Category: Kotlin coding test

Coding tests are a commonly used method to evaluate the abilities of software developers. In particular, algorithm problem-solving skills are considered one of the important factors by many companies. In this course, we will explain the problem-solving process through a problem of determining the direction of a line segment, and we will write the code using the Kotlin language.

Problem Description

Given two points (x1, y1) and (x2, y2), write a program to determine the direction of the line segment formed by these two points. The direction is defined as follows:

• If the line segment rotates clockwise, print ‘CW’.
• If the line segment rotates counterclockwise, print ‘CCW’.
• If the line segment is on a straight line, print ‘C’.

Input

• First line: integers x1, y1 (coordinates of the first point)
• Second line: integers x2, y2 (coordinates of the second point)

Output

Output the direction of the line segment in the format described above.

Problem-Solving Steps

1. Understanding the Problem

To understand the problem, let’s examine the geometric meaning of defining the direction of the line segment. A line segment represents a straight line between two points, and it is essential to determine whether this line makes a clockwise or counterclockwise angle. It will be useful to consider a third point based on the two points to decide the direction.

2. Utilizing the Cross Product of Vectors

To determine the direction of the line segment, we can use the cross product. By using the cross product, we can easily determine the direction of the angle formed by two vectors. The cross product of two vectors A and B is defined as follows:

A = (x2 – x1, y2 – y1), B = (x, y)

Here, B is the point chosen as a reference. Depending on the angle formed by A and the direction of B, we can judge whether it is clockwise, counterclockwise, or on a straight line.

3. Writing Kotlin Code

fun determineDirection(x1: Int, y1: Int, x2: Int, y2: Int): String {
    val direction = (x2 - x1) * (y2) - (y2 - y1) * (x2)

    return when {
        direction > 0 -> "CCW"
        direction < 0 -> "CW"
        else -> "C"
    }
}

4. Implementing and Testing the Complete Function

Let’s implement the complete program to receive input and outputting it in this format.

fun main() {
    val (x1, y1) = readLine()!!.split(" ").map { it.toInt() }
    val (x2, y2) = readLine()!!.split(" ").map { it.toInt() }

    println(determineDirection(x1, y1, x2, y2))
}

5. Explaining the Code

This code performs the function of accepting the coordinates of two points and outputting the direction of the line segment. The ‘determineDirection’ function uses the cross product to calculate the direction, returning ‘CCW’, ‘CW’, or ‘C’ based on the result. The ‘main’ function takes two points’ coordinates from the user and passes them to the function to output the result.

Conclusion

In this course, we examined the process of solving the problem of determining the direction of a line segment. Using the cross product of vectors to determine direction is a very useful approach when solving geometric problems. In the future, when encountering advanced algorithmic problems or geometry-related issues, this foundational knowledge can be applied to solve more complex problems. I hope this process of solving the problem using Kotlin has helped deepen your understanding of the programming language’s syntax and the properties of data structures.

References

• Algorithm Problem-Solving Strategies – Author: Koo Jong-man
• Kotlin Programming – Author: Jason Edmond

If you found this post interesting and useful, please check out other courses as well!

Hello! In this tutorial, we will learn about one of the algorithms called Insertion Sort. Insertion sort is a simple and easy-to-understand sorting algorithm, which is particularly efficient when the data is almost sorted. We will implement the insertion sort algorithm using the Kotlin programming language.

1. What is Insertion Sort?

Insertion sort is a method of sorting a list by ‘inserting’ data one by one. This algorithm works by comparing each element with the sorted list and inserting it into the appropriate position. This method is similar to sorting cards, where you maintain the cards in a sorted order while adding one card at a time.

1.1 Algorithm Overview

• Select one element from the unsorted list.
• Insert the selected element into the appropriate position in the sorted list.
• Repeat this process until all elements are sorted.

2. Time Complexity Analysis

The average time complexity of insertion sort is O(n²). In the worst case, it is also O(n²), and in the best case, it is O(n). This means that the performance is relatively good when the list is almost sorted.

2.1 Space Complexity

Since insertion sort does not require additional storage space, the space complexity is O(1).

3. Implementing Insertion Sort

Now, let’s implement the insertion sort algorithm using Kotlin. First, we will look at the basic structure of insertion sort.

3.1 Basic Insertion Sort Implementation

fun insertionSort(arr: IntArray) {
        for (i in 1 until arr.size) {
            val key = arr[i]
            var j = i - 1
            
            // Move elements greater than key to the right
            while (j >= 0 && arr[j] > key) {
                arr[j + 1] = arr[j]
                j--
            }
            arr[j + 1] = key
        }
    }

3.1.1 Code Explanation

– The insertionSort function takes an integer array arr and sorts it.
– The first element is regarded as already sorted, so the repetition starts from the second element. ( for (i in 1 until arr.size) syntax is used)
– The currently processed element is stored as key, and we find the larger elements and move them to the right.
– A while loop is used to find the correct position to insert key.

3.2 Let’s test Insertion Sort

Let’s actually test the insertion sort algorithm we have implemented. Below is a complete example that includes test code.

fun main() {
        val arr = intArrayOf(5, 3, 1, 4, 2)
        println("Array before sorting: ${arr.joinToString(", ")}")
        
        insertionSort(arr)
        
        println("Array after sorting: ${arr.joinToString(", ")}")
    }

3.2.1 Test Code Explanation

– The main function defines an array to be sorted and prints it.
– After calling the insertionSort function, it prints the sorted array again.

4. Advantages and Disadvantages of Insertion Sort

4.1 Advantages

• It is simple to implement and easy to understand.
• It is efficient for small data sets.
• It is very fast when the data is nearly sorted.

4.2 Disadvantages

• The time complexity is O(n²), making it inefficient. Performance decreases with larger data sets.
• For sorting large amounts of data, other algorithms like merge sort or quick sort may be more suitable.

5. Various Variants

Insertion sort can be made more efficient through various variants. For example, if we use binary search to find the position to insert data, we can optimize the insertion process.

fun insertionSortBinary(arr: IntArray) {
        for (i in 1 until arr.size) {
            val key = arr[i]
            var left = 0
            var right = i - 1
            
            // Find the insertion position of key using binary search
            while (left <= right) {
                val mid = left + (right - left) / 2
                if (arr[mid] > key) right = mid - 1
                else left = mid + 1
            }
            
            // Insert key at the found position
            for (j in i downTo left + 1) {
                arr[j] = arr[j - 1]
            }
            arr[left] = key
        }
    }

5.1 Explanation of Binary Insertion Sort

– The insertionSortBinary function is similar to the traditional insertion sort, but uses binary search to find the position to insert elements.
– Using binary search allows us to find the appropriate insertion position, enhancing efficiency.

6. Summary

In this tutorial, we have learned about insertion sort and implemented it in Kotlin. Insertion sort is a simple yet useful algorithm, especially when dealing with small or nearly sorted data. However, it is advisable to use more efficient algorithms when handling large data sets. Understanding and applying various sorting algorithms will help you develop better programming skills.

7. References

– Algorithm Problem Solving Guide
– Kotlin Official Documentation
– Comparison and Analysis of Various Sorting Algorithms

Thank you! In the next tutorial, we will learn about other sorting algorithms.

In coding tests, algorithm problems usually involve designing an algorithm to solve a specific problem. In this course, we will tackle the problem titled ‘Finding the Order of Buildings’ using Kotlin.

Problem Description

Given input consists of the height and position of multiple buildings. We need to determine the order in which each building is visible, considering all the given buildings. In other words, we aim to find out how the order is determined when one building is behind another, making it not visible.

Input Format

    The first line contains the number of buildings N (1 ≤ N ≤ 200,000).
    Then, for N lines, each building's height H (1 ≤ H ≤ 10^9) and position P (1 ≤ P ≤ 10^9) are given.
    

Output Format

    Output N integers representing the order in which each building is visible.
    

Problem Solving Approach

To solve this problem, we must analyze the relationship between each building’s height and position relative to other buildings. We follow these steps:

1. Sort the provided buildings based on height and position.
2. Check each sorted building one by one to determine if it is visible.
3. Then, count the number of visible buildings to collect the results.

Code Implementation

Now, let’s implement the code in Kotlin to solve the problem based on the discussed approach:

    data class Building(val height: Int, val position: Int)

    fun findVisibleBuildings(buildings: List): List {
        // Sort buildings (sorting by height in descending order)
        val sortedBuildings = buildings.sortedWith(compareByDescending { it.height }.thenBy { it.position })
        val visibleCount = mutableListOf()
        
        var lastHeight = 0
        
        for (building in sortedBuildings) {
            if (building.height > lastHeight) {
                visibleCount.add(building.position)
                lastHeight = building.height
            }
        }
        
        // Sort to return results in original order
        return visibleCount.sorted()
    }

    fun main() {
        val buildingCount = readLine()!!.toInt()
        val buildings = mutableListOf()
        
        for (i in 0 until buildingCount) {
            val (height, position) = readLine()!!.split(" ").map { it.toInt() }
            buildings.add(Building(height, position))
        }
        
        val result = findVisibleBuildings(buildings)
        println(result.joinToString(" "))
    }
    

Code Explanation

The code above provides comments to explain the flow, but to summarize, it consists of the following steps:

1. Create a Building data class to hold the building information.
2. Sort the given buildings in descending order of height.
3. If the current building’s height is greater than the tallest building checked so far, add its position to visibleCount.
4. After checking all buildings, return the results sorted in their original order.

Time Complexity Analysis

The time complexity of this algorithm is O(N log N). This is the sum of the time taken to sort the list of buildings and the time taken to find the tallest building. Therefore, it can efficiently handle large datasets.

Conclusion

Through this course, we learned how to solve the ‘Finding the Order of Buildings’ problem using Kotlin. There are various approaches to solving algorithm problems, and understanding and verifying them is crucial. I hope to further enhance coding skills through various problems in the future.

Future References

• Basic Concepts of Algorithms
• Introduction to Kotlin
• Data Structures and Algorithms

Thank you!