[Algorithms II] Week 4-2 Substring Search

[TOC]

1. Introduction to substring search

"most ingenious algorithm we've seen so far"
pb. having two strings, pattern and text, len(pattern)=M << len(text)=N, try to find pattern in text.

ex. indexOf method of String in java.

2. Brute-Force Substring Search

function signature:
public static int search(String pat, String txt);

brute-force algo: look for pattern at every position of text.

public static int search(String pat, String txt){  
    int N=txt.length(), M=pat.length();  
    for(int i=0; i<=N-M; i++){  
        int j;  
        for(j=0; j<M && pat.charAt(j)==txt.charAt(i+j); j++);  
        if(j==M) return i;  
    }  
    return N;// not found  
}

worst case: when txt/pat are repetitive → MN compares.

problem with brute-force: always backup when mismatch.

brute-force alternative



  • j := number of matched chars in pattern
  • i := index of the end of matched char in text

⇒ do explicite backup when mismatch by i -= j

public static int search(String pat, String txt){  
    int N=txt.length(), M=pat.length(), i=0, j=0;  
    while(i<N && j<M){  
        if(pat.charAt(j)==txt.charAt(i))   
            {j=i++; j++;}  
        else   
            {i=i-j+1; j=0}// <==backup  
    }  
    return j==M ? i-M : N;  
}

challenge: want linear-time guarantee, and want to avoid backup.

3. Knuth-Morris-Pratt

"one of the coolest/trickiest algorithm covered in this course"

intuition

suppose pattern = "BAAAAA",
if we matched 5 chars in pattern and get mismatch on 6th char ⇒ we know the previous 5 chars are "BAAAA" → no need to backup the i pointer.

KMP algorithm: clever method that always avoid backup !

Deterministic finite state automaton (DFA)

  • finite states ,including start and halt state, indexed by j in the subtring pb
  • for each state: exactly one transition for each char in alphabet

ex.
states are 0~6, pat="ABABAC", transitions are indexed by chars in alphabet = {A,B,C}, finish if we reach state-6.

dfa[c][i] = the next state if we are currently in state-i and encoutered char-c.

interpretation of DFA for KMP algo

in the DFA after reading txt[i], the index of state is the number of matched chars in pattern, or length of *longest prefix of pat that is a suffix of txt[0:i]. *

  • need to precompute the dfa[][] array from pattern
  • the pointer i never decrements (thus we can do it in a streaming manner)

if dfa[][] is precomputed, java code is very very simple:

public static int search(String pat, String txt, int[][] dfa){  
    int N=txt.length(), M=pat.length(), i, j=0;  
    for(i=0; i<N && j<M; i++)  
        j = dfa[txt.charAt(i)][j];  
    return j==M ? i-M : N;  
}

running time: linear.
→ key pb: how to build dfa efficiently ?

DFA construction

  • match transition (easy part)

when at state j, for the char c0==pat.charAt(j+1), just go on matching: dfa[c0][j] = j+1
ex. (pat="ABABAC")




  • mismatch transition (hard part)

(for j==0, things are simple: dfa[c][0]=0 for all c!=pat[0])

  • at state j (ie. j chars in pattern are matched), and for c!=pat.charAt(j+1)
  • ⇒ we are in state j: we know the last j chars in input are pat[0...j-1], and followed by char = c, so the last j+1 chars of input string is: pat[0...j-1]+c
  • ⇒ to compute dfa[c][j]: we can simulate as if we backup, ie. i=i-j+1, j=0.
  • if we go back to set j=0, and set i = i-j+1, then i is pointing at pat[1], the text become pat[1...j-1]+c. We then let this string go through our dfa, the state that it achieves is the value of dfa[c][j].

here is a concrete example:
pattern = "ABABAC", state j=5, char c='B'

  • we know the last 6 chars of the input = pat[0...j-1]+c="ABABA"+"B"="ABABAB"
  • if we backup, i will point to pat[1], the string is just pat[1...j-1]+c="BABAB"
  • we use the string "BABAB" as input and go through the partially constructed dfa, and see that we will reach state 4
  • so we know dfa['B'][5]=4

similarly we can get dfa['A'][5]=1, as indicated below:
(pat="ABABAC")


one concern: seems this simulation needs j steps ?
⇒ can be changed to be constant time if we maintain a state X := the state of simulating of input=pat[1...j-1]
we maintain this state X, then for each mismatched char c, we just need to look at dfa[c][X].
(pat="ABABAC")




[Algo]

  • set all matched transitions dfa[c0][j] = j+1 for all c0==pat[j]
  • fill first column (j==0): dfa[c][0]=0 for all c!=pat[0]
  • initialize X=0 (state for empty input string)
  • for j=1 to M:
    • for all c!=pat[0]: set dfa[c][j] = dfa[c][X] (DP here...)
    • update X=dfa[c0][X] ⇒ 注意, 此时X并不等于X+1(最开始dfa[c0][j]=j+1不适用于此), 为什么? 因为c0==pat[j] 而不是pat[X]!! 比如说最开始, j=1的时候X是等于0的!!! (这个弯我饶了好几分钟...)

java code (can be written to be more compate):

public int[][] constructDFA(String pat){  
    int R=256;//ASCII code    
    int M=pat.length();  
    int[][] dfa = new int[R][M];  
    // 1. fill matched transitions: dfa[pat.charAt(j)][j] = j+1   
    for(int j=0;j<M;j++)  
        dfa[pat.charAt(j)][j] = j+1;  
    // 2. fill 1st column --> can be ignored as java int default val=0  
    // 3. fill mismatched transitions     
    int X = 0;  
    for(int j=1;j<M;j++){  
        for(int c=0;c<R;c++)  
            if(c!=pat.charAt(j))  
                dfa[c][j] = dfa[c][X];  
        X = dfa[pat.charAt(j)][X];  
    }  
    return dfa;  
}

running time and space: O(M*R).

prop.
KMP algorithm runs in O(M+N) time, and constructs the dfa in O(M*R) time/space.

这个KMP算法, 我曾经想过好几个小时, 然后最后写出了特别复杂的代码, 虽然可以用但是基本写了就忘掉了. 但是经过老爷子这么一讲, 感觉这次印象深刻了好多. 老爷子NB...

八卦时间:

4. Boyer-Moore

Heuristic in practice.
i does not necessarily go through all txt chars ⇒ i may skip some chars.

intuition

  • for matching: scan chars from right to left (j will decrease when checking)
  • when encoutered a mismatch: we can skip <= M chars (if the char is not in pattern)

ex. (pat="NEEDLE")

→ pb: how to skip?

mismatch character heuristic

note: the i always points to the beginning of the substring (txt[i,...,i+M-1]) to be checked for match.

case 1. mismatched char not in pattern
easy case → just move i to the right of this char.


case 2. mismatched char in pattern

heuristic: line up i with the rightmost char in pattern.
i += skip
where skip length = j - index of rightmost char in pattern

note: this does not always help, in the example below, i even backups:

to avoid backup, in this case we just increment i by 1 (heuristic doesn't help in this case).

implementation

use an array right[] as skip table, right[c] is the index of rightmost occurrence of char c (-1 if c not in pat).

int[] right = new int[M];  
for(int i=0;i<R;i++) right[i] = -1;//value for chars not in pattern  
for(int j=0;j<M;j++){  
    right[pat.charAt(j)]=j;  
}

using this table we can implemente the heuristic algorithm:

public static int search(String pat, String txt, int[] right){  
    int N=txt.length(), M=pat.length();  
    int skip;  
    for(int i=0;i<N-M;i+=skip){  
        skip = 0;  
        for(int j=M-1;j>=0;j++)  
            if(pat.charAt(j)!=txt.charAt(i+j)){// when mismatch happens  
                skip = Math.max(1,j - right[txt.charAt(i+j)]);// skip if we can, else just increment i by 1  
                break;  
            }  
        if(skip==0)// if the above for loop finishes without changing skip --> we are done.    
            return i;  
    }  
    return N;// pattern not found  
}

analysis

property. the Boyer-Moore heuristic (in practice) takes about N/M (sublinear!) compares to search.

好神奇, 比KMP还要简单的算法, 实际效率这么高...

worst-case performance: N*M... 这一点不如KMP.

→ can be improved...

5. Rabin-Karp

两个图灵奖的大神发明的算法..

intuition

basic idea: modular hashing
ex. for strings of numbers

  • compute hash fcn (for number strings is easy: take the string and treat it as a number, then %Q where Q is a big prime number).
  • for a pointer i →corresponds to the substring txt[i, ..., i+M-1] → check hash for match

(below: text=3141592653589793, pattern=26535)

computing the hash function efficiently

let ti be the ith char in txt, the hashcode for substring txt[i,...,i+M-1] is:

⇒ just an M-digit base-R integer modulo Q ! poly(M, R) % Q*. *

  • Honor's method

linear time algorithm for evaluating polynomial.
recursive equation: poly(i, R) = poly(i-1, R)*R+ti

ex. (R=10, M=5)

private long hash(String key, int M){  
    long h=0;  
    for(int i=0;i<M;i++)  
        h = ( h*R + key.charAt(i) ) % Q  
    return h;  
}
  • if we know x_i, the x_i+1 can be infered:


⇒ x_i+1 can be computed in constant time:

⇒ we precompute R^(M-1) and maintain the hash number, and check for match !


public static int search(String txt, String pat){  
    int N=txt.length(), M=pat.length();  
    long pathash = hash(pat, M);      
    int RM = R^(M-1);// <-- pseudo code, store value of R^(M-1)  
    long txthash = hash(txt, M);// txthash will be maintained  
    for(int i=0;i<N-M;i++){  
        if(txthash==pathash && checkMatch(i,txt,pat))   
            return i;  
        txthash = ( (txthash - txt.charAt(i)*RM)*R + txt.charAt(i+M) ) % Q;  
    }  
}

更新txthash的地方可能会有modulo造成的问题... 不过先这样写吧..

for collisions: Monte Carlo vs. Las Vegas

analysis

Theory: if Q is sufficiently large (~M*N^2), the probability of collision is ~1/N.
Practice: choose Q to be sufficiently large, and collision probability is ~1/Q.

Summery

comments powered by Disqus