#delete a node from doubly linked list

[korshubudemycoursesblog]

Mastering Data Structures Using Python: A Complete Guide

When learning programming, mastering Data Structures Using Python is one of the most critical milestones. Python, known for its simplicity and versatility, is a perfect language to delve into data structures, which form the backbone of efficient algorithms. In this blog, we’ll explore the essential data structures in Python, how to use them, and why they’re so vital in programming.

Why Learn Data Structures Using Python?

1. Simplifies Complex Operations

Python's built-in libraries and clean syntax make implementing data structures intuitive. Whether you’re manipulating arrays or designing trees, Python minimizes complexity.

2. High Demand for Python Programmers

The demand for professionals with expertise in Python for data structures is skyrocketing, especially in fields like data science, artificial intelligence, and software engineering.

3. A Foundation for Problem-Solving

Understanding data structures like lists, stacks, queues, and trees equips you to solve complex computational problems efficiently.

What Are Data Structures?

At their core, data structures are ways of organizing and storing data to perform operations like retrieval, insertion, and deletion efficiently. There are two main types:

Linear Data Structures: Data is stored sequentially (e.g., arrays, linked lists).

Non-Linear Data Structures: Data is stored hierarchically (e.g., trees, graphs).

Python, with its versatile libraries, offers tools to implement both types seamlessly.

Essential Data Structures in Python

1. Lists

One of Python's most versatile data structures, lists are dynamic arrays that can store heterogeneous data types.

Example:

python

Copy code

# Creating a list

fruits = ["apple", "banana", "cherry"]

print(fruits[1])  # Output: banana

Features of Lists:

Mutable (elements can be changed).

Supports slicing and iteration.

Used extensively in Python programming for simple data organization.

2. Tuples

Tuples are immutable sequences, often used for fixed collections of items.

Example:

python

Copy code

# Creating a tuple

coordinates = (10, 20)

print(coordinates[0])  # Output: 10

Key Benefits:

Faster than lists due to immutability.

Commonly used in scenarios where data integrity is crucial.

3. Dictionaries

Dictionaries in Python implement hash maps and are perfect for key-value storage.

Example:

python

Copy code

# Creating a dictionary

student = {"name": "John", "age": 22}

print(student["name"])  # Output: John

Why Use Dictionaries?

Quick lookups.

Ideal for scenarios like counting occurrences, storing configurations, etc.

4. Sets

Sets are unordered collections of unique elements, useful for removing duplicates or performing mathematical set operations.

Example:

python

Copy code

# Using sets

numbers = {1, 2, 3, 4, 4}

print(numbers)  # Output: {1, 2, 3, 4}

Applications:

Used in tasks requiring unique data points, such as intersection or union operations.

Advanced Data Structures in Python

1. Stacks

Stacks are linear data structures following the LIFO (Last In, First Out) principle.

Implementation:

python

Copy code

stack = []

stack.append(10)

stack.append(20)

print(stack.pop())  # Output: 20

Use Cases:

Undo operations in text editors.

Browser backtracking functionality.

2. Queues

Queues follow the FIFO (First In, First Out) principle and are used for tasks requiring sequential processing.

Implementation:

python

Copy code

from collections import deque

queue = deque()

queue.append(1)

queue.append(2)

print(queue.popleft())  # Output: 1

Applications:

Customer service simulations.

Process scheduling in operating systems.

3. Linked Lists

Unlike arrays, linked lists store data in nodes connected via pointers.

Types:

Singly Linked Lists

Doubly Linked Lists

Example:

python

Copy code

class Node:

    def __init__(self, data):

        self.data = data

        self.next = None

# Creating nodes

node1 = Node(10)

node2 = Node(20)

node1.next = node2

Benefits:

Efficient insertion and deletion.

Commonly used in dynamic memory allocation.

4. Trees

Trees are hierarchical structures used to represent relationships.

Types:

Binary Trees

Binary Search Trees

Heaps

Example:

python

Copy code

class TreeNode:

    def __init__(self, data):

        self.data = data

        self.left = None

        self.right = None

Applications:

Databases.

Routing algorithms.

5. Graphs

Graphs consist of nodes (vertices) connected by edges.

Representation:

Adjacency List

Adjacency Matrix

Example:

python

Copy code

graph = { 

    "A": ["B", "C"],

    "B": ["A", "D"],

    "C": ["A", "D"],

    "D": ["B", "C"]

}

Applications:

Social networks.

Navigation systems.

Why Python Stands Out for Data Structures

1. Built-In Libraries

Python simplifies data structure implementation with libraries like collections and heapq.

2. Readable Syntax

Beginners and experts alike find Python's syntax intuitive, making learning data structures using Python easier.

3. Versatility

From simple algorithms to complex applications, Python adapts to all.

Common Challenges and How to Overcome Them

1. Understanding Concepts

Some learners struggle with abstract concepts like recursion or tree traversal. Watching tutorial videos or practicing coding challenges can help.

2. Memory Management

Efficient use of memory is critical, especially for large-scale data. Python's garbage collection minimizes these issues.

3. Debugging

Using tools like Python’s pdb debugger helps troubleshoot problems effectively.

[tutort-academy]

How to Ace Your DSA Interview, Even If You're a Newbie

Are you aiming to crack DSA interviews and land your dream job as a software engineer or developer? Look no further! This comprehensive guide will provide you with all the necessary tips and insights to ace your DSA interviews. We'll explore the important DSA topics to study, share valuable preparation tips, and even introduce you to Tutort Academy DSA courses to help you get started on your journey. So let's dive in!

Why is DSA Important?

Before we delve into the specifics of DSA interviews, let's first understand why data structures and algorithms are crucial for software development. DSA plays a vital role in optimizing software components, enabling efficient data storage and processing.

From logging into your Facebook account to finding the shortest route on Google Maps, DSA is at work in various applications we use every day. Mastering DSA allows you to solve complex problems, optimize code performance, and design efficient software systems.

Important DSA Topics to Study

To excel in DSA interviews, it's essential to have a strong foundation in key topics. Here are some important DSA topics you should study:

1. Arrays and Strings

Arrays and strings are fundamental data structures in programming. Understanding array manipulation, string operations, and common algorithms like sorting and searching is crucial for solving coding problems.

2. Linked Lists

Linked lists are linear data structures that consist of nodes linked together. It's important to understand concepts like singly linked lists, doubly linked lists, and circular linked lists, as well as operations like insertion, deletion, and traversal.

3. Stacks and Queues

Stacks and queues are abstract data types that follow specific orderings. Mastering concepts like LIFO (Last In, First Out) for stacks and FIFO (First In, First Out) for queues is essential. Additionally, learn about their applications in real-life scenarios.

4. Trees and Binary Trees

Trees are hierarchical data structures with nodes connected by edges. Understanding binary trees, binary search trees, and traversal algorithms like preorder, inorder, and postorder is crucial. Additionally, explore advanced concepts like AVL trees and red-black trees.

5. Graphs

Graphs are non-linear data structures consisting of nodes (vertices) and edges. Familiarize yourself with graph representations, traversal algorithms like BFS (Breadth-First Search) and DFS (Depth-First Search), and graph algorithms such as Dijkstra's algorithm and Kruskal's algorithm.

6. Sorting and Searching Algorithms

Understanding various sorting algorithms like bubble sort, selection sort, insertion sort, merge sort, and quicksort is essential. Additionally, familiarize yourself with searching algorithms like linear search, binary search, and hash-based searching.

7. Dynamic Programming

Dynamic programming involves breaking down a complex problem into smaller overlapping subproblems and solving them individually. Mastering this technique allows you to solve optimization problems efficiently.

These are just a few of the important DSA topics to study. It's crucial to have a solid understanding of these concepts and their applications to perform well in DSA interviews.

Tips to Follow While Preparing for DSA Interviews

Preparing for DSA interviews can be challenging, but with the right approach, you can maximize your chances of success. Here are some tips to keep in mind:

1. Understand the Fundamentals

Before diving into complex algorithms, ensure you have a strong grasp of the fundamentals. Familiarize yourself with basic data structures, common algorithms, and time and space complexities.

2. Practice Regularly

Consistent practice is key to mastering DSA. Solve a wide range of coding problems, participate in coding challenges, and implement algorithms from scratch. Leverage online coding platforms like LeetCode, HackerRank to practice and improve your problem-solving skills.

3. Analyze and Optimize

After solving a problem, analyze your solution and look for areas of improvement. Optimize your code for better time and space complexities. This demonstrates your ability to write efficient and scalable code.

4. Collaborate and Learn from Others

Engage with the coding community, join study groups, and participate in online forums. Collaborating with others allows you to learn different approaches, gain insights, and improve your problem-solving skills.

5. Mock Interviews and Feedback

Simulate real interview scenarios by participating in mock interviews. Seek feedback from experienced professionals or mentors who can provide valuable insights into your strengths and areas for improvement.

Following these tips will help you build a solid foundation in DSA and boost your confidence for interviews.

Conclusion

Mastering DSA is crucial for acing coding interviews and securing your dream job as a software engineer or developer. By studying important DSA topics, following effective preparation tips, and leveraging Tutort Academy's DSA courses, you'll be well-equipped to tackle DSA interviews with confidence. Remember to practice regularly, seek feedback, and stay curious.

Good luck on your DSA journey!

[bmharwani]

Video tutorial explains a C program to delete a node from doubly linked list. You will learn to delete first node, middle node as well as last node ffrom the doubly linked list. Doubly linked list is also known as two-way linked list. The C program is explained using step by step approach along with figures at each step. Entire source code is explained statement wise. In this tutorial, you will learn   double link list deletion, delete the first node of doubly linked list and delete node at given position in doubly linked list. This online data structures and algorithms tutorial teaches, data structures in c, data structures interview questions and answers and data structures along with data structure programs. By the end of the vide you will be knowing, data structures and algorithms lectures, data structures tutorial videos and online data structures training. Complete coding is explained with figures at each step, you will understand, delete node at given position, data structures interview questions and previous pointer along with linked list using c. If you are looking for, delete a node from linked list, how to delete a node in doubly linked list and algorithm to delete a node from doubly linked list, then this video is for you. For code of deleting a node from doubly linked list, click the following link: http://bmharwani.com/deletefromdoubly.c For more video tutorials on data structures and algorithms, visit: https://www.youtube.com/watch?v=-XXsmBys2DQ&t=0s&list=PLuDr_vb2LpAxVWIk-po5nL5Ct2pHpndLR&index=6 To get notification for new videos, subscribe to my channel: https://youtube.com/c/bintuharwani To see more videos on different computer subjects, visit: http://bmharwani.com

[courseforfree]

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. 

[tonkibasic]

Linked list stack implememntation using two queues 261

Linked list stack implememntation using two queues 261 code#

Fortunately, JavaScript arrays implement this for us in the form of the length property. algorithms (sorting, using stacks and queues, tree exploration algorithms, etc.). A common additional operation for collection data structures is the size, as it allows you to safely iterate the elements and find out if there are any more elements present in the data structure. Data structures: Abstract data types (ADTs), vector, deque, list, queue. Again, it doesn't change the index of the other items in the array, so it is O(1). Similarly, on pop, we simply pop the last value from the array. As it doesn't change the index of the current items, this is O(1). On push, we simply push the new item into the array. Therefore, we can simply implement the operations of this data structure using an array. Fortunately, in JavaScript implementations, array functions that do not require any changes to the index of the current items have an average runtime of O(1). First node have null in link field and second node link have first node address in link field and so on and last node address in. which is head of the stack where pushing and popping items happens at the head of the list. In stack Implementation, a stack contains a top pointer. You will use only one C file (stackfromqueue.c)containing all the functions to design the entire interface. The objective is to implement these push and pop operations such that they operate in O(1) time. A stack can be easily implemented using the linked list. Part 3: Linked List Stack Implementation Using Two Queues Inthis part, you will use two instances of Queue ADT to implement aStack ADT. This fact can be modeled into the type system by using a union of T and undefined. If there are no more items, we can return an out-of-bound value, for example, undefined. The other key operation pops an item from the stack, again in O(1). The first one is push, which adds an item in O(1). StdOut.java A list implemented with a doubly linked list. js is the open source HTML5 audio player. The stack data structure has two key operations. js, a JavaScript library with the goal of making coding accessible to artists, designers, educators, and beginners. We can model this easily in TypeScript using the generic class for items of type T. Using Python, create an implementation of Deque (double-ended queue) with linked list nodes.

Linked list stack implememntation using two queues 261 code#

So deleting of the middle element can be done in O(1) if we just pop the element from the front of the deque.A stack is a last-in/first-out data structure with key operations having a time complexity of O(1). I encountered this question as I am preparing for my code interview. The stack functions worked on from Worksheet 17 were re-implemented using the queue functions worked on from Worksheet 18. Overview: This program is an implementation of a stack using two instances of a queue. Here each new node will be dynamically allocated, so overflow is not possible unless memory is exhausted. Using an array will restrict the maximum capacity of the array, which can lead to stack overflow. You must use only standard operations of a queue - which means only push to back, peek/pop from front, size, and is empty operations are valid. The advantage of using a linked list over arrays is that it is possible to implement a stack that can grow or shrink as much as needed. empty () - Return whether the stack is empty. We will see that the middle element is always the front element of the deque. Stack With Two Queues (Linked List) Zedrimar. pop () - Removes the element on top of the stack. If after the pop operation, the size of the deque is less than the size of the stack, we pop an element from the top of the stack and insert it back into the front of the deque so that size of the deque is not less than the stack. The pop operation on myStack will remove an element from the back of the deque. The number of elements in the deque stays 1 more or equal to that in the stack, however, whenever the number of elements present in the deque exceeds the number of elements in the stack by more than 1 we pop an element from the front of the deque and push it into the stack. Insert operation on myStack will add an element into the back of the deque. We will use a standard stack to store half of the elements and the other half of the elements which were added recently will be present in the deque. Method 2: Using a standard stack and a deque ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.

[statanalytica1-blog]

What is a Python Linked List and Types of Linked List?

As we all know, Python is rapidly evolving. Therefore, we need to understand many things that are very basic and that everyone should be aware of. This blog will look at what a linked list is in Python, how it works, and the functions that can be applied to it. You've probably heard a lot of basic concepts that you should understand as a student, such as data types, strings, arrays, sorting techniques, tuples in Python, and so on. In this tutorial, we'll look at how to create a linked list in Python. So, let's start from the beginning.

Linked List in python

A linked list is a linear data structure in Python that stores data in contiguous memory locations rather than arrays of data items connected by links. In Python, each node in a linked list has a data field and a reference to the linked list's next node. In linked, each element is called a node, and pointers are used to connect them. The head of the linked list is the first node. The size of the linked list is flexible. We can have an endless number of nodes unless the device has enough storage. There are two types of linked lists, which we'll go over in this tutorial one by one.

In Python, a linked list is a node chain, with each node containing data and the address or reference to the next node. The head, which stores the reference to the first node, is the starting point of the linked list.

In Python, the linked list's head.

The first architectural item of the linked list is the 'head node,' or top node in the list. The Head is set to None when the list is first generated because there are no nodes in it. (Note that the linked list doesn't always require a node to begin, and the head option will default to None if one isn't provided.)

Python's Singly Linked List.

In Python, a single pointer connects the next node in a singly linked list to the linked list's next node. The data and pointer for each node in the linked list must be saved. The next pointer in the linked list's last node is null, indicating that the linked list has reached its conclusion.

Python's Doubly Linked List

A singly linked list or a linked list is easier to implement, but traversing it in reverse is far more complicated. To get around this, we can use a Doubly Linked List, in which each node has two pointers: one to point to the previous node and one to point to the next node.

In a doubly-linked list in Python, iteration is more efficient, especially if you need to repeat in reverse, and deletion of specific nodes is more efficient.

To summarise, a doubly linked list is a more sophisticated linked list in which each node has a pointer to the preceding and following nodes in the series. In a doubly-linked list, each node has node data, a piece of information to the next node (next pointer), and a pointer to the previous node (previous pointer) (previous pointer).

Conclusion

We've gone through the linked list and two types of linked lists in Python, singly and doubly-linked lists, in this blog. Although Linked Lists can be intimidating at first, if you get them, trees, graphs, and other data structures become much more understandable! In the future, keep a watch out for more Python-related blogs.

Many students throughout the world are having difficulties with python coding. If you're one of them, we have a team of Python Programming Help professionals who have years of experience. Please do not hesitate to contact our specialists.

[felord]

cop3503 Project 1 – Templated Linked List Solved

Overview

The purpose of this assignment is for you to write a data structure called a Linked List, which utilizes templates (similar to Java’s generics), in order to store any type of data. In addition, the nature of a Linked List will give you some experience dealing with non-contiguous memory organization. This will also give you more experience using pointers and memory management. Pointers, memory allocation, and understand how data is stored in memory will serve you well in a variety of situations, not just for an assignment like this.

Background

Remember Memory? Variables, functions, pointers—everything takes up SOME space in memory. Sometimes that memory is occupied for only a short duration (a temporary variable in a function), sometimes that memory is allocated at the start of a program and hangs around for the lifetime of that program. Visualizing memory can be difficult sometimes, but very helpful. You may see diagrams of memory like this:

Or, you may see diagrams like these:

If you are trying to draw out some representation of memory to help you solve a problem, any of these (or some alternative that makes sense to you) will be fine.

Arrays

Arrays are stored in what is called contiguous memory. A contiguous memory block is one that is not interrupted by anything else (i.e. any other memory block). So if you created an array of 5 integers, each of those integers would be located one after the other in memory, with nothing else occupying memory between them. This is true for all arrays, of any data type.  

All of the data in an application is not guaranteed to be contiguous, nor does it need to be. Arrays are typically the simplest and fastest way to store data, but they have a grand total of zero features. You allocate one contiguous block, but you can’t resize it, removing elements is a pain (and slow), etc. Consider the previous array. What if you wanted to add another element to that block of memory? If the surrounding memory is occupied, you can’t simply overwrite that with your new data element and expect good results.  

someArray = 12; // #badIdea                This will almost certainly break. Other Memory   In this scenario, in order to store one more element you would have to: Create another array that was large enough to store all of the old elements plus the new one Copy over all of the data elements one at a time (including the new element, at the end) Free up the old array—no point in having two copies of the data  

Other Memory Newly available memory Someone else’s memory 10 12 This process has to be repeated each time you want to add to the array (either at the end, or insert in the middle), or remove anything from the array. It can quite costly, in terms of performance, to delete/rebuild an entire array every time you want to make a single change. Cue the Linked List!

Linked List

The basic concept behind a Linked List is simple: It’s a container that stores its elements in a non-contiguous fashion Each element knows about the location of the element which comes after it (and possibly before, more on that later) So instead of a contiguous array, where element 4 comes after element 3, which comes after element 2, etc��� you might have something like this:

Each element in the Linked List (typically referred to as a “node”) stores some data, and then stores a reference (a pointer, in C++), so the node which should come next. The First node knows only about the Second node. The Second node knows only about the Third, etc. The Fourth node has a null pointer as its “next” node, indicating that we’ve reached the end of the data. A real-world example can be helpful as well. Think about a line of people, with one person at the front of the line. That person might know about the person who is next in line, but no further than that (beyond him or herself, the person at the front doesn’t need to know or care). The second person in line might know about the third person in line, but no further. Continuing on this way, the last person in line knows that there is no one else that follows, so that must be the end. Since no one is behind that person, we must be at the end of the line.  

So… What are the advantages of storing data like this? When inserting elements to an array, the entire array has to be reallocated. With a Linked List, only a small number of elements are affected. Only elements surrounding the changed element need to be updated, and all other elements can remain unaffected. This makes the Linked List much more efficient when it comes to adding or removing elements. Now, imagine one person wants to step out of line. If this were an array, all of the data would have to be reconstructed elsewhere. In a Linked List, only three nodes are affected: 1) The person leaving, 2) the person in front of that person, and 3) the person behind that person. Imagine you are the person at the front of the line. You don’t really need to know or care what happens 10 people behind you, as that has no impact on you whatsoever.

If the 5th person in line leaves, the only parts of the line that should be impacted are the 4th, 5th, and 6th spaces. Person 4 has a new “next” Person: whomever was behind the person behind them (Person 6). Person 5 has to be removed from the list. Person 6… actually does nothing. In this example, a Person only cares about whomever comes after them. Since Person 5 was before Person 6, Person 6 is unaffected. (A Linked List could be implemented with two-way information between nodes—more on that later). The same thought-process can be applied if someone stepped into line (maybe a friend was holding their place):

In this case, Person 2 would change their “next” person from Person 3, to the new Person being added. New Guy would have his “next” pointer set to whomever Person 2 was previously keeping track of, Person 3. Because of the ordering process, Person 3 would remain unchanged, as would anyone else in the list (aside from being a bit irritated at the New Guy for cutting in line). So that’s the concept behind a Linked List. A series of “nodes” which are connected via pointer to one another, and inserting/deleting nodes is a faster process than deleting and reconstructing the entire collection of data. Now, how to go about creating that?

Terminology

Node The fundamental building block of a Linked List. A node contains the data actually being stored in the list, as well as 1 or more pointers to other nodes. This is typically implemented as a nested class (see below). Singly-linked A Linked List would be singly-linked if each node only has a single pointer to another node, typically a “next” pointer. This only allows for uni-directional traversal of the data—from beginning to end. Doubly-linked Each node contains 2 pointers: a “next” pointer and a “previous” pointer. This allows for bi-directional traversal of the data—either from front-to-back or backto-front. Head A pointer to the first node in the list, akin to index 0 of an array. Tail A pointer to the last node in the list. May or may not be used, depending on the implementation of the list.  

Nested Classes

The purpose of writing a class is to group data and functionality. The purpose of a nested class is the same—the only difference is where we declare a nested class. We declare a nested class like this: class MyClass { public: // Nested class    class NestedClass { int x, y, z;       NestedClass();       ~NestedClass();       int SomeFunction(); }; private: // Data for "MyClass" NestedClass myData;    NestedClass *somePtr;    float values; MyClass(); // Etc… }; // To create nested classes… // Use the Scope Resolution Operator MyClass::NestedClass someVariable;   // With a class template… TemplateClass foo; TemplateClass::Nested bar;   /* NOTE: You can make nested classes private if you wish, to prevent access to them outside of the encapsulating class. */ Additional reading: http://en.cppreference.com/w/cpp/language/nested_types The nature of the Linked List is that each piece of information knows about the information which follows (or precedes) it. It would make sense, then, to create some nested class to group all of that information together.  

Benefits and Drawbacks

All data structures in programming (C++ or otherwise) have advantages and disadvantages. There is no “one size fits all” data structure. Some are faster (in some cases), some have smaller memory footprints, and some are more flexible in their functionality, which can make life easier for the programmer. Linked List versus Array – Who Wins? Array Linked List Fast access of individual elements as well as iteration over the entire array Changing the Linked List is fast – nodes can be inserted/removed very quickly Random access – You can quickly “jump” to the appropriate memory location of an element Less affected by memory fragmentation, nodes can fit anywhere in memory Changing the array is slow – Have to rebuild the entire array when adding/removing elements No random access, slow iteration Memory fragmentation can be an issue for arrays—need a single, contiguous block large enough for all of the data Extra memory overhead for nodes/pointers Slow to access individual elements

Code Structure

As shown above, the Linked List class itself stores very little data: Pointers to the first and last nodes, and a count. In some implementations, you might only have a pointer to the first node, and that’s it. In addition to those data members, your Linked List class must conform to the following interface:                

Function Reference

Accessors PrintForward Iterator through all of the nodes and print out their values, one a time. PrintReverse Exactly the same as PrintForward, except completely the opposite. NodeCount Wait, we’re storing how many things in this list? FindAll Find all nodes which match the passed in parameter value, and store a pointer to that node in the passed in vector. Use of a parameter like this (passing a something in by reference, and storing data for later use) is called an output parameter. Find Find the first node with a data value matching the passed in parameter, returning a pointer to that node. Returns null if no matching node found. const and nonconst versions. GetNode Given an index, return a pointer to the node at that index. Throws an exception if the index is out of range. Const and non-const versions. Head Returns the head pointer. Const and non-const versions. Tail Returns the tail pointer. Const and non-const versions. Insertion Operations AddHead Create a new Node at the front of the list to store the passed in parameter. AddTail Create a new Node at the end of the list to store the passed in parameter. AddNodesHead Given an array of values, insert a node for each of those at the beginning of the list, maintaining the original order. AddNodesTail Ditto, except adding to the end of the list. InsertAfter Given a pointer to a node, create a new node to store the passed in value, after the indicated node. InsertBefore Ditto, except insert the new node before the indicated node. InsertAt Inserts a new Node to store the first parameter, at the index-th location. So if you specified 4 as the index, the new Node should have 3 Nodes before it. Throws an exception if given an invalid index. Removal Operations RemoveHead Deletes the first Node in the list. Returns whether or not the operation was successful. RemoveTail Deletes the last Node, returning whether or not the operation was successful. Remove Remove ALL Nodes containing values matching that of the passed-in parameter. Returns how many instances were removed. RemoveAt Deletes the index-th Node from the list, returning whether or not the operation was successful. Clear Deletes all Nodes. Don’t forget the node count! Operators operator Overloaded brackets operator. Takes an index, and returns the index-th node. Throws an exception if given an invalid index. Const and non-const versions. operator= After listA = listB, listA == listB is true. operator== Overloaded equality operator. Given listA and listB, is listA equal to listB? What would make one Linked List equal to another? If each of its nodes were equal to the corresponding node of the other. (Similar to comparing two arrays, just with non-contiguous data). Construction / Destruction LinkedList() Default constructor. A head, a tail, and a node counter walk into a bar… and get initialized? Wait, that’s not how that one goes… How many nodes in an empty list? What is head pointing to? What is tail pointing to? Copy Constructor Sets “this” to a copy of the passed in LinkedList. Other list has 10 nodes, with values of 1-10? “this” should too. ~LinkedList() The usual. Clean up your mess. (Delete all the nodes created by the list.)  

Tips

A few tips for this assignment: Remember the "Big Three" or the “Rule of Three” o If you define one of the three special functions (copy constructor, assignment operator, or destructor), you should define the other two Start small, work one bit of functionality at a time. Work on things like Add() and PrintForward() first, as well as accessors (brackets operator, Head()/Tail(), etc). You can’t really test anything else unless those are working. Refer back to the recommended chapters in your textbook as well as lecture videos for an explanation of the details of dynamic memory allocation o There are a lot of things to remember when memory allocation The debugger is your friend! (You have learned how to use it, right?) Make charts, diagrams, sketches of the problem. Memory is inherently difficult to visualize, find a way that works for you. Don’t forget your node count! Read the full article

[t-baba]

Build a Real-time Chat App with Pusher and Vue.js

Apps that communicate in real time are becoming more and more popular nowadays, as they make for a smoother, more natural user experience.

In this tutorial, we’re going to build a real-time chat application using Vue.js powered by ChatKit, a service provided by Pusher. The ChatKit service will provide us with a complete back end necessary for building a chat application on any device, leaving us to focus on building a front-end user interface that connects to the ChatKit service via the ChatKit client package.

Prerequisites

This is an intermediate- to advanced-level tutorial. You’ll need to be familiar with the following concepts to follow along:

Vue.js basics

Vuex fundamentals

employing a CSS framework

You’ll also need Node installed on your machine. You can do this by downloading the binaries from the official website, or by using a version manager. This is probably the easiest way, as it allows you to manage multiple versions of Node on the same machine.

Finally, you’ll need to install Vue CLI globally with the following command:

npm install -g @vue/cli

At the time of writing, Node 10.14.1 and Vue CLI 3.2.1 are the latest versions.

About the Project

We’re going to build a rudimentary chat application similar to Slack or Discord. The app will do the following:

have multiple channels and rooms

list room members and detect presence status

detect when other users start typing

As mentioned earlier, we’re just building the front end. The ChatKit service has a back-end interface that will allows us to manage users, permissions and rooms.

You can find the complete code for this project on GitHub.

Setting up a ChatKit Instance

Let’s create our ChatKit instance, which is similar to a server instance if you’re familiar with Discord.

Go to the ChatKit page on Pusher’s website and click the Sign Up button. You’ll be prompted for an email address and password, as well as the option to sign in with GitHub or Google.

Select which option suits you best, then on the next screen fill out some details such as Name, Account type, User role etc.

Click Complete Onboarding and you’ll be taken to the main Pusher dashboard. Here, you should click the ChatKit Product.

Click the Create button to create a new ChatKit Instance. I’m going to call mine VueChatTut.

We’ll be using the free plan for this tutorial. It supports up to 1,000 unique users, which is more than sufficient for our needs. Head over to the Console tab. You’ll need to create a new user to get started. Go ahead and click the Create User button.

I’m going to call mine “john” (User Identifier) and “John Wick” (Display Name), but you can name yours however you want. The next part is easy: create the two or more users. For example:

salt, Evelyn Salt

hunt, Ethan Hunt

Create three or more rooms and assign users. For example:

General (john, salt, hunt)

Weapons (john, salt)

Combat (john, hunt)

Here’s a snapshot of what your Console interface should like.

Next, you can go to the Rooms tab and create a message using a selected user for each room. This is for testing purposes. Then go to the Credentials tab and take note of the Instance Locator. We’ll need to activate the Test Token Provider, which is used for generating our HTTP endpoint, and take a note of that, too.

Our ChatKit back end is now ready. Let’s start building our Vue.js front end.

Scaffolding the Vue.js Project

Open your terminal and create the project as follows:

vue create vue-chatkit

Select Manually select features and answer the questions as shown below.

Make doubly sure you’ve selected Babel, Vuex and Vue Router as additional features. Next, create the following folders and files as follows:

Make sure to create all the folders and files as demonstrated. Delete any unnecessary files that don’t appear in the above illustration.

For those of you that are at home in the console, here are the commands to do all that:

mkdir src/assets/css mkdir src/store touch src/assets/css/{loading.css,loading-btn.css} touch src/components/{ChatNavBar.vue,LoginForm.vue,MessageForm.vue,MessageList.vue,RoomList.vue,UserList.vue} touch src/store/{actions.js,index.js,mutations.js} touch src/views/{ChatDashboard.vue,Login.vue} touch src/chatkit.js rm src/components/HelloWorld.vue rm src/views/{About.vue,Home.vue} rm src/store.js

When you’re finished, the contents of the src folder should look like so:

. ├── App.vue ├── assets │ ├── css │ │ ├── loading-btn.css │ │ └── loading.css │ └── logo.png ├── chatkit.js ├── components │ ├── ChatNavBar.vue │ ├── LoginForm.vue │ ├── MessageForm.vue │ ├── MessageList.vue │ ├── RoomList.vue │ └── UserList.vue ├── main.js ├── router.js ├── store │ ├── actions.js │ ├── index.js │ └── mutations.js └── views ├── ChatDashboard.vue └── Login.vue

For the loading-btn.css and the loading.css files, you can find them on the loading.io website. These files are not available in the npm repository, so you need to manually download them and place them in your project. Do make sure to read the documentation to get an idea on what they are and how to use the customizable loaders.

Next, we’re going to install the following dependencies:

@pusher/chatkit-client, a real-time client interface for the ChatKit service

bootstrap-vue, a CSS framework

moment, a date and time formatting utility

vue-chat-scroll, which scrolls to the bottom automatically when new content is added

vuex-persist, which saves Vuex state in browser’s local storage

npm i @pusher/chatkit-client bootstrap-vue moment vue-chat-scroll vuex-persist

Do check out the links to learn more about what each package does, and how it can be configured.

Now, let’s configure our Vue.js project. Open src/main.js and update the code as follows:

import Vue from 'vue' import BootstrapVue from 'bootstrap-vue' import VueChatScroll from 'vue-chat-scroll' import App from './App.vue' import router from './router' import store from './store/index' import 'bootstrap/dist/css/bootstrap.css' import 'bootstrap-vue/dist/bootstrap-vue.css' import './assets/css/loading.css' import './assets/css/loading-btn.css' Vue.config.productionTip = false Vue.use(BootstrapVue) Vue.use(VueChatScroll) new Vue({ router, store, render: h => h(App) }).$mount('#app')

Update src/router.js as follows:

import Vue from 'vue' import Router from 'vue-router' import Login from './views/Login.vue' import ChatDashboard from './views/ChatDashboard.vue' Vue.use(Router) export default new Router({ mode: 'history', base: process.env.BASE_URL, routes: [ { path: '/', name: 'login', component: Login }, { path: '/chat', name: 'chat', component: ChatDashboard, } ] })

Update src/store/index.js:

import Vue from 'vue' import Vuex from 'vuex' import VuexPersistence from 'vuex-persist' import mutations from './mutations' import actions from './actions' Vue.use(Vuex) const debug = process.env.NODE_ENV !== 'production' const vuexLocal = new VuexPersistence({ storage: window.localStorage }) export default new Vuex.Store({ state: { }, mutations, actions, getters: { }, plugins: [vuexLocal.plugin], strict: debug })

The vuex-persist package ensures that our Vuex state is saved between page reloads or refreshes.

Our project should be able to compile now without errors. However, don’t run it just yet, as we need to build the user interface.

The post Build a Real-time Chat App with Pusher and Vue.js appeared first on SitePoint.

by Michael Wanyoike via SitePoint https://ift.tt/2YtinR8

[lewiskdavid90]

80% off #Break Away: Programming And Coding Interviews – $10

A course that teaches pointers, linked lists, general programming, algorithms and recursion like no one else

All Levels,  – 21 hours,  83 lectures 

Average rating 4.5/5 (4.5 (242 ratings) Instead of using a simple lifetime average, Udemy calculates a course’s star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.)

Course requirements:

This course requires some basic understanding of a programming language, preferably C. Some solutions are in Java, though Java is not a requirement

Course description:

Programming interviews are like standard plays in professional sport – prepare accordingly. Don’t let Programming Interview gotchas get you down!

Programming interviews differ from real programming jobs in several important aspects, so they merit being treated differently, just like set pieces in sport. Just like teams prepare for their opponent’s playbooks in professional sport, it makes sense for you to approach programming interviews anticipating the interviewer’s playbook This course has been drawn by a team that has conducted hundreds of technical interviews at Google and Flipkart

What’s Covered:

Pointers: Memory layout of pointers and variables, pointer arithmetic, arrays, pointers to pointers, pointers to structures, argument passing to functions, pointer reassignment and modification – complete with visuals to help you conceptualize how things work. Strings: Strings, Character pointers, character arrays, null termination of strings, string.h function implementations with detailed explanations. Linked lists: Visualization, traversal, creating or deleting nodes, sorted merge, reversing a linked list and many many problems and solutions, doubly linked lists. Bit Manipulation: Work with bits and bit operations. Sorting and searching algorithms: Visualize how common sorting and searching algorithms work and the speed and efficiency of those algorithms Recursion: Master recursion with lots of practice! 8 common and uncommon recursive problems explained. Binary search, finding all subsets of a subset, finding all anagrams of a word, the infamous 8 Queens problem, executing dependent tasks, finding a path through a maze, implementing PaintFill, comparing two binary trees Data Structures: Understand queues, stacks, heaps, binary trees and graphs in detail along with common operations and their complexity. Includes code for every data structure along with solved interview problems based on these data structures. Step-by-step solutions to dozens of common programming problems: Palindromes, Game of Life, Sudoku Validator, Breaking a Document into Chunks, Run Length Encoding, Points within a distance are some of the problems solved and explained.

Talk to us!

Mail us about anything – anything! – and we will always reply

Full details Know how to approach and prepare for coding interviews Understand pointer concepts and memory management at a very deep and fundamental level Tackle a wide variety of linked list problems and know how to get started when asked linked list questions as a part of interviews Tackle a wide variety of general pointer and string problems and know how to answer questions on them during interviews Tackle a wide variety of general programming problems which involve just plain logic, no standard algorithms or data structures, these help you get the details right!

Full details YEP! New engineering graduate students who are interviewing for software engineering jobs YEP! Professionals from other fields with some programming knowledge looking to change to a software role YEP! Software professionals with several years of experience who want to brush up on core concepts NOPE! Other technology related professionals who are looking for a high level overview of pointer concepts.

Reviews:

“Algorithms and Data Structures are explained in a simple way. First, the instructor tells the logic, then visualizes it animating boxes and arrows, and finally shows the code. Very easy to follow along. I even solved a number of algorithms just by listening to the description of the logic behind it.” (Anuar AZ)

“It doest the exercises on C and not on Java which is desired for interviews” (Juan)

“The instructor’s knack of teaching complex algorithms in a simple and effective manner.” (RamaKrishna Ganti)

  About Instructor:

Loony Corn

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum Navdeep: longtime Flipkart employee too, and IIT Guwahati alum We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you’ll like them

Instructor Other Courses:

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[mdiongla-se475]

Finally finished HW2

After awhile of debugging FindMaxWeight, I was able to continue on to the other steps. I was getting frustrated in my other post, so I knew i had to take a break from it to get a fresh point of view. 

Then I started working on a method, which took me awhile because I had to brush up on how to carefully delete a node in a doubly linked list. I realized sometimes talking about prev and next can be a little confusing so sometimes I like looking at youtube videos to get a visualization. Once I saw the code and the pictures, I remembered what a learned before. 

Then I was able to finished the code and turned it in the VERY early morning. 

[just4programmers]

Types of Queues in Data Structure

Queue is an important structure for storing and retrieving data and hence is used extensively among all the data structures. Queue, just like any queue (queues for bus or tickets etc.) follows a FIFO mechanism for data retrieval which means the data which gets into the queue first will be the first one to be taken out from it, the second one would be the second to be retrieved and so on.

Types of Queues in Data Structure

Simple Queue

Image Source

As is clear from the name itself, simple queue lets us perform the operations simply. i.e., the insertion and deletions are performed likewise. Insertion occurs at the rear (end) of the queue and deletions are performed at the front (beginning) of the queue list.

All nodes are connected to each other in a sequential manner. The pointer of the first node points to the value of the second and so on.

The first node has no pointer pointing towards it whereas the last node has no pointer pointing out from it.

Circular Queue

Image Source

Unlike the simple queues, in a circular queue each node is connected to the next node in sequence but the last node’s pointer is also connected to the first node’s address. Hence, the last node and the first node also gets connected making a circular link overall.

Priority Queue

Image Source

Priority queue makes data retrieval possible only through a pre determined priority number assigned to the data items.

While the deletion is performed in accordance to priority number (the data item with highest priority is removed first), insertion is performed only in the order.

Doubly Ended Queue (Dequeue)

Image Source

The doubly ended queue or dequeue allows the insert and delete operations from both ends (front and rear) of the queue.

Queues are an important concept of the data structures and understanding their types is very necessary for working appropriately with them.

The post Types of Queues in Data Structure appeared first on The Crazy Programmer.

[kaliforniaco]

Delete all the even nodes from a Doubly Linked List https://t.co/cHNqX3i1SS

Delete all the even nodes from a Doubly Linked List https://t.co/cHNqX3i1SS

— Dave Epps (@dave_epps) October 11, 2018

from Twitter https://twitter.com/dave_epps

[raulbento]

Basic Data Structure (English)

1. What is Data Structure?

Data structure is a way of defining, storing and retrieving data in a structural and systematic way.

2. Sequential access

It means that a group of elements is accessed in a predetermined sequence.

ArrayList: Re-sizable-array implementation of the List interface. Implements all optional list operations, and permits all elements, including null. It is unsynchronized.

Vector:  It is a synchronized ArrayList. It means its thread safe.

3. Linked List 

Doubly-linked list implementation of the List and Deque interfaces. Implements all optional list operations, and permits all elements (including null). It stores the data in nodes that connect with the previous and next nodes since it’s doubly-linked. It could also be singly-linked.

4. Graph

A graph is a pictorial representation of a set of objects where some pairs of objects are connected by links.

5. Stack

Every new elements will be added as the first element, and only the first element can be removed.  The order in which elements come off a stack gives rise to its alternative name, LIFO (for last in, first out). 

6. Queue

Every new elements will be added as the last element, and only the first element can be removed. This makes the queue a First-In-First-Out (FIFO) data structure.

7. Heap

It is a special balanced binary tree data structure where root-node key is compared with its children and arranged accordingly.

8. Tree

A tree is a minimally connected graph having no loops and circuits.

Binary Tree: A binary tree has a special condition that each node can have two children at maximum.

Binary Search Tree: A binary search tree is a binary tree with a special provision where a node's left child must have value less than its parent's value and node's right child must have value greater than its parent value.

AVL Tree: It is height balancing binary search tree. AVL tree checks the height of left and right sub-trees and assures that the difference is not more than 1. This difference is called Balance Factor.

Spanning Tree: A spanning tree is a subset of Graph G, which has all the vertices covered with minimum possible number of edges. A spanning tree does not have cycles and it cannot be disconnected.

9. Set

It can store values without any particular order and no repeated values. It is a computer implementation of the mathematical concept of a finite set. Other variants, called dynamic or mutable sets, allow also the insertion and deletion of elements from the set.

10. Map

It is an object that maps keys to values. A map cannot contain duplicate keys and each key can map to at most one value. Map doesn’t implements Collection because it contains a key and a value.

[itbeatsbookmarks]

(Via: Hacker News)

A BK-tree is a tree data structure specialized to index data in a metric space. A metric space is essentially a set of objects which we equip with a distance function \(d(a, b)\) for every pair of elements \((a, b)\). This distance function must satisfy a set of axioms in order to ensure it's well-behaved. The exact reason why this is required will be explained in the "Search" paragraph below.

The BK-tree data structure was proposed by Burkhard and Keller in 1973 as a solution to the problem of searching a set of keys to find a key which is closest to a given query key. The naive way to solve this problem is to simply compare the query key with every element of the set; if the comparison is done in constant time, this solution is \(O(n)\). On the other hand, a BK-tree is likely to allow fewer comparisons to be made.

Construction of the tree

BK-tree is defined in the following way. An arbitrary element \(a\) is selected as root. Root may have zero or more sub-trees. The \(k\)-th sub-tree is recursively built of all elements \(b\) such that \(d(a,b) = k\).

To see how to construct a BK-tree, let's use a real scenario. We have a dictionary of words and we want to find those that are most similar to a given query word. To gauge how similar two words are, we are going to use the Levenshtein distance. Essentially, it's the minimum number of single-character edits (which can be insertions, deletions or substitutions) required to mutate one word into the other. For example, the distance between "soccer" and "otter" is \(3\), because we can change the first one into the other by deleting the leading s, and then substituting the two central c's with two t's.

Let's use the dictionary

{'some', 'soft', 'same', 'mole', 'soda', 'salmon'}

To construct the tree, we first choose any word as the root node, and then add the other words by calculating their distance from the root. In our case, we can choose "some" to be the root element. Then, after adding the two subsequent words the tree would look like this:

because the distance between "some" and "same" is \(1\) and the distance between "some" and "soft" is \(2\). Now, let's add the next word, "mole". Observe that the distance between "mole" and "some" is again \(2\), so we add it to the tree as a child of "soft", with an edge corresponding to their distance. After adding all the words we obtain the following tree:

Search

Remember that the original problem was to find all the words closest to a given query word. Call \(N\) the maximum allowed distance (which we'll call radius). The algorithm proceeds as follows:

create a candidates list and add the root node to it

take a candidate, compute its distance \(D\) from the query key and compare it with the radius;

selection criterion: add to the candidates list all the children of the current node that, from their parent, have a distance between \(D - N\) and \(D + N\) (inclusive).

Suppose we want to find all the words in our dictionary that are no more distant than \(N = 2\) from the word "sort". Our only candidate is the root node "some". We start by computing

\[D = \mathop{\mathrm{Levenshtein}}(\text{`sort'}, \text{`some'}) = 2\]

Since the radius is \(2,\) we add "some" to the list of results. Then we extend our candidates list with all the children that have a distance from the root node between \(D - N = 0\) and \(D + N = 4\). In this case, all the children satisfy this condition. Moving on, we compute

\[D = \mathop{\mathrm{Levenshtein}}(\text{`sort'}, \text{`same'}) = 3\]

Since \(D > N\), this node is not a result and we move on to "soft"; now

\[D = \mathop{\mathrm{Levenshtein}}(\text{`sort'}, \text{`soft'}) = 1\]

Hence "soft" is an acceptable result. Regarding its children, we take those that have a distance between \(D - N = -1\) and \(D + N = 3\). Again, all of them, but only "soda" is a valid result. Finally, "salmon" is not acceptable. If we sort our results by distance we end up with the following:

[(1, 'soft'), (2, 'some'), (2, 'soda')]

Why does it work?

It's interesting to understand why we are allowed to prune all the children that do not meet the criterion we gave above in point \(3\). In the introduction we said that our distance function \(d\) must satisfy a set of axioms in order for us to obtain the metric space structure. Those axioms are the following. For all elements \(a,b,c\) it must hold:

non-negativity: \(d(a, b) \ge 0\);

\(d(a, b) = 0\) implies \(a = b\) (and vice-versa);

symmetry: \(d(a, b) = d(b, a)\)

triangle inequality: \(d(a, b) \le d(a, c) + d(c, b)\).

The first three are just a formalization of our intuitive notion of "distance", while the last one derives from the relation between sides of a triangle in Euclidean geometry. This is often the most difficult property to demonstrate when we want to prove that a generic distance is actually a metric. As it turns out, the Levenshtein distance satisfies this property and therefore it's a metric. This is why we can use it in the examples above.

Let's call the query key \(\bar x\). Suppose we are evaluating the child \(B\) of an arbitrary node \(A\) inside the tree, which we calculated to be at a distance \(D = d(\bar x, A)\) from the query key. This situation is summarized in the following figure:

Since we assumed that \(d\) is a metric, by the triangle inequality we have

\[d(A, B) \le d(A, \bar x) + d(\bar x, B)\]

from which

\[d(\bar x, B) \ge d(A, B) - d(A, \bar x) = x - D.\]

Using the triangle inequality again, this time with \(d(A, \bar x)\) and \(B\), we obtain

\[d(\bar x, B) \ge d(A, \bar x) - d(A, B) = D - x\]

Since we are only interested in nodes that are at a distance at most \(N\) from the query key \(\bar x\), we impose the constraint \(d(\bar x, B) \le N\). This translates to

\[\begin{cases}x - D \le N\\D - x \le N\end{cases}\]

which is equivalent to

\[D - N \le x \le D + N\]

We have proved that if \(d\) is a metric, we can safely discard nodes that do not meet the above criteria. Finally, note that every child of \(B\) will be at a distance of \(x\) from \(A\) (by construction of the BK-tree) and therefore we can safely prune the whole sub-tree if \(B\) alone does not meet the criterion.

Implementation

This data structure is easy to implement in Python, if we use dictionaries to represent edges.

from collections import deque class BKTree: def __init__(self, distance_func): self._tree = None self._distance_func = distance_func def add(self, node): if self._tree is None: self._tree = (node, {}) return current, children = self._tree while True: dist = self._distance_func(node, current) target = children.get(dist) if target is None: children[dist] = (node, {}) break current, children = target def search(self, node, radius): if self._tree is None: return [] candidates = deque([self._tree]) result = [] while candidates: candidate, children = candidates.popleft() dist = self._distance_func(node, candidate) if dist <= radius: result.append((dist, candidate)) low, high = dist - radius, dist + radius candidates.extend(c for d, c in children.items() if low <= d <= high) return result

The implementation is pretty straightforward and adheres completely to the algorithm we explained above. A few comments:

there's no need to add a root argument to the __init__ method, since any element can be a root node. In our case the first one added will become root;

why is deque even needed? At first I used a set, only to see it fail because dictionaries aren't hashable. We need another data structure that allows \(O(1)\) popping and linear insertion. Built-in deque, being a doubly-linked list, is a natural fit.

Conclusion

The BK-tree is a relatively lesser-known data structure suitable for nearest neighbor search (NNS). It allows a considerable reduction of the search space, if the distance we are working with is a metric. In practice, the speed improvement we get from pruning sub-trees heavily depends on the search space and the radius we select. This is why some experimentation is usually needed for the problem at hand. One area in which BK-tree does well is spell-checking: as long as one keeps the radius to \(1\) or \(2\) the search space is often reduced to under \(10\%\) of the original.

[lesterwilliams1]

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    About Instructor:

Loony Corn

Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore. Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum Navdeep: longtime Flipkart employee too, and IIT Guwahati alum We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy! We hope you will try our offerings, and think you’ll like them

Instructor Other Courses:

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