![PSEUDO-CODE
⢠NaiveStringMatch(Text, Pattern):
⢠n = length(Text)
⢠m = length(Pattern)
⢠for i = 0 to n - m
⢠j = 0
⢠while j < m and Pattern[j] = Text[i + j]
⢠j = j + 1
⢠if j = m
⢠print "Pattern found at position", i](https://image.slidesharecdn.com/stringmatchingalgorithm-1-240318035616-5e667b51/85/String-Matching-algorithm-String-Matching-algorithm-String-Matching-algorithm-6-320.jpg)
![PSEUDO-CODE In this pseudo-code:
⢠T is the text
⢠P is the pattern
⢠d is the number of
characters in the input set.
⢠q is a prime number used as
modulus
⢠n = length[T]
⢠m = length[P]
⢠h = pow(d, m-1) mod q
⢠P = 0
⢠t0 = 0
⢠# Preprocessing: Compute the hash value of the pattern and the first substring of T
⢠for i = 1 to m
⢠P = (d*P + P[i]) mod q
⢠t0 = (d*t0 + T[i]) mod q
⢠# Matching: Slide the window through T and compare hash values
⢠for s = 0 to n-m
⢠if P = ts
⢠if P[1.....m] = T[s+1.....s+m] if s < n-m
⢠ts+1 = (d*(ts - T[s+1]*h) + T[s+m+1]) mod q](https://image.slidesharecdn.com/stringmatchingalgorithm-1-240318035616-5e667b51/85/String-Matching-algorithm-String-Matching-algorithm-String-Matching-algorithm-8-320.jpg)
String Matching algorithm String Matching algorithm String Matching algorithm
- 2. WHAT IS STRING MATCHING ? ⢠In computer science, String searching algorithms, sometimes called string matching algorithms, that try to be find place where one or several string (also called pattern) are found within a larger string or text.
- 3. EXAMPLE A B C C A T D E F SHIFT = 3 C A T PATTERN MATCH TEXT
- 4. STRING MATCHING ALGORITHM ⢠There are many types of String Matching Algorithm like:- 1. The Naive string-matching algorithm. 2. The Rabin-Karp algorithm. 3. String matching with finite automata 4. The Knuth-Morris-Pratt algorithm
- 5. NaĂŻve String Matching Algorithm Input: The algorithm takes two strings as input - the text (longer string) and the pattern (shorter string). Initialization: Let n be the length of the text and m be the length of the pattern. Loop through the text: For each starting position i from 0 to n - m: â˘Initialize a variable j to 0 to track the index in the pattern. â˘While j is less than the length of the pattern m and the character at position i + j in the text matches the character at position j in the pattern: â˘Increment j. â˘If j equals m, it means the entire pattern has been matched starting at position i. â˘Output the position i as the starting index of the pattern in the text. Repeat: Continue this process for all possible starting positions in the text. Output: The algorithm outputs the starting positions in the text where the pattern is found.
- 6. PSEUDO-CODE ⢠NaiveStringMatch(Text, Pattern): ⢠n = length(Text) ⢠m = length(Pattern) ⢠for i = 0 to n - m ⢠j = 0 ⢠while j < m and Pattern[j] = Text[i + j] ⢠j = j + 1 ⢠if j = m ⢠print "Pattern found at position", i
- 7. Rabin-Karp Algorithm Input: The algorithm takes two strings as input - the text (longer string) and the pattern (shorter string). Initialization: ⢠Let n be the length of the text and m be the length of the pattern. ⢠Choose two constants: d, the number of characters in the alphabet, and q, a prime number. Preprocessing: ⢠Compute the hash value of the pattern P and the hash value of the first substring of the text t0. ⢠Iterate over the pattern and the first substring of the text: ⢠Compute the hash value of the pattern using a rolling hash function: P = (d * P + ASCII value of character) mod q. ⢠Compute the hash value of the first substring of the text similarly: t0 = (d * t0 + ASCII value of character) mod q. Matching: ⢠Slide a window of length m over the text from left to right. ⢠For each position s in the text: ⢠Check if the hash value of the pattern matches the hash value of the current substring of the text. ⢠If the hash values match, perform a character-by-character comparison to confirm the match. ⢠If a match is found, output the starting position s as the index where the pattern occurs in the text. ⢠Update the hash value for the next substring using a rolling hash function: ⢠ts+1 = (d * (ts - ASCII value of character at position s * h) + ASCII value of character at position s+m) mod q. ⢠Repeat this process until all positions in the text have been examined. Output: The algorithm outputs the starting positions in the text where the pattern is found.
- 8. PSEUDO-CODE In this pseudo-code: ⢠T is the text ⢠P is the pattern ⢠d is the number of characters in the input set. ⢠q is a prime number used as modulus ⢠n = length[T] ⢠m = length[P] ⢠h = pow(d, m-1) mod q ⢠P = 0 ⢠t0 = 0 ⢠# Preprocessing: Compute the hash value of the pattern and the first substring of T ⢠for i = 1 to m ⢠P = (d*P + P[i]) mod q ⢠t0 = (d*t0 + T[i]) mod q ⢠# Matching: Slide the window through T and compare hash values ⢠for s = 0 to n-m ⢠if P = ts ⢠if P[1.....m] = T[s+1.....s+m] if s < n-m ⢠ts+1 = (d*(ts - T[s+1]*h) + T[s+m+1]) mod q