Author: root

Hello! In this post, we will solve a problem of finding a string using Swift. String problems are common in coding tests, and they greatly help in understanding efficient search algorithms and string processing methods. Through this course, we will solidify our foundation in string processing.

Problem Description

Here is a string search problem:

Given a string haystack and needle, write a function to find the index of the first occurrence of needle in haystack. If needle does not exist, it should return -1.

Examples:

• haystack = "hello", needle = "ll" => Output: 2
• haystack = "aaaaa", needle = "bba" => Output: -1
• haystack = "", needle = " => Output: 0

Problem Approach

To solve this problem, we need to compare the strings sequentially to check where needle appears in haystack. We can follow these steps:

1. First, check the length of needle and compare it with the length of haystack to determine if needle can exist in haystack.
2. Then, extract substrings of the length of needle from each index of haystack and compare them.
3. If the comparison matches, return the index; otherwise, return -1.

Swift Code Implementation

Now, let’s implement the above logic in Swift:

func strStr(_ haystack: String, _ needle: String) -> Int {
        let haystackCount = haystack.count
        let needleCount = needle.count

        // Return 0 when needle is empty
        if needleCount == 0 {
            return 0
        }

        // Return -1 if the length of haystack is shorter than needle
        if haystackCount < needleCount {
            return -1
        }

        // Convert haystack to an array
        let haystackArray = Array(haystack)

        // Compare substring with needle at each index
        for i in 0...(haystackCount - needleCount) {
            var j = 0

            while j < needleCount && haystackArray[i + j] == needle[needle.index(needle.startIndex, offsetBy: j)] {
                j += 1
            }

            // If needle is found
            if j == needleCount {
                return i
            }
        }

        // If needle is not found
        return -1
    }

Code Explanation

Now let’s explain the code in detail:

• func strStr: A function that takes two strings haystack and needle as arguments and performs the string search.
• let haystackCount = haystack.count: Stores the length of haystack.
• let needleCount = needle.count: Stores the length of needle.
• if needleCount == 0: If needle is empty, return 0.
• if haystackCount < needleCount: If the length of haystack is shorter than needle, return -1 as it cannot be found.
• let haystackArray = Array(haystack): Converts the string into an array to allow access to each character.
• for i in 0...(haystackCount - needleCount): Iterates through all indices of haystack. The loop should consider the length of needle for the search range.
• The inner while loop compares the substring from needle with haystack at each index.
• If all comparisons match, it returns index i.
• Finally, it returns -1 if needle is not found.

Test Cases

Let’s write some test cases to validate this code:

print(strStr("hello", "ll"))   // Output: 2
print(strStr("aaaaa", "bba"))     // Output: -1
print(strStr("", ""))              // Output: 0
print(strStr("abcde", "abc"))      // Output: 0
print(strStr("abcde", "xyz"))      // Output: -1

Conclusion

In this lesson, we learned how to solve a string finding problem using Swift. It’s important to carefully check the length of the strings, compare substrings, and define the search range to solve the given problem. String-related problems have various variations, so ensure to practice to handle more diverse cases.

In the next lesson, we will cover another type of string problem. Thank you!

Problem Description

Problem Analysis

This problem involves implementing an algorithm that can count the number of nodes with no child nodes (i.e., leaf nodes) in the given binary tree. You can traverse the binary tree using either a recursive or iterative approach to find the leaf nodes.

Algorithm Design

To find the leaf nodes, the following steps are followed:

1. Define a function to traverse the binary tree.
2. If the current node is not NULL:
• Recursively call the left child node.
• Recursively call the right child node.
• Check if the current node is a leaf node; if it is, increment the count.
3. If it is NULL, the function terminates.

Swift Implementation

Now, let’s implement the above algorithm in Swift. Below is the code for the function that counts the number of leaf nodes:

    class TreeNode {
        var value: Int
        var left: TreeNode?
        var right: TreeNode?
        
        init(value: Int) {
            self.value = value
            self.left = nil
            self.right = nil
        }
    }

    func countLeafNodes(root: TreeNode?) -> Int {
        guard let node = root else {
            return 0
        }
        
        // Check if it is a leaf node
        if node.left == nil && node.right == nil {
            return 1
        }
        
        // Recursively count the number of leaf nodes in the left and right child nodes
        return countLeafNodes(root: node.left) + countLeafNodes(root: node.right)
    }
    

Code Explanation

The above code implements the countLeafNodes function to count the number of leaf nodes in a binary tree. I will describe each part:

• TreeNode class: Defines each node in the binary tree. Each node has a value and left and right children.
• countLeafNodes function: Takes a given node as an argument and returns the number of leaf nodes.
• guard let: Checks if the current node is NULL, and if it is, returns 0 to terminate the search.
• Leaf node check: Returns 1 if the current node has no left and right child nodes.
• Recursive calls: Recursively calls the left and right child nodes and adds up the number of leaf nodes.

Test Cases

Let’s create some test cases to verify that the function works correctly.

    // Create tree
    let root = TreeNode(value: 1)
    let node2 = TreeNode(value: 2)
    let node3 = TreeNode(value: 3)
    let node4 = TreeNode(value: 4)
    let node5 = TreeNode(value: 5)

    // Connect tree structure
    root.left = node2
    root.right = node3
    node2.left = node4
    node2.right = node5

    // Output the number of leaf nodes
    let leafCount = countLeafNodes(root: root)
    print("Number of leaf nodes: \(leafCount)")  // Number of leaf nodes: 3
    

Conclusion

In this article, we explored how to count the number of leaf nodes in a binary tree using Swift. In the process of designing the algorithm and implementing it, we understood the principles of recursive calls and practiced writing actual code. Such problems are frequently encountered in coding tests, so it’s important to practice repeatedly to enhance your proficiency.

Additional Learning Resources

If you want to find more resources related to the problem of counting leaf nodes, consider the following materials:

• GeeksforGeeks: Count Leaf Nodes in Binary Tree
• LeetCode: Number of Leaf Nodes in Binary Tree
• HackerRank: Tree Level Order Traversal

In modern software development, debugging is an essential process. Especially in algorithm tests for employment, it is important not only to solve problems but also to verify the accuracy and efficiency of the code. In this article, we will take a closer look at how to solve algorithm problems using the Swift language and the importance of debugging.

Problem Definition

Problem-Solving Process

Step 1: Understand the Problem

To understand the problem, it is essential to clarify the requirements first. Here, we need to take two integers as input and return their sum. This is a very simple operation, but in coding tests, there may be situations that are not as straightforward as they seem.

Step 2: Design the Algorithm

To solve the problem, we can approach it with a simple set of steps. We will take two integers as input, add them together, and output the result. To do this, we will design the following algorithm:

1. Take the two integers A and B as input.
2. Store the sum of A and B in a variable sum.
3. Return sum.

Step 3: Implement the Code

Let’s implement the above algorithm using Swift.

func addNumbers(A: Int, B: Int) -> Int {
    let sum = A + B
    return sum
}

// Function Test
print(addNumbers(A: 5, B: 10))  // Output: 15

Step 4: Fix Errors Through Debugging

Debugging is the process of verifying that the code we wrote functions as intended. If the function behaves unexpectedly, we need to identify the cause and fix it.

Debugging Techniques

• Using Print Statements: Output the values of variables to verify intermediate processes.
• Condition Checks and Error Verification: Perform additional checks when using boundary conditions or abnormal values.
• Adding Test Cases: Create and run test cases with various input values.

For example, let’s consider the case where A or B might be negative. We can modify the function as follows:

func addNumbers(A: Int, B: Int) -> Int {
    guard A >= 0 && B >= 0 else {
        print("Error: Negative numbers are not allowed.")
        return -1
    }
    let sum = A + B
    return sum
}

// Test - Normal Operation
print(addNumbers(A: 5, B: 10))  // Output: 15

// Test - Abnormal Operation
print(addNumbers(A: -5, B: 10))  // Output: Error: Negative numbers are not allowed.

Step 5: Final Review and Submission

Finally, review the code created and add comments if necessary to enhance readability. In coding tests, it is crucial to ensure that other developers can easily understand my code, considering collaboration.

The Importance of Debugging

Debugging goes beyond merely fixing errors; it provides opportunities to ensure code quality and improve performance through optimization. The problems presented in coding tests are often similar to situations that can arise in real work, so it is important to develop the ability to solve such problems.

Conclusion

We have explored the process of solving algorithm problems using Swift and the importance of debugging. Debugging is a skill essential for successful developers, playing a significant role in enhancing code quality in algorithm tests for employment or actual projects. Furthermore, the experience gained through debugging will greatly assist in solving similar problems in the future.

Today, we will discuss a case of debugging when solving coding test problems using Swift.
To solve algorithmic problems, it is essential to go through the processes of understanding the problem, designing, implementing, and debugging.
Here, we will set up a simple algorithmic problem and look at various issues that may arise during the solution process and how to resolve them through debugging.

Problem Description: Two Sum

Given an integer array nums and an integer target,
the problem is to return the indices of the two numbers such that they add up to target.
There is exactly one solution for each input, and you may not use the same element twice.
The constraints are that the length of the nums array is between 2 and 10,000, and each element is between -10,000 and 10,000.

Input Example

nums = [2, 7, 11, 15], target = 9

Output Example

[0, 1] (2 + 7 = 9)

Problem Solving Process

Step 1: Problem Analysis

The given problem requires finding two numbers, so we could use a nested loop, but
for efficiency, using a hashmap will be a better approach.
Using a hashmap allows us to reduce the time complexity to O(n).

Step 2: Algorithm Design

The problem can be solved with the following steps:

1. Initialize an empty hashmap.
2. Iterate through the array to calculate the current value and the target value.
3. Add the current value as the key and the index as the value to the hashmap.
4. If the target value is in the hashmap, return its index.

Step 3: Code Implementation

Below is the code implemented in Swift:

        
        func twoSum(_ nums: [Int], _ target: Int) -> [Int] {
            var numsDict: [Int: Int] = [:] // Initialize an empty hashmap

            for (index, num) in nums.enumerated() {
                let complement = target - num // Calculate target value

                if let complementIndex = numsDict[complement] {
                    return [complementIndex, index] // Return index
                }

                numsDict[num] = index // Add to hashmap
            }

            return [] // Return an empty array if no result
        }
        
        

Step 4: Code Testing

Create test cases to verify that the implemented function works correctly.

        
        let result = twoSum([2, 7, 11, 15], 9)
        print(result) // [0, 1]
        
        

Debugging Process

Debugging is an essential step in the coding process.
Below is a case where we solved the problem using simple debugging methods:

1. Finding Logical Errors

When using a hashmap like the code above, multiple values can lead to the same target, which may give suboptimal results.
For example, it might return an invalid initial index, so we could add handling for that in the code.

2. Tracking Issues During Deployment

Before deploying code, document the expected results for various input values and
use that to identify discrepancies.
If the actual result differs from the expected result, it may indicate an issue with conditional statements or data structure configuration.

Exception Handling

Moreover, exception handling is a critical part that should not be overlooked.
It is important to provide appropriate responses when users input illegal values or an empty array.
We can add exception handling as follows:

        
        func twoSum(_ nums: [Int], _ target: Int) -> [Int]? {
            guard nums.count >= 2 else {
                return nil // Return nil if input conditions are not met
            }

            var numsDict: [Int: Int] = [:]

            for (index, num) in nums.enumerated() {
                let complement = target - num

                if let complementIndex = numsDict[complement] {
                    return [complementIndex, index]
                }

                numsDict[num] = index
            }

            return nil // Return nil if no result
        }
        
        

Conclusion

Today, we solved the two-sum problem in Swift and learned the importance of debugging in the process.
To solve a problem, it is essential not just to write code but to examine the flow and logic through debugging.
Utilizing debugging in the process of solving algorithmic problems can lead to more efficient solutions.
I encourage you to practice debugging techniques while solving various problems.

Written on: March 1, 2023

Author: Coding Master

Problem Definition

We will address an algorithm problem that minimizes the number of coins needed to make a given amount
based on the types of coins available. For example, if the types of coins are
[1, 5, 10, 25] and the amount is 7,
the number of coins needed is 3 (5 + 1 + 1).

Input

• An array coins containing the types of coins (in int[] format).
• The target amount amount (of int type).

Output

Returns the minimum number of coins. Returns -1 if it is not possible.

Approach to the Problem

This problem can be approached using Dynamic Programming.
The goal is to repeatedly use coins to create the given amount and find the minimum count.
We utilize the properties of optimal substructure and overlapping subproblems with dynamic programming.

Step 1: Initialize the DP Table

First, create an array dp to store the minimum number of coins for each index i.
Initialize dp[0] to 0 and the rest to infinity.

Step 2: Update the DP Table

Loop through each coin and update the amount that can be made with that coin.
Each amount i is updated from the previous amount i - coin
by adding +1 to the coin count.

Swift Code Implementation

            func minCoins(coins: [Int], amount: Int) -> Int {
                // Initialize the DP table
                var dp = Array(repeating: Int.max, count: amount + 1)
                dp[0] = 0

                // Update the DP table
                for coin in coins {
                    for i in coin...(amount) {
                        if dp[i - coin] != Int.max {
                            dp[i] = min(dp[i], dp[i - coin] + 1)
                        }
                    }
                }

                return dp[amount] == Int.max ? -1 : dp[amount]
            }

            // Example usage
            let coins = [1, 5, 10, 25]
            let amount = 7
            let result = minCoins(coins: coins, amount: amount)
            print("Minimum number of coins: \(result)")
        

Time Complexity Analysis

The time complexity of this algorithm is O(m * n).
Here, m is the number of types of coins, and n is the target amount.
The outer loop iterates over the types of coins, and the inner loop iterates over the target amount,
resulting in two nested loops.

Additional Problems and Variations

This problem can be modified to add various requirements.
For example, there may be variations where only specific coins must be used,
or coins cannot be used multiple times.
It is also important to consider such variations.