1621674240

We are going to write a function called `pigIt`

that will accept a string, `str`

, as an argument.

You are given a string and the goal of the function is to translate the string to Pig Latin. To translate the string, you do the following:

- Move the first letter of the word to the end of the word.
- Add “ay” to the end of the word.

That’s it. If the word is only a single letter, skip step number one and just add an “ay” at the end. If the string is a punctuation mark or a number, leave it as is. Leave the cases of the words untouched.

Example:

```
pigIt('Pig latin is cool'); \\ igPay atinlay siay oolcay
pigIt('Hello world !'); \\ elloHay orldway !
```

To begin, we will split the string into an array where each word is its own array element. We assign that array to `strArr`

.

```
let strArr = str.split(' ');
```

Next, we will create an empty array called `pigLatin`

. This is the array we will append each word to after we translate it to Pig Latin.

```
let pigLatin = [];
```

#coding #programming #javascript #algorithms

1674793920

This repository contains JavaScript based examples of many popular algorithms and data structures.

Each algorithm and data structure has its own separate README with related explanations and links for further reading (including ones to YouTube videos).

*Read this in other languages:* *简体中文*, *繁體中文*, *한국어*, *日本語*, *Polski*, *Français*, *Español*, *Português*, *Русский*, *Türkçe*, *Italiana*, *Bahasa Indonesia*, *Українська*, *Arabic*, *Tiếng Việt*, *Deutsch*

*☝ Note that this project is meant to be used for learning and researching purposes only, and it is not meant to be used for production.*

A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

`B`

- Beginner, `A`

- Advanced

`B`

Linked List`B`

Doubly Linked List`B`

Queue`B`

Stack`B`

Hash Table`B`

Heap - max and min heap versions`B`

Priority Queue`A`

Trie`A`

Tree`A`

Binary Search Tree`A`

AVL Tree`A`

Red-Black Tree`A`

Segment Tree - with min/max/sum range queries examples`A`

Fenwick Tree (Binary Indexed Tree)

`A`

Graph (both directed and undirected)`A`

Disjoint Set`A`

Bloom Filter`A`

LRU Cache - Least Recently Used (LRU) cache

An algorithm is an unambiguous specification of how to solve a class of problems. It is a set of rules that precisely define a sequence of operations.

`B`

- Beginner, `A`

- Advanced

**Math**`B`

Bit Manipulation - set/get/update/clear bits, multiplication/division by two, make negative etc.`B`

Binary Floating Point - binary representation of the floating-point numbers.`B`

Factorial`B`

Fibonacci Number - classic and closed-form versions`B`

Prime Factors - finding prime factors and counting them using Hardy-Ramanujan's theorem`B`

Primality Test (trial division method)`B`

Euclidean Algorithm - calculate the Greatest Common Divisor (GCD)`B`

Least Common Multiple (LCM)`B`

Sieve of Eratosthenes - finding all prime numbers up to any given limit`B`

Is Power of Two - check if the number is power of two (naive and bitwise algorithms)`B`

Pascal's Triangle`B`

Complex Number - complex numbers and basic operations with them`B`

Radian & Degree - radians to degree and backwards conversion`B`

Fast Powering`B`

Horner's method - polynomial evaluation`B`

Matrices - matrices and basic matrix operations (multiplication, transposition, etc.)`B`

Euclidean Distance - distance between two points/vectors/matrices`A`

Integer Partition`A`

Square Root - Newton's method`A`

Liu Hui π Algorithm - approximate π calculations based on N-gons`A`

Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up

**Sets**`B`

Cartesian Product - product of multiple sets`B`

Fisher–Yates Shuffle - random permutation of a finite sequence`A`

Power Set - all subsets of a set (bitwise, backtracking, and cascading solutions)`A`

Permutations (with and without repetitions)`A`

Combinations (with and without repetitions)`A`

Longest Common Subsequence (LCS)`A`

Longest Increasing Subsequence`A`

Shortest Common Supersequence (SCS)`A`

Knapsack Problem - "0/1" and "Unbound" ones`A`

Maximum Subarray - "Brute Force" and "Dynamic Programming" (Kadane's) versions`A`

Combination Sum - find all combinations that form specific sum

**Strings**`B`

Hamming Distance - number of positions at which the symbols are different`B`

Palindrome - check if the string is the same in reverse`A`

Levenshtein Distance - minimum edit distance between two sequences`A`

Knuth–Morris–Pratt Algorithm (KMP Algorithm) - substring search (pattern matching)`A`

Z Algorithm - substring search (pattern matching)`A`

Rabin Karp Algorithm - substring search`A`

Longest Common Substring`A`

Regular Expression Matching

**Searches**`B`

Linear Search`B`

Jump Search (or Block Search) - search in sorted array`B`

Binary Search - search in sorted array`B`

Interpolation Search - search in uniformly distributed sorted array

**Sorting**`B`

Bubble Sort`B`

Selection Sort`B`

Insertion Sort`B`

Heap Sort`B`

Merge Sort`B`

Quicksort - in-place and non-in-place implementations`B`

Shellsort`B`

Counting Sort`B`

Radix Sort

**Linked Lists****Trees**`B`

Depth-First Search (DFS)`B`

Breadth-First Search (BFS)

**Graphs**`B`

Depth-First Search (DFS)`B`

Breadth-First Search (BFS)`B`

Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph`A`

Dijkstra Algorithm - finding the shortest paths to all graph vertices from single vertex`A`

Bellman-Ford Algorithm - finding the shortest paths to all graph vertices from single vertex`A`

Floyd-Warshall Algorithm - find the shortest paths between all pairs of vertices`A`

Detect Cycle - for both directed and undirected graphs (DFS and Disjoint Set based versions)`A`

Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph`A`

Topological Sorting - DFS method`A`

Articulation Points - Tarjan's algorithm (DFS based)`A`

Bridges - DFS based algorithm`A`

Eulerian Path and Eulerian Circuit - Fleury's algorithm - Visit every edge exactly once`A`

Hamiltonian Cycle - Visit every vertex exactly once`A`

Strongly Connected Components - Kosaraju's algorithm`A`

Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city

**Cryptography**`B`

Polynomial Hash - rolling hash function based on polynomial`B`

Rail Fence Cipher - a transposition cipher algorithm for encoding messages`B`

Caesar Cipher - simple substitution cipher`B`

Hill Cipher - substitution cipher based on linear algebra

**Machine Learning**`B`

NanoNeuron - 7 simple JS functions that illustrate how machines can actually learn (forward/backward propagation)`B`

k-NN - k-nearest neighbors classification algorithm`B`

k-Means - k-Means clustering algorithm

**Image Processing**`B`

Seam Carving - content-aware image resizing algorithm

**Statistics**`B`

Weighted Random - select the random item from the list based on items' weights

**Evolutionary algorithms**`A`

Genetic algorithm - example of how the genetic algorithm may be applied for training the self-parking cars

**Uncategorized**`B`

Tower of Hanoi`B`

Square Matrix Rotation - in-place algorithm`B`

Jump Game - backtracking, dynamic programming (top-down + bottom-up) and greedy examples`B`

Unique Paths - backtracking, dynamic programming and Pascal's Triangle based examples`B`

Rain Terraces - trapping rain water problem (dynamic programming and brute force versions)`B`

Recursive Staircase - count the number of ways to reach to the top (4 solutions)`B`

Best Time To Buy Sell Stocks - divide and conquer and one-pass examples`A`

N-Queens Problem`A`

Knight's Tour

An algorithmic paradigm is a generic method or approach which underlies the design of a class of algorithms. It is an abstraction higher than the notion of an algorithm, just as an algorithm is an abstraction higher than a computer program.

**Brute Force**- look at all the possibilities and selects the best solution`B`

Linear Search`B`

Rain Terraces - trapping rain water problem`B`

Recursive Staircase - count the number of ways to reach to the top`A`

Maximum Subarray`A`

Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city`A`

Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up

**Greedy**- choose the best option at the current time, without any consideration for the future`B`

Jump Game`A`

Unbound Knapsack Problem`A`

Dijkstra Algorithm - finding the shortest path to all graph vertices`A`

Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph`A`

Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph

**Divide and Conquer**- divide the problem into smaller parts and then solve those parts`B`

Binary Search`B`

Tower of Hanoi`B`

Pascal's Triangle`B`

Euclidean Algorithm - calculate the Greatest Common Divisor (GCD)`B`

Merge Sort`B`

Quicksort`B`

Tree Depth-First Search (DFS)`B`

Graph Depth-First Search (DFS)`B`

Matrices - generating and traversing the matrices of different shapes`B`

Jump Game`B`

Fast Powering`B`

Best Time To Buy Sell Stocks - divide and conquer and one-pass examples`A`

Permutations (with and without repetitions)`A`

Combinations (with and without repetitions)`A`

Maximum Subarray

**Dynamic Programming**- build up a solution using previously found sub-solutions`B`

Fibonacci Number`B`

Jump Game`B`

Unique Paths`B`

Rain Terraces - trapping rain water problem`B`

Recursive Staircase - count the number of ways to reach to the top`B`

Seam Carving - content-aware image resizing algorithm`A`

Levenshtein Distance - minimum edit distance between two sequences`A`

Longest Common Subsequence (LCS)`A`

Longest Common Substring`A`

Longest Increasing Subsequence`A`

Shortest Common Supersequence`A`

0/1 Knapsack Problem`A`

Integer Partition`A`

Maximum Subarray`A`

Bellman-Ford Algorithm - finding the shortest path to all graph vertices`A`

Floyd-Warshall Algorithm - find the shortest paths between all pairs of vertices`A`

Regular Expression Matching

**Backtracking**- similarly to brute force, try to generate all possible solutions, but each time you generate next solution you test if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on a different path of finding a solution. Normally the DFS traversal of state-space is being used.`B`

Jump Game`B`

Unique Paths`B`

Power Set - all subsets of a set`A`

Hamiltonian Cycle - Visit every vertex exactly once`A`

N-Queens Problem`A`

Knight's Tour`A`

Combination Sum - find all combinations that form specific sum

**Branch & Bound**- remember the lowest-cost solution found at each stage of the backtracking search, and use the cost of the lowest-cost solution found so far as a lower bound on the cost of a least-cost solution to the problem, in order to discard partial solutions with costs larger than the lowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-space tree is being used.

**Install all dependencies**

```
npm install
```

**Run ESLint**

You may want to run it to check code quality.

```
npm run lint
```

**Run all tests**

```
npm test
```

**Run tests by name**

```
npm test -- 'LinkedList'
```

**Troubleshooting**

If linting or testing is failing, try to delete the `node_modules`

folder and re-install npm packages:

```
rm -rf ./node_modules
npm i
```

Also make sure that you're using a correct Node version (`>=14.16.0`

). If you're using nvm for Node version management you may run `nvm use`

from the root folder of the project and the correct version will be picked up.

**Playground**

You may play with data-structures and algorithms in `./src/playground/playground.js`

file and write tests for it in `./src/playground/__test__/playground.test.js`

.

Then just simply run the following command to test if your playground code works as expected:

```
npm test -- 'playground'
```

*Big O notation* is used to classify algorithms according to how their running time or space requirements grow as the input size grows. On the chart below you may find most common orders of growth of algorithms specified in Big O notation.

Source: Big O Cheat Sheet.

Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.

Big O Notation | Type | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
---|---|---|---|---|

O(1) | Constant | 1 | 1 | 1 |

O(log N) | Logarithmic | 3 | 6 | 9 |

O(N) | Linear | 10 | 100 | 1000 |

O(N log N) | n log(n) | 30 | 600 | 9000 |

O(N^2) | Quadratic | 100 | 10000 | 1000000 |

O(2^N) | Exponential | 1024 | 1.26e+29 | 1.07e+301 |

O(N!) | Factorial | 3628800 | 9.3e+157 | 4.02e+2567 |

Data Structure | Access | Search | Insertion | Deletion | Comments |
---|---|---|---|---|---|

Array | 1 | n | n | n | |

Stack | n | n | 1 | 1 | |

Queue | n | n | 1 | 1 | |

Linked List | n | n | 1 | n | |

Hash Table | - | n | n | n | In case of perfect hash function costs would be O(1) |

Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(n)) |

B-Tree | log(n) | log(n) | log(n) | log(n) | |

Red-Black Tree | log(n) | log(n) | log(n) | log(n) | |

AVL Tree | log(n) | log(n) | log(n) | log(n) | |

Bloom Filter | - | 1 | 1 | - | False positives are possible while searching |

Name | Best | Average | Worst | Memory | Stable | Comments |
---|---|---|---|---|---|---|

Bubble sort | n | n2 | n2 | 1 | Yes | |

Insertion sort | n | n2 | n2 | 1 | Yes | |

Selection sort | n2 | n2 | n2 | 1 | No | |

Heap sort | n log(n) | n log(n) | n log(n) | 1 | No | |

Merge sort | n log(n) | n log(n) | n log(n) | n | Yes | |

Quick sort | n log(n) | n log(n) | n2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space |

Shell sort | n log(n) | depends on gap sequence | n (log(n))2 | 1 | No | |

Counting sort | n + r | n + r | n + r | n + r | Yes | r - biggest number in array |

Radix sort | n * k | n * k | n * k | n + k | Yes | k - length of longest key |

Folks who are backing this project `∑ = 0`

ℹ️ A few more projects and articles about JavaScript and algorithms on trekhleb.dev

Author: trekhleb

Source Code: https://github.com/trekhleb/javascript-algorithms

License: MIT license

1621674240

We are going to write a function called `pigIt`

that will accept a string, `str`

, as an argument.

You are given a string and the goal of the function is to translate the string to Pig Latin. To translate the string, you do the following:

- Move the first letter of the word to the end of the word.
- Add “ay” to the end of the word.

That’s it. If the word is only a single letter, skip step number one and just add an “ay” at the end. If the string is a punctuation mark or a number, leave it as is. Leave the cases of the words untouched.

Example:

```
pigIt('Pig latin is cool'); \\ igPay atinlay siay oolcay
pigIt('Hello world !'); \\ elloHay orldway !
```

To begin, we will split the string into an array where each word is its own array element. We assign that array to `strArr`

.

```
let strArr = str.split(' ');
```

Next, we will create an empty array called `pigLatin`

. This is the array we will append each word to after we translate it to Pig Latin.

```
let pigLatin = [];
```

#coding #programming #javascript #algorithms

1598015898

Work on real-time JavaScript Snake game project and become a pro

Snake game is an interesting JavaScript project for beginners. Snake game is a single-player game, which we’ve been playing for a very long time. The game mainly consists of two components – snake and fruit. And we just need to take our snake to the food so that it can eat and grow faster and as the number of fruits eaten increases, the length of snake increases which makes the game more interesting. While moving if the snake eats its own body, then the snake dies and the game ends. Now let’s see how we can create this.

To implement the snake game in JavaScript you should have basic knowledge of:

1. Basic concepts of JavaScript

2. HTML

3. CSS

Before proceeding ahead please download source code of Snake Game: Snake Game in JavaScript

HTML builds the basic structure. This file contains some basic HTML tags like div, h1, title, etc. also we’ve used bootstrap (CDN is already included).

index.html:

Code:

```
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>DataFlair Snake game</title>
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css" integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous">
<link rel="stylesheet" href="static/style.css">
</head>
<body>
<div class="container">
<div class ="Jumbotron">
<h1>DataFlair Snake game using vanilla JavaScript</h1>
<h2 class="btn btn-info">
Score: 0
</h2>
<div class="containerCanvas">
<canvas id="canvas" width="500" height="500" class="canvasmain"> </canvas>
</div>
</div>
</div>
<script src="static/fruit.js"></script>
<script src="static/snake.js"></script>
<script src="static/draw.js"></script>
</body>
</html>
```

We have used simple HTML tags except

#javascript tutorial #javascript project #javascript snake game #simple snake game #javascript

1622036598

JavaScript is unarguablly one of the most common things you’ll learn when you start programming for the web. Here’s a small post on JavaScript compound assignment operators and how we use them.

The compound assignment operators consist of a binary operator and the simple assignment operator.

The binary operators, work with two operands. For example a+b where + is the operator and the a, b are operands. Simple assignment operator is used to assign values to a variable(s).

It’s quite common to modify values stored in variables. To make this process a little quicker, we use compound assignment operators.

They are:

- +=
- -+
- *=
- /=

You can also check my video tutorial compound assignment operators.

Let’s consider an example. Suppose price = 5 and we want to add ten more to it.

var price = 5;

price = price + 10;

We added ten to price. Look at the repetitive price variable. We could easily use a compound += to reduce this. We do this instead.

price += 5;

Awesome. Isn’t it? What’s the value of price now? Practice and comment below. If you don’t know how to practice check these lessons.

Lets bring down the price by 5 again and display it.

We use console.log command to display what is stored in the variable. It is very help for debugging.

Debugging let’s you find errors or bugs in your code. More on this later.

price -= 5;

console.log(price);

Lets multiply price and show it.

price *=5;

console.log(price);

and finally we will divide it.

price /=5;

console.log(price);

If you have any doubts, comment below.

#javascript #javascript compound assignment operators #javascript binary operators #javascript simple assignment operator #doers javascript

1606912089

#how to build a simple calculator in javascript #how to create simple calculator using javascript #javascript calculator tutorial #javascript birthday calculator #calculator using javascript and html