks" are added to your Regex engine, languages generated via a Context Free Grammar
will always remain beyond its capabilities. Parsing Context Free - Type 2 grammar requires pushdown automation to "remember" where it is in
a recursive language construct. Anything that requires a recursive evaluation of the grammar rules cannot be parsed using
Regex engines.
To summarize: Lookaround provides some practical benefits to Regex engines but does not "alter the game" on a
theoretical level.
EDIT
Is there some grammar with a complexity somewhere between Type 3 (Regular) and Type 2 (Context Free)?
I believe the answer is no. The reason is because there is no theoretical limit
placed on the size of the NFA/DFA needed to describe a Regular language. It may become arbitrarily large
and therefore impractical to use (or specify). This is where dodges such as "lookaround" are useful. They
provide a short-hand mechanism to specify what would otherwise lead to very large/complex NFA/DFA
specifications. They do not increase the expressiveness of
Regular languages, they just make specifying them more practical. Once you get this point, it becomes
clear that there are a lot of "features" that could be added to Regex engines to make them more
useful in a practical sense - but nothing will make them capable of going beyond the
limits of a Regular language.
The basic difference between a Regular and a Context Free language is that a Regular language
does not contain recursive elements. In order to evaluate a recursive language you need a
Push Down Automation
to "remember" where you are in the recursion. An NFA/DFA does not stack state information so cannot
handle the recursion. So given a non-recursive language definition there will be some NFA/DFA (but
not necessarily a practical Regex expression) to describe it.
I was asked this question in an interview for an internship, and the first solution I suggested was to try and use a regular expression (I usually am a little stumped in interviews). Something like this
(?P<str>[a-zA-Z]+)(?P<n>[0-9]+)
I thought it would match the strings and store them in the variable "str" and the numbers in the variable "n". How, I was not sure of.
So it matches strings of type "a1b2c3", but a problem here is that it also matches strings of type "a1b". Could anyone suggest a solution to deal with this problem?
Also, is there any other regular expression that could solve this problem?
Do you know why "regular expressions" are called "regular"? :-)
That would be too long to explain, I'll just outline the way. To match a pattern (i.e. decide whether a given string is "valid" or "invalid"), a theoretical informatician would use a finite state automaton. That's an abstract machine that has a finite number of states; each tick it reads a char from the input and jumps to another state. The pattern of where to jump from particular state when a particular character is read is fixed. Some states are marked as "OK", some--as "FAIL", so that by examining state of a machine you can check whether your text is "valid" (i.e. a valid e-mail).
For example, this machine only accepts "nice" as its "valid" word (a pic from Wikipedia):
A set of "valid" words such a machine theoretically can distinguish from invalid is called "regular language". Not every set is a regular language: for example, finite state automata are incapable of checking whether parentheses in string are balanced.
But constructing state machines was a complex task, compared to the complexity of defining what "valid" is. So the mathematicians (mainly S. Kleene) noted that every regular language could be described with a "regular expression". They had *s and |s and were the prototypes of what we know as regexps now.
What does it have to do with the problem? The problem in subject is essentially non-regular. It can't be expressed with anything that works like a finite automaton.
The essence is that it should contain a memory cell that is capable to hold an arbitrary number (repetition count in your case). Finite automata and classical regular expressions can not do this.
However, modern regexps are more expressive and are said to be able to check balanced parentheses! But this may serve as a good example that you shouldn't use regexps for tasks they don't suit. Let alone that it contains code snippets; this makes the expression far from being "regular".
Answering the initial question, you can't solve your problem with using anything "regular" only. However, regexps could be aid you in solving this problem, as in tster's answer
Perhaps, I should look closer to tster's answer (do a "+1" there, please!) and show why it's not the "regular expression" solution. One may think that it is, it just contains print statement (not essential) and a loop--and loop concept is compatible with finite state automaton expressive power. But there is one more elusive thing:
while ($line =~ s/^([a-z]+)(\d+)//i)
{
print $1
x # <--- this one
$2;
}
The task of reading a string and a number and printing repeatedly that string given number of times, where the number is an arbitrary integer, is undoable on a finite state machine without additional memory. You use a memory cell to keep that number and decrease it, and check for it to be greater than zero. But this number may be arbitrarily big, and it contradicts with a finite memory available to the finite state machine.
However, there's nothing wrong with classical pattern /([abc]*){5}/ that matches something "regular" repeated fixed number of times. We essentially have states that correspond to "matched pattern once", "matched pattern twice" ... "matched pattern 5 times". There's finite number of them, and that's the gist of the difference.
how about:
while ($line =~ s/^([a-z]+)(\d+)//i)
{
print $1 x $2;
}
Answering your question directly:
No, regular expressions match text and don't print anything, so there is no way to do it solely using regular expressions.
The regular expression you gave will match one string/number pair; you can then print that repeatedly using an appropriate mechanism. The Perl solution from #tster is about as compact as it gets. (It doesn't use the names that you applied in your regex; I'm pretty sure that doesn't matter.)
The remaining details depend on your implementation language.
Nope, this is your basic 'trick question' - no matter how you answer it that answer is wrong unless you have exactly the answer the interviewer was trained to parrot. See the workup of the issue given by Pavel Shved - note that all invocations have 'not' as a common condition, the tool just keeps sliding: Even when it changes state there is no counter in that state
I have a rather advanced book by Kenneth C Louden who is a college prof on the matter, in which it is stated that the issue at hand is codified as "Regex's can't count." The obvious answer to the question seems to me at the moment to be using the lookahead feature of Regex's ...
Probably depends on what build of what brand of regex the interviewer is using, which probably depends of flight-dynamics of Golf Balls.
Nice answers so far. Regular expressions alone are generally thought of as a way to match patterns, not generate output in the manner you mentioned.
Having said that, there is a way to use regex as part of the solution. #Jonathan Leffler made a good point in his comment to tster's reply: "... maybe you need a better regex library in your language."
Depending on your language of choice and the library available, it is possible to pull this off. Using C# and .NET, for example, this could be achieved via the Regex.Replace method. However, the solution is not 100% regex since it still relies on other classes and methods (StringBuilder, String.Join, and Enumerable.Repeat) as shown below:
string input = "aa67bc54c9";
string pattern = #"([a-z]+)(\d+)";
string result = Regex.Replace(input, pattern, m =>
// can be achieved using StringBuilder or String.Join/Enumerable.Repeat
// don't use both
//new StringBuilder().Insert(0, m.Groups[1].Value, Int32.Parse(m.Groups[2].Value)).ToString()
String.Join("", Enumerable.Repeat(m.Groups[1].Value, Int32.Parse(m.Groups[2].Value)).ToArray())
+ Environment.NewLine // comment out to prevent line breaks
);
Console.WriteLine(result);
A clearer solution would be to identify the matches, loop over them and insert them using the StringBuilder rather than rely on Regex.Replace. Other languages may have compact idioms to handle the string multiplication that doesn't rely on other library classes.
To answer the interview question, I would reply with, "it's possible, however the solution would not be a stand-alone 100% regex approach and would rely on other language features and/or libraries to handle the generation aspect of the question since the regex alone is helpful in matching patterns, not generating them."
And based on the other responses here you could beef up that answer further if needed.
I guess my question is best explained with an (simplified) example.
Regex 1:
^\d+_[a-z]+$
Regex 2:
^\d*$
Regex 1 will never match a string where regex 2 matches.
So let's say that regex 1 is orthogonal to regex 2.
As many people asked what I meant by orthogonal I'll try to clarify it:
Let S1 be the (infinite) set of strings where regex 1 matches.
S2 is the set of strings where regex 2 matches.
Regex 2 is orthogonal to regex 1 iff the intersection of S1 and S2 is empty.
The regex ^\d_a$ would be not orthogonal as the string '2_a' is in the set S1 and S2.
How can it be programmatically determined, if two regexes are orthogonal to each other?
Best case would be some library that implements a method like:
/**
* #return True if the regex is orthogonal (i.e. "intersection is empty"), False otherwise or Null if it can't be determined
*/
public Boolean isRegexOrthogonal(Pattern regex1, Pattern regex2);
By "Orthogonal" you mean "the intersection is the empty set" I take it?
I would construct the regular expression for the intersection, then convert to a regular grammar in normal form, and see if it's the empty language...
Then again, I'm a theorist...
I would construct the regular expression for the intersection, then convert to a regular grammar in normal form, and see if it's the empty language...
That seems like shooting sparrows with a cannon. Why not just construct the product automaton and check if an accept state is reachable from the initial state? That'll also give you a string in the intersection straight away without having to construct a regular expression first.
I would be a bit surprised to learn that there is a polynomial-time solution, and I would not be at all surprised to learn that it is equivalent to the halting problem.
I only know of a way to do it which involves creating a DFA from a regexp, which is exponential time (in the degenerate case). It's reducible to the halting problem, because everything is, but the halting problem is not reducible to it.
If the last, then you can use the fact that any RE can be translated into a finite state machine. Two finite state machines are equal if they have the same set of nodes, with the same arcs connecting those nodes.
So, given what I think you're using as a definition for orthogonal, if you translate your REs into FSMs and those FSMs are not equal, the REs are orthogonal.
That's not correct. You can have two DFAs (FSMs) that are non-isomorphic in the edge-labeled multigraph sense, but accept the same languages. Also, were that not the case, your test would check whether two regexps accepted non-identical, whereas OP wants non-overlapping languages (empty intersection).
Also, be aware that the \1, \2, ..., \9 construction is not regular: it can't be expressed in terms of concatenation, union and * (Kleene star). If you want to include back substitution, I don't know what the answer is. Also of interest is the fact that the corresponding problem for context-free languages is undecidable: there is no algorithm which takes two context-free grammars G1 and G2 and returns true iff L(G1) ∩ L(g2) ≠ Ø.
It's been two years since this question was posted, but I'm happy to say this can be determined now simply by calling the "genex" program here: https://github.com/audreyt/regex-genex
$ ./binaries/osx/genex '^\d+_[a-z]+$' '^\d*$'
$
The empty output means there is no strings that matches both regex. If they have any overlap, it will output the entire list of overlaps:
$ runghc Main.hs '\d' '[123abc]'
1.00000000 "2"
1.00000000 "3"
1.00000000 "1"
Hope this helps!
The fsmtools can do all kinds of operations on finite state machines, your only problem would be to convert the string representation of the regular expression into the format the fsmtools can work with. This is definitely possible for simple cases, but will be tricky in the presence of advanced features like look{ahead,behind}.
You might also have a look at OpenFst, although I've never used it. It supports intersection, though.
Excellent point on the \1, \2 bit... that's context free, and so not solvable. Minor point: Not EVERYTHING is reducible to Halt... Program Equivalence for example.. – Brian Postow
[I'm replying to a comment]
IIRC, a^n b^m a^n b^m is not context free, and so (a\*)(b\*)\1\2 isn't either since it's the same. ISTR { ww | w ∈ L } not being "nice" even if L is "nice", for nice being one of regular, context-free.
I modify my statement: everything in RE is reducible to the halting problem ;-)
I finally found exactly the library that I was looking for:
dk.brics.automaton
Usage:
/**
* #return true if the two regexes will never both match a given string
*/
public boolean isRegexOrthogonal( String regex1, String regex2 ) {
Automaton automaton1 = new RegExp(regex1).toAutomaton();
Automaton automaton2 = new RegExp(regex2).toAutomaton();
return automaton1.intersection(automaton2).isEmpty();
}
It should be noted that the implementation doesn't and can't support complex RegEx features like back references. See the blog post "A Faster Java Regex Package" which introduces dk.brics.automaton.
You can maybe use something like Regexp::Genex to generate test strings to match a specified regex and then use the test string on the 2nd regex to determine whether the 2 regexes are orthogonal.
Proving that one regular expression is orthogonal to another can be trivial in some cases, such as mutually exclusive character groups in the same locations. For any but the simplest regular expressions this is a nontrivial problem. For serious expressions, with groups and backreferences, I would go so far as to say that this may be impossible.
I believe kdgregory is correct you're using Orthogonal to mean Complement.
Is this correct?
Let me start by saying that I have no idea how to construct such an algorithm, nor am I aware of any library that implements it. However, I would not be at all surprised to learn that nonesuch exists for general regular expressions of arbitrary complexity.
Every regular expression defines a regular language of all the strings that can be generated by the expression, or if you prefer, of all the strings that are "matched by" the regular expression. Think of the language as a set of strings. In most cases, the set will be infinitely large. Your question asks whether the intersections of the two sets given by the regular expressions is empty or not.
At least to a first approximation, I can't imagine a way to answer that question without computing the sets, which for infinite sets will take longer than you have. I think there might be a way to compute a limited set and determine when a pattern is being elaborated beyond what is required by the other regex, but it would not be straightforward.
For example, just consider the simple expressions (ab)* and (aba)*b. What is the algorithm that will decide to generate abab from the first expression and then stop, without checking ababab, abababab, etc. because they will never work? You can't just generate strings and check until a match is found because that would never complete when the languages are disjoint. I can't imagine anything that would work in the general case, but then there are folks much better than me at this kind of thing.
All in all, this is a hard problem. I would be a bit surprised to learn that there is a polynomial-time solution, and I would not be at all surprised to learn that it is equivalent to the halting problem. Although, given that regular expressions are not Turing complete, it seems at least possible that a solution exists.
I would do the following:
convert each regex to a FSA, using something like the following structure:
struct FSANode
{
bool accept;
Map<char, FSANode> links;
}
List<FSANode> nodes;
FSANode start;
Note that this isn't trivial, but for simple regex shouldn't be that difficult.
Make a new Combined Node like:
class CombinedNode
{
CombinedNode(FSANode left, FSANode right)
{
this.left = left;
this.right = right;
}
Map<char, CombinedNode> links;
bool valid { get { return !left.accept || !right.accept; } }
public FSANode left;
public FSANode right;
}
Build up links based on following the same char on the left and right sides, and you get two FSANodes which make a new CombinedNode.
Then start at CombinedNode(leftStart, rightStart), and find the spanning set, and if there are any non-valid CombinedNodes, the set isn't "orthogonal."
Convert each regular expression into a DFA. From the accept state of one DFA create an epsilon transition to the start state of the second DFA. You will in effect have created an NFA by adding the epsilon transition. Then convert the NFA into a DFA. If the start state is not the accept state, and the accept state is reachable, then the two regular expressions are not "orthogonal." (Since their intersection is non-empty.)
There are know procedures for converting a regular expression to a DFA, and converting an NFA to a DFA. You could look at a book like "Introduction to the Theory of Computation" by Sipser for the procedures, or just search around the web. No doubt many undergrads and grads had to do this for one "theory" class or another.
I spoke too soon. What I said in my original post would not work out, but there is a procedure for what you are trying to do if you can convert your regular expressions into DFA form.
You can find the procedure in the book I mentioned in my first post: "Introduction to the Theory of Computation" 2nd edition by Sipser. It's on page 46, with details in the footnote.
The procedure would give you a new DFA that is the intersection of the two DFAs. If the new DFA had a reachable accept state then the intersection is non-empty.
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....