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Given a regular expression R that describes a regular language (no fancy backreferences). Is there an algorithmic way to construct a regular expression R* that describes the language of all words except those described by R? It should be possible as Wikipedia says:
The regular languages are closed under the various operations, that is, if the languages K and L are regular, so is the result of the following operations: […] the complement ¬L
For example, given the alphabet {a,b,c}, the inverse of the language (abc*)+ is (a|(ac|b|c).*)?
As DPenner has already pointed out in the comments, the inverse of a regular expresion can be exponentially larger than the original expression. This makes inversing regular expressions unsuitable to implement negative partial expression syntax for searching purposes. Is there an algorithm that preserves the O(n*m) runtime characteristic (where n is the size of the regex and m is the length of the input) of regular expression matching and allows for negated subexpressions?
Unfortunately, the answer given by nhahdtdh in the comments is as good as we can do (so far). Whether a given regular expression generates all strings is PSPACE-complete. Since all problems in NP are in PSPACE-complete, an efficient solution to the universality problem would imply that P=NP.
If there were an efficient solution to your problem, would you be able to resolve the universality problem? Sure you would.
Use your efficient algorithm to generate a regular expression for the negation;
Determine whether the resulting regular expression generates the empty set.
Note that the problem "given a regular expression, does it generate the empty set" is fairly straightforward:
The regular expression {} generates the empty set.
(r + s) generates the empty set iff both r and s generate the empty set.
(rs) generates the empty set iff either r or s generates the empty set.
Nothing else generates the empty set.
Basically, it's pretty easy to tell whether a regular expression generates the empty set: just start evaluating the regular expression.
(Note that while the above procedure is efficient in terms of the output length, it might not be efficient in terms of the input length, if the output length is more than polynomially faster than the input length. However, if that were the case, we'd have the same result anyway, i.e., that your algorithm isn't really efficient, since it would take exponentially many steps to generate an exponentially longer output from a given input).
Wikipedia says: ... if there exists at least one regex that matches a particular set then there exist an infinite number of such expressions. We can deduct from this statement that there is an infinite number of expressions that describe the language of all words except those described by R.
Again, (as also #nhahtdh tried to explain) the simplest algorithm to address this question is to extend the scope of evaluation outside the context of the regular expression language itself. That is: match the strings you want to exclude (which represent a finite subset to work with) by using the original regular expression and then treat any failure to match as an actual match (out of an infinite set of other possibilities). So, if the result of the match is negative, your candidate strings are a subset of the valid solutions.
I'm looking for a way to find the smallest possible regular-expression that accepts a sequence.
To make it interesting I don't want any stars(Kleene stars) and preferably no wildcards?
For instance the sequence : 'aaaaaaaa' would be accepted by 'a^8' and a^8 would be the shortest possible expression to accept the sequence.
Does anyone body know how to generate such an expression?
The search space for what you are after will most likely grow exponentially as the string grows, since there is usually a large amount of regular patterns that can match a given string.
I think that in your case you could try using some search heuristic to try and approximate or even manage to find the optimal solution. I do not think that there is a straightforward solution for that (albeit that is just my opinion).
Given that regular expressions and deterministic finite automata are equivalent, you can minimise a given regular expression using any of the algorithms for the minimisation of DFAs. You would of course still need to come up with a regular expression to start with, but if you only need it to accept one string, then the characters of that string are the states. You can then minimise that DFA and convert it to a regular expression.
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.
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....
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.