RABIN KARP algorithm with hash function and hash collision, analysis, algorithm and code for implementation. Besides it contains applications of RABIN KARP algorithm also
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.
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.
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.
This document provides an overview of the Knuth-Morris-Pratt substring search algorithm. It defines the algorithm, describes its history and key components including the prefix function and KMP matcher. An example showing the step-by-step workings of the algorithm on a text and pattern is provided. The algorithm's linear runtime complexity of O(n+m) is compared to other string matching algorithms. Real-world applications including DNA sequence analysis and search engines are discussed.
The Rabin-Karp string matching algorithm calculates a hash value for the pattern and for each substring of the text to compare values efficiently. If hash values match, it performs a character-by-character comparison, otherwise it skips to the next substring. This reduces the number of costly comparisons from O(MN) in brute force to O(N) on average by filtering out non-matching substrings in one comparison each using hash values. Choosing a large prime number when calculating hash values further decreases collisions and false positives.
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.
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
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.
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.
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.
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.
This document provides an overview of the Knuth-Morris-Pratt substring search algorithm. It defines the algorithm, describes its history and key components including the prefix function and KMP matcher. An example showing the step-by-step workings of the algorithm on a text and pattern is provided. The algorithm's linear runtime complexity of O(n+m) is compared to other string matching algorithms. Real-world applications including DNA sequence analysis and search engines are discussed.
The Rabin-Karp string matching algorithm calculates a hash value for the pattern and for each substring of the text to compare values efficiently. If hash values match, it performs a character-by-character comparison, otherwise it skips to the next substring. This reduces the number of costly comparisons from O(MN) in brute force to O(N) on average by filtering out non-matching substrings in one comparison each using hash values. Choosing a large prime number when calculating hash values further decreases collisions and false positives.
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.
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
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(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.
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.
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.
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.
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.
- Bayesian networks can model conditional independencies between variables based on the network structure. Each variable is conditionally independent of its non-descendants given its parents.
- The d-separation algorithm allows determining if two variables are conditionally independent given some evidence by checking if all paths between them are "blocked".
- For trees/forests where each node has at most one parent, inference can be done efficiently in linear time by decomposing probabilities and passing messages between nodes.
The presenation gives a brief detail about a searching algorithm known as "Rabin-Karp Algorithm". The presentation contains the history of the algorithm and its working alongwith the example.
This document describes the Rabin-Karp string matching algorithm. It proposes using hashing to improve on the naive string matching algorithm. The Rabin-Karp algorithm calculates a hash of the pattern string and compares it to the hash of successive substring slices of the text. If the hashes match, it does a character-by-character comparison. This allows it to avoid unnecessary comparisons and achieves an average runtime of O(m+n), improving on the naive algorithm. It also discusses how to mitigate hash collisions.
The Boyer-Moore string matching algorithm was developed in 1977 and is considered one of the most efficient string matching algorithms. It works by scanning the pattern from right to left and shifting the pattern by multiple characters if a mismatch is found, using preprocessing tables. The algorithm constructs a bad character shift table during preprocessing that stores the maximum number of positions a mismatched character can shift the pattern. It then aligns the pattern with the text and checks for matches, shifting the pattern right by the value in the table if a mismatch occurs.
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.
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.
The document discusses the problem of determining the optimal way to fully parenthesize the product of a chain of matrices to minimize the number of scalar multiplications. It presents a dynamic programming approach to solve this problem in four steps: 1) characterize the structure of an optimal solution, 2) recursively define the cost of an optimal solution, 3) compute the costs using tables, 4) construct the optimal solution from the tables. An example is provided to illustrate computing the costs table and finding the optimal parenthesization of a chain of 6 matrices.
The document discusses asymptotic notations that are used to describe the time complexity of algorithms. It introduces big O notation, which describes asymptotic upper bounds, big Omega notation for lower bounds, and big Theta notation for tight bounds. Common time complexities are described such as O(1) for constant time, O(log N) for logarithmic time, and O(N^2) for quadratic time. The notations allow analyzing how efficiently algorithms use resources like time and space as the input size increases.
The document discusses the Knuth-Morris-Pratt string matching algorithm. It begins with an explanation of the string matching problem and an inefficient O(mn) solution. It then introduces the KMP algorithm which uses a prefix function to avoid repeating comparisons, solving the problem in linear O(n) time. The prefix function is computed by analyzing shifts of the pattern against itself. The KMP matcher uses the prefix function to efficiently search the string without backtracking.
The document provides information about B+ trees and height balancing trees. It begins with an introduction to B+ trees, describing their properties, representation, advantages over B-trees, and algorithms for insertion and deletion. It then covers key points about B+ trees, provides examples of height balanced trees like AVL trees and 2-3-4 trees, and gives pseudocode for operations on these trees like calculating balancing factors. The document concludes with solved problems on B+ trees.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
The document discusses algorithm analysis and asymptotic notation. It defines algorithm analysis as comparing algorithms based on running time and other factors as problem size increases. Asymptotic notation such as Big-O, Big-Omega, and Big-Theta are introduced to classify algorithms based on how their running times grow relative to input size. Common time complexities like constant, logarithmic, linear, quadratic, and exponential are also covered. The properties and uses of asymptotic notation for equations and inequalities are explained.
Algorithms Lecture 2: Analysis of Algorithms IMohamed Loey
This document discusses analysis of algorithms and time complexity. It explains that analysis of algorithms determines the resources needed to execute algorithms. The time complexity of an algorithm quantifies how long it takes. There are three cases to analyze - worst case, average case, and best case. Common notations for time complexity include O(1), O(n), O(n^2), O(log n), and O(n!). The document provides examples of algorithms and determines their time complexity in different cases. It also discusses how to combine complexities of nested loops and loops in algorithms.
0 1 knapsack using naive recursive approach and top-down dynamic programming ...Abhishek Singh
This slide contains 0-1 knapsack problem using naive recursive approach and using top-down dynamic programming approach. Here we have used memorization to optimize the recursive approach.
Given two integer arrays val[0...n-1] and wt[0...n-1] that represents values and weights associated with n items respectively. Find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to knapsack capacity W. Here the BRANCH AND BOUND ALGORITHM is discussed .
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.
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.
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.
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.
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.
- Bayesian networks can model conditional independencies between variables based on the network structure. Each variable is conditionally independent of its non-descendants given its parents.
- The d-separation algorithm allows determining if two variables are conditionally independent given some evidence by checking if all paths between them are "blocked".
- For trees/forests where each node has at most one parent, inference can be done efficiently in linear time by decomposing probabilities and passing messages between nodes.
The presenation gives a brief detail about a searching algorithm known as "Rabin-Karp Algorithm". The presentation contains the history of the algorithm and its working alongwith the example.
This document describes the Rabin-Karp string matching algorithm. It proposes using hashing to improve on the naive string matching algorithm. The Rabin-Karp algorithm calculates a hash of the pattern string and compares it to the hash of successive substring slices of the text. If the hashes match, it does a character-by-character comparison. This allows it to avoid unnecessary comparisons and achieves an average runtime of O(m+n), improving on the naive algorithm. It also discusses how to mitigate hash collisions.
The Boyer-Moore string matching algorithm was developed in 1977 and is considered one of the most efficient string matching algorithms. It works by scanning the pattern from right to left and shifting the pattern by multiple characters if a mismatch is found, using preprocessing tables. The algorithm constructs a bad character shift table during preprocessing that stores the maximum number of positions a mismatched character can shift the pattern. It then aligns the pattern with the text and checks for matches, shifting the pattern right by the value in the table if a mismatch occurs.
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.
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.
The document discusses the problem of determining the optimal way to fully parenthesize the product of a chain of matrices to minimize the number of scalar multiplications. It presents a dynamic programming approach to solve this problem in four steps: 1) characterize the structure of an optimal solution, 2) recursively define the cost of an optimal solution, 3) compute the costs using tables, 4) construct the optimal solution from the tables. An example is provided to illustrate computing the costs table and finding the optimal parenthesization of a chain of 6 matrices.
The document discusses asymptotic notations that are used to describe the time complexity of algorithms. It introduces big O notation, which describes asymptotic upper bounds, big Omega notation for lower bounds, and big Theta notation for tight bounds. Common time complexities are described such as O(1) for constant time, O(log N) for logarithmic time, and O(N^2) for quadratic time. The notations allow analyzing how efficiently algorithms use resources like time and space as the input size increases.
The document discusses the Knuth-Morris-Pratt string matching algorithm. It begins with an explanation of the string matching problem and an inefficient O(mn) solution. It then introduces the KMP algorithm which uses a prefix function to avoid repeating comparisons, solving the problem in linear O(n) time. The prefix function is computed by analyzing shifts of the pattern against itself. The KMP matcher uses the prefix function to efficiently search the string without backtracking.
The document provides information about B+ trees and height balancing trees. It begins with an introduction to B+ trees, describing their properties, representation, advantages over B-trees, and algorithms for insertion and deletion. It then covers key points about B+ trees, provides examples of height balanced trees like AVL trees and 2-3-4 trees, and gives pseudocode for operations on these trees like calculating balancing factors. The document concludes with solved problems on B+ trees.
P, NP, NP-Complete, and NP-Hard
Reductionism in Algorithms
NP-Completeness and Cooks Theorem
NP-Complete and NP-Hard Problems
Travelling Salesman Problem (TSP)
Travelling Salesman Problem (TSP) - Approximation Algorithms
PRIMES is in P - (A hope for NP problems in P)
Millennium Problems
Conclusions
The document discusses algorithm analysis and asymptotic notation. It defines algorithm analysis as comparing algorithms based on running time and other factors as problem size increases. Asymptotic notation such as Big-O, Big-Omega, and Big-Theta are introduced to classify algorithms based on how their running times grow relative to input size. Common time complexities like constant, logarithmic, linear, quadratic, and exponential are also covered. The properties and uses of asymptotic notation for equations and inequalities are explained.
Algorithms Lecture 2: Analysis of Algorithms IMohamed Loey
This document discusses analysis of algorithms and time complexity. It explains that analysis of algorithms determines the resources needed to execute algorithms. The time complexity of an algorithm quantifies how long it takes. There are three cases to analyze - worst case, average case, and best case. Common notations for time complexity include O(1), O(n), O(n^2), O(log n), and O(n!). The document provides examples of algorithms and determines their time complexity in different cases. It also discusses how to combine complexities of nested loops and loops in algorithms.
0 1 knapsack using naive recursive approach and top-down dynamic programming ...Abhishek Singh
This slide contains 0-1 knapsack problem using naive recursive approach and using top-down dynamic programming approach. Here we have used memorization to optimize the recursive approach.
Given two integer arrays val[0...n-1] and wt[0...n-1] that represents values and weights associated with n items respectively. Find out the maximum value subset of val[] such that sum of the weights of this subset is smaller than or equal to knapsack capacity W. Here the BRANCH AND BOUND ALGORITHM is discussed .
Knights tour on chessboard using backtrackingAbhishek Singh
The knight is placed o any block of an empty chess board and moving according to the rules of chess, it must visit each square exacty once. Here in the ppt the algorithm along with some visualisation and explanation is given and problem is solved using backtracking approach.
The document discusses the branch and bound algorithm for solving the 15-puzzle problem. It describes the key components of branch and bound including live nodes, e-nodes, and dead nodes. It also defines the cost function used to evaluate nodes as the sum of the path length and number of misplaced tiles. The algorithm generates all possible child nodes from the current node and prunes the search tree by comparing node costs to avoid exploring subtrees without solutions.
Given a text txt[0..n-1] and a pattern pat[0..m-1], write a function search(char pat[], char txt[]) that prints all occurrences of pat[] in txt[]. You may assume that n > m. using kmp algorithm with time complexity O(m+n)
This document discusses using backtracking to solve Sudoku puzzles. It begins with a brief overview of Sudoku, explaining the rules and goal. It then introduces backtracking as a technique that starts with possible moves and backtracks if a partial solution cannot be completed. The key steps of the backtracking algorithm are outlined: finding unassigned cells, trying digits, checking for conflicts, recursively solving, and backtracking if needed. An example puzzle is provided and it is explained how the algorithm would solve it by moving through the steps. Important notes about backtracking are that it will find all solutions but uses significant time and space for large problems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
2. Let text string be T of length N
Pattern string be P of length M
Example
T=“hello world”; N=11;
P=“llo”; M=3
Problem Statement
H E L L O W O R L D
L L O
3. The Rabin-Karp string matching algorithm calculates a hash value
for the pattern, as well as for each M-character subsequence of
given text to be compared.
If the hash values for a particular subsequence are unequal, the
algorithm will calculate the hash value for next M-character
sequence.
If the hash values are equal, the algorithm will do a Brute Force
comparison between the pattern and the M-character sequence.
Therefore there is only one comparison per text subsequence, and
Brute Force is only needed when hash values match.
Note: Only one comparison for sub sequence
Rabin-Karp Algorithm
4. Consider an M-character sequence as an M-
digit number in the alphabet. The text
subsequence t[I ... i+M-1] is mapped to the
number
x(i) = t[i]*3^0+ t[i+1]*b^1+...+ t[i+M-1]*3^(M-
1)
Furthermore, given x(i) we can compute x(i+1)
for the next subsequence t[i+1 .. i+M] in
constant time,as follows:
x(i+1) = (x(i)-(t[i]))/3+(3^(M-1))*t[i+M-1]
Rolling Hash Function
5. A B D A B C
Example
tHash = Hash(“ABD”) = 1*3^0+2*3^1+4*3^2= 43
pHash = Hash(“ABC”) = 1*3^0+2*3^1+3*3^2 = 34
tHash == pHash FALSE
tHash = Hash(“BDA”) = 2*3^0+4*3^1+1*3^2 = 23
tHash == pHash FALSE
tHash = Hash(“DAB”) = 4*3^0+1*3^1+2*3^2 = 25
tHash == pHash FALSE
A B D A B C
A B D A B C
A B D A B C
tHash = Hash(“ABC”) = 30+30+40 = 34
tHash == pHash TRUE
6. TEXT :
PATTERN :
HERE PATTERN MATCHES THE
SUBSTRING SO INDEX NO. 3 IS RETURNED
Incase of HIT
A B D A B C
A B C
7. int main()
{
string s1,s2;
cin>>s1>>s2;
int
l1=s1.size(),l2=s2.size(),
i,j,k;
int hs1=0,p=0,ar[l1];
for(i=0;i<l2;i++)
hs1+=(int)s2[i]*pow(3,i);
int hs2=0;
for(i=0;i<l1;i++)
{
if(i==0)
int c=0;
if(hs2==hs1)
{
for(j=i;j<i+l2;j++)
if(s1[j]==s2[j-
i])
c++;
else
break;
}
if(c==l2)
ar[p++]=i;
}
cout<<l1<<endl;
cout<<"Stored index
8. Hash to two string match then it is called Hit
There is possibility
Hash of “abc” is 34
Hash of “dga” is 34
This is called Hash Collision
Minimize Collision by
Taking mod with prime number
HASH COLLISION
9. Hash of Pattern
O(m)
Hash Comparison
O(n-m+1) = O(n) ; m < n
Average Running Time
O(m+n)
Worst Case Running Time
m comparison in each iteration
O(mn)
ANALYSIS
10. Keyword matching in large files
Good for Plagiarism detection
Searching engines
Database searching
Bioinformatics
APPLICATION