13 KiB
Prefix function and KMP
Prefix function
Prefix function for a given string(S) of length n returns an array \Pi of length n, such that i^{th} element of an array contains length of longest proper prefix of S[0...i] which is also suffix of S[0...i].
Where, S[l...r] represents substring of S starting at index l and ending at index r.
Note: A proper prefix of a string is a prefix that is not equal to the string itself.
For example, \text{prefix function("aaab")} returns \Pi = [0,1,2,0]. Can you figure out why it has value 2 at index $2$(0-based)?
Because S[0...1] is equal to S[1...2].
Note that \Pi[0] is always zero.
Mathematically, it can be written as,
\Pi[i] = \max_ {k = 0 \dots i} \{k : s[0 \dots k-1] = s[i-(k-1) \dots i] \}
Trivial Algorithm
Basic algorithm to find such a value say at index i is to compare all prefixes and suffixes of substring S[0...i] one by one, of course, of same lengths.
for(len = 1; len <= i; len++)
if(s.substr(0,len) == s.substr(i-len+1,len)
pi[i] = max(pi[i], len);
Note that value at any index can not exceed i, $\forall i\in[0,n-1]$.
vector<int> prefix_function(string& s) {
int m = (int)s.size();
vector<int> pf(m, 0);
for (int i = 1; i < m; i++)
for (int len = i; len > 0; len--)
// break at first longest match, efficient!
if (s.substr(0, len) == s.substr(i-len+1, len)) {
pf[i] = len;
break;
}
return pf;
}
Time complexity: O(n^3)
Can we do better?
Efficient Algorithm
We will take advantage of previously computed values as much as possible. We are going to reach at the most efficient version(O(n)) step by step.
Step 1
The value of prefix function at any index i can either increase by at most 1 or decrease by certain amount. That means, \Pi[i] <= \Pi[i-1] + 1. Why?
Suppose, for a given string "s_0s_1s_2s_3s_4s_5s_6s_7s_8", we are told that \Pi[7]=3, means "s_0s_1s_2" and "s_5s_6s_7" are equal substrings.
Now, suppose that s_3 and s_8 are equal, then what can we say about \Pi[8]?
\Pi[8] will be 4. We can see that \Pi[8] = \Pi[7]+1. What if s_3 and s_8 are not equal?
We can not say anything about value of \Pi[8], because depending on the value of s_8, there are many possibilities like,
s_0s_1s_2 = s_6s_7s_8ORs_0s_1 = s_7s_8ORs_0 = s_8OR\Pi[8]=0
But, now it will certainly be less than or equal to \Pi[7].
Therefore, \Pi[i] <= \Pi[i-1] + 1 is always true.
A formal proof for at most one increase can be given as below,
If \Pi[i+1]>\Pi[i]+1, then we can take this suffix ending in position i+1 with the length \Pi[i+1] and remove the last character from it. We end up with a suffix ending in position i with the length \Pi[i+1]−1, which is better than \Pi[i], that means we get a contradiction.
By finding the value of prefix function using this truth, we end up having only O(N) string comparisons because the value of prefix function(in total) increases by at most N steps and also decreases by at most N steps(see in reverse mode). Therefore, total complexity now is $O(N*N)$-as string comparison takes O(N).
vector<int> prefix_function(string& s) {
int m = (int)s.size();
vector<int> pf(m, 0);
for (int i = 1; i < m; i++)
for (int len = pi[i-1] + 1; len > 0; len--)
// break at the first longest match
if (s.substr(0, len) == s.substr(i-len+1, len)) {
pf[i] = len;
break;
}
return pf;
}
Step 2
In step-1, we have seen that the value of prefix function overall increases by at most n steps, but due to string comparisons we ended up having O(N^2) complexity. Here we will eliminate string comparisons completely.
Now, suppose that we are finding value of \Pi[i] for string s, then from the value of \Pi[i-1] we know that prefix of s[0....i-1] of length \Pi[i-1] is equal to the suffix of substring s[0....i-1] of length \Pi[i-1] i.e. s[0...(\Pi[i-1]-1)] = s[(i-\Pi[i-1])...i-1]
As discussed in step-1, if s[\Pi[i-1]] is equal to s[i], then \Pi[i] = \Pi[i-1]+1.
If s[\Pi[i-1]] != s[i], then we are in search for a next longest prefix(say has lenght k) which supports s[0...(k-1)]=s[(i-k)...(i-1)], such that s[k]=s[i] may turn out to be true.
Note that now anyway, value of \Pi[i] will be less than or equal to \Pi[i-1], as discussed in step-1.
If s[k] = s[i], then we can say that \Pi[i]=k+1 because s[0...k] is the longest prefix which matches with a suffix and that is the definition of \Pi[i].
The value-k we are searching is exactly what \Pi[\Pi[i-1]-1] represents. Why?
Points to note:
-
We know that
s[0...(\Pi[i-1]-1)] = s[(i-\Pi[i-1])...i-1]. -
If
s[\Pi[i-1]] != s[i], then value of\Pi[i] <= \Pi[i-1]- from step-1. -
We are searching for
k, whose value must be less than\Pi[i-1]because in case $s[k] = s[i]$(i.e.\Pi[i]=k+1), then\Pi[i] <= \Pi[i-1] \implies k+1<=\Pi[i-1] \implies k < \Pi[i-1]
Therefore, basically we are searching for the longest proper prefix of length less than \Pi[i-1] such that it matches a suffix of $s[0...(\Pi[i-1]-1)]$(which equals to s[(i-\Pi[i-1])...i-1] from point-1 above), which is the definion of the prefix function at index \Pi[i-1] -1 i.e. \Pi[\Pi[i-1]-1].
If s[k]=s[i], then we stop and assign \Pi[i]=k+1. Otherwise, we similarly go like k = \Pi[\Pi[\Pi[i] - 1] - 1], k = \Pi[\Pi[\Pi[\Pi[i] - 1] - 1]-1], ... until k = 0. Then if s[i]=s[0], we assign \Pi[i]=1, and \Pi[i]=0 otherwise.
vector<int> prefix_function(string& s) {
int m = (int)s.size();
vector<int> pf(m);
for (int i = 1; i < m; i++) {
int k = pf[i-1];
while (k > 0 && s[i] != s[k])
k = pf[k-1];
if (s[i] == s[k])
k++;
pf[i] = k;
}
return pf;
}
Time complexity: O(N), where N is the length of the string s. Because, now we are not doing any string comparisons.
Knuth-Morris-Pratt Algorithm(KMP-algo)
KMP algorithm is used to search all occurrences of pattern-string p in a string s in O(N).
For example, p = "ab" and s = "abbbabab", then KMP will find us [0,4,6] because s has 3 occurrences of "ab".
KMP is the most used algorithm in all inbuilt libraries of different programming languages to find "substring matches".
Basic idea here is to create a new string having p as a prefix and s as a suffix i.e. new_str = p + '#' + s.
To make sure that the value of prefix function does not exceed length of p, we will add an additional character which is never going to appear in string s.
Now, we will find prefix function of new_str.
Let say m is the length of p.
\Pi[i] = m, means that new_str[0..m-1] is equal to new-str[i-m...i], which is bacially means $p$(=new_str[0...m-1]) is equal to new_str[i-m...i].
And therefore all indices-i where the values of prefix function \Pi[i] equals to the length of p means it is an occurrence of p in s.
int main()
{
string s,p;
s = "abbbabab";
p = "ab";
int n = s.size(), m = p.size();
// To save memory concatenate
// s in p
p += "#";
p += s;
// p = "ab#abbbabab";
vector<int> pi = prefix_function(p);
// p = "ab#abbbabab";
// ^
// m+1
cout << "occurences in `s` at the following indices: ";
for(int i = m + 1; i < pi.size(); i++) {
if(pi[i] == m) {
cout << i - 2 * m << " ";
}
}
return 0;
}
Find a period of a string
Period of string is the shortest length such that a larger string s can be represented as a concatenation of one or more copies of a substring(t).
For example, s = "ababab" has a period of 2, where t = "ab".
Let's see how to find period of s using value of prefix function of s.
First of all note that length of string $s$(n) is divisible by period of string.
As we know for a string of length n, \Pi[n-1] represents the length of longest proper prefix which is also suffix. Now, let k = n - \Pi[n-1].
Now, if n is divisible by k, then we can divide the string s into multiple blocks of length k.
k=n-\Pi[n-1] \implies \Pi[n-1] = n-k
That means, prefix of length n-k and suffix of length n-k are equal, as per the definition of \Pi[n-1].
If you compare all blocks from the start and the end, then it turns out that all blocks of k size are equal. Which means that k is the period of s, as same blocks of size k repeats in s.
Now, why k is the smallest such period?
Because otherwise the value of \Pi[i-1] will be greater than n-k.
If k does not divide n, then string is not periodic as we cannot divide string into equivalent blocks.
int main()
{
string s;
s = "abbabbabb";
int n = s.size();
vector<int> pi = prefix_function(s);
int possible_period = n - pi[n - 1];
if(n % possible_period == 0) {
cout << "periodic with " << possible_period << endl;
}
else {
cout << "Not periodic string" << endl;
}
return 0;
}
Time Complexity: O(N), N is the length of s.
Compressing a string
Now, we know how to find a period of a string and therefore we can compress string as only one block of size k which repeats all over again and again in s.
To retrive the string back from compressed version, we can attatch its real length i.e. length of s.
int main()
{
string s;
s = "abbabbabb";
int n = s.size();
vector<int> pi = prefix_function(s);
int possible_period = n - pi[n - 1];
if(n % possible_period == 0) {
string compressed = s.substr(0,possible_period);
// A way to represent compressed string
// Attatch real length to retrieve string back
pair<string,int> compressed_str{s.substr(0,possible_period), n};
}
else {
cout << "can not be compressed by this method" << endl;
}
return 0;
}
Number of unique substrings in a string
Problem statement: Find number of unique substrings in a given string s.
Brief idea: Basic idea here is to take an empty string t and add characters one by one from string s and along with that check how many new substrings are created, due to addition of a character in t, using prefix-function.
Let say we have already added some characters to t from s and k is the number of distinct substrings currently. Now, we are a adding character c to t, t = t+c.
Note that total number of new substrings created by appending a character to any string(t) is equal to the length of new string(t=t+c) created. For example, Appending 'd' in "abc" creates 4 new substrings: "d", "cd", "bcd", "abcd".
But how to find number of new unique substrings created by addition of c using prefix function?
Hint: Reverse t.
By reversing t, our task burn down into computing how many prefixes there are that don't appear anywhere else in t, which can be done by finding prefix function of t.
After finding value of prefix function, we will find maximum value $\Pi_{max}$(\Pi_{max} = max\{\Pi[i]\}, \forall i) in the prefix function of reversed t, which shows the length of longest prefix which is already in t as a substring and it also implies that all smaller prefixes are already present as substrings in t.
Therefore, we will deduct this number of already present substrings i.e. \Pi_{max}, from the total number of new substrings i.e. |t|.
Where |t| is the length of t.
Finally, number of new unique substrings created by addition of a character turns out to be |t|-\Pi_{max}.
Note that |t| is the length of t after adding a character.
int prefix_function(string& s)
{
int m = (int)s.size();
vector<int> pf(m);
int mx = 0;
for (int i = 1; i < m; i++) {
int k = pf[i-1];
while (k > 0 && s[i] != s[k])
k = pf[k-1];
if (s[i] == s[k])
k++;
pf[i] = k;
mx = max(mx, pf[i]);
}
return mx;
}
int main()
{
string s,p;
s = "abc";
int n = s.size();
string t, temp;
int unique_substr = 0;
for(int i = 0; i < n; i++) {
t += s[i];
temp = t;
reverse(temp.begin(),temp.end());
unique_substr += (int)t.size() - prefix_function(temp);
}
// Total number of unique substrings
cout << unique_substr << endl;
return 0;
}
Complexity: O(N^2), where N is the length of s.
For each character appended, we are computing prefix function in O(N), which gives a time complexity of O(N^2) in total.