Given two strings str1 and str2 I have a list of matches describing shared substrings as intervals in the form of [str1_beg, str1_end, str2_beg, str2_end]. I want to remove redundant matches where str1_beg, str1_end and str2_beg, str2_end from a match are embedded in some other match.
For each [beg_index, end_index] find [beg_index_new, end_index_new] and remove the ones that satisfy end_index < end_index_new and beg_index >= beg_index_new.
And that's O(n^2)
first of all, you can store your matches more efficiently.
[str_beg,str2_beg,match_len]
This will also make it very easy to check for redundancy, for example
for match in matches:
for i in xrange(len(matches)):
if matches[i][:2] == match[:2] and mathches[i][2] < match[2]:
del matches[i]
I'm assuming your list of matches is assigned to a variable called matches, and has the structure I proposed above, so the ma. I'm using the < operator and not the <= operator, because in the case they are equal, they're the exact same match, and I'm assuming you won't have the same match twice.
Where I'm checking both matche's [:2] slice, I'm chec king the first 2 elements of their lists, which are the starting positions.
Related
I'm trying to find exact matches of strings in Lua including, special characters. I want the example below to return that it is an exact match, but because of the - character it returns nil
index = string.find("test-string", "test-string")
returns nil
index = string.find("test-string", "test-")
returns 1
index = string.find("test-string", "test")
also returns 1
How can I get it to do full matching?
- is a pattern operator in a Lua string pattern, so when you say test-string, you're telling find() to match the string test as few times as possible. So what happens is it looks at test-string, sees test in there, and since - isn't an actual minus sign in this case, it's really looking for teststring.
Do as Mike has said and escape it with the % character.
I found this helpful for better understanding patterns.
You can also ask for a plain substring match that ignores magic characters:
string.find("test-string", "test-string",1,true)
you need to escape special characters in the pattern with the % character.
so in this case you are looking for
local index = string.find('test-string', 'test%-string')
Given a source string s and n equal length strings, I need to find a quick algorithm to return those strings that have at most k characters that are different from the source string s at each corresponding position.
What is a fast algorithm to do so?
PS: I have to claim that this is a academic question. I want to find the most efficient algorithm if possible.
Also I missed one very important piece of information. The n equal length strings form a dictionary, against which many source strings s will be queried upon. There seems to be some sort of preprocessing step to make it more efficient.
My gut instinct is just to iterate over each String n, maintaining a counter of how many characters are different than s, but I'm not claiming it is the most efficient solution. However it would be O(n) so unless this is a known performance problem, or an academic question, I'd go with that.
Sedgewick in his book "Algorithms" writes that Ternary Search Tree allows "to locate all words within a given Hamming distance of a query word". Article in Dr. Dobb's
Given that the strings are fixed length, you can compute the Hamming distance between two strings to determine the similarity; this is O(n) on the length of the string. So, worst case is that your algorithm is O(nm) for comparing your string against m words.
As an alternative, a fast solution that's also a memory hog is to preprocess your dictionary into a map; keys are a tuple (p, c) where p is the position in the string and c is the character in the string at that position, values are the strings that have characters at that position (so "the" will be in the map at {(0, 't'), "the"}, {(1, 'h'), "the"}, {(2, 'e'), "the"}). To query the map, iterate through query string's characters and construct a result map with the retrieved strings; keys are strings, values are the number of times the strings have been retrieved from the primary map (so with the query string "the", the key "thx" will have a value of 2, and the key "tee" will have a value of 1). Finally, iterate through the result map and discard strings whose values are less than K.
You can save memory by discarding keys that can't possibly equal K when the result map has been completed. For example, if K is 5 and N is 8, then when you've reached the 4th-8th characters of the query string you can discard any retrieved strings that aren't already in the result map since they can't possibly have 5 matching characters. Or, when you've finished with the 6th character of the query string, you can iterate through the result map and remove all keys whose values are less than 3.
If need be you can offload the primary precomputed map to a NoSql key-value database or something along those lines in order to save on main memory (and also so that you don't have to precompute the dictionary every time the program restarts).
Rather than storing a tuple (p, c) as the key in the primary map, you can instead concatenate the position and character into a string (so (5, 't') becomes "5t", and (12, 'x') becomes "12x").
Without knowing where in each input string the match characters will be, for a particular string, you might need to check every character no matter what order you check them in. Therefore it makes sense to just iterate over each string character-by-character and keep a sum of the total number of mismatches. If i is the number of mismatches so far, return false when i == k and true when there are fewer than k-i unchecked characters remaining in the string.
Note that depending on how long the strings are and how many mismatches you'll allow, it might be faster to iterate over the whole string rather than performing these checks, or perhaps to perform them only after every couple characters. Play around with it to see how you get the fastest performance.
My method if we're thinking out loud :P I can't see a way to do this without going through each n string, but I'm happy to be corrected. On that it would begin with a pre-process to save a second set of your n strings so that the characters are in ascending order.
The first part of the comparison would then be to check each n string a character at a time say n' to each character in s say s'.
If s' is less than n' then not equal and move to the next s'. If n' is less than s' then go to next n'. Otherwise record a matching character. Repeat this until k miss matches are found or the alternate matches are found and mark n accordingly.
For further consideration, an added pre-processing could be done on each adjacent string in n to see the total number of characters that differ. This could then be used when comparing strings n to s and if sufficient difference exist between these and the adjacent n there may not be a need to compare it?
Given n strings S1, S2, ..., Sn, and an alphabet set A={a_1,a_2,....,a_m}. Assume that the alphabets in each string are all distinct. Now I want to create an inverted-index for each a_i (i=1,2...,m). My inverted-index has also something special: The alphabets in A are in some sequential order, if in the inverted-index a_i has included one string (say S_2), then a_j (j=i+1,i+2,...,m) don't need to include S_2 any more. In short, every string just appears in the inverted list only once. My question is how to build such list in a fast and efficient way? Any time complexity is bounded?
For example, A={a,b,e,g}, S1={abg}, S2={bg}, S3={gae}, S4={g}. Then my inverted-list should be:
a: S1,S3
b: S2 (since S1 has appeared previously, so we don't need to include it here)
e:
g: S4
If I understand your question correctly, a straightforward solution is:
for each string in n strings
find the "smallest" character in the string
put the string in the list for the character
The complexity is proportional to the total length of the strings, multiplying by a constant for the order testing.
If there is a simple way for testing, (e.g. the characters are in alphabetical order and all lower-case, a < will be enough), simply compare them; otherwise, I suggest using a hash table, each pair of which is a character and its order, later simply compare them.
I would like to implement a function with R that removes repeated characters in a string. For instance, say my function is named removeRS, so it is supposed to work this way:
removeRS('Buenaaaaaaaaa Suerrrrte')
Buena Suerte
removeRS('Hoy estoy tristeeeeeee')
Hoy estoy triste
My function is going to be used with strings written in spanish, so it is not that common (or at least correct) to find words that have more than three successive vowels. No bother about the possible sentiment behind them. Nonetheless, there are words that can have two successive consonants (especially ll and rr), but we could skip this from our function.
So, to sum up, this function should replace the letters that appear at least three times in a row with just that letter. In one of the examples above, aaaaaaaaa is replaced with a.
Could you give me any hints to carry out this task with R?
I did not think very carefully on this, but this is my quick solution using references in regular expressions:
gsub('([[:alpha:]])\\1+', '\\1', 'Buenaaaaaaaaa Suerrrrte')
# [1] "Buena Suerte"
() captures a letter first, \\1 refers to that letter, + means to match it once or more; put all these pieces together, we can match a letter two or more times.
To include other characters besides alphanumerics, replace [[:alpha:]] with a regex matching whatever you wish to include.
I think you should pay attention to the ambiguities in your problem description. This is a first stab, but it clearly does not work with "Good Luck" in the manner you desire:
removeRS <- function(str) paste(rle(strsplit(str, "")[[1]])$values, collapse="")
removeRS('Buenaaaaaaaaa Suerrrrte')
#[1] "Buena Suerte"
Since you want to replace letters that appear AT LEAST 3 times, here is my solution:
gsub("([[:alpha:]])\\1{2,}", "\\1", "Buennaaaa Suerrrtee")
#[1] "Buenna Suertee"
As you can see the 4 "a" have been reduced to only 1 a, the 3 r have been reduced to 1 r but the 2 n and the 2 e have not been changed.
As suggested above you can replace the [[:alpha:]] by any combination of [a-zA-KM-Z] or similar, and even use the "or" operator | inside the squre brackets [y|Q] if you want your code to affect only repetitions of y and Q.
gsub("([a|e])\\1{2,}", "\\1", "Buennaaaa Suerrrtee")
# [1] "Buenna Suerrrtee"
# triple r are not affected and there are no triple e.
Is there a function for counting the number of times a particular keyword is contained in a dataset?
For example, if dataset <- c("corn", "cornmeal", "corn on the cob", "meal") the count would be 3.
Let's for the moment assume you wanted the number of element containing "corn":
length(grep("corn", dataset))
[1] 3
After you get the basics of R down better you may want to look at the "tm" package.
EDIT: I realize that this time around you wanted any-"corn" but in the future you might want to get word-"corn". Over on r-help Bill Dunlap pointed out a more compact grep pattern for gathering whole words:
grep("\\<corn\\>", dataset)
Another quite convenient and intuitive way to do it is to use the str_count function of the stringr package:
library(stringr)
dataset <- c("corn", "cornmeal", "corn on the cob", "meal")
# for mere occurences of the pattern:
str_count(dataset, "corn")
# [1] 1 1 1 0
# for occurences of the word alone:
str_count(dataset, "\\bcorn\\b")
# [1] 1 0 1 0
# summing it up
sum(str_count(dataset, "corn"))
# [1] 3
You can also do something like the following:
length(dataset[which(dataset=="corn")])
I'd just do it with string division like:
library(roperators)
dataset <- c("corn", "cornmeal", "corn on the cob", "meal")
# for each vector element:
dataset %s/% 'corn'
# for everything:
sum(dataset %s/% 'corn')
You can use the str_count function from the stringr package to get the number of keywords that match a given character vector.
The pattern argument of the str_count function accepts a regular expression that can be used to specify the keyword.
The regular expression syntax is very flexible and allows matching whole words as well as character patterns.
For example the following code will count all occurrences of the string "corn" and will return 3:
sum(str_count(dataset, regex("corn")))
To match complete words use:
sum(str_count(dataset, regex("\\bcorn\\b")))
The "\b" is used to specify a word boundary. When using str_count function, the default definition of word boundary includes apostrophe. So if your dataset contains the string "corn's", it would be matched and included in the result.
This is because apostrophe is considered as a word boundary by default. To prevent words containing apostrophe from being counted, use the regex function with parameter uword = T. This will cause the regular expression engine to use the unicode TR 29 definition of word boundaries. See http://unicode.org/reports/tr29/tr29-4.html. This definition does not consider apostrophe as a word boundary.
The following code will give the number of time the word "corn" occurs. Words such as "corn's" will not be included.
sum(str_count(dataset, regex("\\bcorn\\b", uword = T)))