String Matching algorithm String Matching algorithm String Matching algorithm

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String matching algorithms try to find where a pattern string is found within a larger text string. The naive string matching algorithm compares characters one by one between the pattern and each substring of the text of the same length. The Rabin-Karp algorithm uses a rolling hash to quickly compare the hash of the pattern to the hash of each substring, only doing a full character comparison if the hashes match. Both algorithms output the starting positions in the text where the pattern is found.

String Matching algorithm String Matching algorithm String Matching algorithm

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.

Most read

EXAMPLE
A B C C A T D E F
SHIFT = 3
C A T
PATTERN MATCH
TEXT

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

Naïve 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:
•Initialize a variable j to 0 to track the index in the pattern.
•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:
•Increment j.
•If j equals m, it means the entire pattern has been matched starting at position i.
•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.

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

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.

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

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Artificial intelligence Presented by JM.jmansha170

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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. Naïve 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: •Initialize a variable j to 0 to track the index in the pattern. •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: •Increment j. •If j equals m, it means the entire pattern has been matched starting at position i. •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

String Matching algorithm String Matching algorithm String Matching algorithm

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