String Matching Algorithms-The Naive Algorithm
Download as pptx, pdf2 likes1,180 views
String matching algorithms are used to find patterns within larger strings or texts. The example shows a text string "A B C A B A A C A B" and a pattern "A B A A" with a shift of 3. The naive string matching algorithm is described which compares characters between the text and pattern from index 0 to the string lengths to find all valid shifts where the pattern occurs in the text.
1 of 16
Downloaded 65 times
![THE NAIVE ALGORITHM
T[n]=T[0,1…n-1]
P[m]=P[0,1…m-1]
NAIVE-STRING-MATCHER(T,P)
1) n = T.length
2) m = P.length
3) for s=0 to n-m
4) if P[0…m-1]==T[s+0….s+m-1]
5) print” Pattern occurs with shift ” s](https://image.slidesharecdn.com/stringmatchingalgorithms-thenaivealgorithm-180718073714/85/String-Matching-Algorithms-The-Naive-Algorithm-7-320.jpg)
Ad
Recommended
Pattern matching
Pattern matchingshravs_188 The document summarizes and provides code examples for four pattern matching algorithms:
1. The brute force algorithm checks each character position in the text to see if the pattern starts there, running in O(mn) time in worst case.
2. The Boyer-Moore algorithm uses a "bad character" shift and "good suffix" shift to skip over non-matching characters in the text, running faster than brute force.
3. The Knuth-Morris-Pratt algorithm uses a failure function to determine the maximum shift of the pattern on a mismatch, avoiding wasteful comparisons.
4. The failure function allows KMP to skip portions of the text like Boyer-Moore, running
Rabin Carp String Matching algorithm
Rabin Carp String Matching algorithmsabiya sabiya The document discusses the Rabin-Karp substring search algorithm. It defines the algorithm as a string search method that compares hash values rather than strings themselves, allowing the hash of the next text position to be efficiently computed from the current position's hash. The document provides an example application of the algorithm, explains its O(n+m) running time complexity, and lists applications such as bioinformatics and plagiarism detection.
String matching, naive,
String matching, naive,Amit Kumar Rathi This document discusses string matching algorithms. It begins with an introduction to the naive string matching algorithm and its quadratic runtime. Then it proposes three improved algorithms: FC-RJ, FLC-RJ, and FMLC-RJ, which attempt to match patterns by restricting comparisons based on the first, first and last, or first, middle, and last characters, respectively. Experimental results show that these three proposed algorithms outperform the naive algorithm by reducing execution time, with FMLC-RJ working best for three-character patterns.
String matching algorithm
String matching algorithmAlokeparna Choudhury The document discusses string matching algorithms. It introduces the naive O(mn) algorithm and describes how it works by performing character-by-character comparisons. It then introduces the Knuth-Morris-Pratt (KMP) algorithm, which improves the runtime to O(n) by using a prefix function to avoid re-checking characters. The prefix function encapsulates information about how the pattern matches shifts of itself. The KMP algorithm uses the prefix function to avoid backtracking during matching. An example is provided to illustrate how the KMP algorithm works on a sample string and pattern.
Kmp
Kmpakruthi k The Knuth-Morris-Pratt algorithm is a linear-time string matching algorithm that improves on the naive algorithm. It works by preprocessing the pattern string to determine where matches can continue after a mismatch. This allows it to avoid re-examining characters. The algorithm computes a prefix function during preprocessing to determine the size of the longest prefix that is also a suffix. It then uses this information to efficiently determine where to continue matching after a mismatch by avoiding backtracking.
String matching algorithms
String matching algorithmsMahdi Esmailoghli This document summarizes and compares several string matching algorithms: the Naive Shifting Algorithm, Rabin-Karp Algorithm, Finite Automaton String Matching, and Knuth-Morris-Pratt (KMP) Algorithm. It provides high-level descriptions of each algorithm, including their time complexities, which range from O(n*m) for the Naive algorithm to O(n) for the Rabin-Karp, Finite Automaton, and KMP algorithms. It also includes examples and pseudocode to illustrate how some of the algorithms work.
A* Algorithm
A* AlgorithmDr. C.V. Suresh Babu This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
String matching algorithms
String matching algorithmsAshikapokiya12345 This document discusses string matching algorithms. It defines string matching as finding a pattern within a larger text or string. It then summarizes two common string matching algorithms: the naive algorithm and Rabin-Karp algorithm. The naive algorithm loops through all possible shifts of the pattern and directly compares characters. Rabin-Karp also shifts the pattern but compares hash values of substrings first before checking individual characters to reduce comparisons. The document provides examples of how each algorithm works on sample strings.
String matching algorithms-pattern matching.
String matching algorithms-pattern matching.Swapan Shakhari The document summarizes three string matching algorithms: Knuth-Morris-Pratt algorithm, Boyer-Moore string search algorithm, and Bitap algorithm. It provides details on each algorithm, including an overview, inventors, pseudocode, examples, and explanations of how they work. The Knuth-Morris-Pratt algorithm uses information about the pattern string to skip previously examined characters when a mismatch occurs. The Boyer-Moore algorithm uses preprocessing of the pattern to calculate shift amounts to skip alignments. The Bitap algorithm uses a bit array and bitwise operations to efficiently compare characters.
String Matching (Naive,Rabin-Karp,KMP)
String Matching (Naive,Rabin-Karp,KMP)Aditya pratap Singh This document discusses and compares several algorithms for string matching:
1. The naive algorithm compares characters one by one and has O(mn) runtime, where m and n are the lengths of the pattern and text.
2. Rabin-Karp uses hashing to compare substrings, running in O(m+n) time. It calculates hash values for the pattern and text substrings.
3. Knuth-Morris-Pratt improves on naive by using the prefix function to avoid re-checking characters, running in O(m+n) time. It constructs a state machine from the pattern to skip matching.
Boyer more algorithm
Boyer more algorithmKritika Purohit The document discusses the Boyer-Moore string searching algorithm. It works by preprocessing the pattern string and comparing characters from right to left. If a mismatch occurs, it uses two heuristics - bad character and good suffix - to determine the shift amount. The bad character heuristic shifts past mismatching characters, while the good suffix heuristic looks for matching suffixes to allow larger shifts. The algorithm generally gets faster as the pattern length increases, running in sub-linear time on average. It has applications in tasks like virus scanning and database searching that require high-speed string searching.
String matching algorithms(knuth morris-pratt)
String matching algorithms(knuth morris-pratt)Neel Shah The Knuth-Morris-Pratt algorithm improves upon the brute force string matching approach by utilizing a prefix function to avoid re-checking characters. It runs in O(m+n) time and O(m) space, where m is the pattern length and n is the string length. The prefix function encapsulates how the pattern matches shifts of itself, enabling the algorithm to avoid redundant comparisons when a mismatch occurs.
Dinive conquer algorithm
Dinive conquer algorithmMohd Arif The document discusses the divide and conquer algorithm design technique. It begins by explaining the basic approach of divide and conquer which is to (1) divide the problem into subproblems, (2) conquer the subproblems by solving them recursively, and (3) combine the solutions to the subproblems into a solution for the original problem. It then provides merge sort as a specific example of a divide and conquer algorithm for sorting a sequence. It explains that merge sort divides the sequence in half recursively until individual elements remain, then combines the sorted halves back together to produce the fully sorted sequence.
N queen problem
N queen problemRidhima Chowdhury This document presents an overview of the N-Queen problem and its solution using backtracking. It discusses how the N-Queen problem was originally proposed as a chess puzzle in 1848 and involved placing N queens on an N×N chessboard so that no two queens attack each other. It then explains how backtracking can be used to systematically place queens on the board one by one and remove placements that result in conflicts until all queens are placed or no more placements are possible. Examples are given showing the backtracking process and solution trees for 4x4 boards. The time complexity of this backtracking solution is analyzed to be O(N!).
Algorithms Lecture 4: Sorting Algorithms I
Algorithms Lecture 4: Sorting Algorithms IMohamed Loey The document discusses three sorting algorithms: bubble sort, selection sort, and insertion sort. Bubble sort works by repeatedly swapping adjacent elements that are in the wrong order. Selection sort finds the minimum element and swaps it into the sorted portion of the array. Insertion sort inserts elements into the sorted portion of the array, swapping as needed to put the element in the correct position. Both selection sort and insertion sort have a time complexity of O(n^2) in the worst case.
Rabin Karp ppt
Rabin Karp pptshreyasBharadwaj15 The Rabin-Karp algorithm is a string-searching algorithm that uses hashing to find patterns in strings.
The Rabin-Karp algorithm makes use of hash functions and the rolling hash technique.
Optimal binary search tree
Optimal binary search treeKavya P The document discusses the algorithm for constructing an optimal binary search tree. It involves calculating the cost table C and root table R based on the probabilities of keys. The cost C[i,j] is calculated recursively as the minimum of C[i,k-1] + C[k+1,j] + sum of probabilities from i to j. This is done by iterating through all subtrees until the overall minimum cost C[1,n] is found, with the corresponding roots in R. The example calculates the optimal binary search tree for keys A, B, C, D with probabilities 0.1, 0.2, 0.4, 0.3, finding a minimum cost of 1.7.
Divide and conquer
Divide and conquerDr Shashikant Athawale The document discusses divide and conquer algorithms. It describes divide and conquer as a design strategy that involves dividing a problem into smaller subproblems, solving the subproblems recursively, and combining the solutions. It provides examples of divide and conquer algorithms like merge sort, quicksort, and binary search. Merge sort works by recursively sorting halves of an array until it is fully sorted. Quicksort selects a pivot element and partitions the array into subarrays of smaller and larger elements, recursively sorting the subarrays. Binary search recursively searches half-intervals of a sorted array to find a target value.
Lecture optimal binary search tree 
Lecture optimal binary search tree Divya Ks This document discusses optimal binary search trees and provides an example problem. It begins with basic definitions of binary search trees and optimal binary search trees. It then shows an example problem with keys 1, 2, 3 and calculates the cost as 17. The document explains how to use dynamic programming to find the optimal binary search tree for keys 10, 12, 16, 21 with frequencies 4, 2, 6, 3. It provides the solution matrix and explains that the minimum cost is 2 with the optimal tree as 10, 12, 16, 21.
Convex hull
Convex hullShiwangi Thakur The document discusses several algorithms for computing the convex hull of a set of points, including brute force, quick hull, divide and conquer, Graham's scan, and Jarvis march. It provides details on the time complexity of each algorithm, ranging from O(n^2) for brute force to O(n log n) for quick hull, divide and conquer, and Graham's scan. Jarvis march runs in O(nh) time where h is the number of points on the convex hull.
KMP String Matching Algorithm
KMP String Matching Algorithmkalpanasatishkumar The Knuth-Morris-Pratt (KMP) string matching algorithm searches for occurrences of a pattern string within a main text string. It uses a prefix function to determine where the next match could begin without re-checking characters, allowing it to run in linear time. The algorithm consists of computing the prefix function, then performing the matching. It checks characters from left to right, using the prefix table to "shift" to the next possible match location when a mismatch occurs.
Greedy Algorithm
Greedy AlgorithmWaqar Akram This document provides an introduction to greedy algorithms. It defines greedy algorithms as algorithms that make locally optimal choices at each step in the hope of finding a global optimum. The document then provides examples of problems that can be solved using greedy algorithms, including counting money, scheduling jobs, finding minimum spanning trees, and the traveling salesman problem. It also provides pseudocode for a general greedy algorithm and discusses some properties of greedy algorithms.
Rabin karp string matcher
Rabin karp string matcherAmit Kumar Rathi The document discusses the Rabin-Karp algorithm for string matching. It defines Rabin-Karp as a string search algorithm that compares hash values of strings rather than the strings themselves. It explains that Rabin-Karp works by calculating a hash value for the pattern and text subsequences to compare, and only does a brute force comparison when hash values match. The worst-case complexity is O(n-m+1)m but the average case is O(n+m) plus processing spurious hits. Real-life applications include bioinformatics to find protein similarities.
Constraint satisfaction problems (csp) 
Constraint satisfaction problems (csp) Archana432045 This document discusses constraint satisfaction problems (CSPs). It defines CSPs as problems with variables that must satisfy constraints. CSPs can represent many real-world problems and are solved through constraint satisfaction methods. The document outlines CSP components like variables, domains, and constraints. It also describes representing problems as CSPs, solving CSPs through backtracking search, and the role of heuristics like minimum remaining values in improving the search process.
RABIN KARP ALGORITHM STRING MATCHING
RABIN KARP ALGORITHM STRING MATCHINGAbhishek Singh RABIN KARP algorithm with hash function and hash collision, analysis, algorithm and code for implementation. Besides it contains applications of RABIN KARP algorithm also
convex hull
convex hullravikirankalal The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
Hashing
HashingDawood Faheem Abbasi hashing is encryption process mostly used in programming language for security purpose.
This presentation will you understand all about hashing and also different techniques used in it for encryption process
Daa notes 3
Daa notes 3smruti sarangi This file contains the concepts of Class P, Class NP, NP- completeness, Travelling Salesman Person problem, Clique Problem, Vertex cover problem, Hamiltonian problem, FFT and DFT.
4 report format
4 report formatAshikapokiya12345 The document summarizes string matching algorithms. It discusses the naive string matching algorithm which compares characters at each shift to find matches. It also discusses the Rabin-Karp algorithm which uses hashing to match the hash value of the pattern to the hash value of substrings in the text. If the hash values match, it then checks for an exact character match. The Rabin-Karp algorithm has better average-case performance than the naive algorithm but the same worst-case performance of O((n-m+1)m) time.
4 report format
4 report formatAshikapokiya12345 The document summarizes string matching algorithms. It discusses the naive string matching algorithm which compares characters at each shift to find matches. It also describes the Rabin-Karp algorithm which uses hashing to match the hash value of the pattern to the hash value of substrings in the text. If the hash values match, it then checks for an exact character match. The Rabin-Karp algorithm has better average-case performance than the naive algorithm but the same worst-case performance of O((n-m+1)m) time.
More Related Content
What's hot (20)
String matching algorithms-pattern matching.
String matching algorithms-pattern matching.Swapan Shakhari The document summarizes three string matching algorithms: Knuth-Morris-Pratt algorithm, Boyer-Moore string search algorithm, and Bitap algorithm. It provides details on each algorithm, including an overview, inventors, pseudocode, examples, and explanations of how they work. The Knuth-Morris-Pratt algorithm uses information about the pattern string to skip previously examined characters when a mismatch occurs. The Boyer-Moore algorithm uses preprocessing of the pattern to calculate shift amounts to skip alignments. The Bitap algorithm uses a bit array and bitwise operations to efficiently compare characters.
String Matching (Naive,Rabin-Karp,KMP)
String Matching (Naive,Rabin-Karp,KMP)Aditya pratap Singh This document discusses and compares several algorithms for string matching:
1. The naive algorithm compares characters one by one and has O(mn) runtime, where m and n are the lengths of the pattern and text.
2. Rabin-Karp uses hashing to compare substrings, running in O(m+n) time. It calculates hash values for the pattern and text substrings.
3. Knuth-Morris-Pratt improves on naive by using the prefix function to avoid re-checking characters, running in O(m+n) time. It constructs a state machine from the pattern to skip matching.
Boyer more algorithm
Boyer more algorithmKritika Purohit The document discusses the Boyer-Moore string searching algorithm. It works by preprocessing the pattern string and comparing characters from right to left. If a mismatch occurs, it uses two heuristics - bad character and good suffix - to determine the shift amount. The bad character heuristic shifts past mismatching characters, while the good suffix heuristic looks for matching suffixes to allow larger shifts. The algorithm generally gets faster as the pattern length increases, running in sub-linear time on average. It has applications in tasks like virus scanning and database searching that require high-speed string searching.
String matching algorithms(knuth morris-pratt)
String matching algorithms(knuth morris-pratt)Neel Shah The Knuth-Morris-Pratt algorithm improves upon the brute force string matching approach by utilizing a prefix function to avoid re-checking characters. It runs in O(m+n) time and O(m) space, where m is the pattern length and n is the string length. The prefix function encapsulates how the pattern matches shifts of itself, enabling the algorithm to avoid redundant comparisons when a mismatch occurs.
Dinive conquer algorithm
Dinive conquer algorithmMohd Arif The document discusses the divide and conquer algorithm design technique. It begins by explaining the basic approach of divide and conquer which is to (1) divide the problem into subproblems, (2) conquer the subproblems by solving them recursively, and (3) combine the solutions to the subproblems into a solution for the original problem. It then provides merge sort as a specific example of a divide and conquer algorithm for sorting a sequence. It explains that merge sort divides the sequence in half recursively until individual elements remain, then combines the sorted halves back together to produce the fully sorted sequence.
N queen problem
N queen problemRidhima Chowdhury This document presents an overview of the N-Queen problem and its solution using backtracking. It discusses how the N-Queen problem was originally proposed as a chess puzzle in 1848 and involved placing N queens on an N×N chessboard so that no two queens attack each other. It then explains how backtracking can be used to systematically place queens on the board one by one and remove placements that result in conflicts until all queens are placed or no more placements are possible. Examples are given showing the backtracking process and solution trees for 4x4 boards. The time complexity of this backtracking solution is analyzed to be O(N!).
Algorithms Lecture 4: Sorting Algorithms I
Algorithms Lecture 4: Sorting Algorithms IMohamed Loey The document discusses three sorting algorithms: bubble sort, selection sort, and insertion sort. Bubble sort works by repeatedly swapping adjacent elements that are in the wrong order. Selection sort finds the minimum element and swaps it into the sorted portion of the array. Insertion sort inserts elements into the sorted portion of the array, swapping as needed to put the element in the correct position. Both selection sort and insertion sort have a time complexity of O(n^2) in the worst case.
Rabin Karp ppt
Rabin Karp pptshreyasBharadwaj15 The Rabin-Karp algorithm is a string-searching algorithm that uses hashing to find patterns in strings.
The Rabin-Karp algorithm makes use of hash functions and the rolling hash technique.
Optimal binary search tree
Optimal binary search treeKavya P The document discusses the algorithm for constructing an optimal binary search tree. It involves calculating the cost table C and root table R based on the probabilities of keys. The cost C[i,j] is calculated recursively as the minimum of C[i,k-1] + C[k+1,j] + sum of probabilities from i to j. This is done by iterating through all subtrees until the overall minimum cost C[1,n] is found, with the corresponding roots in R. The example calculates the optimal binary search tree for keys A, B, C, D with probabilities 0.1, 0.2, 0.4, 0.3, finding a minimum cost of 1.7.
Divide and conquer
Divide and conquerDr Shashikant Athawale The document discusses divide and conquer algorithms. It describes divide and conquer as a design strategy that involves dividing a problem into smaller subproblems, solving the subproblems recursively, and combining the solutions. It provides examples of divide and conquer algorithms like merge sort, quicksort, and binary search. Merge sort works by recursively sorting halves of an array until it is fully sorted. Quicksort selects a pivot element and partitions the array into subarrays of smaller and larger elements, recursively sorting the subarrays. Binary search recursively searches half-intervals of a sorted array to find a target value.
Lecture optimal binary search tree
Lecture optimal binary search tree Divya Ks This document discusses optimal binary search trees and provides an example problem. It begins with basic definitions of binary search trees and optimal binary search trees. It then shows an example problem with keys 1, 2, 3 and calculates the cost as 17. The document explains how to use dynamic programming to find the optimal binary search tree for keys 10, 12, 16, 21 with frequencies 4, 2, 6, 3. It provides the solution matrix and explains that the minimum cost is 2 with the optimal tree as 10, 12, 16, 21.
Convex hull
Convex hullShiwangi Thakur The document discusses several algorithms for computing the convex hull of a set of points, including brute force, quick hull, divide and conquer, Graham's scan, and Jarvis march. It provides details on the time complexity of each algorithm, ranging from O(n^2) for brute force to O(n log n) for quick hull, divide and conquer, and Graham's scan. Jarvis march runs in O(nh) time where h is the number of points on the convex hull.
KMP String Matching Algorithm
KMP String Matching Algorithmkalpanasatishkumar The Knuth-Morris-Pratt (KMP) string matching algorithm searches for occurrences of a pattern string within a main text string. It uses a prefix function to determine where the next match could begin without re-checking characters, allowing it to run in linear time. The algorithm consists of computing the prefix function, then performing the matching. It checks characters from left to right, using the prefix table to "shift" to the next possible match location when a mismatch occurs.
Greedy Algorithm
Greedy AlgorithmWaqar Akram This document provides an introduction to greedy algorithms. It defines greedy algorithms as algorithms that make locally optimal choices at each step in the hope of finding a global optimum. The document then provides examples of problems that can be solved using greedy algorithms, including counting money, scheduling jobs, finding minimum spanning trees, and the traveling salesman problem. It also provides pseudocode for a general greedy algorithm and discusses some properties of greedy algorithms.
Rabin karp string matcher
Rabin karp string matcherAmit Kumar Rathi The document discusses the Rabin-Karp algorithm for string matching. It defines Rabin-Karp as a string search algorithm that compares hash values of strings rather than the strings themselves. It explains that Rabin-Karp works by calculating a hash value for the pattern and text subsequences to compare, and only does a brute force comparison when hash values match. The worst-case complexity is O(n-m+1)m but the average case is O(n+m) plus processing spurious hits. Real-life applications include bioinformatics to find protein similarities.
Constraint satisfaction problems (csp)
Constraint satisfaction problems (csp) Archana432045 This document discusses constraint satisfaction problems (CSPs). It defines CSPs as problems with variables that must satisfy constraints. CSPs can represent many real-world problems and are solved through constraint satisfaction methods. The document outlines CSP components like variables, domains, and constraints. It also describes representing problems as CSPs, solving CSPs through backtracking search, and the role of heuristics like minimum remaining values in improving the search process.
RABIN KARP ALGORITHM STRING MATCHING
RABIN KARP ALGORITHM STRING MATCHINGAbhishek Singh RABIN KARP algorithm with hash function and hash collision, analysis, algorithm and code for implementation. Besides it contains applications of RABIN KARP algorithm also
convex hull
convex hullravikirankalal The document discusses the convex hull algorithm. It begins by defining a convex hull as the shape a rubber band would take if stretched around pins on a board. It then provides explanations of extreme points, edges, and applications of convex hulls. Various algorithms for finding convex hulls are presented, including divide and conquer in O(n log n) time and Jarvis march in O(n^2) time in the worst case.
Hashing
HashingDawood Faheem Abbasi hashing is encryption process mostly used in programming language for security purpose.
This presentation will you understand all about hashing and also different techniques used in it for encryption process
Daa notes 3
Daa notes 3smruti sarangi This file contains the concepts of Class P, Class NP, NP- completeness, Travelling Salesman Person problem, Clique Problem, Vertex cover problem, Hamiltonian problem, FFT and DFT.
Similar to String Matching Algorithms-The Naive Algorithm (20)
4 report format
4 report formatAshikapokiya12345 The document summarizes string matching algorithms. It discusses the naive string matching algorithm which compares characters at each shift to find matches. It also discusses the Rabin-Karp algorithm which uses hashing to match the hash value of the pattern to the hash value of substrings in the text. If the hash values match, it then checks for an exact character match. The Rabin-Karp algorithm has better average-case performance than the naive algorithm but the same worst-case performance of O((n-m+1)m) time.
4 report format
4 report formatAshikapokiya12345 The document summarizes string matching algorithms. It discusses the naive string matching algorithm which compares characters at each shift to find matches. It also describes the Rabin-Karp algorithm which uses hashing to match the hash value of the pattern to the hash value of substrings in the text. If the hash values match, it then checks for an exact character match. The Rabin-Karp algorithm has better average-case performance than the naive algorithm but the same worst-case performance of O((n-m+1)m) time.
String matching Algorithm by Foysal
String matching Algorithm by FoysalFoysal Mahmud Here i discuss 3 algorithm about String matching.
Those algorithm are:
1. The naive algorithm.
2. The Rabin-Krap algorithm.
3. The Knuth-Morris-Pratt algorithm.
i hope,by readinng this slide, it is easy to undarstand those algorithm.
String_Matching_algorithm String_Matching_algorithm .pptx
String_Matching_algorithm String_Matching_algorithm .pptxpraweenkumarsahu9 The document discusses string matching algorithms. It describes the naive string matching algorithm which compares characters in a pattern to a text sequentially. It also describes the Rabin-Karp algorithm which compares hash values of substrings instead of the strings themselves. The document provides examples and pseudocode for the naive string matching algorithm. It analyzes the time complexity of the Rabin-Karp algorithm.
06. string matching
06. string matchingOnkar Nath Sharma This document discusses string matching algorithms and their complexity. It introduces the string matching problem of finding all valid shifts where a pattern occurs in a text. It describes the naive algorithm that checks for a match between the pattern and text at each possible shift in O((n-m+1)m) time. It also mentions more advanced algorithms like the Knuth-Morris-Pratt algorithm and using finite automata that have better time complexities.
Naive String Matching Algorithm | Computer Science
Naive String Matching Algorithm | Computer ScienceTransweb Global Inc In this approach, the pattern is made to slide over text one by one and test for a match. If a match is found while testing, then it returns the starting index number from where the pattern is found in the text and then slides by 1 again to check for subsequent matches of the pattern in the text. Copy the link given below and paste it in new browser window to get more information on Naive String Matching Algorithm:- http://www.transtutors.com/homework-help/computer-science/naive-string-matching-algorithm.aspx
String matching algorithms
String matching algorithmsDepartment of Computer Science, University of Peshawar A basic introduction to "String matching algorithms". Useful for Biotechnology, Bioinformatics and Computer science.
String Match | Computer Science
String Match | Computer ScienceTransweb Global Inc Matching the String with a pattern is known as String Match”. Now, what are these strings and patterns? Alright, the string is that which is to be checked entered by the user and is matched with the pattern which is already in the database. Copy the link given below and paste it in new browser window to get more information on String Match:- www.transtutors.com/homework-help/computer-science/string-match.aspx
String Matching Algorithms: Naive, KMP, Rabin-Karp
String Matching Algorithms: Naive, KMP, Rabin-KarpNAtional Institute of TEchnology Rourkela , Galgotias University This presentation provides a clear and concise overview of popular string matching algorithms used in computer science, especially in areas like text search, bioinformatics, and compiler design.
🔍 Topics Covered:
What is String Matching?
Naive Algorithm (Brute Force)
Knuth-Morris-Pratt (KMP) Algorithm
Rabin-Karp Algorithm
Gp 27[string matching].pptx![Gp 27[string matching].pptx](https://cdn.slidesharecdn.com/ss_thumbnails/gp27stringmatching-230422183348-6f0879e9-thumbnail.jpg?width=560&fit=bounds)
Gp 27[string matching].pptxSumitYadav641839 It is string matching pattern from large text.it contains pseudo code, algorithm, advantages, drawbacks, conclusion and references as well.
String Matching algorithm String Matching algorithm String Matching algorithm
String Matching algorithm String Matching algorithm String Matching algorithmpraweenkumarsahu9 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.
25 String Matching
25 String MatchingAndres Mendez-Vazquez The string matching problem is a classic of algorithms. In this class, we only look at the Rabin-Karpp algorithm as a classic example of the string matching algorithms
Naive string matching
Naive string matchingAbhishek Singh Given presentation tell us about string, string matching and the navie method of string matching. Well this method has O((n-m+1)*m) time complexicity. It also tells the problem with naive approach and gives list of approaches which can be applied to reduce the time complexicity
StringMatching-Rabikarp algorithmddd.pdf
StringMatching-Rabikarp algorithmddd.pdfbhagabatijenadukura The document discusses string matching algorithms. It begins by introducing the problem of finding a pattern string P of length M within a text string T of length N, where typically N >> M. It then describes the naive brute force approach of checking for matches at each text position, having complexity of Θ(MN). The document next introduces the Knuth-Morris-Pratt (KMP) algorithm, which uses a failure function to skip over parts of the text where there cannot be a match, reducing complexity to Θ(N). Finally, it covers the Rabin-Karp algorithm, which uses hashing to filter out non-matching candidates before checking for exact matches, achieving overall complexity of Θ(N).
String matching algorithms
String matching algorithmsDr Shashikant Athawale This document discusses and defines four common algorithms for string matching:
1. The naive algorithm compares characters one by one with a time complexity of O(MN).
2. The Knuth-Morris-Pratt (KMP) algorithm uses pattern preprocessing to skip previously checked characters, achieving linear time complexity of O(N+M).
3. The Boyer-Moore (BM) algorithm matches strings from right to left and uses pattern preprocessing tables to skip more characters than KMP, with sublinear worst-case time complexity of O(N/M).
4. The Rabin-Karp (RK) algorithm uses hashing techniques to find matches in text substrings, with time complexity of
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM 
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM NETAJI SUBHASH ENGINEERING COLLEGE , KOLKATA This document discusses different pattern recognition algorithms that could be implemented in real-time data sets. It begins by defining pattern recognition and providing examples. It then discusses why pattern recognition is important and lists several applications. The document goes on to describe three main approaches to pattern recognition - statistical, syntactic, and neural pattern recognition - and provides examples for each. It then provides more detailed descriptions and pseudocode for several specific algorithms, including KMP, Boyer-Moore, Rabin-Karp, naive string matching, and brute-force string matching. It concludes by discussing future work improving algorithm complexity and potential applications in biometric identification.
Naive string matching algorithm
Naive string matching algorithmKiran K Algorithm to find whether a given Pattern is present in Text or not and return the index if the pattern is found.
Knuth–Morris–Pratt Algorithm | Computer Science
Knuth–Morris–Pratt Algorithm | Computer ScienceTransweb Global Inc In Software Engineering, the Knuth–Morris–Pratt string looking calculation (or KMP calculation) scans for events of a "word" W inside a principle "content string" S by utilizing the perception that when a jumble happens, the word itself epitomizes adequate data to figure out where the following match could start, accordingly bypassing reconsideration of beforehand coordinated characters. Copy the link given below and paste it in new browser window to get more information on Knuth-Morris-Pratt-Algorithm:- http://www.transtutors.com/homework-help/computer-science/knuth-marris-pratt-algorithm.aspx
Extending Boyer-Moore Algorithm to an Abstract String Matching Problem
Extending Boyer-Moore Algorithm to an Abstract String Matching ProblemLiwei Ren任力偉 The bad character shift rule of Boyer-Moore string search algorithm is studied in this paper for the purpose of extending it to more general string match problems. An abstract problem of string match is defined in general. An optimized string match algorithm based one the bad character heuristics is proposed to solve the abstract match problem efficiently.
String Matching Algorithms: Naive, KMP, Rabin-Karp
String Matching Algorithms: Naive, KMP, Rabin-KarpNAtional Institute of TEchnology Rourkela , Galgotias University
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM NETAJI SUBHASH ENGINEERING COLLEGE , KOLKATA
Ad
More from Adeel Rasheed (20)
Multan to Sukkur Motorway (M5)
Multan to Sukkur Motorway (M5)Adeel Rasheed In these slides i describe the cost, history and other important information related to Multan to Sukkur Motorway (M5).
Religious Conflict
Religious ConflictAdeel Rasheed The document defines religious conflict and discusses three types: inter-religious conflicts between different religious groups, intra-religious conflicts within religious groups over religious matters, and sectarian violence arising from perceptions of superiority between religious subdivisions. It provides examples of each type and notes religious conflicts can be caused by disagreements between religious beliefs and governmental policies.
R Studio (Report)
R Studio (Report)Adeel Rasheed RStudio is a free and open source integrated development environment (IDE) for R, a programming language for statistical computing and graphics. It was founded in 2010 and combines a source code editor, build automation tools and a debugger to help users write and test R code. The document then provides information on installing and using RStudio, including how to start the desktop or server versions, work with projects, read data files, debug code, and use the source code editor.
History of computer (1st to 5th Generations)
History of computer (1st to 5th Generations)Adeel Rasheed In these slides, I describe all the important points about all generations of computers. History of computer (1st to 5th Generations)
CSS Cascade Style Sheet
CSS Cascade Style SheetAdeel Rasheed On these slides. I explain all the properties and values of CSS Cascade Style Sheet (CSS).
How to define CSS class or id. How to implement CSS on the HTML page.
Zooming and Its Types
Zooming and Its TypesAdeel Rasheed Zooming enlarges images to make details more visible and clear. There are two types of zooming: optical zoom uses lens movement to magnify the image, maintaining quality, while digital zoom is image processing that crops the edges and compromises quality by expanding pixels. Optical zoom is achieved through physical extension of the camera lens, while digital zoom manipulates pixels after image capture.
User Interface & Its Types
User Interface & Its TypesAdeel Rasheed In these slides i describe user interface also its types. And put the other useful and important headings on it.
Questions About Android Application Development
Questions About Android Application DevelopmentAdeel Rasheed In this file i describe all the important questions about android application development.
What is Android?
What is an Activity?
Describe android application architecture?
What is explicit intent?
What is APK format?
What is implicit intent?
What language does Android support to develop an application?
Human and Machine Learning
Human and Machine LearningAdeel Rasheed Human learning involves acquiring knowledge, behaviors, and skills through processing information. It can occur through education, personal development, or other training. Machine learning is a form of artificial intelligence where algorithms allow computers to evolve behaviors based on empirical data in order to improve performance on tasks over time. Common applications of machine learning include search engines, medical diagnosis, and game playing.
Connection Establishment & Flow and Congestion Control
Connection Establishment & Flow and Congestion ControlAdeel Rasheed On these slides i describe all the detail about Connection Establishment & Flow and Congestion Control. For more detail visit: https://chauhantricks.blogspot.com/
IP Address - IPv4 & IPv6
IP Address - IPv4 & IPv6Adeel Rasheed IP addresses are numeric identifiers assigned to devices connected to a network. IPv4 uses 32-bit addresses represented in dotted decimal notation, while IPv6 uses 128-bit addresses represented by 8 groups of hexadecimal digits separated by colons. IP addresses have two parts - a network portion allocated by ISPs and a host portion assigned to individual devices. IPv4 classes (A, B, C, D, E) determine how many bits are used for the network vs host portions. IPv6 supports a much larger address space and easier auto-configuration compared to IPv4.
Computer Crime
Computer CrimeAdeel Rasheed Computer crimes refer to illegal activities that involve a computer system, such as hacking, identity theft, or distributing malware. Some common types of computer crimes include hacking into systems without authorization, stealing copyrighted material, cyberbullying or stalking others online, and fraud. Computer crimes are also known as cyber crimes, electronic crimes, or e-crimes.
Privacy and Security Information
Privacy and Security InformationAdeel Rasheed The document discusses various aspects of computer privacy and security. It outlines ways to protect privacy such as using passwords and encryption. Password protection allows only authorized users to access a computer, while public/private key encryption scrambles messages during transmission. The document also discusses how email privacy can be compromised if packets are intercepted during routing over the internet. It then covers information security strategies like restricting access and using biometrics. Malicious programs, or malware, are designed to damage systems, and examples provided are viruses, worms, and Trojan horses. The document concludes with a discussion of ethics issues involving copyright, digital rights management, and plagiarism.
Code of Conduct - Code of Practice & Standards
Code of Conduct - Code of Practice & StandardsAdeel Rasheed In which we explain introduction, importance, sections, steps of creation, responsibilities, violations and some other important contents about codes of conduct.
Self Adaptive Systems
Self Adaptive SystemsAdeel Rasheed The complexity of current software-based systems has led the software engineering community to look for inspiration in diverse related fields (e.g., robotics, artificial intelligence) as well as other areas (e.g., biology) to find new ways of designing and managing systems and services.
Ethernet - LAN
Ethernet - LANAdeel Rasheed Ethernet is a family of computer networking technologies for local area networks (LANs) and metropolitan area networks (MANs). It was commercially introduced in 1980 and first standardized in 1983 as IEEE 802.3, and has since been refined to support higher bit rates and longer link distances.
Synchronous and Asynchronous Transmission
Synchronous and Asynchronous TransmissionAdeel Rasheed Synchronous communication requires that the transmitting and receiving devices have synchronized clocks running at the same rate to allow data to flow continuously in blocks or frames in a full duplex mode, making it efficient and reliable for transferring large amounts of data, as is used for chat rooms, video calls, and phone conversations. In asynchronous transmission, data is sent intermittently without an external clock, flowing in a half duplex mode one byte at a time, generally with 8 data bits plus a start and stop bit, and is used for letters, emails, television, and radio.
Classes and Objects in C#
Classes and Objects in C#Adeel Rasheed A class defines the data and behavior of a type by grouping together variables, methods, and events. It supports encapsulation by allowing fields and methods to be declared as instance or static members. A class is declared using the class keyword followed by the class name and body surrounded by curly braces. Objects are instances of a class that can access members using the dot operator. Constructors are special methods that initialize an object when created with the new operator.
Intel Microprocessors 8086 Documentation
Intel Microprocessors 8086 DocumentationAdeel Rasheed On this file i provide all the documentation of Intel Microprocessors 8086 for assignment and project.
Applications of Computer
Applications of ComputerAdeel Rasheed Computers have countless applications across all fields including science, medicine, education, banking, crime investigation, entertainment, sports, advertising, social networking, government, stock trading, ticket reservations, the military, meteorology, business, book publishing, space, agriculture, and research. They are used for calculations, data storage, communication, education, banking transactions, medical imaging, security systems, gaming, movie making, statistical analysis, social media, record keeping, weapon development, weather forecasting, accounting, publishing, and more. Computers have revolutionized our lives and are ubiquitous in today's world.
Ad
Recently uploaded (20)
Creating an Accessible Future-How AI-powered Accessibility Testing is Shaping...
Creating an Accessible Future-How AI-powered Accessibility Testing is Shaping...Impelsys Inc. Web accessibility is a fundamental principle that strives to make the internet inclusive for all. According to the World Health Organization, over a billion people worldwide live with some form of disability. These individuals face significant challenges when navigating the digital landscape, making the quest for accessible web content more critical than ever.
Enter Artificial Intelligence (AI), a technological marvel with the potential to reshape the way we approach web accessibility. AI offers innovative solutions that can automate processes, enhance user experiences, and ultimately revolutionize web accessibility. In this blog post, we’ll explore how AI is making waves in the world of web accessibility.
Oracle Cloud and AI Specialization Program
Oracle Cloud and AI Specialization ProgramVICTOR MAESTRE RAMIREZ Oracle Cloud and AI Specialization Program
Agentic AI: Beyond the Buzz- LangGraph Studio V2
Agentic AI: Beyond the Buzz- LangGraph Studio V2Shashikant Jagtap Presentation given at the LangChain community meetup London
https://lu.ma/9d5fntgj
Coveres
Agentic AI: Beyond the Buzz
Introduction to AI Agent and Agentic AI
Agent Use case and stats
Introduction to LangGraph
Build agent with LangGraph Studio V2
vertical-cnc-processing-centers-drillteq-v-200-en.pdf
vertical-cnc-processing-centers-drillteq-v-200-en.pdfAmirStern2 מכונות CNC קידוח אנכיות הן הבחירה הנכונה והטובה ביותר לקידוח ארונות וארגזים לייצור רהיטים. החלק נוסע לאורך ציר ה-x באמצעות ציר דיגיטלי מדויק, ותפוס ע"י צבת מכנית, כך שאין צורך לבצע setup (התאמות) לגדלים שונים של חלקים.
Establish Visibility and Manage Risk in the Supply Chain with Anchore SBOM
Establish Visibility and Manage Risk in the Supply Chain with Anchore SBOMAnchore Over 70% of any given software application consumes open source software (most likely not even from the original source) and only 15% of organizations feel confident in their risk management practices.
With the newly announced Anchore SBOM feature, teams can start safely consuming OSS while mitigating security and compliance risks. Learn how to import SBOMs in industry-standard formats (SPDX, CycloneDX, Syft), validate their integrity, and proactively address vulnerabilities within your software ecosystem.
PyData - Graph Theory for Multi-Agent Integration
PyData - Graph Theory for Multi-Agent Integrationbarqawicloud Graph theory is a well-known concept for algorithms and can be used to orchestrate the building of multi-model pipelines. By translating tasks and dependencies into a Directed Acyclic Graph, we can orchestrate diverse AI models, including NLP, vision, and recommendation capabilities. This tutorial provides a step-by-step approach to designing graph-based AI model pipelines, focusing on clinical use cases from the field.
Domino IQ – What to Expect, First Steps and Use Cases
Domino IQ – What to Expect, First Steps and Use Casespanagenda Webinar Recording: https://www.panagenda.com/webinars/domino-iq-what-to-expect-first-steps-and-use-cases/
HCL Domino iQ Server – From Ideas Portal to implemented Feature. Discover what it is, what it isn’t, and explore the opportunities and challenges it presents.
Key Takeaways
- What are Large Language Models (LLMs) and how do they relate to Domino iQ
- Essential prerequisites for deploying Domino iQ Server
- Step-by-step instructions on setting up your Domino iQ Server
- Share and discuss thoughts and ideas to maximize the potential of Domino iQ
Ben Blair - Operating Safely in a Vibe Coding World
Ben Blair - Operating Safely in a Vibe Coding WorldAWS Chicago AWS Community Day Midwest 2025
Ben Blair
Operating Safely in a Vibe Coding World
Viral>Wondershare Filmora 14.5.18.12900 Crack Free Download
Viral>Wondershare Filmora 14.5.18.12900 Crack Free DownloadPuppy jhon ➡ 🌍📱👉COPY & PASTE LINK👉👉👉 ➤ ➤➤ https://drfiles.net/
Wondershare Filmora Crack is a user-friendly video editing software designed for both beginners and experienced users.
Developing Schemas with FME and Excel - Peak of Data & AI 2025
Developing Schemas with FME and Excel - Peak of Data & AI 2025Safe Software When working with other team members who may not know the Esri GIS platform or may not be database professionals; discussing schema development or changes can be difficult. I have been using Excel to help illustrate and discuss schema design/changes during meetings and it has proven a useful tool to help illustrate how a schema will be built. With just a few extra columns, that Excel file can be sent to FME to create new feature classes/tables. This presentation will go thru the steps needed to accomplish this task and provide some lessons learned and tips/tricks that I use to speed the process.
TrustArc Webinar - 2025 Global Privacy Survey
TrustArc Webinar - 2025 Global Privacy SurveyTrustArc How does your privacy program compare to your peers? What challenges are privacy teams tackling and prioritizing in 2025?
In the sixth annual Global Privacy Benchmarks Survey, we asked global privacy professionals and business executives to share their perspectives on privacy inside and outside their organizations. The annual report provides a 360-degree view of various industries' priorities, attitudes, and trends. See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar features an expert panel discussion and data-driven insights to help you navigate the shifting privacy landscape. Whether you are a privacy officer, legal professional, compliance specialist, or security expert, this session will provide actionable takeaways to strengthen your privacy strategy.
This webinar will review:
- The emerging trends in data protection, compliance, and risk
- The top challenges for privacy leaders, practitioners, and organizations in 2025
- The impact of evolving regulations and the crossroads with new technology, like AI
Predictions for the future of privacy in 2025 and beyond
If You Use Databricks, You Definitely Need FME
If You Use Databricks, You Definitely Need FMESafe Software DataBricks makes it easy to use Apache Spark. It provides a platform with the potential to analyze and process huge volumes of data. Sounds awesome. The sales brochure reads as if it is a can-do-all data integration platform. Does it replace our beloved FME platform or does it provide opportunities for FME to shine? Challenge accepted
Floods in Valencia: Two FME-Powered Stories of Data Resilience
Floods in Valencia: Two FME-Powered Stories of Data ResilienceSafe Software In October 2024, the Spanish region of Valencia faced severe flooding that underscored the critical need for accessible and actionable data. This presentation will explore two innovative use cases where FME facilitated data integration and availability during the crisis. The first case demonstrates how FME was used to process and convert satellite imagery and other geospatial data into formats tailored for rapid analysis by emergency teams. The second case delves into making human mobility data—collected from mobile phone signals—accessible as source-destination matrices, offering key insights into population movements during and after the flooding. These stories highlight how FME's powerful capabilities can bridge the gap between raw data and decision-making, fostering resilience and preparedness in the face of natural disasters. Attendees will gain practical insights into how FME can support crisis management and urban planning in a changing climate.
Secure Access with Azure Active Directory
Secure Access with Azure Active DirectoryVICTOR MAESTRE RAMIREZ Secure Access with Azure Active Directory
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/06/state-space-models-vs-transformers-for-ultra-low-power-edge-ai-a-presentation-from-brainchip/
Tony Lewis, Chief Technology Officer at BrainChip, presents the “State-space Models vs. Transformers for Ultra-low-power Edge AI” tutorial at the May 2025 Embedded Vision Summit.
At the embedded edge, choices of language model architectures have profound implications on the ability to meet demanding performance, latency and energy efficiency requirements. In this presentation, Lewis contrasts state-space models (SSMs) with transformers for use in this constrained regime. While transformers rely on a read-write key-value cache, SSMs can be constructed as read-only architectures, enabling the use of novel memory types and reducing power consumption. Furthermore, SSMs require significantly fewer multiply-accumulate units—drastically reducing compute energy and chip area.
New techniques enable distillation-based migration from transformer models such as Llama to SSMs without major performance loss. In latency-sensitive applications, techniques such as precomputing input sequences allow SSMs to achieve sub-100 ms time-to-first-token, enabling real-time interactivity. Lewis presents a detailed side-by-side comparison of these architectures, outlining their trade-offs and opportunities at the extreme edge.
Mastering AI Workflows with FME - Peak of Data & AI 2025
Mastering AI Workflows with FME - Peak of Data & AI 2025Safe Software Harness the full potential of AI with FME: From creating high-quality training data to optimizing models and utilizing results, FME supports every step of your AI workflow. Seamlessly integrate a wide range of models, including those for data enhancement, forecasting, image and object recognition, and large language models. Customize AI models to meet your exact needs with FME’s powerful tools for training, optimization, and seamless integration
How Advanced Environmental Detection Is Revolutionizing Oil & Gas Safety.pdf
How Advanced Environmental Detection Is Revolutionizing Oil & Gas Safety.pdfRejig Digital Unlock the future of oil & gas safety with advanced environmental detection technologies that transform hazard monitoring and risk management. This presentation explores cutting-edge innovations that enhance workplace safety, protect critical assets, and ensure regulatory compliance in high-risk environments.
🔍 What You’ll Learn:
✅ How advanced sensors detect environmental threats in real-time for proactive hazard prevention
🔧 Integration of IoT and AI to enable rapid response and minimize incident impact
📡 Enhancing workforce protection through continuous monitoring and data-driven safety protocols
💡 Case studies highlighting successful deployment of environmental detection systems in oil & gas operations
Ideal for safety managers, operations leaders, and technology innovators in the oil & gas industry, this presentation offers practical insights and strategies to revolutionize safety standards and boost operational resilience.
👉 Learn more: https://www.rejigdigital.com/blog/continuous-monitoring-prevent-blowouts-well-control-issues/
Oracle Cloud Infrastructure AI Foundations
Oracle Cloud Infrastructure AI FoundationsVICTOR MAESTRE RAMIREZ Oracle Cloud Infrastructure AI Foundations
Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...
Scaling GenAI Inference From Prototype to Production: Real-World Lessons in S...Anish Kumar Presented by: Anish Kumar
LinkedIn: https://www.linkedin.com/in/anishkumar/
This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks).
This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights.
Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...
“State-space Models vs. Transformers for Ultra-low-power Edge AI,” a Presenta...Edge AI and Vision Alliance
String Matching Algorithms-The Naive Algorithm
- 1. STRING MATCHING ALGORITHMS A B C A B A A C A B SHIFT=3 A B A A
- 2. BY: Adeel Rasheed For More Slides and Information Visit: https://chauhantricks.blogspot.com/
- 3. WHAT IS STRING MATCHING • In computer science, string searching algorithms, sometimes called string matching algorithms, that try to find a place where one or several string (also called pattern) are found within a larger string or text.
- 4. EXAMPLE STRING MATCHING PROBLEM TEXTA B C A B A A C A B SHIFT=3 A B A A
- 5. Types of SMA The Naive string-matching algorithm The Rabin-Krap algorithm String matching with finite automata The Knuth-Morris-Pratt algorithm
- 6. THE NAIVE ALGORITHM The idea of the naive solution is just to make a comparison character by character of the text for all pattern. It returns all the valid shifts found. P=Pattern T=Text S=Shift
- 7. THE NAIVE ALGORITHM T[n]=T[0,1…n-1] P[m]=P[0,1…m-1] NAIVE-STRING-MATCHER(T,P) 1) n = T.length 2) m = P.length 3) for s=0 to n-m 4) if P[0…m-1]==T[s+0….s+m-1] 5) print” Pattern occurs with shift ” s
- 8. EXAMPLE SUPPOSE, T=1011101110 P=111 FIND ALL VALIDSHIFT…… 1 0 1 1 1 0 1 1 1 0 1 1 1P=Patter n T=Text S=0
- 9. 1 0 1 1 1 0 1 1 1 0 1 1 1 S=1
- 10. 1 0 1 1 1 0 1 1 1 0 1 1 1 S=2 So, S=2 is a valid shift…
- 11. 1 0 1 1 1 0 1 1 1 0 S=3 1 1 1
- 12. 1 0 1 1 1 0 1 1 1 0 S=4 1 1 1
- 13. 1 0 1 1 1 0 1 1 1 0 1 1 1 S=5
- 14. 1 0 1 1 1 0 1 1 1 0 S=6 1 1 1 So, S=6 is a valid shift…
- 15. 1 0 1 1 1 0 1 1 1 0 1 1 1 S=7