Category: Javascript Coding Test

Author: [Your Name]

Date: [Date]

1. Introduction

Coding tests are an important part of the software developer hiring process, as they assess problem-solving abilities in algorithms. In this course, we will take a detailed look at solving card game-related problems using JavaScript. Card games are a familiar type of game for many people and provide an opportunity to develop basic algorithmic thinking. JavaScript is primarily used in web-based environments, so many jobs require proficiency in JavaScript.

2. Problem Description

Below is an algorithm problem related to card games.

Problem: Organizing Cards

You are playing a card game with N cards. Each card has a unique number from 1 to N. Your goal is to sort the cards in order from the smallest number to the largest number. However, there are many cards, making it difficult to do this manually.

Write a function to sort the given array of cards. The function should take the length of the array as input and output the sorted array.

Example:

• Input: [3, 1, 4, 2]
• Output: [1, 2, 3, 4]

3. Approach to the Problem

To solve this problem, we will follow these steps:

1. Analyze the elements of the input array to determine which sorting algorithm is most suitable.
2. Implement the selected sorting algorithm in JavaScript code.
3. Validate the obtained results through test cases.

4. Code Implementation

There are various sorting algorithms that can be used in JavaScript. Commonly used sorting algorithms include:

• Bubble Sort
• Selection Sort
• Insertion Sort
• Quick Sort
• Merge Sort
• JavaScript Built-in Sort Method (sort)

This time, we will use the built-in method sort() of JavaScript to solve the problem.

function sortCards(cards) {
            return cards.sort((a, b) => a - b);
        }
        
        // Test
        const unsortedCards = [3, 1, 4, 2];
        const sortedCards = sortCards(unsortedCards);
        console.log(sortedCards);  // [1, 2, 3, 4]
        

5. Code Explanation

The sortCards function implemented above sorts the given array of cards. This function includes the following steps:

1. cards.sort((a, b) => a - b): The sort() method is used to sort the array of cards. This method performs string sorting by default, so a callback function is provided for numeric sorting.
2. The callback function compares the two arguments a and b to determine their order based on the result. If the value of a - b is negative, a is considered to come before b.
3. Returns the sorted array.

6. Test Cases

Let’s run various test cases to validate the accuracy of the function.

function testSortCards() {
            console.assert(JSON.stringify(sortCards([3, 1, 4, 2])) === JSON.stringify([1, 2, 3, 4]), "Test Case 1 Failed");
            console.assert(JSON.stringify(sortCards([10, 5, 3, 8])) === JSON.stringify([3, 5, 8, 10]), "Test Case 2 Failed");
            console.assert(JSON.stringify(sortCards([-1, 0, 1])) === JSON.stringify([-1, 0, 1]), "Test Case 3 Failed");
            console.assert(JSON.stringify(sortCards([4, 4, 4])) === JSON.stringify([4, 4, 4]), "Test Case 4 Failed");
            console.log("All test cases pass");
        }
        
        testSortCards();

The above testSortCards function contains tests for various scenarios and outputs which test case failed if any test fails.

7. Performance Considerations

The JavaScript sort() method has an average time complexity of O(n log n). Therefore, it performs excellently even for large data sets. However, if you use a sorting algorithm that you implemented yourself, the performance may vary depending on the implementation. In particular, inefficient algorithms like bubble sort or selection sort can lead to performance degradation when processing large amounts of data, so it is advisable to choose efficient algorithms.

8. Conclusion

In this course, we implemented a solution to a card game problem using JavaScript and looked closely at the approach to the problem and the code. We confirmed that the built-in sorting method sort() in JavaScript can simplify solving this problem. Algorithm problems can be a challenging endeavor, but attempting various methods and accumulating experience is essential. I encourage you to utilize various algorithms and data in solving problems in the future.

Hello! In this course, we will provide an in-depth explanation of an algorithm problem using JavaScript, specifically “Maze Exploration.” This is one of the frequently appearing problem types in coding tests, where the goal is to find a path from the starting point to the destination in a given maze. This problem can be solved using graph search algorithms such as BFS (Breadth-First Search) or DFS (Depth-First Search).

Problem Description

Problem: Check if there is a path from the starting point (start) to the destination (end) in a given 2D array representing a maze. The path in the maze can follow ‘0’ (a traversable space), while ‘1’ (an obstacle) cannot be passed.

For example, let’s assume the maze is as follows:

[
    [0, 1, 0, 0, 0],
    [0, 1, 0, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 1, 0, 0, 0],
    [0, 0, 0, 1, 0]
]

The starting point is (0, 0) and the destination is (4, 4). In other words, we need to determine whether there exists a path from (0, 0) to (4, 4) while exploring the maze.

Input and Output

• Input: 2D array (maze), starting point (start), destination (end)
• Output: Whether a path exists (true/false)

Approach to Problem Solving

To solve this problem, we can use BFS (Breadth-First Search) or DFS (Depth-First Search) algorithms. BFS is suitable for finding the shortest path, but since we only need to check for the existence of a path in this problem, we can also solve it using DFS.

Algorithm Explanation

1. **Basic Setup**: The following process is needed to explore the maze.

1. Add all nodes to be explored to the stack (for DFS).
2. Mark explored nodes as visited to prevent duplicate exploration.
3. Move in all possible directions (up, down, left, right) from the current position.
4. If the target position is reached, return true.
5. If all paths have been explored and the target is not reached, return false.

JavaScript Implementation

Now, let’s implement the above algorithm in JavaScript. Below is the specific code for the DFS algorithm for maze exploration:

function isPathExist(maze, start, end) {
    const rows = maze.length;
    const cols = maze[0].length;

    // Movement direction array (up, down, left, right)
    const directions = [
        [-1, 0], // up
        [1, 0],  // down
        [0, -1], // left
        [0, 1]   // right
    ];

    // Initialize stack and visited array
    const stack = [start];
    const visited = Array.from({ length: rows }, () => Array(cols).fill(false));
    visited[start[0]][start[1]] = true;

    // DFS exploration
    while (stack.length > 0) {
        const [x, y] = stack.pop();

        // If the destination is reached
        if (x === end[0] && y === end[1]) {
            return true;
        }

        // Move in each direction
        for (const [dx, dy] of directions) {
            const newX = x + dx;
            const newY = y + dy;

            // Check range and visit status
            if (newX >= 0 && newX < rows && newY >= 0 && newY < cols &&
                maze[newX][newY] === 0 && !visited[newX][newY]) {
                visited[newX][newY] = true;
                stack.push([newX, newY]);
            }
        }
    }

    // If unreachable
    return false;
}

// Test
const maze = [
    [0, 1, 0, 0, 0],
    [0, 1, 0, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 1, 0, 0, 0],
    [0, 0, 0, 1, 0]
];
console.log(isPathExist(maze, [0, 0], [4, 4])); // true

Code Explanation

The above code implements the process of finding a path from the starting point to the destination in a given maze using DFS (Depth-First Search). The main steps are as follows:

1. Initial Setup: First, initialize the number of rows and columns in the maze and set the movement directions in an array. The directions we can move are up, down, left, and right.
2. Initialize Stack and Visited Array: Use a stack to explore paths for DFS, marking visited positions as 'true'.
3. DFS Iteration: Pop a position from the stack to get the current position and check if it is the destination. If the destination is reached, return true.
4. Check Move Possibility: Check all directions to confirm if the new position is within range, has no obstacles, and hasn’t been visited before adding it to the stack.

Performance Analysis

The time complexity of this algorithm is O(V+E), where V is the number of vertices (i.e., all positions in the maze) and E is the number of edges (i.e., the number of possible movements from each position). In the worst case, we need to explore all positions, which is why this complexity is necessary. The space complexity is O(V) as space is required for the visited array.

Conclusion

In this lecture, we discussed how to solve the maze exploration problem using JavaScript. We learned basic code and techniques to check the existence of a path through the maze using the DFS algorithm. These types of problems frequently appear in coding tests, so practice various modified problems to build your confidence.

In the next lecture, we will cover more complex maze exploration problems or other types of algorithm problems, so please look forward to it!

Problem Definition

Resolve the following problem. You need to implement a function that sorts the given integer in descending order and returns those digits as a single integer. For example, if the input is 42145, it should return 54421.

Input

• A single integer n (0 ≤ n ≤ 1,000,000,000)

Output

• An integer sorted in descending order

Approach

To solve the problem, follow the steps below:

1. Convert the given integer to a string.
2. Convert the string to an array, containing each digit in the array.
3. Sort the array in descending order.
4. Combine the sorted array back into a string, then convert it to an integer and return it.

Code Implementation

Below is an example of how the above approach is implemented in code:

function sortDigitsDescending(n) {
    // Step 1: Convert the integer to a string
    const strNum = n.toString();
    
    // Step 2: Convert the string to an array
    const digitsArray = strNum.split('');
    
    // Step 3: Sort the array in descending order
    digitsArray.sort((a, b) => b - a);
    
    // Step 4: Combine the sorted array into a string and convert to integer
    const sortedNumber = parseInt(digitsArray.join(''), 10);
    
    return sortedNumber;
}

Code Explanation

The code above works as follows:

• The function sortDigitsDescending(n) takes an integer n as a parameter.
• It uses the toString() method to convert the number to a string.
• The split('') method separates each digit of the string into an array.
• The sort() method sorts the elements of the array in descending order. It compares each digit by converting them to numbers.
• The join('') method combines the sorted array back into a single string, then parseInt() converts it to an integer and returns it.

Test Cases

Now it is necessary to validate the function with various test cases:

console.log(sortDigitsDescending(42145)); // 54421
console.log(sortDigitsDescending(123456789)); // 987654321
console.log(sortDigitsDescending(0)); // 0
console.log(sortDigitsDescending(10000)); // 10000
console.log(sortDigitsDescending(9876543210)); // 9876543210

Performance Considerations

This algorithm has a time complexity of O(n log n) depending on the length of the input. Here, n is the number of digits. During the sorting of the digits, JavaScript’s internal sorting algorithm is utilized, which guarantees O(n log n) performance in the worst case.

Conclusion

We have successfully implemented an algorithm to sort the digits of a given integer in descending order. In this process, we utilized JavaScript’s string and array methods to solve the problem simply and efficiently. An important aspect of solving algorithmic problems is to break down the problem into smaller parts and make the code writing clear at each stage. As you tackle various algorithmic problems, try to apply this approach.

Hello! Today we will discuss the ‘Finding Prime Numbers’ problem, which frequently appears in JavaScript coding tests. A prime number is a natural number greater than 1 that cannot be formed by multiplying two smaller natural numbers. For example, 2, 3, 5, 7, and 11 are all prime numbers. In this article, we will implement an algorithm to find prime numbers and explain the process in detail.

1. Problem Definition

Given an integer N, write a program that outputs all prime numbers less than or equal to N.

Input: An integer N (1 ≤ N ≤ 10,000)

Output: Print all prime numbers less than or equal to N in a single line

2. Algorithm Selection

The representative algorithm to find prime numbers is the Sieve of Eratosthenes. This algorithm is very efficient and suitable for finding prime numbers less than or equal to N. The reasons for using this algorithm are as follows:

• It has a time complexity of O(N log log N), making it very fast.
• The memory usage is moderate, allowing it to handle up to N = 10,000 smoothly.

2.1 Explanation of the Sieve of Eratosthenes Algorithm

The Sieve of Eratosthenes algorithm proceeds through the following steps:

1. Add all numbers from 2 to N to a list.
2. Confirm the first number in the list, which is 2, as a prime. Remove all multiples of 2 from the list.
3. Confirm the next number (3) as a prime and remove all multiples of 3. Repeat this process.
4. Continue only while the current number is less than or equal to the square root of N.

3. Writing Code

Now, let’s write the code in JavaScript based on the above algorithm.

function sieveOfEratosthenes(N) {
    const primes = [];
    const isPrime = new Array(N + 1).fill(true);
    isPrime[0] = isPrime[1] = false; // 0 and 1 are not prime numbers.

    for (let i = 2; i <= N; i++) {
        if (isPrime[i]) {
            primes.push(i); // i is a prime number.
            for (let j = i * 2; j <= N; j += i) {
                isPrime[j] = false; // Multiples of i are not prime numbers.
            }
        }
    }
    return primes;
}

// Example of usage
const N = 100; // Input N.
const primeNumbers = sieveOfEratosthenes(N);
console.log(primeNumbers.join(' ')); // Output the prime numbers.

4. Code Analysis

Let’s take a look at the written code step by step:

• const isPrime = new Array(N + 1).fill(true);: This creates an array of numbers up to N and initializes all values to true.
• isPrime[0] = isPrime[1] = false;: Since 0 and 1 are not prime, they are set to false.
• The for loop checks the numbers from 2 to N. If isPrime[i] is true, it means i is a prime number. Add this number to the primes array.
• Also, iterate through all multiples of i and set them to false.
• Through this process, the final array containing only prime numbers is returned.

5. Test Cases

Now, let’s run some test cases to verify that our implementation works well.

console.log(sieveOfEratosthenes(10)); // [2, 3, 5, 7]
console.log(sieveOfEratosthenes(50)); // [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
console.log(sieveOfEratosthenes(100)); // [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]

6. Conclusion

Today we learned how to find prime numbers in JavaScript. We explored an efficient method of locating primes using the Sieve of Eratosthenes, and based on that, we wrote practical code. I hope this code helps enhance your algorithm skills further. Additionally, I hope it aids you in preparing for coding tests!

7. Additional Learning Resources

If you want to see more materials and solve problems, please refer to the following resources:

• LeetCode – Various algorithm problems and solutions
• HackerRank – Coding test problems and practices
• Codewars – Coding practice by solving problems in various languages