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UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2008 PowerPoint Presentation
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UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2008

UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2008

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UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2008

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  1. UMass Lowell Computer Science 91.503Analysis of AlgorithmsProf. Karen DanielsFall, 2008 Tuesday, 12/2/08 String Matching Algorithms Chapter 32

  2. Ch 32 String Matching Automata Chapter Dependencies You’re responsible for material in Sections 32.1-32.4 of this chapter.

  3. String Matching Algorithms Motivation & Basics

  4. String Matching Problem Motivations: text-editing, pattern matching in DNA sequences 32.1 Text: array T [1...n] Pattern: array P [1...m] Array Element: Character from finite alphabet S Pattern P occurs with shift s in T if P [1...m] = T [s+1...s + m] source: 91.503 textbook Cormen et al.

  5. String Matching Algorithms • Naive Algorithm • Worst-case running time in O((n-m+1) m) • Rabin-Karp • Worst-case running time in O((n-m+1) m) • Better than this on average and in practice • Finite Automaton-Based • Worst-case running time in O(n + m|S|) • Knuth-Morris-Pratt • Worst-case running time in O(n + m)

  6. ab abcca cca abcca Notation & Terminology • S* = set of all finite-length strings formed using characters from alphabet S • Empty string: e • |x| = length of string x • w is a prefix of x: w x • w is a suffix of x: w x • prefix, suffix are transitive

  7. Overlapping Suffix Lemma 32.1 32.3 32.1 source: 91.503 textbook Cormen et al.

  8. String Matching Algorithms Naive Algorithm

  9. Naive String Matching How to do better? worst-case running time is in Q((n-m+1)m) 32.4 source: 91.503 textbook Cormen et al.

  10. String Matching Algorithms Rabin-Karp

  11. Rabin-Karp Algorithm • Assume each character is digit in radix-d notation (e.g. d=10) • p = decimal value of pattern • ts = decimal value of substring T[s+1..s+m] for s = 0,1...,n-m • Strategy: • compute p in O(m) time (which is in O(n)) • compute all ti values in total of O(n) time • find all valid shifts s in O(n) time by comparing p with each ts • Compute p in O(m) time using Horner’s rule: • p = P[m] + d(P[m-1] + d(P[m-2] + ... + d(P[2] + dP[1]))) • Compute t0 similarly from T[1..m] in O(m) time • Compute remaining ti’s in O(n-m) time • ts+1 = d(ts - d m-1T[s+1]) + T[s+m+1] source: 91.503 textbook Cormen et al.

  12. Rabin-Karp Algorithm But... p, ts may be large, so use mod 32.5 source: 91.503 textbook Cormen et al.

  13. Rabin-Karp Algorithm (continued) source: 91.503 textbook Cormen et al. But... • ts+1 = d(ts - d m-1T[s+1]) + T[s+m+1] • d m-1 mod q p = 31415 spurious hit

  14. Rabin-Karp Algorithm (continued) source: 91.503 textbook Cormen et al.

  15. d is radix. q is modulus Q(m) in Q(n) high-order digit position for m-digit window Preprocessing Q(m) Matching loop invariant: when line 10 executed ts=T[s+1..s+m] mod q Q((n-m+1)m) rule out spurious hit Q(m) Try all possible shifts Rabin-Karp Algorithm (continued) What input generates worst case? worst-case running time is in Q((n-m+1)m) source: 91.503 textbook Cormen et al.

  16. Rabin-Karp Algorithm (continued) source: 91.503 textbook Cormen et al. d is radix q is modulus Q(m) in Q(n) high-order digit position for m-digit window Worst Case Preprocessing Q(m) Matching loop invariant: when line 10 executed ts=T[s+1..s+m] mod q Q((n-m+1)m) rule out spurious hit Q(m) Try all possible shifts Average Case Assume reducing mod q is like random mapping from S* to Zq set of all finite-length strings formed from S # spurious hits is in O(n/q) Estimate (chance that ts= p mod q) = 1/q Expected matching time = O(n) + O(m(v + n/q)) (v = # valid shifts) preprocessing + ts updates explicit matching comparisons If v is in O(1) and q >= m average-case running time is in O(n+m)

  17. String Matching Algorithms Finite Automata

  18. Finite Automata 32.6 source: 91.503 textbook Cormen et al. Strategy: Build automaton for pattern, then examine each text character once. worst-case running time is in Q(n) + automaton creation time

  19. Finite Automata source: 91.503 textbook Cormen et al.

  20. String-Matching Automaton Pattern = P = ababaca Automaton accepts strings ending in P 32.7 source: 91.503 textbook Cormen et al.

  21. Automaton’s operational invariant String-Matching Automaton Suffix Function for P: s (x) = length of longest prefix of P that is a suffix of x 32.3 32.4 at each step: keeps track of longest pattern prefix that is a suffix of what has been read so far source: 91.503 textbook Cormen et al.

  22. String-Matching Automaton Simulate behavior of string-matching automaton that finds occurrences of pattern P of length m in T[1..n] Worst Case assuming automaton has already been created... worst-case running time of matching is in Q(n) source: 91.503 textbook Cormen et al.

  23. String-Matching Automaton (continued) Correctness of matching procedure... 32.4 32.3 32.3 to be proved next… source: 91.503 textbook Cormen et al.

  24. String-Matching Automaton (continued) Correctness of matching procedure... 32.2 32.8 32.8 32.2 source: 91.503 textbook Cormen et al.

  25. source: 91.503 textbook Cormen et al. String-Matching Automaton (continued) Correctness of matching procedure... 32.3 32.9 32.2 32.1 32.9 32.3

  26. String-Matching Automaton (continued) Correctness of matching procedure... 32.4 32.3 32.3 source: 91.503 textbook Cormen et al.

  27. String-Matching Automaton (continued) source: 91.503 textbook Cormen et al. worst-case running time of automaton creation is in O(m3 |S|) Worst Case can be improved to: O(m|S|) worst-case running time of entire string-matching strategy is in O(m|S|) + O(n) automaton creation time pattern matching time

  28. String Matching Algorithms Knuth-Morris-Pratt

  29. Knuth-Morris-Pratt Overview • Achieve Q(n+m) time by shortening automaton preprocessing time below O(m|S|) • Approach: • don’t precompute automaton’s transition function • calculate enough transition data “on-the-fly” • obtain data via “alphabet-independent” pattern preprocessing • pattern preprocessing compares pattern against shifts of itself

  30. Knuth-Morris-Pratt Algorithm determine how pattern matches against itself 32.10 source: 91.503 textbook Cormen et al.

  31. Knuth-Morris-Pratt Algorithm 32.5 Equivalently, what is largest k < q such that Pk Pq? Prefix function p shows how pattern matches against itself p(q) is length of longest prefix of P that is a proper suffix of Pq Example: source: 91.503 textbook Cormen et al.

  32. Knuth-Morris-Pratt Algorithm Worst Case Q(m) in Q(n) # characters matched using amortized analysis scan text left-to-right Q(m+n) next character does not match Q(n) next character matches Is all of P matched? using amortized analysis Look for next match source: 91.503 textbook Cormen et al.

  33. Worst Case Potential Method Amortized Analysis k = current state of algorithm initial potential value Q(m) in Q(n) potential decreases source: 91.503 textbook Cormen et al. Knuth-Morris-Pratt Algorithm Potential is never negative since p (k) >= 0 for all k amortized cost of loop body is in O(1) Q(m) loop iterations potential increases by <=1 in each execution of for loop body

  34. Knuth-Morris-Pratt Algorithm Correctness... source: 91.503 textbook Cormen et al.

  35. Knuth-Morris-Pratt Algorithm 32.5 Correctness... 32.6 32.6 32.1 source: 91.503 textbook Cormen et al.

  36. Knuth-Morris-Pratt Algorithm Correctness... 32.11 32.5 source: 91.503 textbook Cormen et al.

  37. Knuth-Morris-Pratt Algorithm 32.6 Correctness... 32.5 32.5 32.7 32.6 source: 91.503 textbook Cormen et al.