Javascript Coding Test Course, ‘Finding the Good Number’

Introduction

Having a basic understanding of programming languages is essential for solving algorithm problems, which are a crucial element of coding tests. In particular, JavaScript is a language that is essential for web development and frontend fields, and many companies are presenting problems using JavaScript. In this course, we will learn the applicability of JavaScript through the problem of ‘Good Numbers’ and explain the algorithm problem-solving process in detail.

Problem Description

The ‘Good Numbers’ problem is about finding numbers that satisfy certain conditions among the given numbers. The specifics of this problem are as follows:

Problem: Given an array of positive integers, write a function that removes duplicates from the array and prints all numbers less than 10. Additionally, calculate the average of the remaining numbers up to one decimal place and return it.

Input and Output Format

Input: An array of positive integers arr ([1, 2, 2, 3, 10, 5, 7, 15])
Output:
1. List of numbers less than 10 after removing duplicates
2. Average of the remaining numbers (up to one decimal place)

Example

    Input: [1, 2, 2, 3, 10, 5, 7, 15]
    Output: 
    Numbers after removing duplicates (less than 10): [1, 2, 3, 5, 7]
    Average: 3.6
    

Problem-Solving Approach

To solve the problem, we need to follow the steps of removing duplicates from the array and calculating the average of the filtered array. We will look at how to perform these tasks step by step using JavaScript.

Step 1: Remove Duplicates

We can use the Set object to remove duplicate elements from the array. The Set object does not allow duplicates automatically, so we can easily obtain the desired result using this object.


const arr = [1, 2, 2, 3, 10, 5, 7, 15];
const uniqueArr = [...new Set(arr)];
console.log(uniqueArr); // [1, 2, 3, 10, 5, 7, 15]
    

Step 2: Filtering

Now, we will perform the task of filtering numbers less than 10 from the array with duplicates removed. We can use JavaScript’s filter() method to extract only the elements that meet the condition.


const filteredArr = uniqueArr.filter(num => num < 10);
console.log(filteredArr); // [1, 2, 3, 5, 7]
    

Step 3: Calculate Average

To calculate the average of the filtered array, we can use the reduce() method. We can get the average by summing all the elements of the array and dividing the total by the number of elements.


const average = filteredArr.reduce((acc, num) => acc + num, 0) / filteredArr.length;
console.log(average.toFixed(1)); // 3.6
    

Complete Code

Now, we will integrate all the described processes into a single function to write the final code.


function goodNumbers(arr) {
    const uniqueArr = [...new Set(arr)];
    const filteredArr = uniqueArr.filter(num => num < 10);
    const average = filteredArr.reduce((acc, num) => acc + num, 0) / filteredArr.length;
    return {
        filteredNumbers: filteredArr,
        average: average.toFixed(1)
    };
}

const inputArr = [1, 2, 2, 3, 10, 5, 7, 15];
const result = goodNumbers(inputArr);
console.log(result);
    

Result

When the code above is executed, the following result can be obtained.


{
    filteredNumbers: [1, 2, 3, 5, 7],
    average: "3.6"
}
    

Optimization and Considerations

The above problem-solving method works well in practical applications. However, when dealing with large datasets, additional considerations regarding performance may be necessary. For example, while using a Set is a convenient way to extract unique values, it may lead to increased memory usage if the size of the array is very large. In such cases, several methods can be considered to improve the algorithm’s performance:

  • Explore methods to remove duplicates and filter at the same time.
  • Improve performance based on the choice of data structure.

Conclusion

In this course, we explored the process of solving the ‘Good Numbers’ problem using JavaScript. By removing duplicates, filtering numbers according to conditions, and calculating averages, we were able to enhance our basic algorithm problem-solving skills. It is important to practice solving such problems frequently while preparing for coding tests. I hope you gain a deeper understanding by utilizing the various features of JavaScript.

Did You Find This Helpful?

If this course has helped you prepare for JavaScript coding tests, try challenging other algorithm problems as well. Feel free to leave any difficulties or questions in the comments. I hope you have the opportunity to share your learning journey and grow together.

JavaScript Coding Test Course, Hacking Effectively

JavaScript is one of the most important languages in web development, and its significance is further highlighted in algorithm problem solving. Many companies evaluate applicants’ problem-solving skills and coding abilities through coding tests. This article will detail the overall approach to coding tests and the problem-solving process through algorithm problems that can be solved with JavaScript.

Problem Description

Problem 1: Sum of Two Numbers

Given an integer array nums and an integer target, write a function that returns the indices of the two numbers in the array that add up to target.

For example:

  • If nums = [2, 7, 11, 15] and target = 9, it should return [0, 1]. (2 + 7 = 9)

Approach to the Problem

To solve this problem, you can take the following approach.

  1. Using a nested loop: This method involves iterating through the two elements of the array to calculate the sum. However, the time complexity is O(n2), making it inefficient.
  2. Using a hashmap: This allows solving the problem in one pass. You store the required numbers in a hashmap and check if the difference between the current number and the target exists in the hashmap. The time complexity of this method is O(n).

Solution: Code using Hashmap

function twoSum(nums, target) {
    const map = new Map(); // Initialize the hashmap

    for (let i = 0; i < nums.length; i++) {
        const complement = target - nums[i]; // Calculate the required value

        if (map.has(complement)) {
            return [map.get(complement), i]; // Return the indices
        }
        
        map.set(nums[i], i); // Add the current number to the hashmap
    }
    
    return []; // Return an empty array if no result is found
}

Code Explanation

The code above defines the twoSum function. The function takes two parameters: an integer array nums and an integer target.

  1. Initialize the hashmap (map).
  2. Iterate through the given array nums.
  3. Calculate the complement for each number. (The result of subtracting the current value from the target value)
  4. Check if the hashmap contains the complement. If it does, return the current index and the stored index.
  5. Add the current number to the hashmap.

Review

Using a hashmap to solve the problem was efficient. The reason is that the code operates with a complexity of O(n), allowing it to respond quickly to all input cases. By solving various problems and understanding the solutions while preparing for coding tests, you can gain a deep understanding of algorithms and data structures.

Conclusion

Success in JavaScript coding tests relies on the ability to read and understand problems, as well as the ability to select appropriate algorithms. The sum of two numbers problem discussed today is not particularly complex, but it serves as good practice for developing algorithmic thinking. Keep solving more problems to improve your skills!

JavaScript Coding Test Course, Finding Desired Integer

One of the most important skills in preparing for JavaScript coding tests is the ability to accurately understand the given problem and efficiently solve it. In this course, we will take a detailed look at the process of solving an algorithm problem under the topic ‘Finding a Desired Integer’.

Problem Description

Implement a function that finds a specific integer in a given array and returns the index of that integer. If the specific integer is not in the array, it should return -1.

The function definition is as follows:

function findInteger(arr: number[], target: number): number

Input:

  • arr: An array of integers to search (0 ≤ arr.length ≤ 10^5)
  • target: The integer to find (-10^9 ≤ target ≤ 10^9)

Output:

  • If target exists in arr, return the index of target
  • If target does not exist in arr, return -1

Problem Analysis

To understand the problem, it is helpful to look at some examples of the input array.

  • Example 1: findInteger([1, 2, 3, 4, 5], 3)Output: 2 (index of 3)
  • Example 2: findInteger([10, 20, 30], 25)Output: -1 (25 is not in the array)
  • Example 3: findInteger([1, 2, 3, 4, 5], 5)Output: 4 (index of 5)

This problem involves finding a specific integer in an array of integers, so the most common method would be to traverse the array to find that integer. However, in the worst-case scenario, the array can be up to 100,000 in length, so an efficient solution is needed.

Solution Approach

To solve this problem, we can consider two approaches:

  • Linear search (O(n))
  • Binary search (O(log n) if the array is sorted)

Linear search is a method of traversing through all elements of the array and comparing them. This method is simple to implement, but in the worst case, it takes O(n) time. However, binary search is only possible if the given array is sorted. Therefore, we cannot exclude the possibility that the array may not be sorted in this problem. Hence, we will choose the linear search method.

Implementation

Below is a specific example of the function’s implementation:


function findInteger(arr, target) {
    for (let i = 0; i < arr.length; i++) {
        if (arr[i] === target) {
            return i; // Return index when the target is found
        }
    }
    return -1; // Return -1 if the target is not found
}
            

The code goes through the following process:

  1. It traverses the given array arr using a for loop.
  2. It compares each element arr[i] to target.
  3. If they match, it returns the corresponding index i.
  4. If it reaches the end of the array without finding the target, it returns -1.

Now let’s test this function:


console.log(findInteger([1, 2, 3, 4, 5], 3)); // 2
console.log(findInteger([10, 20, 30], 25)); // -1
console.log(findInteger([1, 2, 3, 4, 5], 5)); // 4
            

Time Complexity Analysis

The time complexity of the above algorithm is O(n). The maximum number of searches required is proportional to the length of the array. In the worst case, all elements of the array may need to be compared.

The space complexity is O(1), as it does not use any additional data structures and only utilizes the original array, keeping the memory usage constant.

Conclusion

In this course, we explored how to solve the ‘Finding a Desired Integer’ problem using JavaScript. We practiced important skills in preparing for coding tests by analyzing the problem, selecting the appropriate algorithm, and implementing it. By repeatedly going through such processes and encountering various problems, you can significantly improve your skills. Keep solving various algorithm problems and find your own solutions.

JavaScript Coding Test Course, Dividing Segments into Groups

This course aims to cover “Grouping Line Segments,” which is one of the frequently asked problems in JavaScript coding tests.
This problem tests the process of finding overlapping line segments among the given segments and grouping them accordingly.
We will examine various situations and considerations that may arise while solving algorithmic problems in detail.

Problem Definition

Problem: Given an array of line segments, return the number of groups formed by overlapping line segments.

For example, let’s assume we are given the following line segments:


Line Segments: [[1, 3], [2, 4], [5, 6], [7, 10], [9, 11]]

There are two groups in this array:

  • First Group: [[1, 3], [2, 4]]
  • Second Group: [[5, 6], [7, 10], [9, 11]]

Approach to the Problem

To solve this problem, we can use the following approach:

  1. Sorting: Sort the line segments based on their start or end points.
  2. Grouping: Traverse through the sorted line segments and group overlapping segments together.

Step 1: Sorting the Line Segments

Sort the line segments based on their starting points. This makes it easier to determine when segments overlap.

Step 2: Implementing the Grouping Logic

While traversing the sorted line segments, check if the current segment overlaps with the previous one.
If they do not overlap, start a new group; if they do overlap, add the current segment to that group.

Example Code

The following JavaScript code is written based on the above logic.


function groupLines(lines) {
    // 1. Sort line segments based on starting points
    lines.sort((a, b) => a[0] - b[0]);

    let groups = [];
    let currentGroup = [];

    for (let i = 0; i < lines.length; i++) {
        const line = lines[i];

        if (currentGroup.length === 0) {
            currentGroup.push(line);
        } else {
            // If the start of the current segment is less than or equal to the end of the previous segment, they overlap.
            if (line[0] <= currentGroup[currentGroup.length - 1][1]) {
                currentGroup.push(line);
            } else {
                // If they do not overlap, save the group and start a new one
                groups.push(currentGroup);
                currentGroup = [line];
            }
        }
    }

    // Add the last group
    if (currentGroup.length > 0) {
        groups.push(currentGroup);
    }

    return groups.length;
}

// Example input
const lines = [[1, 3], [2, 4], [5, 6], [7, 10], [9, 11]];
console.log(groupLines(lines));  // Output: 2

Code Explanation

The code above groups the line segments through the following processes:

  1. Sorting: Sorted the array of segments in ascending order based on starting points.
  2. Group Searching: Checked if the current segment overlaps with the previous one while traversing each segment.
  3. Group Saving: When encountering a non-overlapping segment, saved the current group and started a new one.

Complexity Analysis

The time complexity of this algorithm is mainly determined by the sorting part. Sorting takes O(n log n), and the process of traversing the segments and grouping them takes O(n).
Therefore, the overall time complexity is O(n log n).

The space complexity is O(n) in the worst case where no segments overlap.

Conclusion

In this course, we learned how to determine and group overlapping line segments through the problem “Grouping Line Segments.”
We explored the process of effectively solving the problem using basic algorithm techniques like sorting and searching.

Such algorithm problems often appear in real coding tests, so practicing the approaches mentioned above and solving various variations is important.
We will be covering useful coding test problems in the next course as well, so stay tuned!

Javascript Coding Test Course, Finding the Greatest Common Divisor

Topic: Finding the Greatest Common Divisor

Problem Description

Write a function that calculates the greatest common divisor (GCD) of two given integers a and b.
The greatest common divisor is the largest number that divides both numbers.

Input and Output Format

  • Input: Two positive integers a, b (1 ≤ a, b ≤ 109)
  • Output: The greatest common divisor of the two numbers

Example

        Input: 48, 18
        Output: 6
    

Approach to the Problem

There are various methods to find the greatest common divisor, but utilizing the famous Euclidean Algorithm can solve it efficiently.
This algorithm is based on the following principle:

  • The GCD of two integers a and b can be found by repeatedly calculating a % b until b becomes 0.
  • That is, GCD(a, b) = GCD(b, a % b), and when b becomes 0, a is the greatest common divisor.

Explanation of the Euclidean Algorithm

The Euclidean algorithm operates in the following steps:

  1. Prepare a and b. If b is not 0, proceed to the next step.
  2. Calculate r = a % b to obtain the new remainder.
  3. Update the value of a to b, and the value of b to r.
  4. Repeat this process until b becomes 0.
  5. As a result, a will be the greatest common divisor.

JavaScript Implementation

The code implementing the Euclidean algorithm in JavaScript is as follows:

        function gcd(a, b) {
            while (b !== 0) {
                const r = a % b;
                a = b;
                b = r;
            }
            return a;
        }

        // Test
        const result = gcd(48, 18);
        console.log(result); // 6
    

Time Complexity Analysis

The time complexity of the Euclidean algorithm is O(log(min(a, b))).
The performance is generally good depending on the ratio of the two numbers, and it is particularly efficient when dealing with large numbers.

Additional Problems and Exercises

If you are comfortable with finding the greatest common divisor, try solving the following problems:

  • Write a function to calculate the least common multiple when given two integers. (Use the fact that LCM(a, b) = a * b / GCD(a, b).)
  • Write a function to find the greatest common divisor of all elements in a given array.

Conclusion

In this article, we explored how to solve the problem of finding the greatest common divisor using JavaScript.
We learned an efficient approach through the Euclidean algorithm.
Such fundamental algorithms are frequently used in coding tests and practical applications, so thorough practice is essential.

References