#dequeue in data structure with example
Explore tagged Tumblr posts
Visit Tumblr Blog
Explore Tumblr blogs with no restrictions, modern design and the best experience.
Last Seen Tumblr Blogs
Fun Fact
Tumblr’s reach among the 26-to-35-year-olds in the US is 11%.
Link
Data Structures and Algorithms from Zero to Hero and Crack Top Companies 100+ Interview questions (Java Coding)
What you’ll learn
Java Data Structures and Algorithms Masterclass
Learn, implement, and use different Data Structures
Learn, implement and use different Algorithms
Become a better developer by mastering computer science fundamentals
Learn everything you need to ace difficult coding interviews
Cracking the Coding Interview with 100+ questions with explanations
Time and Space Complexity of Data Structures and Algorithms
Recursion
Big O
Dynamic Programming
Divide and Conquer Algorithms
Graph Algorithms
Greedy Algorithms
Requirements
Basic Java Programming skills
Description
Welcome to the Java Data Structures and Algorithms Masterclass, the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.
At 44+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Java. You will see 100+ Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft and how-to face Interviews with comprehensive visual explanatory video materials which will bring you closer to landing the tech job of your dreams!
Learning Java is one of the fastest ways to improve your career prospects as it is one of the most in-demand tech skills! This course will help you in better understanding every detail of Data Structures and how algorithms are implemented in high-level programming languages.
We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer.
After finishing this course, you will be able to:
Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting, and dynamic programming to solve programming challenges.
Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications
Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets
Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.
Why this course is so special and different from any other resource available online?
This course will take you from the very beginning to very complex and advanced topics in understanding Data Structures and Algorithms!
You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course.
You will also see Interview Questions done at the top technology companies such as Apple, Amazon, Google, and Microsoft.
I cover everything you need to know about the technical interview process!
So whether you are interested in learning the top programming language in the world in-depth and interested in learning the fundamental Algorithms, Data Structures, and performance analysis that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next technical interview this is the course for you!
And this is what you get by signing up today:
Lifetime access to 44+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want
Friendly and fast support in the course Q&A whenever you have questions or get stuck
FULL money-back guarantee for 30 days!
This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms, increase your earning potential, or just want a job with more freedom, this is the right course for you!
The topics that are covered in this course.
Section 1 – Introduction
What are Data Structures?
What is an algorithm?
Why are Data Structures And Algorithms important?
Types of Data Structures
Types of Algorithms
Section 2 – Recursion
What is Recursion?
Why do we need recursion?
How does Recursion work?
Recursive vs Iterative Solutions
When to use/avoid Recursion?
How to write Recursion in 3 steps?
How to find Fibonacci numbers using Recursion?
Section 3 – Cracking Recursion Interview Questions
Question 1 – Sum of Digits
Question 2 – Power
Question 3 – Greatest Common Divisor
Question 4 – Decimal To Binary
Section 4 – Bonus CHALLENGING Recursion Problems (Exercises)
power
factorial
products array
recursiveRange
fib
reverse
palindrome
some recursive
flatten
capitalize first
nestedEvenSum
capitalize words
stringifyNumbers
collects things
Section 5 – Big O Notation
Analogy and Time Complexity
Big O, Big Theta, and Big Omega
Time complexity examples
Space Complexity
Drop the Constants and the nondominant terms
Add vs Multiply
How to measure the codes using Big O?
How to find time complexity for Recursive calls?
How to measure Recursive Algorithms that make multiple calls?
Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple, and Microsoft)
Product and Sum
Print Pairs
Print Unordered Pairs
Print Unordered Pairs 2 Arrays
Print Unordered Pairs 2 Arrays 100000 Units
Reverse
O(N) Equivalents
Factorial Complexity
Fibonacci Complexity
Powers of 2
Section 7 – Arrays
What is an Array?
Types of Array
Arrays in Memory
Create an Array
Insertion Operation
Traversal Operation
Accessing an element of Array
Searching for an element in Array
Deleting an element from Array
Time and Space complexity of One Dimensional Array
One Dimensional Array Practice
Create Two Dimensional Array
Insertion – Two Dimensional Array
Accessing an element of Two Dimensional Array
Traversal – Two Dimensional Array
Searching for an element in Two Dimensional Array
Deletion – Two Dimensional Array
Time and Space complexity of Two Dimensional Array
When to use/avoid array
Section 8 – Cracking Array Interview Questions (Amazon, Facebook, Apple, and Microsoft)
Question 1 – Missing Number
Question 2 – Pairs
Question 3 – Finding a number in an Array
Question 4 – Max product of two int
Question 5 – Is Unique
Question 6 – Permutation
Question 7 – Rotate Matrix
Section 9 – CHALLENGING Array Problems (Exercises)
Middle Function
2D Lists
Best Score
Missing Number
Duplicate Number
Pairs
Section 10 – Linked List
What is a Linked List?
Linked List vs Arrays
Types of Linked List
Linked List in the Memory
Creation of Singly Linked List
Insertion in Singly Linked List in Memory
Insertion in Singly Linked List Algorithm
Insertion Method in Singly Linked List
Traversal of Singly Linked List
Search for a value in Single Linked List
Deletion of a node from Singly Linked List
Deletion Method in Singly Linked List
Deletion of entire Singly Linked List
Time and Space Complexity of Singly Linked List
Section 11 – Circular Singly Linked List
Creation of Circular Singly Linked List
Insertion in Circular Singly Linked List
Insertion Algorithm in Circular Singly Linked List
Insertion method in Circular Singly Linked List
Traversal of Circular Singly Linked List
Searching a node in Circular Singly Linked List
Deletion of a node from Circular Singly Linked List
Deletion Algorithm in Circular Singly Linked List
A method in Circular Singly Linked List
Deletion of entire Circular Singly Linked List
Time and Space Complexity of Circular Singly Linked List
Section 12 – Doubly Linked List
Creation of Doubly Linked List
Insertion in Doubly Linked List
Insertion Algorithm in Doubly Linked List
Insertion Method in Doubly Linked List
Traversal of Doubly Linked List
Reverse Traversal of Doubly Linked List
Searching for a node in Doubly Linked List
Deletion of a node in Doubly Linked List
Deletion Algorithm in Doubly Linked List
Deletion Method in Doubly Linked List
Deletion of entire Doubly Linked List
Time and Space Complexity of Doubly Linked List
Section 13 – Circular Doubly Linked List
Creation of Circular Doubly Linked List
Insertion in Circular Doubly Linked List
Insertion Algorithm in Circular Doubly Linked List
Insertion Method in Circular Doubly Linked List
Traversal of Circular Doubly Linked List
Reverse Traversal of Circular Doubly Linked List
Search for a node in Circular Doubly Linked List
Delete a node from Circular Doubly Linked List
Deletion Algorithm in Circular Doubly Linked List
Deletion Method in Circular Doubly Linked List
Entire Circular Doubly Linked List
Time and Space Complexity of Circular Doubly Linked List
Time Complexity of Linked List vs Arrays
Section 14 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple, and Microsoft)
Linked List Class
Question 1 – Remove Dups
Question 2 – Return Kth to Last
Question 3 – Partition
Question 4 – Sum Linked Lists
Question 5 – Intersection
Section 15 – Stack
What is a Stack?
What and Why of Stack?
Stack Operations
Stack using Array vs Linked List
Stack Operations using Array (Create, isEmpty, isFull)
Stack Operations using Array (Push, Pop, Peek, Delete)
Time and Space Complexity of Stack using Array
Stack Operations using Linked List
Stack methods – Push, Pop, Peek, Delete, and isEmpty using Linked List
Time and Space Complexity of Stack using Linked List
When to Use/Avoid Stack
Stack Quiz
Section 16 – Queue
What is a Queue?
Linear Queue Operations using Array
Create, isFull, isEmpty, and enQueue methods using Linear Queue Array
Dequeue, Peek and Delete Methods using Linear Queue Array
Time and Space Complexity of Linear Queue using Array
Why Circular Queue?
Circular Queue Operations using Array
Create, Enqueue, isFull and isEmpty Methods in Circular Queue using Array
Dequeue, Peek and Delete Methods in Circular Queue using Array
Time and Space Complexity of Circular Queue using Array
Queue Operations using Linked List
Create, Enqueue and isEmpty Methods in Queue using Linked List
Dequeue, Peek and Delete Methods in Queue using Linked List
Time and Space Complexity of Queue using Linked List
Array vs Linked List Implementation
When to Use/Avoid Queue?
Section 17 – Cracking Stack and Queue Interview Questions (Amazon, Facebook, Apple, Microsoft)
Question 1 – Three in One
Question 2 – Stack Minimum
Question 3 – Stack of Plates
Question 4 – Queue via Stacks
Question 5 – Animal Shelter
Section 18 – Tree / Binary Tree
What is a Tree?
Why Tree?
Tree Terminology
How to create a basic tree in Java?
Binary Tree
Types of Binary Tree
Binary Tree Representation
Create Binary Tree (Linked List)
PreOrder Traversal Binary Tree (Linked List)
InOrder Traversal Binary Tree (Linked List)
PostOrder Traversal Binary Tree (Linked List)
LevelOrder Traversal Binary Tree (Linked List)
Searching for a node in Binary Tree (Linked List)
Inserting a node in Binary Tree (Linked List)
Delete a node from Binary Tree (Linked List)
Delete entire Binary Tree (Linked List)
Create Binary Tree (Array)
Insert a value Binary Tree (Array)
Search for a node in Binary Tree (Array)
PreOrder Traversal Binary Tree (Array)
InOrder Traversal Binary Tree (Array)
PostOrder Traversal Binary Tree (Array)
Level Order Traversal Binary Tree (Array)
Delete a node from Binary Tree (Array)
Entire Binary Tree (Array)
Linked List vs Python List Binary Tree
Section 19 – Binary Search Tree
What is a Binary Search Tree? Why do we need it?
Create a Binary Search Tree
Insert a node to BST
Traverse BST
Search in BST
Delete a node from BST
Delete entire BST
Time and Space complexity of BST
Section 20 – AVL Tree
What is an AVL Tree?
Why AVL Tree?
Common Operations on AVL Trees
Insert a node in AVL (Left Left Condition)
Insert a node in AVL (Left-Right Condition)
Insert a node in AVL (Right Right Condition)
Insert a node in AVL (Right Left Condition)
Insert a node in AVL (all together)
Insert a node in AVL (method)
Delete a node from AVL (LL, LR, RR, RL)
Delete a node from AVL (all together)
Delete a node from AVL (method)
Delete entire AVL
Time and Space complexity of AVL Tree
Section 21 – Binary Heap
What is Binary Heap? Why do we need it?
Common operations (Creation, Peek, sizeofheap) on Binary Heap
Insert a node in Binary Heap
Extract a node from Binary Heap
Delete entire Binary Heap
Time and space complexity of Binary Heap
Section 22 – Trie
What is a Trie? Why do we need it?
Common Operations on Trie (Creation)
Insert a string in Trie
Search for a string in Trie
Delete a string from Trie
Practical use of Trie
Section 23 – Hashing
What is Hashing? Why do we need it?
Hashing Terminology
Hash Functions
Types of Collision Resolution Techniques
Hash Table is Full
Pros and Cons of Resolution Techniques
Practical Use of Hashing
Hashing vs Other Data structures
Section 24 – Sort Algorithms
What is Sorting?
Types of Sorting
Sorting Terminologies
Bubble Sort
Selection Sort
Insertion Sort
Bucket Sort
Merge Sort
Quick Sort
Heap Sort
Comparison of Sorting Algorithms
Section 25 – Searching Algorithms
Introduction to Searching Algorithms
Linear Search
Linear Search in Python
Binary Search
Binary Search in Python
Time Complexity of Binary Search
Section 26 – Graph Algorithms
What is a Graph? Why Graph?
Graph Terminology
Types of Graph
Graph Representation
The graph in Java using Adjacency Matrix
The graph in Java using Adjacency List
Section 27 – Graph Traversal
Breadth-First Search Algorithm (BFS)
Breadth-First Search Algorithm (BFS) in Java – Adjacency Matrix
Breadth-First Search Algorithm (BFS) in Java – Adjacency List
Time Complexity of Breadth-First Search (BFS) Algorithm
Depth First Search (DFS) Algorithm
Depth First Search (DFS) Algorithm in Java – Adjacency List
Depth First Search (DFS) Algorithm in Java – Adjacency Matrix
Time Complexity of Depth First Search (DFS) Algorithm
BFS Traversal vs DFS Traversal
Section 28 – Topological Sort
What is Topological Sort?
Topological Sort Algorithm
Topological Sort using Adjacency List
Topological Sort using Adjacency Matrix
Time and Space Complexity of Topological Sort
Section 29 – Single Source Shortest Path Problem
what is Single Source Shortest Path Problem?
Breadth-First Search (BFS) for Single Source Shortest Path Problem (SSSPP)
BFS for SSSPP in Java using Adjacency List
BFS for SSSPP in Java using Adjacency Matrix
Time and Space Complexity of BFS for SSSPP
Why does BFS not work with Weighted Graph?
Why does DFS not work for SSSP?
Section 30 – Dijkstra’s Algorithm
Dijkstra’s Algorithm for SSSPP
Dijkstra’s Algorithm in Java – 1
Dijkstra’s Algorithm in Java – 2
Dijkstra’s Algorithm with Negative Cycle
Section 31 – Bellman-Ford Algorithm
Bellman-Ford Algorithm
Bellman-Ford Algorithm with negative cycle
Why does Bellman-Ford run V-1 times?
Bellman-Ford in Python
BFS vs Dijkstra vs Bellman Ford
Section 32 – All Pairs Shortest Path Problem
All pairs shortest path problem
Dry run for All pair shortest path
Section 33 – Floyd Warshall
Floyd Warshall Algorithm
Why Floyd Warshall?
Floyd Warshall with negative cycle,
Floyd Warshall in Java,
BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,
Section 34 – Minimum Spanning Tree
Minimum Spanning Tree,
Disjoint Set,
Disjoint Set in Java,
Section 35 – Kruskal’s and Prim’s Algorithms
Kruskal Algorithm,
Kruskal Algorithm in Python,
Prim’s Algorithm,
Prim’s Algorithm in Python,
Prim’s vs Kruskal
Section 36 – Cracking Graph and Tree Interview Questions (Amazon, Facebook, Apple, Microsoft)
Section 37 – Greedy Algorithms
What is a Greedy Algorithm?
Well known Greedy Algorithms
Activity Selection Problem
Activity Selection Problem in Python
Coin Change Problem
Coin Change Problem in Python
Fractional Knapsack Problem
Fractional Knapsack Problem in Python
Section 38 – Divide and Conquer Algorithms
What is a Divide and Conquer Algorithm?
Common Divide and Conquer algorithms
How to solve the Fibonacci series using the Divide and Conquer approach?
Number Factor
Number Factor in Java
House Robber
House Robber Problem in Java
Convert one string to another
Convert One String to another in Java
Zero One Knapsack problem
Zero One Knapsack problem in Java
Longest Common Sequence Problem
Longest Common Subsequence in Java
Longest Palindromic Subsequence Problem
Longest Palindromic Subsequence in Java
Minimum cost to reach the Last cell problem
Minimum Cost to reach the Last Cell in 2D array using Java
Number of Ways to reach the Last Cell with given Cost
Number of Ways to reach the Last Cell with given Cost in Java
Section 39 – Dynamic Programming
What is Dynamic Programming? (Overlapping property)
Where does the name of DC come from?
Top-Down with Memoization
Bottom-Up with Tabulation
Top-Down vs Bottom Up
Is Merge Sort Dynamic Programming?
Number Factor Problem using Dynamic Programming
Number Factor: Top-Down and Bottom-Up
House Robber Problem using Dynamic Programming
House Robber: Top-Down and Bottom-Up
Convert one string to another using Dynamic Programming
Convert String using Bottom Up
Zero One Knapsack using Dynamic Programming
Zero One Knapsack – Top Down
Zero One Knapsack – Bottom Up
Section 40 – CHALLENGING Dynamic Programming Problems
Longest repeated Subsequence Length problem
Longest Common Subsequence Length problem
Longest Common Subsequence problem
Diff Utility
Shortest Common Subsequence problem
Length of Longest Palindromic Subsequence
Subset Sum Problem
Egg Dropping Puzzle
Maximum Length Chain of Pairs
Section 41 – A Recipe for Problem Solving
Introduction
Step 1 – Understand the problem
Step 2 – Examples
Step 3 – Break it Down
Step 4 – Solve or Simplify
Step 5 – Look Back and Refactor
Section 41 – Wild West
Download
To download more paid courses for free visit course catalog where 1000+ paid courses available for free. You can get the full course into your device with just a single click. Follow the link above to download this course for free.
3 notes
·
View notes
Text
C Program to implement Deque using circular array
Implement Deque using circular array Write a C Program to implement Deque using circular array. Here’s simple Program to implement Deque using circular array in C Programming Language. What is Queue ? Queue is also an abstract data type or a linear data structure, in which the first element is inserted from one end called REAR, and the deletion of existing element takes place from the other end…
View On WordPress
#c data structures#c queue programs#dequeue in c using arrays#dequeue in data structure using c#dequeue in data structure with example#dequeue program in c using array#double ended queue in c#double ended queue in c using arrays#double ended queue in data structure#double ended queue in data structure program
0 notes
Photo
Still going with applying effects to each affected unit in battle (I’m getting close!) so instead I wanted to talk a bit about data structures in Gamemaker Studio. However, the structures here are pretty much common to all languages
So, there are a few, but the main ones are lists, queues, stacks, maps, and grids. Just to summarise briefly:
Lists are exactly what they sound like - an ordered list of values.
A queue is like a list except adding things to the queue goes right at the bottom, and “dequeueing” will take the topmost item off the queue and return it - you might have heard of the term “First In, Last Out”, which applies here.
A stack is like a queue except adding things to the stack will add them at the TOP instead of the bottom (this is useful when things interrupt other things. This is your “Last in, First Out” data structure.
A map (sometimes called a dict in other languages) is an unordered list of pairs: a “key” and an associated value.
Now the crux of the matter is that these four data structures are all somewhat similar in that they’re only one dimensional. Take the list, for example. What if you want to store an enemy’s id, plus the attack he’s using, plus when his attack is set to go off.
Now, you have a couple of options here. The first is that you can nest data structures within data structures (say, a map for each list entry containing enemy details). However, data structures have to be created and cleaned up or you will get memory leaks, so this isn’t my favoured approach.
The second is that you can create an object for each entry, pass the object id into the list, and fill the object with the relevant variables. This is an approach I’m leaning towards but I’m slightly hesitant as gamemaker objects come with built in variables and overhead processing, which could impact performance-wise (and again, if you neglect to destroy those objects later, you’re going to get memory leaks). As an aside, Gamemaker Studio 2 plans to implement “lightweight” objects with reduced overhead compared to regular objects, so yay for that whenever it comes out and whenever I can afford to buy GMS2!
So here’s where grids come into play. When you think of a grid, think Microsoft Excel - you have a 2D array, with each row being a new entry, and each column containing similar data for each row. Grids are basically perfect. Except that Gamemaker doesn’t natively support some of the cool things you can do with other data structures. You can’t write a one-liner and shuffle grids like you can with lists. You can’t queue data and append it to the bottom of the grid, or stack it and add it to the top of the grid. Similarly, you can’t dequeue an item and have it removed from the top of the grid immediately. You also can’t return a value from a value-key pair like you can with a map.
But the beauty of programming is, if something doesn’t exist, you can make it happen yourself! So that brings me to the above picture - I’ve written up a list of the missing features to turn grids into the ultimate data structure, adding the missing functionality of the others! Now, these scripts aren’t complete by any means, but I think a full suite of functions will make working with grids much easier, and bypass the limitations of the other data structures!
#game#videogame#devblog#gamedev#scifi#spacegame#dungeon crawler#rpg#entropy#gamemaker#programming#pixel art#robot#indiedev
40 notes
·
View notes
Text
300+ TOP SAP ABAP Objective Questions and Answers
SAP ABAP Multiple Choice Questions :-
1. This data type has a default length of one and a blank default value. A: I B: N C: C D: D Ans:C 2. A DATA statement may appear only at the top of a program, before START-OFSELECTION. A: True B: False Ans:B 3. If a field, NAME1, is declared as a global data object, what will be output by the following code? report zabaprg. DATA: name1 like KNA1-NAME1 value 'ABAP programmer'. name1 = 'Customer name'. CLEAR name1. perform write_name. FORM write_name. name1 = 'Material number'. WRITE name1. ENDFORM. A: Customer name B: ABAP programmer C: Material number D: None of the above Ans:C 4. All of these allow you to step through the flow of a program line-by-line except: A: Enter /h then execute B: Execute in debug mode C: Enter /i then execute D: Set a breakpoint Ans: C 5. Which of the following may NOT be modified using the ABAP Dictionary transaction? A: Type groups B: Search help C: Lock objects D: Function groups Ans:D 6. In a line of code, text-100, is an example of which type of text element? A: Text symbol B: Selection text C: Text title D: Text identifier Ans:A 7. The editor function that formats and indents the lines of code automatically is called ____. A: Auto align B: Pretty printer C: Generate version D: Syntax check Ans:B 8. A DO loop increments the system field ____. A: SY-LOOPI B: SY-TABIX C: SY-LSIND D: SY-INDEX Ans: D 9. The event that is processed after all data has been read but before the list is displayed is: A: END-OF-PAGE. B: START-OF-SELECTION. C: END-OF-SELECTION. D: AT LINE-SELECTION. Ans:A ? C 10. The field declared below is of what data type? DATA: new_fld(25). A: P B: N C: I D: C Ans: D
SAP ABAP MCQs 11. In regard to the INITIALIZATION event, which of the following is NOT a true statement? A: Executed before the selection screen is displayed. B: You should use SET PF-STATUS here. C: You can assign different values to PARAMETERS and SELECT-OPTIONS here. D: Executed one time when you start the report. Ans: B 12. The event AT SELECTION-SCREEN OUTPUT. occurs before the selection screen is displayed and is the best event for assigning default values to selection criteria. A: True B: False Ans: B 13. The business (non-technical) definition of a table field is determined by the field's ____. A: domain B: field name C: data type D: data element Ans: D 14. In regard to the three-tier client/server architecture, which of the following is a true statement? A: The presentation server processes the SAP program logic. B: An application server is responsible for updating database tables. C: Typically, there is a one-to-one ratio of database servers to presentation servers. D: The application server layer is the level between a presentation server and a database server. Ans: D,B 15. What will be output by the code below? DATA: alph type I value 3. write: alph. WHILE alph > 2. write: alph. alph = alph - 1. ENDWHILE. A: 3 B: 3 2 C: 3 3 2 D: 3 3 Ans: D 16. To allow the user to enter a single value on a selection screen, use the ABAP keyword ____. A: SELECT-OPTIONS. B: PARAMETERS. C: RANGES. D: DATA. Ans: B 17. What will be output by the following code? DATA: BEGIN OF itab OCCURS 0, fval type i, END OF itab. itab-fval = 1. APPEND itab. itab-fval = 2. APPEND itab. REFRESH itab. WRITE: /1 itab-fval. A: 1 B: 2 C: blank D: 0 Ans: B 18. You can define your own key fields when declaring an internal table. A: True B: False Ans: A 19. When modifying an internal table within LOOP AT itab. _ ENDLOOP. you must include an index number. A: True B: False Ans : B 20. If itab contains 20 rows, what will SY-TABIX equal when the program reaches the WRITE statement below? SY-TABIX = 10. LOOP AT itab. count_field = count_field + 1. ENDLOOP. WRITE: /1 count_field. A: 0 B: 10 C: 20 D: 30 Ans: C 21. Adding a COMMIT WORK statement between SELECT_ENDSELECT is a good method for improving performance. A: True B: False Ans:B 22. To select one record for a matching primary key, use ____. A: SELECT B: SELECT INTO C: SELECT SINGLE D: SELECT ENTRY Ans: C 23. In regard to MOVE-CORRESPONDING, which of the following is NOT a true statement? A: Moves the values of components with identical names. B: Fields without a match are unchanged. C: Corresponds to one or more MOVE statements. D: Moves the values of components according to their location. Ans: D 24. The ABAP keyword for adding authorizations to a program is ____. A: AUTH-CHECK B: AUTHORITY-CHECK C: AUTHORIZATION-CHECK D: AUTHORITY-OBJECT Ans:B 25. To read an exact row number of an internal table, use this parameter of the READ TABLE statement. A: INDEX B: TABIX C: ROW D: WHERE Ans: B ? A 26. To remove lines from a database table, use ____. A: UPDATE B: MODIFY C: ERASE D: DELETE Ans: D 27. Which table type would be most appropriate for accessing table rows using an index. A: Hashed table B: Standard table C: Sorted table D: None of these may be accessed using an index. Ans: C 28. The following code indicates: SELECTION-SCREEN BEGIN OF BLOCK B1. PARAMETERS: myparam(10) type C, Myparam2(10) type N, SELECTION-SCREEN END OF BLOCK. A: Draw a box around myparam and myparam2 on the selection screen. B: Allow myparam and myparam2 to be ready for input during an error dialog. C: Do not display myparam and myparam2 on the selection screen. D: Display myparam and myparam2 only if both fields have default values. Ans: A 29. The following code reorders the rows so that: DATA: itab LIKE kna1 OCCURS 0 WITH HEADER LINE. itab-name1 = 'Smith'. itab-ort01 = 'Miami'. APPEND itab. itab-name1 = 'Jones'. itab-ort01 = 'Chicago'. APPEND itab. itab-name1 = 'Brown'. itab-ort01 = 'New York'. APPEND itab. SORT itab BY name1 ort01. A: Smith appears before Jones B: Jones appears before Brown C: Brown appears before Jones D: Miami appears before New York Ans: C 30. If a table contains many duplicate values for a field, minimize the number of records returned by using this SELECT statement addition. A: MIN B: ORDER BY C: DISTINCT D: DELETE Ans:C 31. When writing a SELECT statement, you should place as much load as possible on the database server and minimize the load on the application server. A: True B: False Ans: B 32. All of the following pertain to interactive reporting in ABAP except: A: Call transactions and other programs from a list. B: Secondary list shows detail data. C: Good for processing lists in background. D: AT USER-COMMAND Ans:C 33. In regard to a function group, which of the following is NOT a true statement? A: Combines similar function modules. B: Shares global data with all its function modules. C: Exists within the ABAP workbench as an include program. D: Shares subroutines with all its function modules. Ans: D 34. Errors to be handled by the calling program are defined in a function module's ____. A: exceptions interface B: source code C: exporting interface D: main program Ans :A 35. In regard to the START-OF-SELECTION event, which of the following is a true statement? A: Executed before the selection screen is displayed. B: This is the only event in which a SELECT statement may be coded. C: Executed when the user double-clicks a list row. D: Automatically started by the REPORT statement. Ans:D 36. The order in which an event appears in the ABAP code determines when the event is processed. A: True B: False Ans: B 37. The SAP service that ensures data integrity by handling locking is called: A: Update B: Dialog C: Enqueue/Dequeue D: Spool Ans: C 38. What standard data type is the following user-defined type? TYPES: user_type. A: N B: C C: I D: Undefined Ans: B 39. Which ABAP program attribute provides access protection? A: Status B: Application C: Development class D: Authorization group Ans:D 40. Page headers for a secondary list should be coded in which event? A: TOP-OF-PAGE. B: START-OF-SELECTION. C: TOP-OF-PAGE DURING LINE-SELECTION. D: AT USER-COMMAND. Ans: C 41. Given: PERFORM subroutine USING var. The var field is known as what type of parameter? A: Formal B: Actual C: Static D: Value Ans:B 42. The following statement will result in a syntax error.DATA: price(3) type p decimals 2 value '100.23'. A: True B: False Ans: B 43. The following code indicates:CALL SCREEN 300. A: Start the PAI processing of screen 300. B: Jump to screen 300 without coming back. C: Temporarily branch to screen 300. * D: Exit screen 300. Ans:C 44. Which of the following would be stored in a table as master data? A: Customer name and address B: Sales order items C: Accounting invoice header D: Vendor credit memo Ans: A 45. In relation to an internal table as a formal parameter, because of the STRUCTURE syntax, it is possible to: A: Use the DESCRIBE statement within a subroutine. B: Loop through the internal table within a subroutine. C: Access the internal table fields within a subroutine. D: Add rows to the internal table within a subroutine. Ans: C 46. This data type has a default length of one and a default value = '0'. A: P B: C C: N D: I Ans: C 47. To prevent duplicate accesses to a master data field: A: Create an index on the master data field. B: Remove nested SELECT statements. C: Use SELECT SINGLE. D: Buffer the data in an internal table. Ans: A ? C 48. In regard to the code below, which of the following is not a true statement? TABLES: KNA1. GET KNA1. Write: /1 kna1-kunnr. END-OF-SELECTION. A: The GET event is processed while a logical database is running. B: All the fields from table KNA1 may be used in the GET event. C: You can code the GET event elsewhere in the same program. D: None of the above. Ans: D 49. The following code indicates: SELECT fld1 FROM tab1 INTO TABLE itab UP TO 100 ROWS WHERE fld7 = pfld7. A: Itab will contain 100 rows. B: Only the first 100 records of tab1 are read. C: If itab has less than 100 rows before the SELECT, SY-SUBRC will be set to 4. D: None of the above. Ans: D 50. To place a checkbox on a list, use A: WRITE CHECKBOX. B: FORMAT CHECKBOX ON. C: WRITE fld AS CHECKBOX. D: MODIFY LINE WITH CHECKBOX. Ans:C 51. Which of the following is NOT a true statement in regard to a sorted internal table type? A: May only be accessed by its key. B: Its key may be UNIQUE or NON-UNIQUE. C: Entries are sorted according to its key when added. D: A binary search is used when accessing rows by its key. Ans: A 52. The following code indicates: CALL SCREEN 9000 STARTING AT 10 5 ENDING AT 60 20 A: Screen 9000 is called with the cursor at coordinates (10,5)(60,20). B: Screen 9000 must be of type "Modal dialog box." C: Display screen 9000 in a full window. D: Screen 9000 may only contain an ABAP list. Ans:A 53. After a DESCRIBE TABLE statement SY-TFILL will contain A: The number of rows in the internal table. B: The current OCCURS value. C: Zero, if the table contains one or more rows. D: The length of the internal table row structure. Ans:A 54. Function module source code may not call a subroutine. A: True B: False Ans: B 55. This data type has a default length of eight and a default value = '00000000'. A: P B: D C: N D: C Ans: B 56. Within the source code of a function module, errors are handled via the keyword: A: EXCEPTION B: RAISE C: STOP D: ABEND Ans:B 57. Which of these is NOT a valid type of function module? A: Normal B: Update C: RFC D: Dialog Ans:D 58. To call a local subroutine named calculate answer, use this line of code: A: PERFORM calculate answer. B: CALL calculate answer. C: USING calculate answer. D: SUB calculate answer. Ans:A 59. Given: DO. Write: /1 'E equals MC squared.'. ENDDO. This will result in ____. A: output of 'E equals MC squared.' on a new line one time B: an endless loop that results in an abend error C: output of 'E equals MC squared.' on a new line many times D: a loop that will end when the user presses ESC Ans.B 60. The following code indicates write: /5 'I Love ABAP'. A: Output 'I Lov' on the current line B: Output 'I Love ABAP' starting at column 5 on the current line C: Output 'I Lov' on a new line D: Output 'I Love ABAP' starting at column 5 on a new line Ans: D 61. Which of the following is NOT a component of the default standard ABAP report header? A: Date and Time B: List title C: Page number D: Underline Ans: A 62. A select statement has built-in authorization checks. A: True B: False Ans:B 63. A BDC program is used for all of the following except: A: Downloading data to a local file B: Data interfaces between SAP and external systems C: Initial data transfer D: Entering a large amount of data Ans:B 64. Page footers are coded in the event: A: TOP-OF-PAGE. B: END-OF-SELECTION. C: NEW-PAGE. D: END-OF-PAGE. Ans:D 65. Page headers for a secondary/details list can be coded in the event: A: GET. B: INITIALIZATION. C: TOP-OF-PAGE DURING LINE-SELECTION. D: NEW-PAGE. Ans:C 66. To both add or change lines of a database table, use ____. A: INSERT B: UPDATE C: APPEND D: MODIFY Ans:D 67. To select one record for a matching primary key, use ____. A: SELECT B: SELECT INTO C: SELECT SINGLE D: SELECT ENTRY Ans:C 68. After adding rows to an internal table with COLLECT, you should avoid adding More rows with APPEND. A: True B: False Ans:A 69. The output for the following code will be report zabaprg. DATA: my_field type I value 99. my_field = my_field + 1. clear my_field. WRITE: 'The value is', my_field left-justified. A: The value is 99 B: The value is 100 C: The value is 0 D: None of the above Ans: C 70. If this code results in an error, the remedy is SELECT * FROM tab1 WHERE fld3 = pfld3. WRITE: /1 tab1-fld1, tab1-fld2. ENDSELECT. A: Add a SY-SUBRC check. B: Change the * to fld1 fld2. C: Add INTO (tab1-fld1, tab1-fld2). D: There is no error. Ans: C,D 71. To summarize the contents of several matching lines into a single line, use this SELECT statement clause. A: INTO B: WHERE C: FROM D: GROUP BY Ans:D 72. What is output by the following code? DATA: BEGIN OF itab OCCURS 0, letter type c, END OF itab. itab-letter = 'A'. APPEND itab. itab-letter = 'B'. APPEND itab. itab-letter = 'C'. APPEND itab. itab-letter = 'D'. APPEND itab. LOOP AT itab. SY-TABIX = 2. WRITE itab-letter. EXIT. ENDLOOP. A: A B: A B C D C: B D: B C D Ans: A 73. All of the following are considered to be valid ABAP modularization techniques except: A: Subroutine B: External subroutine C: Field-group D: Function module Ans:C 74. To create a list of the top 25 customers, you should use A: DELETE ADJACENT DUPLICATES B: READ TABLE itab INDEX 25 C: LOOP AT itab FROM 25 D: APPEND SORTED BY Ans:D 75. Which of these sentences most accurately describes the GET VBAK LATE. event? A: This event is processed before the second time the GET VBAK event is processed. B: This event is processed after all occurrences of the GET VBAK event are completed. C: This event will only be processed after the user has selected a basic list row. D: This event is only processed if no records are selected from table VBAK. Ans:B 76. In an R/3 environment, where is the work of a dialog program performed? A: On the application server using a dialog work process service. B: On the presentation server using a dialog work process service. C: On the database server using a dialog work process service. D: None of the above. Ans: A 77. In regard to Native SQL, which of the following is NOT a true statement? A: A CONNECT to the database is done automatically. B: You must specify the SAP client. C: The tables that you address do not have to exist in the ABAP Dictionary. D: Will run under different database systems. Ans:D 78. To change one or more lines of a database table, use ____. A: UPDATE B: INSERT C: INTO D: MOD Ans:A 79. Which is the correct sequence of events? A: AT SELECTION-SCREEN, TOP-OF-PAGE, INITIALIZATION B: START-OF-SELECTION, AT USER-COMMAND, GET dbtab C: INITIALIZATION, END-OF-SELECTION, AT LINE-SELECTION D: GET dbtab, GET dbtab LATE, START-OF-SELECTION Ans:B 80. Which of the following is NOT a numeric data type? A: I B: N C: P D: F Ans: B SAP ABAP Questions and Answers pdf Download Read the full article
2 notes
·
View notes
Text
Prerequisites I - DS 101
Abstract Data Type
An abstract piece of code that is re-usable and serves the purpose by hiding the inner workings of it and only exposing the interface for generic usage is known as an abstract data type.
For example, a user who needs to implement a railway booking engine to serve the user requests based on first-in, first-out (FIFO) method, will have to implement the queue for the specific use-case with operations such as enqueue & dequeue. The disadvantage of this approach is that the queue will have to be reimplemented for other use-cases that simulate the queue. This can be solved by having an abstract data type queue which implements the basic functionality (operations) of the queue and can be imported anywhere else it is required.
An abstract data type (ADT) can be thought of as a data declaration packaged along with the operations that are meaningful for the data.
ADT Implementations
Most data is required to be stored as either as linear or as a non-linear structure. An example of a linear and non-linear data structure would be an array and a tree respectively.
We have two basic structures to implement complex ADT list (both linear and non-linear): arrays and linked-lists.
Arrays can be used to store sequential data, where the data in a specific cell can be accessed efficiently if the index (address) is known, but it is very inefficient to add or remove new/existing elements. On the other hand, linked-lists do us a great favor in efficiently adding and removing nodes (atomic units of linked-lists), but are inefficient at accessing a node directly.
Node: It is a data structure that has a data part and link part (holds multiple links if it is non-linear), pointing to the next element(s).
Analysis of Algorithms
When there are no loops in a program (it is linear, which means it is a set of instructions), the efficiency of the program directly depends on the speed of the CPU. Hence, algorithm analysis focuses mostly on the loops (given that recursion can always be converted to a loop). The efficiency of a program (or an algorithm) is always a function of the number of elements to be processed/accessed.
Example:
n = 11 for i in range(n): # will create a list of numbers from 0 to 10 for j in range(i): # some code here return
The above code would run 55 times, as the inner loop would be a dependant on the value of i for each ith loop. When the value of i is 0, the inner loop runs 0 times, when i is 1, the inner loop runs 1 times and so on till the value of i is 10. If we choose the see the pattern, it would basically be: 1 + 2 + 3 + ... + 10. And if we consider it as a function of n, then: f(n) = n*(n+1)/2.
Big-O Notation
It is a notation to describe/represent the worst case runtime of an algorithm. The Big-O, as in "on the order of" is an approximate measure of efficiency and since it is only an estimate, we can focus only on the dominant factor in the equation.
Considering the previous example, the worst case scenario would be O(n^2).
Significance and other notations.
There are other notations of different measures of efficiency such as the theta notation and the omega notation. As with the Big-O notation defines the upper bound of the function, the omega function defines the lower bound or the best case runtime.
The theta notation is basically a function with 2 constants, where one represents an upper bound and the other represents the lower bound. Given a function f(n), there exists k and K such that n*k
2 notes
·
View notes
Text
Queue in java
Hierarchy of interfaces in Java Collection Framework: Relationship between. Next we are adding 5 strings in random order into the priority queue.
See Ĭopyright © 2000–2019, Robert Sedgewick and Kevin Wayne. Queue is a Collection that allows duplicate elements but not null elements. The first line tells us that we are creating a priority queue: Queue testStringsPQ new PriorityQueue () PriorityQueue is available in java.util package.
Chronicle Queue is a persisted low-latency messaging framework for high performance and critical. However, there's one exception that we'll touch upon later. This project covers the Java version of Chronicle Queue. Each of these methods exists in two forms: one throws an exception if the operation fails, the other returns a special value (either null or false, depending on the. Besides basic Collection operations, queues provide additional insertion, extraction, and inspection operations. In fact, most Queues we'll encounter in Java work in this first in, first out manner often abbreviated to FIFO. A collection designed for holding elements prior to processing. * * This implementation uses a singly linked list with a static nested class for * linked-list nodes. After we declare our Queue, we can add new elements to the back, and remove them from the front. Recommended Articles This is a guide to Queue in Java. We have seen examples of the most commonly used methods in queue operation. There are alternative methods available for all the methods. It supports operations like insertion, retrieval, and removal. Hence we create the instance of the LinkedList and the PriorityQueue class and assign it to the queue interface. Since the Queue is an interface, we cannot create an instance of it. ****************************************************************************** * Compilation: javac Queue.java * Execution: java Queue enqueue and dequeue * operations, along with methods for peeking at the first item, * testing if the queue is empty, and iterating through * the items in FIFO order. The Queue is a special interface in Java that is used to hold the elements in insertion order. Queue interface is a part of Java Collections that consists of two implementations: LinkedList and PriorityQueue are the two classes that implement the Queue interface. Below is the syntax highlighted version of Queue.java The Queue interface of the Java collections framework provides the functionality of the queue data structure.
0 notes
Text
BFS Algorithm
Breadth-First Search Algorithm
Breadth-First Search Algorithm or BFS is the most widely utilized method.
BFS is a graph traversal approach in which you start at a source node and layer by layer through the graph, analyzing the nodes directly related to the source node. Then, in BFS traversal, you must move on to the next-level neighbor nodes.
According to the BFS, you must traverse the graph in a breadthwise direction:
· To begin, move horizontally and visit all the current layer’s nodes.
· Continue to the next layer.
Breadth-First Search uses a queue data structure to store the node and mark it as “visited” until it marks all the neighboring vertices directly related to it. The queue operates on the First In First Out (FIFO) principle, so the node’s neighbors will be viewed in the order in which it inserts them in the node, starting with the node that was inserted first.
Read More
Why Do You Need Breadth-First Search Algorithm?
There are several reasons why you should use the BFS Algorithm to traverse graph data structure. The following are some of the essential features that make the BFS algorithm necessary:
· The BFS algorithm has a simple and reliable architecture.
· The BFS algorithm helps evaluate nodes in a graph and determines the shortest path to traverse nodes.
· The BFS algorithm can traverse a graph in the fewest number of iterations possible.
· The iterations in the BFS algorithm are smooth, and there is no way for this method to get stuck in an infinite loop.
· In comparison to other algorithms, the BFS algorithm’s result has a high level of accuracy.
In this tutorial, next, you will look at the pseudocode for the breadth-first search algorithm.
Pseudocode Of Breadth-First Search Algorithm
The breadth-first search algorithm’s pseudocode is:
Read More
Bredth_First_Serach( G, A ) // G ie the graph and A is the source node
Let q be the queue
q.enqueue( A ) // Inserting source node A to the queue
Mark A node as visited.
While ( q is not empty )
B = q.dequeue( ) // Removing that vertex from the queue, which will be visited by its neighbour
Processing all the neighbors of B
For all neighbors of C of B
If C is not visited, q. enqueue( C ) //Stores C in q to visit its neighbour
Mark C a visited
For a better understanding, you will look at an example of a breadth-first search algorithm later in this tutorial.
Example of Breadth-First Search Algorithm
In a tree-like structure, graph traversal requires the algorithm to visit, check, and update every single un-visited node. The sequence in which graph traversals visit the nodes on the graph categorizes them.
The BFS algorithm starts at the first starting node in a graph and travels it entirely. After traversing the first node successfully, it visits and marks the next non-traversed vertex in the graph.
Step 1: In the graph, every vertex or node is known. First, initialize a queue.
Step 2: In the graph, start from source node A and mark it as visited.
Step 3: Then you can observe B and E, which are unvisited nearby nodes from A. You have two nodes in this example, but here choose B, mark it as visited, and enqueue it alphabetically.
Step 4: Node E is the next unvisited neighboring node from A. You enqueue it after marking it as visited.
Step 5: A now has no unvisited nodes in its immediate vicinity. As a result, you dequeue and locate A.
Read More
Step 6: Node C is an unvisited neighboring node from B. You enqueue it after marking it as visited.
Step 7: Node D is an unvisited neighboring node from C. You enqueue it after marking it as visited.
Step 8: If all of D’s adjacent nodes have already been visited, remove D from the queue.
Step 9: Similarly, all nodes near E, B, and C nodes have already been visited; therefore, you must remove them from the queue.
Step 10: Because the queue is now empty, the bfs traversal has ended.
Complexity Of Breadth-First Search Algorithm
The time complexity of the breadth-first search algorithm : The time complexity of the breadth-first search algorithm can be stated as O(|V|+|E|) because, in the worst case, it will explore every vertex and edge. The number of vertices in the graph is |V|, while the edges are |E|.
The space complexity of the breadth-first search algorithm : You can define the space complexity as O(|V|), where |V| is the number of vertices in the graph, and different data structures are needed to determine which vertices have already been added to the queue. This is also the space necessary for the graph, which varies depending on the graph representation used by the algorithm’s implementation.
You will see some bfs algorithm applications now that you’ve grasped the complexity of the breadth-first search method.
Application Of Breadth-First Search Algorithm
The breadth-first search algorithm has the following applications:
· For Unweighted Graphs, You Must Use the Shortest Path and Minimum Spacing Tree.
Read More
The shortest path in an unweighted graph is the one with the fewest edges. You always reach a vertex from a given source using the fewest amount of edges when utilizing breadth-first. In unweighted graphs, any spanning tree is the Minimum Spanning Tree, and you can identify a spanning tree using either depth or breadth-first traversal.
· Peer to Peer Network
Breadth-First Search is used to discover all neighbor nodes in peer-to-peer networks like BitTorrent.
· Crawlers in Search Engine
Crawlers create indexes based on breadth-first. The goal is to start at the original page and follow all of the links there, then repeat. Crawlers can also employ Depth First Traversal. However, the benefit of breadth-first traversal is that the depth or layers of the created tree can be limited.
· Social Networking Websites
You can use a breadth-first search to identify persons within a certain distance ‘d’ from a person in social networks up to ‘d’s levels.
· GPS Navigation System
To find all nearby locations, utilize the breadth-first search method.
· Broadcasting Network
A broadcast packet in a network uses breadth-first search to reach all nodes.
· Garbage Collection
Cheney’s technique uses the breadth-first search for duplicating garbage collection. Because of the better locality of reference, breadth-first search is favored over the Depth First Search algorithm.
· Cycle Detection in Graph
Cycle detection in undirected graphs can be done using either Breadth-First Search or Depth First Search. BFS can also be used to find cycles in a directed graph.
· Identifying Routes
To see if there is a path between two vertices, you can use either Breadth-First or Depth First Traversal.
· Finding All Nodes Within One Connected Component
To locate all nodes reachable from a particular node, you can use either Breadth-First or Depth First Traversal.
Read More
Code Implementation of Breadth-First Search Algorithm
Breadth-First Search Algorithm Code Implementation:
#include<stdio.h>
#include<conio.h>
#include<stdlib.h>
int twodimarray[10][10],queue[10],visited[10],n,i,j,front=0,rear=-1;
void breadthfirstsearch(int vertex) // breadth first search function
{
for (i=1;i<=n;i++)
if(twodimarray[vertex][i] && !visited[i])
queue[++rear]=i;
if(front<=rear)
{
visited[queue[front]]=1;
breadthfirstsearch(queue[front++]);
}
}
int main() {
int x;
printf(“\n Enter the number of vertices:”);
scanf(“%d”,&n);
for (i=1;i<=n;i++) {
queue[i]=0;
visited[i]=0;
}
printf(“\n Enter graph value in form of matrix:\n”);
for (i=1;i<=n;i++)
for (j=1;j<=n;j++)
scanf(“%d”,&twodimarray[i][j]);
printf(“\n Enter the source node:”);
scanf(“%d”,&x);
breadthfirstsearch(x);
printf(“\n The nodes which are reachable are:\n”);
for (i=1;i<=n;i++)
if(visited[i])
printf(“%d\t”,i);
else
printf(“\n Breadth first search is not possible”);
getch();
}
1 note
·
View note
Text
BFS Algorithm
Breadth-First Search Algorithm
Breadth-First Search Algorithm or BFS is the most widely utilized method.
BFS is a graph traversal approach in which you start at a source node and layer by layer through the graph, analyzing the nodes directly related to the source node. Then, in BFS traversal, you must move on to the next-level neighbor nodes.
According to the BFS, you must traverse the graph in a breadthwise direction:
· To begin, move horizontally and visit all the current layer's nodes.
· Continue to the next layer.
Breadth-First Search uses a queue data structure to store the node and mark it as "visited" until it marks all the neighboring vertices directly related to it. The queue operates on the First In First Out (FIFO) principle, so the node's neighbors will be viewed in the order in which it inserts them in the node, starting with the node that was inserted first.
Read More
Why Do You Need Breadth-First Search Algorithm?
There are several reasons why you should use the BFS Algorithm to traverse graph data structure. The following are some of the essential features that make the BFS algorithm necessary:
· The BFS algorithm has a simple and reliable architecture.
· The BFS algorithm helps evaluate nodes in a graph and determines the shortest path to traverse nodes.
· The BFS algorithm can traverse a graph in the fewest number of iterations possible.
· The iterations in the BFS algorithm are smooth, and there is no way for this method to get stuck in an infinite loop.
· In comparison to other algorithms, the BFS algorithm's result has a high level of accuracy.
In this tutorial, next, you will look at the pseudocode for the breadth-first search algorithm.
Pseudocode Of Breadth-First Search Algorithm
The breadth-first search algorithm's pseudocode is:
Read More
Bredth_First_Serach( G, A ) // G ie the graph and A is the source node
Let q be the queue
q.enqueue( A ) // Inserting source node A to the queue
Mark A node as visited.
While ( q is not empty )
B = q.dequeue( ) // Removing that vertex from the queue, which will be visited by its neighbour
Processing all the neighbors of B
For all neighbors of C of B
If C is not visited, q. enqueue( C ) //Stores C in q to visit its neighbour
Mark C a visited
For a better understanding, you will look at an example of a breadth-first search algorithm later in this tutorial.
Example of Breadth-First Search Algorithm
In a tree-like structure, graph traversal requires the algorithm to visit, check, and update every single un-visited node. The sequence in which graph traversals visit the nodes on the graph categorizes them.
The BFS algorithm starts at the first starting node in a graph and travels it entirely. After traversing the first node successfully, it visits and marks the next non-traversed vertex in the graph.
Step 1: In the graph, every vertex or node is known. First, initialize a queue.
Step 2: In the graph, start from source node A and mark it as visited.
Step 3: Then you can observe B and E, which are unvisited nearby nodes from A. You have two nodes in this example, but here choose B, mark it as visited, and enqueue it alphabetically.
Step 4: Node E is the next unvisited neighboring node from A. You enqueue it after marking it as visited.
Step 5: A now has no unvisited nodes in its immediate vicinity. As a result, you dequeue and locate A.
Read More
Step 6: Node C is an unvisited neighboring node from B. You enqueue it after marking it as visited.
Step 7: Node D is an unvisited neighboring node from C. You enqueue it after marking it as visited.
Step 8: If all of D's adjacent nodes have already been visited, remove D from the queue.
Step 9: Similarly, all nodes near E, B, and C nodes have already been visited; therefore, you must remove them from the queue.
Step 10: Because the queue is now empty, the bfs traversal has ended.
Complexity Of Breadth-First Search Algorithm
The time complexity of the breadth-first search algorithm : The time complexity of the breadth-first search algorithm can be stated as O(|V|+|E|) because, in the worst case, it will explore every vertex and edge. The number of vertices in the graph is |V|, while the edges are |E|.
The space complexity of the breadth-first search algorithm : You can define the space complexity as O(|V|), where |V| is the number of vertices in the graph, and different data structures are needed to determine which vertices have already been added to the queue. This is also the space necessary for the graph, which varies depending on the graph representation used by the algorithm's implementation.
You will see some bfs algorithm applications now that you’ve grasped the complexity of the breadth-first search method.
Application Of Breadth-First Search Algorithm
The breadth-first search algorithm has the following applications:
· For Unweighted Graphs, You Must Use the Shortest Path and Minimum Spacing Tree.
Read More
The shortest path in an unweighted graph is the one with the fewest edges. You always reach a vertex from a given source using the fewest amount of edges when utilizing breadth-first. In unweighted graphs, any spanning tree is the Minimum Spanning Tree, and you can identify a spanning tree using either depth or breadth-first traversal.
· Peer to Peer Network
Breadth-First Search is used to discover all neighbor nodes in peer-to-peer networks like BitTorrent.
· Crawlers in Search Engine
Crawlers create indexes based on breadth-first. The goal is to start at the original page and follow all of the links there, then repeat. Crawlers can also employ Depth First Traversal. However, the benefit of breadth-first traversal is that the depth or layers of the created tree can be limited.
· Social Networking Websites
You can use a breadth-first search to identify persons within a certain distance 'd' from a person in social networks up to 'd's levels.
· GPS Navigation System
To find all nearby locations, utilize the breadth-first search method.
· Broadcasting Network
A broadcast packet in a network uses breadth-first search to reach all nodes.
· Garbage Collection
Cheney's technique uses the breadth-first search for duplicating garbage collection. Because of the better locality of reference, breadth-first search is favored over the Depth First Search algorithm.
· Cycle Detection in Graph
Cycle detection in undirected graphs can be done using either Breadth-First Search or Depth First Search. BFS can also be used to find cycles in a directed graph.
· Identifying Routes
To see if there is a path between two vertices, you can use either Breadth-First or Depth First Traversal.
· Finding All Nodes Within One Connected Component
To locate all nodes reachable from a particular node, you can use either Breadth-First or Depth First Traversal.
Read More
Code Implementation of Breadth-First Search Algorithm
Breadth-First Search Algorithm Code Implementation:
#include<stdio.h>
#include<conio.h>
#include<stdlib.h>
int twodimarray[10][10],queue[10],visited[10],n,i,j,front=0,rear=-1;
void breadthfirstsearch(int vertex) // breadth first search function
{
for (i=1;i<=n;i++)
if(twodimarray[vertex][i] && !visited[i])
queue[++rear]=i;
if(front<=rear)
{
visited[queue[front]]=1;
breadthfirstsearch(queue[front++]);
}
}
int main() {
int x;
printf("\n Enter the number of vertices:");
scanf("%d",&n);
for (i=1;i<=n;i++) {
queue[i]=0;
visited[i]=0;
}
printf("\n Enter graph value in form of matrix:\n");
for (i=1;i<=n;i++)
for (j=1;j<=n;j++)
scanf("%d",&twodimarray[i][j]);
printf("\n Enter the source node:");
scanf("%d",&x);
breadthfirstsearch(x);
printf("\n The nodes which are reachable are:\n");
for (i=1;i<=n;i++)
if(visited[i])
printf("%d\t",i);
else
printf("\n Breadth first search is not possible");
getch();
}
1 note
·
View note
Text
The Breadth-First Search Algorithm
Graphs can be traversed in a depth-first or breadth-first manner. The first one is particularly useful when your data is a DAG (directed, acyclical graph), but both can be applied to any given kind of graph, including trees.
Consider this example:
This is a graph with 9 nodes, which I have key colored to show the depth of every layer of nodes. Hopefully this clears out the topology a bit. This graph can be represented as a 9 x 9 adjacency matrix as shown above (if you have N nodes, necessarily you need N x N size in your matrix).
The key to traversing the graph in a breadth-first manner is the implementation of two auxiliary data structures:
An N-sized array to keep track of what nodes you have already visited in your traversal, to avoid cycles when going down the graph by layers.
A queue of nodes of dynamic size, which will queue the nodes in order as you visit them, and dequeue them as you traverse down.
So in this example, you would do this, if you wished to traverse the tree starting from node 0:
Enqueue 0
Dequeue 0
Enqueue all its children, 1, 2, 3 (queue = 1, 2, 3)
Add 0 to output
Mark 0 as visited
Dequeue 1
If not visited yet, enqueue all its children, 4 and 5 (queue = 2, 3, 4, 5)
Add 1 to output
Mark 1 as visited
Dequeue 2
If not visited yet, enqueue all its children, 3 and 6 (queue = 3, 4, 5, 6) ... REPEAT UNTIL THE QUEUE IS EMPTY and all nodes have been visited
This is a sample implementation:
import java.util.Queue; import java.util.ArrayDeque; import java.util.List; import java.util.ArrayList; public class Main { public static int N = 9; public static void main(String[] args) { int[][] graph = new int[N][N]; graph[0][1] = 1; graph[0][2] = 1; graph[0][3] = 1; graph[1][4] = 1; graph[1][5] = 1; graph[2][3] = 1; graph[2][6] = 1; graph[3][7] = 1; graph[3][8] = 1; System.out.println(bfs(graph, 0).toString()); } private static List bfs(int[][] graph, int startNode) { List<Integer> result = new ArrayList<>(); //visited array of N elements to avoid cycles boolean[] visited = new boolean[N]; Queue<Integer> queue = new ArrayDeque<>(); int current = startNode; queue.add(current); visited[startNode] = true; while (!queue.isEmpty()) { int currentNode = queue.poll(); result.add(currentNode); // Explore all neighbors of the current node for (int i = 0; i < graph[currentNode].length; i++) { if (graph[currentNode][i] == 1 && !visited[i]) { queue.add(i); visited[i] = true; } } } return result; } }
0 notes
Link
As the name suggests, the queue is the type of data structure that follows the FIFO (First In – First Out) mechanism. In simple words, it is a linear data structure that resembles somewhere like a stack but has the difference of entry and exit in the elements i.e unlike stacks. It is open at both ends. One end is used to insert data (enqueue) and the other end is used to remove data (dequeue).
Queues are just like lines at a ticket counter or a cash-counter where the first entering person is the first exiting person as well.
0 notes
Text
Queue IN Data Structure - tccicomputercoaching.com
Data structure: It’s a way in computer science to organize data properly in such a way that we can use data efficiently.
What is Queue?
Queue is also an abstract data type or a linear data structure, in which the first element is inserted from one end called REAR (also called tail), and the deletion of existing element takes place from the other end called as FRONT (also called head). This makes queue as FIFO data structure, which means that element inserted first will also be removed first.
Like Stack, Queue is also an ordered list of elements of similar data types.
A queue is a particular kind of abstract data type or collection in which the entities in the collection are kept in order and the principal (or only) operations on the collection are the addition of entities to the rear terminal position, known as enqueue, and removal of entities from the front terminal position, known as dequeue. This makes the queue a First-In-First-Out (FIFO) data structure. In a FIFO data structure, the first element added to the queue will be the first one to be removed. This is equivalent to the requirement that once a new element is added, all elements that were added before have to be removed before the new element can be removed.
Real Example of Queue:
A queue at window of movie-ticket. The person who is standing first, its turn comes first, he/her comes first out.
Queue can be implemented using an Array, Stack or Linked List. The easiest way of implementing a queue is by using an Array. A bounded queue is a queue limited to a fixed number of items.
One characteristic of a queue is that it does not have a specific capacity. Regardless of how many elements are already contained, a new element can always be added. It can also be empty, at which point removing an element will be impossible until a new element has been added again.
Fixed length arrays are limited in capacity, but it is not true that items need to be copied towards the head of the queue.
Operations –
1. Create Queue Operation– This operation takes the maximum number of elements (maxElements) the Queue can hold as an argument, creates a Queue according to it and returns a pointer to the Queue.
2. Enqueue Operation – This Operation takes the pointer to the top of the queue Q and the item (element) to be inserted as arguments. Check for the emptiness of queue.
3. Dequeue Operation – This Operation takes the pointer to the top of the stack S as an argument and will then dequeue an element.
4. Front function – This function takes the pointer to the top of the queue Q as an argument and returns the front element of the queue Q.
Queue overflow results from trying to add an element onto a full queue and Queue underflow happens when trying to remove an element from an empty queue.
If you like this post then please share and like the post.
To Learn More About data structure in ahemdabad and , Computer Class
Visit us @ tccicomputercoaching.com
Call us @ 98256 18292
#Online data structure in Ahmedabad#Online Computer Class#Online engineering course#Programming Language in Ahmedabad#TCCI
0 notes
Link
Data Structures and Algorithms in C programming language – coding interviews questions/projects. Linked List, Recursion
DATA STRUCTURES
Created by Deepali Srivastava
Last updated 8/2019
English
English Subs [Auto-generated]
What you’ll learn
Understand the details of Data Structures and algorithms through animations
Learn to write programs for different Data Structures and Algorithms in C language
Get the confidence to face programming interviews
Test your knowledge with over 100 Quiz questions
Learn how to analyse algorithms
Get the ability to write and trace recursive algorithms
Requirements
Basic knowledge of programming in any language
Description
This “Data Structures and Algorithms In C” course is thoroughly detailed and uses lots of animations to help you visualize the concepts.
Subtitles are available for the first section only. Closed Captioning for rest of the sections is in progress and are available as [Auto-generated].
This “Data Structures and Algorithms in C” tutorial will help you develop a strong background in Data Structures and Algorithms. The course is broken down into easy to assimilate short lectures, and after each topic there is a quiz that can help you to test your newly acquired knowledge. The examples are explained with animations to simplify the learning of this complex topic. Complete working programs are shown for each concept that is explained.
This course provides a comprehensive explanation of structures like linked lists, stacks and queues, binary search trees, heap, searching, hashing. Various sorting algorithms with implementation and analysis are included in this tutorial. Concept of recursion is very important for designing and understanding certain algorithms so the process of recursion is explained with the help of several examples.
This course covers following topics with C language implementation :
Algorithm Analysis, Big O notation, Time complexity, Singly linked list, Reversing a linked list, Doubly linked list, Circular linked list, Linked list concatenation, Sorted linked list.
Stack, Queue, Circular Queue, Dequeue, Priority queue, Polish Notations, Infix to Postfix, Evaluation of Postfix, Binary Tree, Binary Search Tree, Tree Traversal (inorder, preorder, postorder, level order), Recursion, Heap, Searching, Hashing
Sorting : Selection, Bubble, Insertion, Shell, Merging, Recursive Merge, Iterative Merge, Quick, Heap, Binary tree, Radix, Address calculation sort
Throughout the course, a step by step approach is followed to make you understand different Data Structures and Algorithms. You will see implementation of different data structures in C language and algorithms are explained in step-wise manner. Through this course you can build a strong foundation and it will help you to crack Data Structures and Algorithms coding interviews questions and work on projects. Good foundation on Data Structures and Algorithms interview topics helps you to attempt tricky interview questions.
In this Data Structures and Algorithms course, C language is used for implementing various concepts, but you can easily implement them in any other programming languages like C++, C#, Java, Python.
What students are saying about this course-
“This is exactly how I hoped to learn data structure and algorithm, PLUS, it’s using C!!”
“Instructor is teaching in very well and efficient manner with a good pace ,clears every doubts and teaches concepts deeply.”
“Great class, explains topics very well, better than any college class I ever took.”
“yes this course has helped me a lot in discovering new topics and the example programs are also quite helpful.”
“I really appreciate the way the steps are broken down incrementally.”
“Deepali does a great job in explaining all the concept and the course is very well organized. First the concept is explained on paper and then there is a walk through of the code, and then execution of the code. I have learnt a great deal from this course.”
“I am taking notes and writing code side by side watching videos which makes it beneficial to understand the code and easier to grasp the concept of the topic rather than just copying the source code. Thank you Deepali Mam for not giving the source it was better to write the code by myself. The videos are informative, detailed and right on point with step by step code programs and I feel learned a lot taking your course then the class which I took at University. This course made my base of data structures in C pretty strong thank you for that.”
“This is an awesome course. If you need to understand then try to write every code yourself then try to analyze it. that’s how you can gain confidence.”
“I’m re-learning something what I am learn years ago, and this course is perfect for my need.”
“Its quite helpful, it nicely supplements what you have studied in the book.”
“Excellent presentation and content. Easily comprehensible. Since Data Structures and Algorithms are heart of computer science will give a 5 star for this kind of knowledge resource.”
“It is one of the best courses that I have ever taken in Structures and C.”
“Good foundation course covering the fundamentals of structures in C.”
“It’s definitely a good course for beginners who have basic knowledge in C and want to learn Data Structures and Algorithms. Really good explanation by the instructor with experience of even writing a book on Data structures.”
“Till now its above expectations.Mam I am also following your both book “C in Depth” and “Data Structure Using C”.”
“Great in depth explanations of the data structures and algorithms covered.”
“good and perfect teaching for basic levels for beginners in data structures.”
“Very good clarification and reference for common data structures and algorithms.”
This Data Structures and Algorithms In C online course on udemy will help software developers to refresh the concepts studied in book / pdf and also to students learning from referred book / pdf.
Who this course is for:
Programmers looking for jobs
Programmers wanting to write efficient code
Computer Science students having Data Structures as part of their curriculum
Non Computer science students wanting to enter IT industry
Size: 752MB
DOWNLOAD TUTORIAL
The post DATA STRUCTURES AND ALGORITHMS THROUGH C IN DEPTH appeared first on GetFreeCourses.Me.
0 notes
Text
COP3503 Lab 5 – Array-Based Stack and Queue Solved
Overview
In this assignment, you will be implementing your own Array-Based Stack (ABS) and Array-Based Queue. A stack is a linear data structure which follows the last in, first out (LIFO) property. LIFO means that the data most recently added is the first data to be removed. (Imagine a stack of books, or a stack of papers on a desk—the first one to be removed is the last one placed on top.) A queue is another linear data structure that follows the first in, first out (FIFO) property. FIFO means that the data added first is the first to be removed (like a line in a grocery store-- the first person in line is the first to checkout).
Stack Behavior
Push – Add something to the top of the stack. Pop – Remove something from the top of the stack and return it, Example of LIFO operations-the data most recently added is the first to be removed
Queue Behavior
Enqueue – Add something to end of the queue. Dequeue – Remove something from the front of the queue. Example of FIFO operations-the newest data is last to be removed
Description
Your ABS and ABQ will be template classes, and thus will be able to hold any data type. (Many data structures follow this convention—reuse code whenever you can!) As with previous classes that use dynamic memory, you must be sure to define The Big Three: The Copy Constructor, the Assignment Operator, and the Destructor. Data will be stored using a dynamically allocated array (hence the array-based stack and queue). You may use any other variables/function in your class to make implementation easier. By default, your ABS and ABQ will have a scale factor 2.0f. Attempting to push() or enqueue() an item onto an ABS/ABQ that is full will resize the current capacity to current_capacity*scale_factor. When calling pop() or dequeue(), if the “percent full” (e.g. current size / max capacity) becomes strictly less than 1/scale_factor, resized the storage array to current_capacity/scale_factor. Why increase (or decrease) the size by any amount other than one? Short answer: performance! If you are increasing or decreasing the size of a container, it’s reasonable to assume that you will want to increase or decrease the size again at some point, requiring another round of allocate, copy, delete, etc. Increasing the capacity by more than you might need (right now) or waiting to reduce the total capacity allows you to avoid costly dynamic allocations, which can improve performance—especially in situations in which this resizing happens frequently. This tradeoff to this approach is that it will use more memory, but this speed-versus-memory conflict is something that programmers have been dealing with for a long time. An example of the resizing scheme to be implement on a stack.
Stack Functions
Your ABS must support the following methods: Method Description ABS() Default constructor. Maximum capacity should be set to 1, and current size set to 0; ABS(int capacity) Constructor for an ABS with the specified starting maximum capacity. ABS(const ABS &d) Copy Constructor ABS &operator=(const ABS &d) Assignment operator. ~ABS() Destructor void push(T data) Add a new item to the top of the stack and resize if necessary. T pop() Remove the item at the top of the stack, resizes if necessary, and return the value removed. Throws -1 if the stack is empty. T peek() Return the value of the item at the top of the stack, without removing it. Throws -1 if the stack is empty. unsigned int getSize() Returns the current number of items in the ABS. unsigned int getMaxCapacity() Returns the current max capacity of the ABS. T* getData() Returns the array representing the stack. Addition methods may be added as deemed necessary.
Queue Functions
Your ABQ must support the following functions Method Description ABQ() Default constructor. Maximum capacity should be set to 1, and current size set to 0; ABQ(int capacity) Constructor for an ABQ with the specified starting maximum capacity. ABQ(const ABS &d) Copy Constructor ABQ &operator=(const ABQ &d) Assignment operator. ~ABQ() Destructor void enqueue(T data) Add a new item to end of the queue and resizes if necessary. T dequeue() Remove the item at front of the queue, resizes if necessary, and return the value removed. Throws -1 if the queue is empty. T peek() Return the value of the item at the front of the queue, without removing it. Throws -1 if the queue is empty. unsigned int getSize() Returns the current number of items in the ABQ. unsigned int getMaxCapacity() Returns the current max capacity of the ABQ. T* getData() Returns the array representing the queue. Addition methods may be added as deemed necessary. S
Extra Credit (1%)
You can earn up to 1% extra credit (toward your final course grade) for this lab by doing everything listed below. DO NOT work on extra credit until your standard lab scored full points. If your standard lab does not score full points, you cannot earn any extra credit. Update ABS/ABQ Update your ABS and ABQ to use a scale factor other than 2.0f, and modify any methods that should behave differently as a result: ABS(int capacity, float scale_factor) : Constructor for an ABS with the specified starting capacity, and a custom scale factor. ABQ(int capacity, float scale_factor) : Constructor for an ABQ with the specified starting capacity, and a custom scale factor. Add an extra attribute to your ABS and ABQ, total_resizes, which is a count of how many times the ABS/ABQ has been resized. Add an accessor for this attribute: unsigned int getTotalResizes(): Returns the total number of times the ABS has been resized. Analysis Report Create a testing file that can perform the following tasks, each with an ABS/ABQ that starts with a max capacity of 2: push N items onto an empty ABS. pop N items off an ABS with N items already on it. enqueue N items onto an empty ABQ. dequeue N items off an ABQ with N items already on it. For each of the tasks, record: How long it takes to perform the task ○ Use the or libraries How many resizes were performed during the task Do the tasks for each of the following possible combinations of Scale Factor and N*: Scale Factor N 1.5 10 000 000 2.0 30 000 000 3.0 50 000 000 10.0 75 000 000 100.0 100 000 000 Depending on your computer specs, a significantly high N may take a very long time or crash the program. If necessary, you may pick 5 different values of N but be sure to vary their size decently to get the most interesting results. You should have 100 different sets of data (4 tasks * 5 scale factors * 5 Ns). Graph the data for each scale factor, with N being the independent variable and time being the dependent variable. Include the number of resizes for each task somewhere on the graph as well. You should have 10 graphs, one for each scale factor per ABS/ABQ. Write a 1-2 page analysis of your results. Make note of any trends in the data. Answer the following questions in your report. How does N affect the time it takes for? What are the effects of changing scale factor? How do both of these affect the number of times the ABS will be resized? What seems to be the best scale factor, and why? How can you explain differences between the performances of your ABS and ABQ? Submission (on Canvas): Your updated ABS.h file Read the full article
0 notes
Text
Stack, Queue and Heaps Part - 1
Stacks are dynamic data structures that follow the Last Out (LIFO) principle. The last item to be inserted a stack is the first one to be deleted from it.
For example, you have a stack of trays on a table. The tray at the top of the stack is the first item to be moved if you require a tray from that stack.
Inserting and deleting elements
have restrictions on the insertion and deletion of elements. Elements can be inserted or deleted only from one end of the stack i.e. the top. The element at the top is called the top element. The operations of inserting and deleting elements are called push) and pop) respectively.
When the top element of a stack is deleted, if the stack remains non-empty, then the element just below the previous top element becomes the new top element of the stack.
For example, in the stack of trays, if you take the tray on the top and do not replace it, then the second tray automatically becomes the top element (tray) of that stack.
Queue
Queues are data structures that follow the First In First Out (FIFO) i.e. first element that is added to the queue is the first one to be removed.
Elements are always added to the back and removed from the front. Think of it as a line of people waiting for a bus. The person who is at the beginning of the line is the first one to enter the bus.
Variables used
• : Array in which queue is simulated
• : Maximum number of elements that can be stored in a queue]
• : Points at the index where the next deletion will be performed
• : Points at the index where the next insertion will be performed
Functions supported
Queues support the following fundamental functions:
Enqueue
function adds an element to the back of the queue.
Dequeue
function removes the element from the front of the queue.
Heaps
is a special case of balanced binary tree data structure where the root-node key is compared with its children and arranged accordingly. If α has child node β then −
key(α) ≥ key(β)
As the value of parent is greater than that of , this property generates Max Heap. Based on this criteria, a heap can be of two types −
For Input → 35 33 42 10 14 19 27 44 26 31
Hash Table:
Hash Table is a data structure which stores data in an associative manner. In a hash table, data is stored in an array format, where each data value has its own unique index value. Access of data becomes very fast if we know the index of the desired data.
Thus, it becomes a data structure in which insertion and search operations are very fast irrespective of the size of the data. Hash Table uses an array as a storage medium and uses to generate an index where an element is to be inserted or is to be located from.
Hashing is a technique to convert a range of key values into a range of indexes of an array. We’re going to use to get a range of key values. Consider an example of of size 20, and the following items are to be stored. Item in the (keyvalue) format.
For more details on our products and services, please feel free to visit us at outsource ecommerce software, outsource nodejs developer, outsource, outsource your project, outsource ruby developer
0 notes
Text
We’ve all heard that if you already know a programming language, learning another one will be easier. Well, as a JavaScript developer I must say, learning Python (again!) is going to be easier than learning PHP or JavaScript the first time around. I’ve tried learning Python years ago when my son became interested in programming and I sure wish I’d kept up with it. Going into day 2 of learning Python, I’ve be introduced to lists, tuples and ranges. All examples in this post are using version 3.7.2.
Lists
Lists are a built-in python data structure (sequence types). They are very similar to arrays in JavaScript, a list of items, separated by commas and surrounded by brackets.
JavaScript Array
Python List
Lists are mutable, meaning they can be changed after it is created. They are an ordered sequence of items. holding a collection of things; strings, numbers, booleans, etc. They are zero indexed based structures. You can access an item by their index number which is their position in the list.
>>> my_list [1, True, 3, 'candy', 3.4, 'Cardi B'] >>> my_list[5] 'Cardi B' >>>
An easy way to always access the last item in the list is by using the len method. No matter the length of the list, you can have access to the last item.
>>> my_list [1, True, 3, 'candy', 3.4, 'Cardi B'] >>> my_list[len(my_list)-1] 'Cardi B' >>>
Since lists are zero indexed, you can always access the first item, no matter the length by the [0] index.
>>> my_list[0] 1 >>> my_list [1, True, 3, 'candy', 3.4, 'Cardi B'] >>>
Slicing up a list is a great way to access a group of items from the list. Instead of just accessing an item one at a time, the slice object gives you access by where you want to start, where you want to stop and the steps in between.
[start index:end index: step]
>> my_list [1, 2, 3, 4, 5] >>> my_list[0:2] [1, 2] >>>
[0:2] starting at index 0 and up to, but not including index 2. Also, you don’t need the 0, you can leave it off and it’s understood as 0.
>>> my_list[:7:2] [1, 3, 5, 7] >>>
[0:9:2] starting at index 0 and up to, but not including index 9 counting by 2s.
>>> my_list = [1,2,3,4,5,6,7,8,9,10] >>> my_list[0:9:2] [1, 3, 5, 7, 9] >>>
To return a list that returns every other item (by 2s).
>>> my_list[::2] [1, 3, 5, 7, 9] >>>
Reverse a list.
>> my_list[::-1] [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] >>>
Lists are mutable. You can update and change them any way you like. Reference the index and assign a new item.
>>>my_list = [1,2,3,4,5,6,7,8,9,10] > my_list[0] = 'a' >>> my_list ['a', 2, 3, 4, 5, 6, 7, 8, 9, 10]
Functions
Loved the familiarity of these good old methods. Python makes these functions available for quick and easy data manipulation. First up:
.pop() returns and removes items from any position in the list. Using pop() without any index will remove and return the last item in the list.
>>> my_list ['a', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'bug'] >>> my_list.pop() 'bug'
Using .pop() with an index value, you can target any item you like to be popped off and returned.
>>> my_list= [1,2,True,'apricot',5,'Cardi B'] >>> my_list.pop(4) 5 >>> my_list [1, 2, True, 'apricot', 'Cardi B'] >>>
.pop() can behave like either a queue or a stack. Both are data structures in programming and process data in very different ways. Queue is my favorite, I see FIFO, I think SQS (AWS), first-in first-out (FIFO). Any item added at the end (enqueue) will be the last one to be accessed (dequeue). You have to do an awful lot of popping to get to the last one if your queue is contains a lot of items. pop(0) will make your list behave like a queue .pop() will make your list behave like a stack. A stack just what it sounds like, laundry. It is a first-in last-out structure. Any item added at the end of the stack will be the first out off the stack. If your favorite jeans are folded first and are at the bottom of the stack, you may as well grab the ones off the top and work your way down. You don’t want to look like you’re re-running your jeans, do you?
Learn more about queues and stacks here.
Next up is .remove() which is great when you want to remove an item by it’s value. Strange thing is, if that item is a Boolean, it’ll remove the item in the 0 index. I didn’t know that was a thing until I just tested it.
> my_list= [1,2,True,'apricot',5,'Cardi B'] >>> my_list.remove(True) >>> my_list [2, True, 'apricot', 5, 'Cardi B'] >>> my_list.remove(5) >>> my_list [2, True, 'apricot', 'Cardi B']
.reverse() is a handy for when you need to swap the order of the list.
>>> my_list ['a', 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> my_list.reverse() >>> my_list [10, 9, 8, 7, 6, 5, 4, 3, 2, 'a'] >>> my_list.reverse() >>> my_list ['a', 2, 3, 4, 5, 6, 7, 8, 9, 10]
Sometimes you just want to throw an item on the end, .append() does the trick. Add the item inside the method and voila!
>>> my_list ['a', 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> my_list.append('bug') >>> my_list ['a', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'bug']
Last one is .insert(). You can add a new item to any position you want to in your list. Specify the index and the value and insert away. It’ll make room and put your item right where you want it.
>>> my_list= [1,2,True,'apricot',5,'Cardi B'] >>> my_list.insert(2, 43.9) >>> my_list [1, 2, 43.9, True, 'apricot', 5, 'Cardi B']
Tuples
Tupes are immutable and fixed-sized, meaning they cannot be changed. They’re good for items that don’t need to change, like x and y coordinates. You can’t assign an item to an index, again immutable. To create a tuple, list items in parentheses, separated by commas. someTuple = (some item, another item, last item,…)
>>> coord = (4.0, 3.2) >>> coord[1] 3.2 >>> coord[1] = 2.9 Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'tuple' object does not support item assignment
You can create a tuple from a tuple. This will not change the original tuple, just copy it’s values into a new one. Note the comma after the item in parens.
>>> coord = (4.0, 3.2) >>> big_coord = coord + (0.8,) >>> big_coord (4.0, 3.2, 0.8)
Since tuples are immutable, modifying isn’t possible, but you can overwrite one.
>>> coord = (4.0, 3.2) >>> coord (4.0, 3.2) >>> coord = (2.2, 5.1) >>> coord (2.2, 5.1)
This is pretty cool about tuples. You can unpack the items from a tuple and assign them to variables. As long as the number of items and variables are equal, you can easily unpack and assign those values.
>>> user_info=("Smith", "Rasheeda", "F", 342492) >>> user_info ('Smith', 'Rasheeda', 'F', 342492) >>> fn, ln, mi, id = user_info >>> fn 'Smith' >>> ln 'Rasheeda' >>> mi 'F' >>> id 342492
The last thing about tuples is that they remind me they can be used as template literals in that, you can use a tuple to do string substitution.
>>> print("My favorite flavors of ice cream are %s and %s." % ("butter almond", "strawberry")) My favorite flavors of ice cream are butter almond and strawberry. >>>
Ranges
The last thing I covered was ranges. A great tool to quickly create a list of items in a range with just one method. Very similar to slicing, but way better if you want to create a quick list of numbers. To get those numbers in to a list, use the list function. The values needed are the start, stop and the step. If you leave the step of, the list is in numerical order. range(start, stop, range)
>>> range(2,22,2) range(2, 22, 2) >>> list(range(2,22,2)) [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] >>>
Not a bad first few days with Python. Great stuff here and there is so much to learn. Stay tuned for tomorrows post. I think I’ll go over dictionaries.
Day 2 of Learning Python: Lists, Tuples and Ranges We've all heard that if you already know a programming language, learning another one will be easier.
0 notes
Text
Data Structures & Algorithms in Swift Full Release Now Available!
Hey, Swifties! The full release of our Data Structures & Algorithms in Swift book is now available!
The early access release of this book — complete with the theory of data structures and algorithms in Swift — debuted at our conference RWDevCon 2018. Since then, the team has been hard at work creating a robust catalogue of challenges — 18 chapters in all — to test what you’ve learned and grow your expertise. The book is structured with the theory chapters alternating with the challenge chapters to keep you on track to nail down the fundamental and more advanced concepts.
Why Do You Need This Book?
Understanding how data structures and algorithms work in code is crucial for creating efficient and scalable apps. Swift’s Standard Library has a small set of general purpose collection types, yet they don’t give you what you need for every case.
Moreover, you’ll find these concepts helpful for your professional and personal development as a developer.
When you interview for a software engineering position, chances are that you’ll be tested on data structures and algorithms. Having a strong foundation in data structures and algorithms is the “bar” for many companies with software engineering positions.
Knowing the strategies used by algorithms to solve tricky problems gives you ideas for improvements you can make to your own code. Knowing more data structures than just the standard array and dictionary also gives you a bigger collection of tools that you can use to build your own apps.
Here’s what’s contained in the full release of the book:
Section I: Introduction
Chapter 1: Why Learn Data Structures & Algorithms?: Data structures are a well-studied area, and the concepts are language agnostic; a data structure from C is functionally and conceptually identical to the same data structure in any other language, such as Swift. At the same time, the high-level expressiveness of Swift make it an ideal choice for learning these core concepts without sacrificing too much performance.
Chapter 2: Swift Standard Library: Before you dive into the rest of this book, you’ll first look at a few data structures that are baked into the Swift language. The Swift standard library refers to the framework that defines the core components of the Swift language. Inside, you’ll find a variety of tools and types to help build your Swift apps.
Section II: Elementary Data Structures
Chapter 3: Linked List: A linked list is a collection of values arranged in a linear unidirectional sequence. A linked list has several theoretical advantages over contiguous storage options such as the Swift Array, including constant time insertion and removal from the front of the list, and other reliable performance characteristics.
Chapter 5: Stacked Data Structure: The stack data structure is identical in concept to a physical stack of objects. When you add an item to a stack, you place it on top of the stack. When you remove an item from a stack, you always remove the topmost item. Stacks are useful, and also exceedingly simple. The main goal of building a stack is to enforce how you access your data.
Chapter 7: Queues: Lines are everywhere, whether you are lining up to buy tickets to your favorite movie, or waiting for a printer machine to print out your documents. These real-life scenarios mimic the queue data structure. Queues use first-in-first-out ordering, meaning the first element that was enqueued will be the first to get dequeued. Queues are handy when you need to maintain the order of your elements to process later.
Easy-to-understand examples show key concepts, such as trees!
Section III: Trees
Chapter 9: Trees: The tree is a data structure of profound importance. It is used to tackle many recurring challenges in software development, such as representing hierarchical relationships, managing sorted data, and facilitating fast lookup operations. There are many types of trees, and they come in various shapes and sizes.
Chapter 11: Binary Trees: In the previous chapter, you looked at a basic tree where each node can have many children. A binary tree is a tree where each node has at most two children, often referred to as the left and right children. Binary trees serve as the basis for many tree structures and algorithms. In this chapter, you’ll build a binary tree and learn about the three most important tree traversal algorithms.
Chapter 13: Binary Search Trees: A binary search tree facilitates fast lookup, addition, and removal operations. Each operation has an average time complexity of O(log n), which is considerably faster than linear data structures such as arrays and linked lists.
Chapter 15: AVL Trees: In the previous chapter, you learned about the O(log n) performance characteristics of the binary search tree. However, you also learned that unbalanced trees can deteriorate the performance of the tree, all the way down to O(n). In 1962, Georgy Adelson-Velsky and Evgenii Landis came up with the first self-balancing binary search tree: the AVL Tree.
Helpful visuals demonstrate how to organize and sort data!
Chapter 17: Tries: The trie (pronounced as “try”) is a tree that specializes in storing data that can be represented as a collection, such as English words. The benefits of a trie are best illustrated by looking at it in the context of prefix matching, which is what you’ll do in this chapter.
Chapter 19: Binary Search: Binary search is one of the most efficient searching algorithms with a time complexity of O(log n). This is comparable with searching for an element inside a balanced binary search tree. To perform a binary search, the collection must be able to perform index manipulation in constant time, and must be sorted.
Chapter 21: The Heap Data Structure: A heap is a complete binary tree, also known as a binary heap, that can be constructed using an array. Heaps come in two flavors: Max heaps and Min heaps. Have you seen the movie Toy Story, with the claw machine and the squeaky little green aliens? Imagine that the claw machine is operating on your heap structure, and will always pick the minimum or maximum value, depending on the flavor of heap.
Chapter 23: Priority Queue: Queues are simply lists that maintain the order of elements using first-in-first-out (FIFO) ordering. A priority queue is another version of a queue that, instead of using FIFO ordering, dequeues elements in priority order. A priority queue is especially useful when you need to identify the maximum or minimum value given a list of elements.
Section IV: Sorting Algorithms
Chapter 25: O(n²) Sorting Algorithms: O(n²) time complexity is not great performance, but the sorting algorithms in this category are easy to understand and useful in some scenarios. These algorithms are space efficient; they only require constant O(1) additional memory space. In this chapter, you’ll be looking at the bubble sort, selection sort, and insertion sort algorithms.us shapes and sizes.
Chapter 27: Merge Sort: In this chapter, you’ll look at a completely different model of sorting. So far, you’ve been relying on comparisons to determine the sorting order. Radix sort is a non-comparative algorithm for sorting integers in linear time. There are multiple implementations of radix sort that focus on different problems. To keep things simple, in this chapter you’ll focus on sorting base 10 integers while investigating the least significant digit (LSD) variant of radix sort.
Chapter 29: Radix Sort: A binary search tree facilitates fast lookup, addition, and removal operations. Each operation has an average time complexity of O(log n), which is considerably faster than linear data structures such as arrays and linked lists.
Chapter 31: Heapsort: Heapsort is another comparison-based algorithm that sorts an array in ascending order using a heap. This chapter builds on the heap concepts presented in Chapter 21, “The Heap Data Structure.” Heapsort takes advantage of a heap being, by definition, a partially sorted binary tree.
Chapter 33: Quicksort: Quicksort is another divide and conquer technique that introduces the concept of partitions and a pivot to implement high performance sorting. You‘ll see that while it is extremely fast for some datasets, for others it can be a bit slow.
Real-world examples help you apply the book’s concepts in a concrete and relevant way!
Section V: Graphs
Chapter 35: Graphs: What do social networks have in common with booking cheap flights around the world? You can represent both of these real-world models as graphs! A graph is a data structure that captures relationships between objects. It is made up of vertices connected by edges. In a weighted graph, every edge has a weight associated with it that represents the cost of using this edge. This lets you choose the cheapest or shortest path between two vertices.
Chapter 37: Breadth-First Search: In the previous chapter, you explored how graphs can be used to capture relationships between objects. Several algorithms exist to traverse or search through a graph’s vertices. One such algorithm is the breadth-first search algorithm, which can be used to solve a wide variety of problems, including generating a minimum spanning tree, finding potential paths between vertices, and finding the shortest path between two vertices.
Chapter 39: Depth-First Search: In the previous chapter, you looked at breadth-first search where you had to explore every neighbor of a vertex before going to the next level. In this chapter, you will look at depth-first search, which has applications for topological sorting, detecting cycles, path finding in maze puzzles, and finding connected components in a sparse graph.
Chapter 41: Dijkstra’s Algorithm: Have you ever used the Google or Apple Maps app to find the shortest or fastest from one place to another? Dijkstra’s algorithm is particularly useful in GPS networks to help find the shortest path between two places. Dijkstra’s algorithm is a greedy algorithm, which constructs a solution step-by-step, and picks the most optimal path at every step.
Chapter 43: Prim’s Algorithm: In previous chapters, you’ve looked at depth-first and breadth-first search algorithms. These algorithms form spanning trees. In this chapter, you will look at Prim’s algorithm, a greedy algorithm used to construct a minimum spanning tree. A minimum spanning tree is a spanning tree with weighted edges where the total weight of all edges is minimized. You’ll learn how to implement a greedy algorithm to construct a solution step-by-step, and pick the most optimal path at every step.
The book moves beyond fundamentals to more advanced concepts, such as Dijkstra’s Algorithm!
Data Structures and Algorithms in Swift will teach you how to implement the most popular and useful data structures, and when and why you should use one particular data structure or algorithm over another.
This set of basic data structures and algorithms will serve as an excellent foundation for building more complex and special-purpose constructs. And the high-level expressiveness of Swift makes it an ideal choice for learning these core concepts without sacrificing performance.
Get your own copy:
If you’ve pre-ordered Data Structures & Algorithms in Swift, you can log in to the store and download the final version here.
If you haven’t already bought this must-have addition to your development library, head over to our store page to grab your copy today!
Questions about the book? Ask them in the comments below!
The post Data Structures & Algorithms in Swift Full Release Now Available! appeared first on Ray Wenderlich.
Data Structures & Algorithms in Swift Full Release Now Available! published first on https://medium.com/@koresol
0 notes