1. 1/39 COMP170 Tutorial 13: Pattern Matching T: P:

2. 2/39 Overview 1. What is Pattern Matching? 2. The Naive Algorithm 3. The Boyer-Moore Algorithm 4. The Rabin-Karp Algorithm 5. Questions

3. 3/39 1. What is Pattern Matching? Definition: – given a text string T and a pattern string P, find the pattern inside the text  T: “the rain in spain stays mainly on the plain”  P: “n th” Applications: – text editors, Search engines (e.g. Google), image analysis

4. 4/39 String Concepts Assume S is a string of size m. A substring S[i.. j] of S is the string fragment between indexes i and j. A prefix of S is a substring S[0.. i] A suffix of S is a substring S[i.. m-1] – i is any index between 0 and m-1

5. 5/39 Examples Substring S[1..3] == "ndr" All possible prefixes of S: – "andrew", "andre", "andr", "and", "an”, "a" All possible suffixes of S: – "andrew", "ndrew", "drew", "rew", "ew", "w" andrew S 05

6. 6/39 2. The Naive Algorithm Check each position in the text T to see if the pattern P starts in that position andrew T: rewP: andrew T: rewP:.. P moves 1 char at a time through T

7. 7/39 Algorithm and Analysis Brutal force continued Naive-Search(T,P) 01 for s  0 to n – m 02 j  0 03 // check if T[s..s+m–1] = P[0..m–1] 04 while T[s+j] = P[j] do 05 j  j + 1 06 if j = m return s 07 return –1

8. 8/39 The brute force algorithm is fast when the alphabet of the text is large – e.g. A..Z, a..z, 1..9, etc. It is slower when the alphabet is small – e.g. 0, 1 (as in binary files, image files, etc.) Example of a worst case: – T: "aaaaaaaaaaaaaaaaaaaaaaaaaah" – P: "aaah" Example of a more average case: – T: "a string searching example is standard" – P: "store" continued

9. 9/39 Reverse naive algorithm Why not search from the end of P? – Boyer and Moore Reverse-Naive-Search(T,P) 01 for s  0 to n – m 02 j  m – 1 // start from the end 03 // check if T[s..s+m–1] = P[0..m–1] 04 while T[s+j] = P[j] do 05 j  j - 1 06 if j < 0 return s 07 return –1 Running time is exactly the same as of the naive algorithm…

10. 10/39 3. The Boyer-Moore Algorithm The Boyer-Moore pattern matching algorithm is based on two techniques. 1. The looking-glass technique – find P in T by moving backwards through P, starting at its end

11. 11/39 2. The character-jump technique – when a mismatch occurs at T[i] =/= P[m-1] – the character in pattern P[m-1] is not the same as T[i] There are 2 possible cases. x T i b P

12. 12/39 Case 1 If P contains x somewhere, then try to shift P right to align the last occurrence of x in P with T[i]. x a T i b P x c x a T b P x c ? ?

13. 13/39 Case 2 If the character T[i] does not appear in P, then shift P to align P[0] with T[i+1]. x a T i b P d c x a T i new b P d c ? ? No x in P ? 0

14. 14/39 Case 3 If T[i] = P[m-1] and the match is incomplete, align T[i] with the last occurrence of T[i] in P. x a T i b a P a c x a T i new b a P a c ? ? ?

15. 15/39 Boyer-Moore Example (1) T: P:

16. 16/39 Boyer-Moore algorithm To implement, we need to find out for each character c in the alphabet, the amount of shift needed if P[m-1] aligns with the character c in the input text and they don’t match. This takes O(m + A) time, where A is the number of possible characters. Afterwards, matching P with substrings in T is very fast in practice. Example: Suppose the alphabet is {a, b,c} and the pattern is ababbb. Then, shift[c] = 6 shift[a] = 3 shift[b] = 1

17. 17/39 Analysis Boyer-Moore worst case running time is O(nm + A) But, Boyer-Moore is fast when the alphabet (A) is large, slow when the alphabet is small. – e.g. good for English text, poor for binary Boyer-Moore is significantly faster than brute force for searching English text.

18. 18/39 Fingerprint idea Assume: – We can compute a fingerprint f(P) of P in O(m) time. – If f(P)  f(T[s.. s+m–1]), then P  T[s.. s+m–1] – We can compare fingerprints in O(1) – We can compute f’ = f(T[s+1.. s+m]) from f(T[s.. s+m–1]), in O(1) f f’

19. 19/39 Algorithm with Fingerprints Let the alphabet  ={ 0,1,2,3,4,5,6,7,8,9 } Let fingerprint to be just a decimal number, i.e., f(“ 1045 ”) = 1*10 3 + 0*10 2 + 4*10 1 + 5 = 1045 Fingerprint-Search(T,P) 01 fp  compute f(P) 02 f  compute f(T[0..m–1]) 03 for s  0 to n – m do 04 if fp = f return s 05 f  (f – T[s]*10 m-1 )*10 + T[s+m] 06 return –1 f new f T[s]T[s] T[s+m] Running time O(m+n) Where is the catch?

20. 20/39 Using a Hash Function Problem: – we can not assume we can do arithmetics with m-digits- long numbers in O(1) time Solution: Use a hash function h = f mod q – For example, if q = 7, h(“ 52 ”) = 52 mod 7 = 3 – h(S 1 )  h(S 2 )  S 1  S 2 – But h(S 1 ) = h(S 2 ) does not imply S 1 =S 2 !  For example, if q = 7, h(“ 73 ”) = 3, but “ 73 ”  “ 52 ” Basic “mod q” arithmetics: – (a+b) mod q = (a mod q + b mod q) mod q – (a*b) mod q = (a mod q)*(b mod q) mod q

21. 21/39 Preprocessing and Stepping Preprocessing: – fp = P[m-1] + 10*(P[m-2] + 10*(P[m-3]+ … … + 10*(P[1] + 10*P[0])…)) mod q – In the same way compute ft from T[0..m-1] – Example: P = “ 2531 ”, q = 7, what is fp? Stepping: – ft = (ft – T[s]*10 m-1 mod q)*10 + T[s+m]) mod q – 10 m-1 mod q can be computed once in the preprocessing – Example: Let T[ … ] = “ 5319 ”, q = 7, what is the corresponding ft? ft new ft T[s]T[s] T[s+m]

22. 22/39 Rabin-Karp Algorithm Rabin-Karp-Search(T,P) 01 q  a prime larger than m 02 c  10 m-1 mod q // run a loop multiplying by 10 mod q 03 fp  0; ft  0 04 for i  0 to m-1 // preprocessing 05 fp  (10*fp + P[i]) mod q 06 ft  (10*ft + T[i]) mod q 07 for s  0 to n – m // matching 08 if fp = ft then // run a loop to compare strings 09 if P[0..m-1] = T[s..s+m-1] return s 10 ft  ((ft – T[s]*c)*10 + T[s+m]) mod q 11 return –1 How many character comparisons are done if T = “ 2531978 ” and P = “ 1978 ”?

23. 23/39 Analysis If q is a prime, the hash function distributes m-digit strings evenly among the q values – Thus, only every q-th value of shift s will result in matching fingerprints (which will require comparing stings with O(m) comparisons) Expected running time (if q > m): – Outer loop: O(n-m) – All inner loops: – Total time: O(n-m) Worst-case running time: O((n-m+1)m)

24. 24/39 Rabin-Karp in Practice If the alphabet has d characters, interpret characters as radix-d digits (replace 10 with d in the algorithm). Choosing prime q > m can be done with randomized algorithms in O(m), or q can be fixed to be the largest prime so that 10*q fits in a computer word. Rabin-Karp is simple and can be easily extended to two-dimensional pattern matching.

25. 25/39 Question 1 What is the worst case complexity of the Naïve algorithm? Find an example of the worst case. What is the worst case complexity of the BM algorithm? Find an example of the worst case.

26. 26/39 Question 2 Illustrate how does BM work for the following pattern matching problem. T: abacaabadcabacabaabb P: abacab

27. 27/39 Answer to question 1 Example of a worst case for Naïve algorithm: – T: "aaaaaaaaaaaaaaaaaaaaaaaaaah" – P: "aaah“ Time complexity O(mn)

28. 28/39 BM Worst Case Example T: "aaaaa…a" P: "baaaaa“ Complexity – O(mn+A) T: P:

29. 29/39 Answer to question (2) T: P: