Got an interesting one, and can't come up with any solid ideas, so thought maybe someone else may have done something similar.
I want to be able to identify strings of letters in a longer sentence that are not words and remove them. Essentially things like kuashdixbkjshakd
Everything annoyingly is in lowercase which makes it more difficult, but since I only care about English, I'm essentially looking for the opposite of consonant clusters, groups of them that don't make phonetically pronounceable sounds.
Has anyone heard of/done something like this before?
EDIT: this is what ChatGpt tells me
It is difficult to provide a comprehensive list of combinations of consonants that have never appeared in a word in the English language. The English language is a dynamic and evolving language, and new words are being created all the time. Additionally, there are many regional and dialectal variations of the language, which can result in different sets of words being used in different parts of the world.
It is also worth noting that the frequency of use of a particular combination of consonants in the English language is difficult to quantify, as the existing literature on the subject is limited. The best way to determine the frequency of use of a particular combination of consonants would be to analyze a large corpus of written or spoken English.
In general, most combinations of consonants are used in some words in the English language, but some combinations of consonants may be relatively rare. Some examples of relatively rare combinations of consonants in English include "xh", "xw", "ckq", and "cqu". However, it is still possible that some words with these combinations of consonants exist.
You could try to pass every single word inside the sentence to a function that checks wether the word is listed inside a dictionary. There is a good number of dictionary text files on GitHub. To speed up the process: use a hash map :)
You could also use an auto-corretion API or a library.
Algorithm to combine both methods:
Run sentence through auto correction
Run every word through dictionary
Delete words that aren't listed in the dictionary
This could remove typos and words that are non-existent.
You could train a simple model on sequences of characters which are permitted in the language(s) you want to support, and then flag any which contain sequences which are not in the training data.
The LangId language detector in SpamAssassin implements the Cavnar & Trenkle language-identification algorithm which basically uses a sliding window over the text and examines the adjacent 1 to 5 characters at each position. So from the training data "abracadabra" you would get
a 5
ab 2
abr 2
abra 2
abrac 1
b 2
br 2
bra 2
brac 1
braca 1
:
With enough data, you could build a model which identifies unusual patterns (my suggestion would be to try a window size of 3 or smaller for a start, and train it on several human languages from, say, Wikipedia) but it's hard to predict how precise exactly this will be.
SpamAssassin is written in Perl and it should not be hard to extract the language identification module.
As an alternative, there is a library called libtextcat which you can run standalone from C code if you like. The language identification in LibreOffice uses a fork which they adapted to use Unicode specifically, I believe (though it's been a while since I last looked at that).
Following Cavnar & Trenkle, all of these truncate the collected data to a few hundred patterns; you would probably want to extend this to cover up to all the 3-grams you find in your training data at least.
Perhaps see also Gertjan van Noord's link collection: https://www.let.rug.nl/vannoord/TextCat/
Depending on your test data, you could still get false positives e.g. on peculiar Internet domain names and long abbreviations. Tweak the limits for what you want to flag - I would think that GmbH should be okay even if you didn't train on German, but something like 7 or more letters long should probably be flagged and manually inspected.
This will match words with more than 5 consonants (you probably want "y" to not be considered a consonant, but it's up to you):
\b[a-z]*[b-z&&[^aeiouy]]{6}[a-z]*\b
See live demo.
5 was chosen because I believe witchcraft has the longest chain of consonants of any English word. You could dial back "6" in the regex to say 5 or even 4 if you don't mind matching some outliers.
My question is a continuation of this one. Basically, I have a table of words like so:
HAT18178_890909.098070313.1
HAT18178_890909.098070313.2
HAT18178_890909.143412462.1
HAT18178_890909.143412462.2
For my purposes, I do not need the terminal .1 or .2 for this set of names. I can manually write the following regex (using Python syntax):
r = re.compile('(.*\.\d+)\.\d+')
However, I cannot guarantee that my next set of names will have a similar structure where the final 2 characters will be discardable - it could be 3 characters (i.e. .12) and the separator could change as well (i.e. . to _).
What is the appropriate way to either explicitly learn a regex or to determine which characters are unnecessary?
It's an interesting problem.
X y
HAT18178_890909.098070313.1 HAT18178_890909.098070313
HAT18178_890909.098070313.2 HAT18178_890909.098070313
HAT18178_890909.143412462.1 HAT18178_890909.143412462
HAT18178_890909.143412462.2 HAT18178_890909.143412462
The problem is that there is not a single solution but many.
Even for a human it is not clear what the regex should be that you want.
Based on this data, I would think the possibilities to learn are:
Just match a fixed width of 25: .{25}
Fixed first part: HAT18178_890909.
Then:
There's only 2 varying numbers on each single spot (as you show 2 cases).
So e.g. [01] (either 0 or 1), [94] the next spot and so on would be a good solution.
The obvious one would be \d+
But it could also be \d{9}
You see, there are multiple correct answers.
These regexes would still work if the second point would be an underscore instead.
My conclusion:
The problem is that it is much more work to prepare the data for machine learning than it is to create a regex. If you want to be sure you cover everything, you need to have complete data, so then a regex is probably less effort.
You could split on non-alphanumeric characters;
[^a-zA-Z0-9']+
That would get you, in this case, few strings like this:
HAT18178
890909
098070313
1
From there on you can simply discard the last one if that's never necessary, and continue on processing the first sequences
So I'm working on some cleanup in haxeflixel, and I need to validate a csv map, so I'm using a regex to check if its ok (don't mention the ending commas, I know thats not valid csv but I want to allow it), and I think I have a decent regex for doing that, and it seems to work well on flash, but c++ crashes, and neko gives me this error: An error occured while running pcre_exec....
here is my regex, I'm sorry its long, but I have no idea where the problem is...
^(([ ]*-?[0-9]+[ ]*,?)+\r?\n?)+$
if anyone knows what might be going on I'd appreciate it,
Thanks,
Nico
ps. there are probably errors in my regex for checking csv, but I can figure those out, its kind of enjoyable, I'd rather just know what specifically could be causing this:)
edit: ah, I've just noticed this doesn't happen on all strings, once I narrow it down to what strings, I will post one... as for what I'm checking for, its basically just to make sure theres no weird xml header, or any non integer value in the map file, basically it should validate this:
1,1,1,1
1,1,1,1
1,1,1,1
or this:
1,1,1,1,
1,1,1,1,
1,1,1,1,
but not:
xml blahh blahh>
1,m,1,1
1,1,b,1
1,1,1,1
xml>
(and yes I know thats not valid xml;))
edit: it gets stranger:
so I'm trying to determine what strings crash it, and while this still wouldnt explain a normal map crashing, its definatly weird, and has the same result:
what happens is:
this will fail a .match() test, but not crash:
a
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
while this will crash the program:
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
1,*a*,1,1,1,1,1,1,1,1,1,1,1,1,1
To be honest, you wrote one of the worst regexps I ever seen. It actually looks like it was written specifically to be as slow as possible. I write it not to offend you, but to express how much you need to learn to write regexps(hint: writing your own regexp engine is a good exercise).
Going to your problem, I guess it just runs out of memory(it is extremely memory intensive). I am not sure why it happens only on pcre targets(both neko and cpp targets use pcre), but I guess it is about memory limits per regexp run in pcre or some heuristics in other targets to correct such miswritten regexps.
I'd suggest something along the lines of
~/^(( *-?[0-9]+ *,)* *-?[0-9]+ *,?\r?\n)*(( *-?[0-9]+ *,)* *-?[0-9]+ *,?\r?\n?)$/
There, "~/" and last "/" are haxe regexp markers.
I wasnt extensively testing it, just a run on your samples, but it should do the job(probably with a bit of tweaking).
Also, just as a hint, I'd suggest you to split file into lines first before running any regexps, it will lower memmory usage(or you will need to hold only a part of your text in memory) and simplify your regexp.
I'd also note that since you will need to parse csv anyhow(for any properly formed input, which are prevailing in your data I guess), it might be much faster to do all the tests while actually parsing.
Edit: the answer to question "why it eats so much memory"
Well, it is not a short topic, and that's why I proposed to you to write your own regexp engine. There are some differences in implementations, but generally imagine regexp engine works like that:
parses your regular expression and builds a graph of all possible states(state is basically a symbol value and a number of links to other symbols which can follow it).
sets up a list of read pointer and state pointer pairs, current state list, consisting of regexp initial state and a pointer to matched string first letter
sets up read pointer to the first symbol of symbol string
sets up state poiter to initial state of regexp
takes up one pair from current state list and stores it as current state and current read pointer
reads symbol under current read pointer
matches it with symbols in states which current state have links to, and makes a list of states that matched.
if there is a final regexp state in this list, goes to 12
for each item in this list adds a pair of next read pointer(which is current+1) and item to the current state list
if the current state list is empty, returns false, as string didn't match the regexp
goes to 6
here it is, in a final state of matched regexp, returns true, string matches regexp.
Of course, there are some differences between regexp engines, and some of them eliminate some problems afaik. And of course they also have pseudosymbols, groupings, they need to store the positions regexp and groups matched, they have lookahead and lookbehind and also grouping references which makes it a bit(quite a humble measure) more complex and forces to use a bit more complex data structures, but the main idea is the same. So, here we are and your problem is clearly seen from algorithm. The less specific you are about what you want to match and the more there chances for engine to match the same substring as different paths in state graph, the more memory and processor time it will consume, exponentionally.
Try to model how regexp engine matches regexp (a+a+)+b on strings aaaaaab, ab, aa, aaaaaaaaaa, aaaaaaaaaaaaaaaaaaaaaaaaaaaaaa (Don't try the last one, it would take hours or days to compute on a modern PC.)
Also, it worth to note that some regexp engines do things in a bit different way so they can handle this situations properly, but there always are ways to make regexp extremely slow.
And another thing to note is that I may hav ebeen wrong about the exact memory problem. This case it may be processor too, and before that it may be engine limits on memory/processor kicking in, not exactly system starving of memory.
I have some SQLCLR code for working with Regular Expresions. But now that it is getting migrated into Azure, which does not allow SQLCLR, that's out. I need to find a way to do regex in pure T-SQL.
Master Data Services are not available because the dev edition of MSSQL we have is not R2.
All ideas appreciated, thanks.
Regular expression match samples that need handling
(culled from regexlib and other places over the past few years)
email address
^[\w-]+(\.[\w-]+)*#([a-z0-9-]+(\.[a-z0-9-]+)*?\.[a-z]{2,6}|(\d{1,3}\.){3}\d{1,3})(:\d{4})?$
dollars
^(\$)?(([1-9]\d{0,2}(\,\d{3})*)|([1-9]\d*)|(0))(\.\d{2})?$
uri
^(http|https|ftp)\://([a-zA-Z0-9\.\-]+(\:[a-zA-Z0-9\.&%\$\-]+)*#)*((25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9])\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9]|0)\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[1-9]|0)\.(25[0-5]|2[0-4][0-9]|[0-1]{1}[0-9]{2}|[1-9]{1}[0-9]{1}|[0-9])|localhost|([a-zA-Z0-9\-]+\.)*[a-zA-Z0-9\-]+\.(com|edu|gov|int|mil|net|org|biz|arpa|info|name|pro|aero|coop|museum|[a-zA-Z]{2}))(\:[0-9]+)*(/($|[a-zA-Z0-9\.\,\?\'\\\+&%\$#\=~_\-]+))*$
one numeric digit
^\d$
percentage
^-?[0-9]{0,2}(\.[0-9]{1,2})?$|^-?(100)(\.[0]{1,2})?$
height notation
^\d?\d'(\d|1[01])"$
numbers between 1 1000
^([1-9]|[1-9]\d|1000)$
credit card numbers
^((4\d{3})|(5[1-5]\d{2})|(6011))-?\d{4}-?\d{4}-?\d{4}|3[4,7]\d{13}$
list of years
^([1-9]{1}[0-9]{3}[,]?)*([1-9]{1}[0-9]{3})$
days of the week
^(Sun|Mon|(T(ues|hurs))|Fri)(day|\.)?$|Wed(\.|nesday)?$|Sat(\.|urday)?$|T((ue?)|(hu?r?))\.?$
time on 12 hour clock
(?<Time>^(?:0?[1-9]:[0-5]|1(?=[012])\d:[0-5])\d(?:[ap]m)?)
time on 24 hour clock
^(?:(?:(?:0?[13578]|1[02])(\/|-|\.)31)\1|(?:(?:0?[13-9]|1[0-2])(\/|-|\.)(?:29|30)\2))(?:(?:1[6-9]|[2-9]\d)?\d{2})$|^(?:0?2(\/|-|\.)29\3(?:(?:(?:1[6-9]|[2-9]\d)?(?:0[48]|[2468][048]|[13579][26])|(?:(?:16|[2468][048]|[3579][26])00))))$|^(?:(?:0?[1-9])|(?:1[0-2]))(\/|-|\.)(?:0?[1-9]|1\d|2[0-8])\4(?:(?:1[6-9]|[2-9]\d)?\d{2})$
usa phone numbers
^\(?[\d]{3}\)?[\s-]?[\d]{3}[\s-]?[\d]{4}$
Unfortunately, you will not be able to move your CLR function(s) to SQL Azure. You will need to either use the normal string functions (PATINDEX, CHARINDEX, LIKE, and so on) or perform these operations outside of the database.
EDIT Adding some information for the examples added to the question.
Email address
This one is always controversial because people disagree about which version of the RFC they want to support. The original didn't support apostrophes, for example (or at least people insist that it didn't - I haven't dug it up from the archives and read it myself, admittedly), and it has to be expanded quite often for new TLDs (once for 4-letter TLDs like .info, then again for 6-letter TLDs like .museum). I've often heard quite knowledgeable people state that perfect e-mail validation is impossible, and having previously worked for an e-mail service provider, I can tell you that it was a constantly moving target. But for the simplest approaches, see the question TSQL Email Validation (without regex).
One numeric digit
Probably the easiest one of the bunch:
WHERE #s LIKE '[0-9]';
Credit card numbers
Assuming you strip out dashes and spaces, which you should do in any case. Note that this isn't an actual check of the credit card number algorithm to ensure that the number itself is actually valid, just that it conforms to the general format (AmEx = 15 digits starting with a 3, the rest are 16 digits - Visa starts with a 4, MasterCard starts with a 5, Discover starts with 6 and I think there's one that starts with a 7 (though that may just be gift cards of some kind)):
WHERE #s + ' ' LIKE '[3-7]'+ REPLICATE('[0-9]', 14) + '[0-9 ]';
If you want to be a little more precise at the cost of being long-winded, you can say:
WHERE (LEN(#s) = 15 AND #s LIKE '3' + REPLICATE('[0-9]', 14))
OR (LEN(#s) = 16 AND #s LIKE '[4-7]' + REPLICATE('[0-9]', 15));
USA phone numbers
Again, assuming you're going to strip out parentheses, dashes and spaces first. Pretty sure a US area code can't start with a 1; if there are other rules, I am not aware of them.
WHERE #s LIKE '[2-9]' + REPLICATE('[0-9]', 9);
-----
I'm not going to go further, because a lot of the other expressions you've defined can be extrapolated from the above. Hopefully this gives you a start. You should be able to Google for some of the others to see how other people have replicated the patterns with T-SQL. Some of them (like days of the week) can probably just be checked against a table - seems overkill to do an invasie pattern matching for a set of 7 possible values. Similarly with a list of 1000 numbers or years, these are things that will be much easier (and probably more efficient) to check if the numeric value is in a table rather than convert it to a string and see if it matches some pattern.
I'll state again that a lot of this will be much better if you can cleanse and validate the data before it gets into the database in the first place. You should strive to do this wherever possible, because without CLR, you just can't do powerful RegEx inside SQL Server.
Ken Henderson wrote about ways to replicate RegEx without CLR, but they require sp_OA* procedures, which are even less likely to ever see the light of day in Azure than CLR. Most of the other articles you'll find online use an approach similar to Ken's or use complex use of built-in string functions.
Which portions of RegEx specifically are you trying to replicate? Can you show an example of the input/output of one of your functions? Perhaps it will be easy to convert to get similar results using the built-in string functions like PATINDEX.
Intro
I work in a facility where we have microscopes. These guys can be asked to generate 4D movies of a sample: they take e.g. 10 pictures at different Z position, then wait a certain amount of time (next timepoint) and take 10 slices again.
They can be asked to save a file for each slice, and they use an explicit naming pattern, something like 2009-11-03-experiment1-Z07-T42.tif. The file names are numbered to reflect the Z position and the time point
Question
Once you have all these file names, you can use a regex pattern to extract the Z and T value, if you know the backbone pattern of the file name. This I know how to do.
The question I have is: do you know a way to automatically generate regex pattern from the file name list? For instance, there is an awesome tool on the net that does similar thing: txt2re.
What algorithm would you use to parse all the file name list and generate a most likely regex pattern?
There is a Perl module called String::Diff which has the ability to generate a regular expression for two different strings. The example it gives is
my $diff = String::Diff::diff_regexp('this is Perl', 'this is Ruby');
print "$diff\n";
outputs:
this\ is\ (?:Perl|Ruby)
Maybe you could feed pairs of filenames into this kind of thing to get an initial regex. However, this wouldn't give you capturing of numbers etc. so it wouldn't be completely automatic. After getting the diff you would have to hand-edit or do some kind of substitution to get a working final regex.
First of all, you are trying to do this the hard way. I suspect that this may not be impossible but you would have to apply some artificial intelligence techniques and it would be far more complicated than it is worth. Either neural networks or a genetic algorithm system could be trained to recognize the Z numbers and T numbers, assuming that the format of Z[0-9]+ and T[0-9]+ is always used somewhere in the regex.
What I would do with this problem is to write a Python script to process all of the filenames. In this script, I would match twice against the filename, one time looking for Z[0-9]+ and one time looking for T[0-9]+. Each time I would count the matches for Z-numbers and T-numbers.
I would keep four other counters with running totals, two for Z-numbers and two for T-numbers. Each pair would represent the count of filenames with 1 match, and the ones with multiple matches. And I would count the total number of filenames processed.
At the end, I would report as follows:
nnnnnnnnnn filenames processed
Z-numbers matched only once in nnnnnnnnnn filenames.
Z-numbers matched multiple times in nnnnnn filenames.
T-numbers matched only once in nnnnnnnnnn filenames.
T-numbers matched multiple times in nnnnnn filenames.
If you are lucky, there will be no multiple matches at all, and you could use the regexes above to extract your numbers. However, if there are any significant number of multiple matches, you can run the script again with some print statements to show you example filenames that provoke a multiple match. This would tell you whether or not a simple adjustment to the regex might work.
For instance, if you have 23,768 multiple matches on T-numbers, then make the script print every 500th filename with multiple matches, which would give you 47 samples to examine.
Probably something like [ -/.=]T[0-9]+[ -/.=] would be enough to get the multiple matches down to zero, while also giving a one-time match for every filename. Or at worst, [0-9][ -/.=]T[0-9]+[ -/.=]
For Python, see this question about TemplateMaker.