This document provides an introduction to MATLAB. It discusses that MATLAB is a high-performance language for technical computing that integrates computation, visualization, and programming. It can be used for tasks like math and computation, algorithm development, modeling, simulation, prototyping, data analysis, and scientific graphics. MATLAB uses arrays as its basic data type and allows matrix and vector problems to be solved more quickly than with other languages. The document then provides examples of entering matrices, using basic MATLAB commands and functions, plotting graphs, and writing MATLAB code in M-files.
This is about a topic of compiler design, LR and SLR parsing algorithm and LR grammar, Canonical collection and Item, Conflict in LR parsing shift reduce. Classification of Bottom up parsing.
The document discusses transactions and transaction management in database systems. It defines transactions as logical units of work that must follow the ACID properties of atomicity, consistency, isolation, and durability. Transactions access and update data using operations like read and write. The transaction model ensures concurrent transactions execute reliably by enforcing serializability through techniques like conflict analysis and precedence graphs. Maintaining serializability guarantees the isolation property and prevents anomalous behavior from transaction interleaving.
This document provides an introduction to database concepts including definitions of data, information, and databases. It discusses the data processing cycle and differences between manual and computerized data processing. It also describes database users like system analysts, application programmers, and end users. Additionally, it covers database features such as redundancy control, data integrity, data sharing, and security. It discusses data abstraction, database models including hierarchical, network and relational models, as well as normalization. Other topics include database architecture, physical and logical data independence, and entity-relationship diagrams.
The document describes the greedy method algorithm design technique. It works in steps, selecting the best available option at each step until all options are exhausted. Many problems can be formulated as finding a feasible subset that optimizes an objective function. A greedy algorithm works in stages, making locally optimal choices at each stage to arrive at a global optimal solution. Several examples are provided to illustrate greedy algorithms for problems like change making, machine scheduling, container loading, knapsack problem, job sequencing with deadlines, and single-source shortest paths. Pseudocode is given for some of the greedy algorithms.
The document discusses syntax-directed translation using attribute grammars. Attribute grammars assign semantic values or attributes to the symbols in a context-free grammar. A depth-first traversal of the parse tree executes semantic rules that calculate the values of attributes. There are two types of attributes: synthesized attributes which are computed bottom-up and inherited attributes which are passed top-down. L-attributed grammars allow efficient evaluation by passing inherited attributes left-to-right during a depth-first traversal.
This document defines deterministic finite automata (DFAs) and discusses their components, representations, operations, and properties. A DFA consists of a finite set of states, a finite alphabet, a transition function, a start state, and a set of accepting/final states. DFAs can be represented by transition tables or diagrams. The product and complement operations on DFAs are defined, and it is shown that these preserve regularity of languages. The accessible and minimal parts of a DFA are also discussed.
The document provides an introduction to MATLAB, describing the main environment components like the command window and workspace. It explains basic MATLAB functions and variables, arrays, control flow statements, M-files, and common plotting and data analysis tools. Examples are given of different array operations, control structures, and building simple MATLAB functions and scripts.
This document provides an introduction to MATLAB. It discusses that MATLAB is a high-performance language for technical computing that integrates computation, visualization, and programming. It can be used for tasks like math and computation, algorithm development, modeling, simulation, prototyping, data analysis, and scientific graphics. MATLAB uses arrays as its basic data type and allows matrix and vector problems to be solved more quickly than with other languages. The document then provides examples of entering matrices, using basic MATLAB commands and functions, plotting graphs, and writing MATLAB code in M-files.
This is about a topic of compiler design, LR and SLR parsing algorithm and LR grammar, Canonical collection and Item, Conflict in LR parsing shift reduce. Classification of Bottom up parsing.
The document discusses transactions and transaction management in database systems. It defines transactions as logical units of work that must follow the ACID properties of atomicity, consistency, isolation, and durability. Transactions access and update data using operations like read and write. The transaction model ensures concurrent transactions execute reliably by enforcing serializability through techniques like conflict analysis and precedence graphs. Maintaining serializability guarantees the isolation property and prevents anomalous behavior from transaction interleaving.
This document provides an introduction to database concepts including definitions of data, information, and databases. It discusses the data processing cycle and differences between manual and computerized data processing. It also describes database users like system analysts, application programmers, and end users. Additionally, it covers database features such as redundancy control, data integrity, data sharing, and security. It discusses data abstraction, database models including hierarchical, network and relational models, as well as normalization. Other topics include database architecture, physical and logical data independence, and entity-relationship diagrams.
The document describes the greedy method algorithm design technique. It works in steps, selecting the best available option at each step until all options are exhausted. Many problems can be formulated as finding a feasible subset that optimizes an objective function. A greedy algorithm works in stages, making locally optimal choices at each stage to arrive at a global optimal solution. Several examples are provided to illustrate greedy algorithms for problems like change making, machine scheduling, container loading, knapsack problem, job sequencing with deadlines, and single-source shortest paths. Pseudocode is given for some of the greedy algorithms.
The document discusses syntax-directed translation using attribute grammars. Attribute grammars assign semantic values or attributes to the symbols in a context-free grammar. A depth-first traversal of the parse tree executes semantic rules that calculate the values of attributes. There are two types of attributes: synthesized attributes which are computed bottom-up and inherited attributes which are passed top-down. L-attributed grammars allow efficient evaluation by passing inherited attributes left-to-right during a depth-first traversal.
This document defines deterministic finite automata (DFAs) and discusses their components, representations, operations, and properties. A DFA consists of a finite set of states, a finite alphabet, a transition function, a start state, and a set of accepting/final states. DFAs can be represented by transition tables or diagrams. The product and complement operations on DFAs are defined, and it is shown that these preserve regularity of languages. The accessible and minimal parts of a DFA are also discussed.
The document provides an introduction to MATLAB, describing the main environment components like the command window and workspace. It explains basic MATLAB functions and variables, arrays, control flow statements, M-files, and common plotting and data analysis tools. Examples are given of different array operations, control structures, and building simple MATLAB functions and scripts.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
Στο παρόν φυλλάδιο μελετάμε ένα από τα πιο κοινά προβλήματα που εμφανίζονται στα μαθηματικά,
τα γραμμικά συστήματα. Παρουσιάζουμε το γενικό πρόβλημα της επίλυσης ενός
συστήματος με m εξισώσεις και n αγνώστους και την πιο γνωστή μέθοδο επίλυσης, την μέθοδο απαλοιφής του Gauss, χωρίς να δίνουμε
άλλους τρόπους επίλυσης που χρησιμοποιούν επαυξημένους πίνακες και ορίζουσες.
The document describes the process of minimizing a deterministic finite automaton (DFA) using the Myhill-Nerode theorem. It involves 4 steps: 1) creating a table of all state pairs, 2) marking pairs where one state is final and the other is not, 3) transitively marking additional pairs, 4) combining any remaining unmarked pairs into single states. An example is provided where the given 6-state DFA is minimized to a 3-state DFA using this process. Terminology used in DFA minimization is also defined.
Chapter summary and solutions to end-of-chapter exercises for "Data Visualization: Principles and Practice" book by Alexandru C. Telea
Chapter provides an overview of a number of methods for visualizing tensor data. It explains principal component analysis as a technique used to process a tensor matrix and extract from it information that can directly be used in its visualization. It forms a fundamental part of many tensor data processing and visualization algorithms. Section 7.4 shows how the results of the principal component analysis can be visualized using the simple color-mapping techniques. Next parts of the chapter explain how same data can be visualized using tensor glyphs, and streamline-like visualization techniques.
In contrast to Slicer, which is a more general framework for analyzing and visualizing 3D slice-based data volumes, the Diffusion Toolkit focuses on DT-MRI datasets, and thus offers more extensive and easier to use options for fiber tracking.
The document discusses code generation and register allocation techniques. It begins by describing register allocation and assignment approaches and their advantages and disadvantages. It then discusses various issues in register allocation like which values reside in registers and which register. It describes register allocation as an NP-hard problem and common heuristics like graph coloring. The rest of the document discusses specific strategies for register allocation like global register allocation, usage counts, and register allocation via graph coloring and interference graphs. It also discusses techniques like register spilling when there are not enough registers.
Arrays in C allow storing of homogeneous data items in contiguous memory locations. An array variable stores multiple data items of the same data type. Arrays provide advantages like code optimization through accessing elements with fewer lines of code, easy traversal and sorting of data using loops, and random access of any element. The main disadvantage is that arrays have a fixed size set at declaration. Key array terminology includes size, type, base address, index, and range. Arrays can be declared and initialized at the time of declaration or by taking input from the user. Multidimensional arrays like 2D arrays represent data in rows and columns like a matrix.
The document discusses heaps and priority queues. It provides an overview of using a complete binary tree and array-based representation to implement a heap-based priority queue. Key points include: storing nodes in an array allows easy access to parent and child nodes, a heap is a complete binary tree where each parent has a higher priority value than its children, and priority queues can be implemented efficiently using heaps.
Maharishi University of Management (MSc Computer Science test questions)Dharma Kshetri
The document provides sample code for 3 programming challenges:
1) Write a function to return the second largest integer in an array.
2) Write a function to return the difference between the sum of odd and even numbers in an array.
3) Write a function to return a substring of characters from an input character array based on a start position and length. The function returns null if the start/length are invalid.
Semantic analysis is a pass by a compiler that adds semantic information to the parse tree and performs certain checks based on this information. It logically follows the parsing phase, in which the parse tree is generated, and logically precedes the code generation phase, in which executable code is generated.
Adbms 11 object structure and type constructorVaibhav Khanna
Unique Identity:
An OO database system provides a unique identity to each independent object stored in the database.
This unique identity is typically implemented via a unique, system-generated object identifier, or OID
The main property required of an OID is that it be immutable
Specifically, the OID value of a particular object should not change.
This preserves the identity of the real-world object being represented.
Type Constructors:
In OO databases, the state (current value) of a complex object may be constructed from other objects (or other values) by using certain type constructors.
The three most basic constructors are atom, tuple, and set.
Other commonly used constructors include list, bag, and array.
The document discusses different database normalization forms including first normal form (1NF), second normal form (2NF), third normal form (3NF), Boyce-Codd normal form (BCNF), and how to convert a relational schema into BCNF. It provides examples of relational schemas and their functional dependencies to illustrate when a schema satisfies or violates each normal form. The conversion process to BCNF involves finding the minimal cover of functional dependencies, identifying dependencies that violate BCNF, and decomposing the schema into multiple schemas in BCNF.
An array is a container that holds a fixed number of values of the same type. An array's length is determined when it is created and cannot be changed. The document then provides an example of creating an integer array called "scores" with 4 elements to store the scores of 4 cricket teams. It demonstrates accessing the elements of the array using indexes and printing the team scores.
Theory of automata and formal languageRabia Khalid
KleenE Star Closure, Plus operation, recursive definition of languages, INTEGER, EVEN, factorial, PALINDROME, languages of strings, cursive definition of RE, defining languages by RE,Examples
This document discusses automata theory and focuses on grammars, languages, and finite state machines. It defines key terminology like alphabets, strings, languages, and regular expressions. It explains Chomsky's hierarchy of formal languages from type-3 regular languages to type-0 recursively enumerable languages. The document also discusses finite state automata (FSA), deterministic finite automata (DFA), non-deterministic finite automata (NFA), context-free grammars, pushdown automata, and Turing machines. Examples of grammars, languages, and finite state machines are provided to illustrate these concepts.
This document discusses sparse matrices. It defines a sparse matrix as a matrix with more zero values than non-zero values. Sparse matrices can save space by only storing the non-zero elements and their indices rather than allocating space for all elements. Two common representations for sparse matrices are the triplet representation, which stores the non-zero values and their row and column indices, and the linked representation, which connects the non-zero elements. Applications of sparse matrices include solving large systems of equations.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
Στο παρόν φυλλάδιο μελετάμε ένα από τα πιο κοινά προβλήματα που εμφανίζονται στα μαθηματικά,
τα γραμμικά συστήματα. Παρουσιάζουμε το γενικό πρόβλημα της επίλυσης ενός
συστήματος με m εξισώσεις και n αγνώστους και την πιο γνωστή μέθοδο επίλυσης, την μέθοδο απαλοιφής του Gauss, χωρίς να δίνουμε
άλλους τρόπους επίλυσης που χρησιμοποιούν επαυξημένους πίνακες και ορίζουσες.
The document describes the process of minimizing a deterministic finite automaton (DFA) using the Myhill-Nerode theorem. It involves 4 steps: 1) creating a table of all state pairs, 2) marking pairs where one state is final and the other is not, 3) transitively marking additional pairs, 4) combining any remaining unmarked pairs into single states. An example is provided where the given 6-state DFA is minimized to a 3-state DFA using this process. Terminology used in DFA minimization is also defined.
Chapter summary and solutions to end-of-chapter exercises for "Data Visualization: Principles and Practice" book by Alexandru C. Telea
Chapter provides an overview of a number of methods for visualizing tensor data. It explains principal component analysis as a technique used to process a tensor matrix and extract from it information that can directly be used in its visualization. It forms a fundamental part of many tensor data processing and visualization algorithms. Section 7.4 shows how the results of the principal component analysis can be visualized using the simple color-mapping techniques. Next parts of the chapter explain how same data can be visualized using tensor glyphs, and streamline-like visualization techniques.
In contrast to Slicer, which is a more general framework for analyzing and visualizing 3D slice-based data volumes, the Diffusion Toolkit focuses on DT-MRI datasets, and thus offers more extensive and easier to use options for fiber tracking.
The document discusses code generation and register allocation techniques. It begins by describing register allocation and assignment approaches and their advantages and disadvantages. It then discusses various issues in register allocation like which values reside in registers and which register. It describes register allocation as an NP-hard problem and common heuristics like graph coloring. The rest of the document discusses specific strategies for register allocation like global register allocation, usage counts, and register allocation via graph coloring and interference graphs. It also discusses techniques like register spilling when there are not enough registers.
Arrays in C allow storing of homogeneous data items in contiguous memory locations. An array variable stores multiple data items of the same data type. Arrays provide advantages like code optimization through accessing elements with fewer lines of code, easy traversal and sorting of data using loops, and random access of any element. The main disadvantage is that arrays have a fixed size set at declaration. Key array terminology includes size, type, base address, index, and range. Arrays can be declared and initialized at the time of declaration or by taking input from the user. Multidimensional arrays like 2D arrays represent data in rows and columns like a matrix.
The document discusses heaps and priority queues. It provides an overview of using a complete binary tree and array-based representation to implement a heap-based priority queue. Key points include: storing nodes in an array allows easy access to parent and child nodes, a heap is a complete binary tree where each parent has a higher priority value than its children, and priority queues can be implemented efficiently using heaps.
Maharishi University of Management (MSc Computer Science test questions)Dharma Kshetri
The document provides sample code for 3 programming challenges:
1) Write a function to return the second largest integer in an array.
2) Write a function to return the difference between the sum of odd and even numbers in an array.
3) Write a function to return a substring of characters from an input character array based on a start position and length. The function returns null if the start/length are invalid.
Semantic analysis is a pass by a compiler that adds semantic information to the parse tree and performs certain checks based on this information. It logically follows the parsing phase, in which the parse tree is generated, and logically precedes the code generation phase, in which executable code is generated.
Adbms 11 object structure and type constructorVaibhav Khanna
Unique Identity:
An OO database system provides a unique identity to each independent object stored in the database.
This unique identity is typically implemented via a unique, system-generated object identifier, or OID
The main property required of an OID is that it be immutable
Specifically, the OID value of a particular object should not change.
This preserves the identity of the real-world object being represented.
Type Constructors:
In OO databases, the state (current value) of a complex object may be constructed from other objects (or other values) by using certain type constructors.
The three most basic constructors are atom, tuple, and set.
Other commonly used constructors include list, bag, and array.
The document discusses different database normalization forms including first normal form (1NF), second normal form (2NF), third normal form (3NF), Boyce-Codd normal form (BCNF), and how to convert a relational schema into BCNF. It provides examples of relational schemas and their functional dependencies to illustrate when a schema satisfies or violates each normal form. The conversion process to BCNF involves finding the minimal cover of functional dependencies, identifying dependencies that violate BCNF, and decomposing the schema into multiple schemas in BCNF.
An array is a container that holds a fixed number of values of the same type. An array's length is determined when it is created and cannot be changed. The document then provides an example of creating an integer array called "scores" with 4 elements to store the scores of 4 cricket teams. It demonstrates accessing the elements of the array using indexes and printing the team scores.
Theory of automata and formal languageRabia Khalid
KleenE Star Closure, Plus operation, recursive definition of languages, INTEGER, EVEN, factorial, PALINDROME, languages of strings, cursive definition of RE, defining languages by RE,Examples
This document discusses automata theory and focuses on grammars, languages, and finite state machines. It defines key terminology like alphabets, strings, languages, and regular expressions. It explains Chomsky's hierarchy of formal languages from type-3 regular languages to type-0 recursively enumerable languages. The document also discusses finite state automata (FSA), deterministic finite automata (DFA), non-deterministic finite automata (NFA), context-free grammars, pushdown automata, and Turing machines. Examples of grammars, languages, and finite state machines are provided to illustrate these concepts.
This document discusses sparse matrices. It defines a sparse matrix as a matrix with more zero values than non-zero values. Sparse matrices can save space by only storing the non-zero elements and their indices rather than allocating space for all elements. Two common representations for sparse matrices are the triplet representation, which stores the non-zero values and their row and column indices, and the linked representation, which connects the non-zero elements. Applications of sparse matrices include solving large systems of equations.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
2. Матрицаны шығару
Python программалау тілінде матрицаларды шығару
үшін бірнеше тәсілдер қолданылады.
Тізімді өңдеп экранға шығару үшін әдеттегідей екі
цикл қолданылады. Бірінші цикл жол нөмірлерін, ал
екінші цикл жолдағы элементтер нөмірлерін анықтайды.
Мысалы, екі өлшемді сандық массивті жол экранға
шығару:
a = [[1, 2, 3, 4], [5, 6], [7, 8, 9]]
for I in range(len(a)): немесе:
for j in range(len(a[i])):
print (a[i][j], end=' ')
print()
НЕМЕСЕ join() әдісін қолданып:
a = [[1, 2, 3, 4], [5, 6], [7, 8, 9]]
for row in a:
print (' '.join([str(elem) for elem in row]))
Нәтижесі:
1 2 3 4
5 6
7 8 9
for row in a:
for elem in row:
print (elem, end=' ')
print()
3. Матрицаны енгізу
Python программалау тілінде матрицаларды енгізу
үшін бірнеше тәсілдер қолданылады.
Матрицаны пернетақтадан енгізу:
a = {}
for i in range(3):
for j in range(3):
a[i,j] = int(input())
for i in range(3):
print(*[a[i,j] for j in range(3)])
Нәтижесі:
1
2
3
4
5
6
7
8
9
1 2 3
4 5 6
7 8 9
4. Матрицаны енгізу
Матрицаны кездейсоқ сандармен толтыру үшін
random модулін қолданамыз.
from random import random
print(«Жолдар саны:")
n=int(input())
print(«Бакғаналар саны")
m=int(input())
matrix = []
for i in range(5):
row = []
for j in range(5):
row.append(int(random()*10))
matrix.append(row)
for row in matrix:
print(row)
Нәтижесі:
Жолдар саны:
3
Бағаналар саны:
3
[0, 7, 9]
[7, 6, 9]
[6, 1, 8]
>>>
5. Матрицаны енгізу
Матрицаны кездейсоқ сандармен толтырудың екінші
тәсілі:
import random
print(«Жолдар саны:")
n=int(input())
print(«Бағаналар саны:")
m=int(input())
for i in range(n):
a=[[random.randint(-20, 20)] for j in range(m)]
print(a) Нәтижесі:
Жолдар саны:
3
Бағаналар саны:
3
[[-11], [15], [-10]]
[[-16], [5], [-8]]
[[-3], [-17], [11]]
>>>
6. Матрица
Матрицадан максималды элементтер қосындысы бар
жолды және бағананы нықтау керек.
from random import random
matrix = []
for i in range(5):
row = []
for j in range(5):
row.append(int(random()*10))
matrix.append(row)
for row in matrix:
print(row)
maxRow = 0
idRow = 0
i = 0
7. Матрица
for row in matrix:
if sum(row) > maxRow:
maxRow = sum(row)
idRow = i
i += 1
print(idRow, '-', maxRow)
maxCol = 0
idCol = 0
for i in range(5):
colSum = 0
for j in range(5):
colSum += matrix[j][i]
if colSum > maxCol:
maxCol = colSum
idCol = i
print(idCol, '-', maxCol)
Нәтижесі:
[7, 9, 6, 3, 5]
[2, 9, 3, 7, 2]
[4, 7, 3, 9, 1]
[8, 6, 2, 2, 4]
[5, 2, 2, 3, 4]
0 - 30
1 - 33
>>>
8. Random модулінің функциялары
Random модулінде кездейсоқ сандарды, әріптерді кездейоқ
түрде генерациялау үшін, реттіліктін элементтерін кездейсоқ
таңдау үшін функциялары бар.
random.randrange(start, stop, step) – реттіліктен таңдалған
кездейсоқ сандарды қайтарады;
random.randint(A, B) - N, A ≤ N ≤ B кездейсоқ бүтін
сандар;
random.random() – 0.0-ден 1.0-ге дейінгі кездейсоқ сандар;
random.uniform(A, B) – А-дан В-ға дейінгі қалқымалы
нүктелі кездейсоқ сандар;
random.sample(population, k) – population реттіліктен k
ұзындықты тізім;
random.choice(sequence) – реттіліктен таңдалған кездейсоқ
санды қайтарады (sequence-реттілік).
9. Random модулі
Мысал: Кез-келген парольды генерациялау.
import random
str1 = '123456789'
str2 = 'qwertyuiopasdfghjklzxcvbnm'
str3 = str2.upper()
print(str3)
str4 = str1+str2+str3
print(str4)
ls = list(str4)
random.shuffle(ls)
psw = ''.join([random.choice(ls) for x in range(12)])
print(psw)
Нәтижесі:
str3: QWERTYUIOPASDFGHJKLZXCVBNM
str4: 123456789qwertyuiopasdfghjklzxcvbnmQWERTYUIOPASDFGHJKLZXCVBNM
psw: pQsqWsQFuAOK
>>>