Recently, I was asked the following problem during an interview.
Given a string S, I need to find another string S2 such that S2 is a subsequence of S and also S is a subsequence of S2+reverse(S2). Here '+' means concatenation. I need to output the min possible length of S2 for given S.
I was told that this is a dynamic programming problem however I was unable to solve it. Can somebody help me with this problem?
EDIT-
Is there a way to do this in O(N2) or less.
There are 2 important aspects in this problem.
Since we need S as a substring of S2+reverse(S2), S2 should have
atleast n/2 length.
After concatenation of S2 and reverse(S2), there is a pattern where
the alphabets repeats such as
So the solution is to check from the center of S to end of S for any consecutive elements. If you find one then check the elements on either side as shown.
Now if you are able to reach till the end of the string, then the minimum number of elements (result) is the distance from start to the point where you find consecutive elements. In this example its C i.e 3.
We know that this may not happen always. i.e you may not be able to find consecutive elements at the center. Let us say the consecutive elements are after the center then we can do the same test.
Main string
Substring
Concatenated string
Now arrives the major doubt. Why we consider only the left side starting from center? The answer is simple, the concatenated string is made by S+reverse(S). So we are sure that the last element in the substring comes consecutive in the concatenated string. There is no way that any repetition in the first half of the main string can give a better result because at least we should have the n alphabets in the final concatenated string
Now the matter of complexity:
Searching for consecutive alphabets give a maximum of O(n)
Now checking elements on either side iteratively can give a worst case complexity of O(n). i.e maximum n/2 comparisons.
We may fail many times doing the second check so the we have a multiplicative relation between the complexities i.e O(n*n).
I believe this is a correct solution and didn't find any loophole yet.
Let's say that S2 is "apple". Then we can make this assumption:
S2 + reverseS2 >= S >= S2
"appleelppa" >= S >= "apple"
So the given S will something including "apple" to not more than "appleelppe". It could be "appleel" or "appleelpp".
String S ="locomotiffitomoc";
// as you see S2 string is "locomotif" but
// we don't know S2 yet, so it's blank
String S2 = "";
for (int a=0; a<S.length(); a++) {
try {
int b = 0;
while (S.charAt(a - b) == S.charAt(a + b + 1))
b++;
// if this for loop breaks that means that there is a character that doesn't match the rule
// if for loop doesn't break but throws an exception we found it.
} catch (Exception e) {
// if StringOutOfBoundsException is thrown this means end of the string.
// you can check this manually of course.
S2 = S.substring(0,a+1);
break;
}
}
System.out.println(S2); // will print out "locomotif"
Congratulations, you found the minimum S2.
Each character from S can be includes in S2 or not. With that we can construct recursion that tries two cases:
first character of S is used for cover,
first character of S is not
used for cover,
and calculate minimum of these two covers. To implement this, it is enough to track how much of S is covered with already chosen S2+reverse(S2).
There are optimizations where we know what result is (found cover, can't have cover), and it is not needed to take first character for cover if it will not cover something.
Simple python implementation:
cache = {}
def S2(S, to_cover):
if not to_cover: # Covered
return ''
if not S: # Not covered
return None
if len(to_cover) > 2*len(S): # Can't cover
return None
key = (S, to_cover)
if key not in cache:
without_char = S2(S[1:], to_cover) # Calculate with first character skipped
cache[key] = without_char
_f = to_cover[0] == S[0]
_l = to_cover[-1] == S[0]
if _f or _l:
# Calculate with first character used
with_char = S2(S[1:], to_cover[int(_f):len(to_cover)-int(_l)])
if with_char is not None:
with_char = S[0] + with_char # Append char to result
if without_char is None or len(with_char) <= len(without_char):
cache[key] = with_char
return cache[key]
s = '21211233123123213213131212122111312113221122132121221212321212112121321212121132'
c = S2(s, s)
print len(s), s
print len(c), c
How to find that any anagram of String 1 is sub string of String 2?
Eg :-
String 1 =rove
String 2=stackoverflow
So it will return true as anagram of "rove" is "over" which is sub-string of String 2
On edit: my first answer was quadratic in the worst case. I've tweaked it to be strictly linear:
Here is an approach based on the notion of a sliding window: Create a dictionary keyed by the letters of the first dictionary with frequency counts of the letters for the corresponding values. Think of this as a dictionary of targets which need to be matched by m consecutive letters in the second string, where m is the length of the first string.
Start by processing the first m letters in the second string. For each such letter if it appears as a key in the target dictionary decrease the corresponding value by 1. The goal is to drive all target values to 0. Define discrepancy to be the sum of the absolute values of the values after processing the first window of m letters.
Repeatedly do the following: check if discrepancy == 0 and return Trueif it does. Otherwise -- take the character m letters ago and check if it is a target key and if so -- increase the value by 1. In this case, this either increases or decreases the discrepancy by 1, adjust accordingly. Then get the next character of the second string and process it as well. Check if it is a key in the dictionary and if so adjust the value and the discrepancy as appropriate.
Since there are no nested loop and each pass through the main loop involves just a few dictionary lookups, comparisons, addition and subtractions, the overall algorithm is linear.
A Python 3 implementation (which shows the basic logic of how the window slides and the target counts and discrepancy are adjusted):
def subAnagram(s1,s2):
m = len(s1)
n = len(s2)
if m > n: return false
target = dict.fromkeys(s1,0)
for c in s1: target[c] += 1
#process initial window
for i in range(m):
c = s2[i]
if c in target:
target[c] -= 1
discrepancy = sum(abs(target[c]) for c in target)
#repeatedly check then slide:
for i in range(m,n):
if discrepancy == 0:
return True
else:
#first process letter from m steps ago from s2
c = s2[i-m]
if c in target:
target[c] += 1
if target[c] > 0: #just made things worse
discrepancy +=1
else:
discrepancy -=1
#now process new letter:
c = s2[i]
if c in target:
target[c] -= 1
if target[c] < 0: #just made things worse
discrepancy += 1
else:
discrepancy -=1
#if you get to this stage:
return discrepancy == 0
Typical output:
>>> subAnagram("rove", "stack overflow")
True
>>> subAnagram("rowe", "stack overflow")
False
To stress-test it, I downloaded the complete text of Moby Dick from Project Gutenberg. This has over 1 million characters. "Formosa" is mentioned in the book, hence an anagram of "moors" appears as a substring of Moby Dick. But, not surprisingly, no anagram of "stackoverflow" appears in Moby Dick:
>>> f = open("moby dick.txt")
>>> md = f.read()
>>> f.close()
>>> len(md)
1235186
>>> subAnagram("moors",md)
True
>>> subAnagram("stackoverflow",md)
False
The last call takes roughly 1 second to process the complete text of Moby Dick and verify that no anagram of "stackoverflow" appears in it.
Let L be the length of String1.
Loop over String2 and check if each substring of length L is an anagram of String1.
In your example, String1 = rove and String2 = stackoverflow.
stackoverflow
stac and rove are not anagrams, so move to the next substring of length L.
stackoverflow
tack and rove are not anagrams, and so on till you find the substring.
A faster method would be to check if the last letter in the current substring is present in String1 i.e., once you find that stac and rove are not anagrams, and see that 'c' (which is the last letter of the current substring) is not present in rove, you can simply skip that substring entirely and get the next substring from 'k'.
i.e. stackoverflow
stac and rove are not anagrams. 'c' is not present in 'rove', so simply skip over this substring and check from 'k':
stackoverflow
This will significantly reduce the number of comparisons.
Edit:
Here is a Python 2 implementation of the method explained above.
NOTE: This implementation works under the assumption that all characters in both strings are in lowercase and they consist only of the characters a -z.
def isAnagram(s1, s2):
c1 = [0] * 26
c2 = [0] * 26
# increase character counts for each string
for i in s1:
c1[ord(i) - 97] += 1
for i in s2:
c2[ord(i) - 97] += 1
# if the character counts are same, they are anagrams
if c1 == c2:
return True
return False
def isSubAnagram(s1, s2):
l = len(s1)
# s2[start:end] represents the substring in s2
start = 0
end = l
while(end <= len(s2)):
sub = s2[start:end]
if isAnagram(s1, sub):
return True
elif sub[-1] not in s1:
start += l
end += l
else:
start += 1
end += 1
return False
Output:
>>> print isSubAnagram('rove', 'stackoverflow')
True
>>> print isSubAnagram('rowe', 'stackoverflow')
False
It can be done in O(n^3) pre-processing, and O(klogk) per query where: n is the size of the "given string" (string 2 in your example) and k is the size of the query (string 1 in your example).
Pre process:
For each substring s of string2: //O(n^2) of those
sort s
store s in some data base (hash table, for example)
Query:
given a query q:
sort q
check if q is in the data base
if it is - it's an anagram of some substring
otherwise - it is not.
This answer assumes you are going to check multiple "queries" (string 1's) for a single string (string 2), and thus tries to optimize the complexity for each query.
As discussed in comments, you can do the pro-process step lazily - that means, when you first encounter a query of length k insert to the DS all substrings of length k, and proceed as original suggestion.
You may need to create all the possible combination of String1 that is rove like rove,rvoe,reov.. Then check this any of this combination is in String2.
//Two string are considered and check whether Anagram of the second string is
//present in the first string as part of it (Substring)
//e.g. 'atctv' 'cat' will return true as 'atc' is anagram of cat
//Similarly 'battex' is containing an anagram of 'text' as 'ttex'
public class SubstringIsAnagramOfSecondString {
public static boolean isAnagram(String str1, String str2){
//System.out.println(str1+"::" + str2);
Character[] charArr = new Character[str1.length()];
for(int i = 0; i < str1.length(); i++){
char ithChar1 = str1.charAt(i);
charArr[i] = ithChar1;
}
for(int i = 0; i < str2.length(); i++){
char ithChar2 = str2.charAt(i);
for(int j = 0; j<charArr.length; j++){
if(charArr[j] == null) continue;
if(charArr[j] == ithChar2){
charArr[j] = null;
}
}
}
for(int j = 0; j<charArr.length; j++){
if(charArr[j] != null)
return false;
}
return true;
}
public static boolean isSubStringAnagram(String firstStr, String secondStr){
int secondLength = secondStr.length();
int firstLength = firstStr.length();
if(secondLength == 0) return true;
if(firstLength < secondLength || firstLength == 0) return false;
//System.out.println("firstLength:"+ firstLength +" secondLength:" + secondLength+
//" firstLength - secondLength:" + (firstLength - secondLength));
for(int i = 0; i < firstLength - secondLength +1; i++){
if(isAnagram(firstStr.substring(i, i+secondLength),secondStr )){
return true;
}
}
return false;
}
public static void main(String[] args) {
System.out.println("isSubStringAnagram(xyteabc,ate): "+ isSubStringAnagram("xyteabc","ate"));
}
}
There is a secret message that is a string of length at least 2 containing only the characters A..Z.
Apply a sequence of "operations" to it. An Operation is shown in the example.
Given the final encrypted string, count the number of possible ways you could have produced this string using one or more repeated operations applied to a source string. Operations are distinct even if they give the same encryption of your secret message. I.E. : There are four distinct separate ways to obtain AAA from AA.
Here is an example:
The encrypted string is: ABABA. The output would be: 8. Here are the different ways you could have produced ABABA:
Start with AB -> AB+A -> AB+ABA
Start with AB -> AB+A -> ABA+BA
3.Start with ABA -> AB+ABA
Start with ABA -> ABA+BA
Start with BA -> A+BA -> AB+ABA
Start with BA -> A+BA -> ABA+BA
Start with ABAB -> ABAB+A
Start with BABA -> A+BABA
Could you please help suggest an algorithm to solve this. I thought of trying recursion but on bigger inputs my code runs way to slow.
Notice that, at each step of your derivation, you wind up with a substring of the "encrypted" string. There are quadratically many (i.e. O(n^2)) such substrings.
You can find the number of ways to derive the final string from each of its suffixes and prefixes straightforwardly.
So, you have quadratically many possible subproblems, and you can break a problem down into a complete set of subproblems, do the counting on each subproblem, and add the results up.
You get a dynamic programming algorithm out of this by solving each subproblem only once. This dynamic programming algorithm can be made to run in cubic time.
Not sure if this is correct, but here's a recursive attempt in JavaScript:
function f(str){
var total = 0
function g(ptr,len){
for (var i=1; i<= (len%2 == 0 ? len/2 - 1: (len - 1)/2); i++){
var left = str.substr(ptr,len - i),
suffix = str.substr(ptr + len - i,i),
right = str.substr(ptr + i,len - i),
prefix = str.substr(ptr,i),
recurseL = false,
recurseR = false
if (suffix == left.substr(-i)){
total++
recurseL = true
}
if (suffix == left.substr(0,i)){
total++
recurseL = true
}
if (prefix == right.substr(-i)){
total++
recurseR = true
}
if (prefix == right.substr(0,i)){
total++
recurseR = true
}
if (recurseR)
g(ptr + i,len - i)
if (recurseL)
g(ptr,len - i)
}
return total
}
return g(0,str.length)
}
f("ABABA")
Output:
8
Sorry for the long title :)
In this problem, we have string S of length n, and string T of length m. We can check whether S is a subsequence of string T in time complexity O(n+m). It's really simple.
I am curious about: what if we can delete at most K successive characters? For example, if K = 2, we can make "ab" from "accb", but not from "abcccb". I want to check if it's possible very fast.
I could only find obvious O(nm): check if it's possible for every suffix pairs in string S and string T. I thought maybe greedy algorithm could be possible, but if K = 2, the case S = "abc" and T = "ababbc" is a counterexample.
Is there any fast solution to solve this problem?
(Update: I've rewritten the opening of this answer to include a discussion of complexity and to discussion some alternative methods and potential risks.)
(Short answer, the only real improvement above the O(nm) approach that I can think of is to observe that we don't usually need to compute all n times m entries in the table. We can calculate only those cells we need. But in practice it might be very good, depending on the dataset.)
Clarify the problem: We have a string S of length n, and a string T of length m. The maximum allowed gap is k - this gap is to be enforced at the beginning and end of the string also. The gap is the number of unmatched characters between two matched characters - i.e. if the letters are adjacent, that is a gap of 0, not 1.
Imagine a table with n+1 rows and m+1 columns.
0 1 2 3 4 ... m
--------------------
0 | ? ? ? ? ? ?
1 | ? ? ? ? ? ?
2 | ? ? ? ? ? ?
3 | ? ? ? ? ? ?
... |
n | ? ? ? ? ? ?
At first, we we could define that the entry in row r and column c is a binary flag that tells us whether the first r characters of of S is a valid k-subsequence of the first c characters of T. (Don't worry yet how to compute these values, or even whether these values are useful, we just need to define them clearly first.)
However, this binary-flag table isn't very useful. It's not possible to easily calculate one cell as a function of nearby cells. Instead, we need each cell to store slightly more information. As well as recording whether the relevant strings are a valid subsequence, we need to record the number of consecutive unmatched characters at the end of our substring of T (the substring with c characters). For example, if the first r=2 characters of S are "ab" and the first c=3 characters of T are "abb", then there are two possible matches here: The first characters obviously match with each other, but the b can match with either of the latter b. Therefore, we have a choice of leaving one or zero unmatched bs at the end. Which one do we record in the table?
The answer is that, if a cell has multiple valid values, then we take the smallest one. It's logical that we want to make life as easy as possible for ourselves while matching the remainder of the string, and therefore that the smaller the gap at the end, the better. Be wary of other incorrect optmizations - we do not want to match as many characters as possible or as few characters. That can backfire. But it is logical, for a given pair of strings S,T, to find the match (if there are any valid matches) that minimizes the gap at the end.
One other observation is that if the string S is much shorter than T, then it cannot match. This depends on k also obviously. The maximum length that S can cover is rk, if this is less than c, then we can easily mark (r,c) as -1.
(Any other optimization statements that can be made?)
We do not need to compute all the values in this table. The number of different possible states is k+3. They start off in an 'undefined' state (?). If a matching is not possible for the pair of (sub)strings, the state is -. If a matching is possible, then the score in the cell will be a number between 0 and k inclusive, recording the smallest possible number of unmatched consecutive characters at the end. This gives us a total of k+3 states.
We are interested only in the entry in the bottom right of the table. If f(r,c) is the function that computes a particular cell, then we are interested only in f(n,m). The value for a particular cell can be computed as a function of the values nearby. We can build a recursive algorithm that takes r and c as input and performs the relevant calculations and lookups in term of the nearby values. If this function looks up f(r,c) and finds a ?, it will go ahead and compute it and then store the answer.
It is important to store the answer as the algorithm may query the same cell many times. But also, some cells will never be computed. We just start off attempting to calculate one cell (the bottom right) and just lookup-and-calculate-and-store as necessary.
This is the "obvious" O(nm) approach. The only optimization here is the observation that we don't need to calculate all the cells, therefore this should bring the complexity below O(nm). Of course, with really nasty datasets, you may end up calculating almost all of the cells! Therefore, it's difficult to put an official complexity estimate on this.
Finally, I should say how to compute a particular cell f(r,c):
If r==0 and c <= k, then f(r,c) = 0. An empty string can match any string with up to k characters in it.
If r==0 and c > k, then f(r,c) = -1. Too long for a match.
There are only two other ways a cell can have a successful state. We first try:
If S[r]==T[c] and f(r-1,c-1) != -1, then f(r,c) = 0. This is the best case - a match with no trailing gap.
If that didn't work, we try the next best thing. If f(r,c-1) != -1 and f(r,c) < k, then f(r,c) = f(r,c-1)+1.
If neither of those work, then f(r,c) = -1.
The rest of this answer is my initial, Haskell-based approach. One advantage of it is that it 'understands' that it needn't compute every cell, only computing cells where necessary. But it could make the inefficiency of calculating one cell many times.
*Also note that the Haskell approach is effectively approaching the problem in a mirror image - it trying to build matches from the end substrings of S and T where minimal leading bunch of unmatched characters. I don't have the time to rewrite it in its 'mirror image' form!
A recursive approach should work. We want a function that will take three arguments, int K, String S, and String T. However, we don't just want a boolean answer as to whether S is a valid k-subsequence of T.
For this recursive approach, if S is a valid k-subsequence, we also want to know about the best subsequence possible by returning how few characters from the start of T can be dropped. We want to find the 'best' subsequence. If a k-subsequence is not possible for S and T, then we return -1, but if it is possible then we want to return the smallest number of characters we can pull from T while retaining the k-subsequence property.
helloworld
l r d
This is a valid 4-subsequence, but the biggest gap has (at most) four characters (lowo). This is the best subsequence because it leaves a gap of just two characters at the start (he). Alternatively, here is another valid k-subsequence with the same strings, but it's not as good because it leaves a gap of three at the start:
helloworld
l r d
This is written in Haskell, but it should be easy enough to rewrite in any other language. I'll break it down in more detail below.
best :: Int -> String -> String -> Int
-- K S T return
-- where len(S) <= len(T)
best k [] t_string -- empty S is a subsequence of anything!
| length(t_string) <= k = length(t_string)
| length(t_string) > k = -1
best k sss#(s:ss) [] = (-1) -- if T is empty, and S is non-empty, then no subsequence is possible
best k sss#(s:ss) tts#(t:ts) -- both are non-empty. Various possibilities:
| s == t && best k ss ts /= -1 = 0 -- if s==t, and if best k ss ts != -1, then we have the best outcome
| best k sss ts /= -1
&& best k sss ts < k = 1+ (best k sss ts) -- this is the only other possibility for a valid k-subsequence
| otherwise = -1 -- no more options left, return -1 for failure.
A line-by-line analysis:
(A comment in Haskell starts with --)
best :: Int -> String -> String -> Int
A function that takes an Int, and two Strings, and that returns an Int. The return value is to be -1 if a k-subsequence is not possible. Otherwise it will return an integer between 0 and K (inclusive) telling us the smallest possible gap at the start of T.
We simply deal with the cases in order.
best k [] t -- empty S is a subsequence of anything!
| length(t) <= k = length(t)
| length(t) > k = -1
Above, we handle the case where S is empty ([]). This is simple, as an empty string is always a valid subsequence. But to test if it is a valid k-subsequence, we must calculate the length of T.
best k sss#(s:ss) [] = (-1)
-- if T is empty, and S is non-empty, then no subsequence is possible
That comment explains it. This leaves us with the situations where both strings are non-empty:
best k sss#(s:ss) tts#(t:ts) -- both are non-empty. Various possibilities:
| s == t && best k ss ts /= -1 = 0 -- if s==t, and if best k ss ts != -1, then we have the best outcome
| best k sss ts /= -1
&& best k sss ts < k = 1+ (best k sss ts) -- this is the only other possibility for a valid k-subsequence
| otherwise = -1 -- no more options left, return -1 for failure.
tts#(t:ts) matches a non-empty string. The name of the string is tts. But there is also a convenient trick in Haskell to allow you to give names to the first letter in the string (t) and the remainder of the string (ts). Here ts should be read aloud as the plural of t - the s suffix here means 'plural'. We say have have a t and some ts and together they make the full (non-empty) string.
That last block of code deals with the case where both strings are non-empty. The two strings are called sss and tts. But to save us the hassle of writing head sss and tail sss to access the first letter, and the string-remainer, of the string, we simply use #(s:ss) to tell the compiler to store those quantities into variables s and ss. If this was C++ for example, you'd get the same effect with char s = sss[0]; as the first line of your function.
The best situation is that the first characters match s==t and the remainder of the strings are a valid k-subsequence best k sss ts /= -1. This allows us to return 0.
The only other possibility for success if if the current complete string (sss) is a valid k-subsequence of the remainder of the other string (ts). We add 1 to this and return, but making an exception if the gap would grow too big.
It's very important not to change the order of those last five lines. They are order in decreasing order of how 'good' the score is. We want to test for, and return the very best possibilities first.
Naive recursive solution. Bonus := return value is the number of ways that the string can be matched.
#include <stdio.h>
#include <string.h>
unsigned skipneedle(char *haystack, char *needle, unsigned skipmax)
{
unsigned found,skipped;
// fprintf(stderr, "skipneedle(%s,%s,%u)\n", haystack, needle, skipmax);
if ( !*needle) return strlen(haystack) <= skipmax ? 1 : 0 ;
found = 0;
for (skipped=0; skipped <= skipmax ; haystack++,skipped++ ) {
if ( !*haystack ) break;
if ( *haystack == *needle) {
found += skipneedle(haystack+1, needle+1, skipmax);
}
}
return found;
}
int main(void)
{
char *ab = "ab";
char *test[] = {"ab" , "accb" , "abcccb" , "abcb", NULL}
, **cpp;
for (cpp = test; *cpp; cpp++ ) {
printf( "[%s,%s,%u]=%u \n"
, *cpp, ab, 2
, skipneedle(*cpp, ab, 2) );
}
return 0;
}
An O(p*n) solution where p = number of subsequences possible of S in T.
Scan the string T and maintain a list of possible subsequences of S that would have
1. Index of last character found and
2. Number of characters to be deleted found
Continue to update this list at each character of T.
Not sure if this is what your asking for, but you could create a list of characters from each String, and search for instances of the one list in the other, then if(list2.length-K > list1.length) return false.
Following is a proposed algorithm : - O(|T|*k) average case
1> scan T and store character indices in Hash Table :-
eg. S = "abc" T = "ababbc"
Symbol table entries : -
a = 1 3
b = 2 4 5
c = 6
2.> as we know isValidSub(S,T) = isValidSub(S(0,j),T) && (isValidSub(S(j+1,N),T)||....isValidSub(S(j+K,T),T))
a.> we will use the bottom up approach to solve above problem
b.> we will maintain an valid array Valid(len(S)) where each record points to a Hash Table (Explained as we go along solving further)
c.> Start from the last element of S, Look up for the indices stored corresponding to the character in Symbol Table
eg. in above example S[last] = "c"
in Symbol Table c = 6
Now we put records like (5,6) , (4,6) ,.... (6-k-1,6) into Hash table at Valid(last)
Explanation : - as s(6,len(S)) is valid subsequence hence s(0,6-i) ++ s(6,len(S)) (where i is in range(1,k+1)) is also valid subsequence provided s(0,6-i) is valid subsequence.
3.> start filling up Valid Array from last to 0 element : -
a.> take a indice from hash table entry corresponding to S[j] where j is current indice of Valid Array we are analysing.
b.> Check whether indice is in Valid(j+1) if less then add (indice-i,indice) where i in range(1,k+1) into Valid(j) Hash Table
example:-
S = "abc" T = "ababbc"
iteration 1 :
j = len(S) = 3
S[3] = 'c'
Symbol Table : c = 6
add (5,6),(4,6),(3,6) as K = 2 in Valid(j)
Valid(3) = {(5,6),(4,6),(3,6)}
j = 2
iteration 2 :
S[j] = 'b'
Symbol table: b = 2 4 5
Look up 2 in Valid(3) => not found => skip
Look up 4 in Valid(3) => found => add Valid(2) = {(3,4),(2,4),(1,4)}
Look up 5 in Valid(3) => found => add Valid(2) = {(3,4),(2,4),(1,4),(4,5)}
j = 1
iteration 3:
S[j] = "a"
Symbol Table : a = 1 3
Look up 1 in Valid(2) => not found
Look up 3 in Valid(2) => found => stop as it is last iteration
END
as 3 is found in Valid(2) that means there exists a valid subsequence starting at in T
Start = 3
4.> Reconstruct the solution moving downwards in Valid Array :-
example :
Start = 3
Look up 3 in Valid(2) => found (3,4)
Look up 4 in Valid(3) => found (4,6)
END
reconstructed solution (3,4,6) which is indeed valid subsequence
Remember (3,5,6) can also be a solution if we had added (3,5) instead of (3,4) in that iteration
Analysis of Time complexity & Space complexity : -
Time Complexity :
Step 1 : Scan T = O(|T|)
Step 2 : fill up all Valid entries O(|T|*k) using HashTable lookup is aprox O(1)
Step 3 : Reconstruct solution O(|S|)
Overall average case Time : O(|T|*k)
Space Complexity:
Symbol table = O(|T|+|S|)
Valid table = O(|T|*k) can be improved with optimizations
Overall space = O(|T|*k)
Java Implementation: -
public class Subsequence {
private ArrayList[] SymbolTable = null;
private HashMap[] Valid = null;
private String S;
private String T;
public ArrayList<Integer> getSubsequence(String S,String T,int K) {
this.S = S;
this.T = T;
if(S.length()>T.length())
return(null);
S = S.toLowerCase();
T = T.toLowerCase();
SymbolTable = new ArrayList[26];
for(int i=0;i<26;i++)
SymbolTable[i] = new ArrayList<Integer>();
char[] s1 = T.toCharArray();
char[] s2 = S.toCharArray();
//Calculate Symbol table
for(int i=0;i<T.length();i++) {
SymbolTable[s1[i]-'a'].add(i);
}
/* for(int j=0;j<26;j++) {
System.out.println(SymbolTable[j]);
}
*/
Valid = new HashMap[S.length()];
for(int i=0;i<S.length();i++)
Valid[i] = new HashMap<Integer,Integer >();
int Start = -1;
for(int j = S.length()-1;j>=0;j--) {
int index = s2[j] - 'a';
//System.out.println(index);
for(int m = 0;m<SymbolTable[index].size();m++) {
if(j==S.length()-1||Valid[j+1].containsKey(SymbolTable[index].get(m))) {
int value = (Integer)SymbolTable[index].get(m);
if(j==0) {
Start = value;
break;
}
for(int t=1;t<=K+1;t++) {
Valid[j].put(value-t, value);
}
}
}
}
/* for(int j=0;j<S.length();j++) {
System.out.println(Valid[j]);
}
*/
if(Start != -1) { //Solution exists
ArrayList subseq = new ArrayList<Integer>();
subseq.add(Start);
int prev = Start;
int next;
// Reconstruct solution
for(int i=1;i<S.length();i++) {
next = (Integer)Valid[i].get(prev);
subseq.add(next);
prev = next;
}
return(subseq);
}
return(null);
}
public static void main(String[] args) {
Subsequence sq = new Subsequence();
System.out.println(sq.getSubsequence("abc","ababbc", 2));
}
}
Consider a recursive approach: let int f(int i, int j) denote the minimum possible gap at the beginning for S[i...n] matching T[j...m]. f returns -1 if such matching does not exist. Here's the implementation of f:
int f(int i, int j){
if(j == m){
if(i == n)
return 0;
else
return -1;
}
if(i == n){
return m - j;
}
if(S[i] == T[j]){
int tmp = f(i + 1, j + 1);
if(tmp >= 0 && tmp <= k)
return 0;
}
return f(i, j + 1) + 1;
}
If we convert this recursive approach to a dynamic programming approach, then we can have a time complexity of O(nm).
Here's an implementation that usually* runs in O(N) and takes O(m) space, where m is length(S).
It uses the idea of a surveyor's chain:
Imagine a series of poles linked by chains of length k.
Achor the first pole at the beginning of the string.
Now cary the next pole forward until you find a character match.
Place that pole. If there is slack, move on to the next character;
else the previous pole has been dragged forward, and you need to go back
and move it to the next nearest match.
Repeat until you reach the end or run out of slack.
typedef struct chain_t{
int slack;
int pole;
} chainlink;
int subsequence_k_impl(char* t, char* s, int k, chainlink* link, int len)
{
char* match=s;
int extra = k; //total slack in the chain
//for all chars to match, including final null
while (match<=s+len){
//advance until we find spot for this post or run out of chain
while (t[link->pole] && t[link->pole]!=*match ){
link->pole++; link->slack--;
if (--extra<0) return 0; //no more slack, can't do it.
}
//if we ran out of ground, it's no good
if (t[link->pole] != *match) return 0;
//if this link has slack, go to next pole
if (link->slack>=0) {
link++; match++;
//if next pole was already placed,
while (link[-1].pole < link->pole) {
//recalc slack and advance again
extra += link->slack = k-(link->pole-link[-1].pole-1);
link++; match++;
}
//if not done
if (match<=s+len){
//currrent pole is out of order (or unplaced), move it next to prev one
link->pole = link[-1].pole+1;
extra+= link->slack = k;
}
}
//else drag the previous pole forward to the limit of the chain.
else if (match>=s) {
int drag = (link->pole - link[-1].pole -1)- k;
link--;match--;
link->pole+=drag;
link->slack-=drag;
}
}
//all poles planted. good match
return 1;
}
int subsequence_k(char* t, char* s, int k)
{
int l = strlen(s);
if (strlen(t)>(l+1)*(k+1))
return -1; //easy exit
else {
chainlink* chain = calloc(sizeof(chainlink),l+2);
chain[0].pole=-1; //first pole is anchored before the string
chain[0].slack=0;
chain[1].pole=0; //start searching at first char
chain[1].slack=k;
l = subsequence_k_impl(t,s,k,chain+1,l);
l=l?chain[1].pole:-1; //pos of first match or -1;
free(chain);
}
return l;
}
* I'm not sure of the big-O. I initially thought it was something like O(km+N). In testing, it averages less than 2N for good matches and less than N for failed matches.
...but.. there is a strange degenerate case. For random strings selected from an alphabet of size A, it gets much slower when k = 2A+1. Even this case it's better than O(Nm), and the performance returns to O(N) when k is increased or decreased slightly. Gist Here if anyone is curious.
Given two strings S and T, where the T is the pattern string. Find if any scrambled form of pattern string exists as SubString in the string S and if present return the start index.
Example:
String S: abcdef
String T: efd
String S has "def", a combination of search string T: "efd".
I have found a solution with a run time of O(m*n). I am working on a linear time solution where I used to HashMaps (static one, maintained for String T, and another a dynamic copy of the previous HashMap used for checking the current substring of T). I'd start checking at the next character where it fails. But this runs in O(m*n) in worst case.
I'd like to get some pointers to make it work in O(m+n) time. Any help would be appreciated.
First of all, I would like to know boundaries for string S length (m) and pattern T length (n).
There exist one general idea but complexity of the solution based on it depends on the pattern length. Complexity varies from O(m) to O(m*n^2) for short patterns with length<=100 and O(n) for long patterns.
Fundamental theorem of arithmetic states that every integer number can be uniquely represented as a product of prime numbers.
Idea - I guess, your alphabet is english letters. So, alphabet size is 26. Let's replace first letter with first prime, second letter with the second and so on. I mean the following replacement: a->2b->3c->5d->7e->11 and so on.
Let's denote product of primes corresponding for the letters of some string as prime product(string). For example, primeProduct(z) will be 101 as 101 is 26-th prime number, primeProduct(abc) will be 2*3*5=30,primeProduct(cba) will also be 5*3*2=30.
Why we choose prime numbers? If we replace a ->2; b ->3, c->4, we won't be able to decipher for exapmle 4 - is it "c" or "aa".
Solution for the short patterns case:
For the string S, we should calculate in linear time prime product for all prefixes. I mean we have to create array A such that A[0] = primeProduct(S[0]), A[1] = primeProduct(S[0]S[1]), A[N] = primeProduct(S). Sample implementation:
A[0] = getPrime(S[0]);
for(int i=1;i<S.length;i++)
A[i]=A[i-1]*getPrime(S[i]);
Searching pattern T. Calculate primeProduct(T). For all 'windows' in S which have the same length with pattern compare it's primeProduct with primeProduct(pattern). If currentWindow is equal to the pattern or currentWindow is a scrumbled form(anagramm) of the pattern primeProducts will be the same.
Important note! We have prepared array A for fast computing primeProduct for any substring of S. primeProduct of(S[i],S[i+1],...S[j]) = getPrime(S[i])*...*getPrime(S[j]) = A[j]/A[i-1];
Complexity: if pattern length is <=9, even 'zzzzzzzzz' is 101^9<=MAX_LONG_INT; All calculations fit in standart long type and complexity is O(N)+O(M) where N is for calculating primeProduct of pattern and M is iterating over all windows in S. If length<=100 you have to add complexity of mul/div long numbers that's why complexity becomes O(m*n^2). length of 101^length is O(N) mul/div of such long numbers is O(N^2)
For the long patterns with length>=1000 it's better to store some hash map(prime,degree). Array of prefixes will become array of hash maps and A[j]/A[i-1] trick will become differenceBetween(A[j] and A[i-1] hashmaps's key sets).
Would this JavaScript example be linear time?
<script>
function matchT(t,s){
var tMap = [], answer = []
//map the character count in t
for (var i=0; i<t.length; i++){
var chr = t.charCodeAt(i)
if (tMap[chr]) tMap[chr]++
else tMap[chr] = 1
}
//traverse string
for (var i=0; i<s.length; i++){
if (tMap[s.charCodeAt(i)]){
var start = i, j = i + 1, tmp = []
tmp[s.charCodeAt(i)] = 1
while (tMap[s.charCodeAt(j)]){
var chr = s.charCodeAt(j++)
if (tmp[chr]){
if (tMap[chr] > tmp[chr]) tmp[chr]++
else break
}
else tmp[chr] = 1
}
if (areEqual (tmp,tMap)){
answer.push(start)
i = j - 1
}
}
}
return answer
}
//function to compare arrays
function areEqual(arr1,arr2){
if (arr1.length != arr2.length) return false
for (var i in arr1)
if (arr1[i] != arr2[i]) return false
return true
}
</script>
Output:
console.log(matchT("edf","ghjfedabcddef"))
[3, 10]
If the alphabet is not too large (say, ASCII), then there is no need to use a hash to take care of strings.
Just use a big array which is of the same size as the alphabet, and the existence checking becomes O(1). Thus the whole algorithm becomes O(m+n).
Let us consider for the given example,
String S: abcdef
String T: efd
Create a HashSet which consists of the characters present in the Substring T. So, the set consists of .
Generate a label for the Substring T: 1e1f1d. (number of occurences of each characters + the character itself, can be done using technique similar to count sort)
Now we have to generate labels for the input of the sub-string's length.
Let us start from the first position, which has character a. Since it is not present we do not create any sub-string and move to the next character b. Similarly, to character c and then stop at d.
Since d is present in the HashSet start generating labels(of the sub-string length) for each time the character appears. We can do this in different function to avoid clearing the count array(doing this reduces the complexity from O(m*n) to O(m+n)). If at any point the input string does not consists of the Substring T we can start the label generation from the next position(since the position till the break occurred cannot be a part of the anagram).
So, by generating the labels we can solve the problem in linear O(m+n) time complexity.
m: length of the input string,
n: length of the sub string.
That Code below I used for the pattern searching questions in GFG its accepted in all test cases and works in linear time.
// { Driver Code Starts
import java.util.*;
class Implement_strstr
{
public static void main(String args[])
{
Scanner sc = new Scanner(System.in);
int t = sc.nextInt();
sc.nextLine();
while(t>0)
{
String line = sc.nextLine();
String a = line.split(" ")[0];
String b = line.split(" ")[1];
GfG g = new GfG();
System.out.println(g.strstr(a,b));
t--;
}
}
}// } Driver Code Ends
class GfG
{
//Function to locate the occurrence of the string x in the string s.
int strstr(String a, String d)
{
if(a.equals("") && d.equals("")) return 0;
if(a.length()==1 && d.length()==1 && a.equals(d)) return 0;
if(d.length()==1 && a.charAt(a.length()-1)==d.charAt(0)) return a.length()-1;
int t=0;
int pl=-1;
boolean b=false;
int fl=-1;
for(int i=0;i<a.length();i++)
{
if(pl!=-1)
{
if(i==pl+1 && a.charAt(i)==d.charAt(t))
{
t++;
pl++;
if(t==d.length())
{
b=true;
break;
}
}
else
{
fl=-1;
pl=-1;
t=0;
}
}
else
{
if(a.charAt(i)==d.charAt(t))
{
fl=i;
pl=i;
t=1;
}
}
}
return b?fl:-1;
}
}
Here is the link to the question https://practice.geeksforgeeks.org/problems/implement-strstr/1