What is the time complexity of Unique Morse Code Words?

The optimal solution has a time complexity of O(N*M).

What is the space complexity of Unique Morse Code Words?

The space complexity is O(N*M).

What data structures are used to solve Unique Morse Code Words?

This problem uses Array, Hash Table, String. Understanding these concepts is essential for solving this Easy problem efficiently.

What are the key insights for solving Unique Morse Code Words?

Insight 1: The core idea is to iterate through the words, transform each word into its Morse code representation, and store these representations in a set to ensure uniqueness. Insight 2: Using a set data structure is crucial for efficiently determining the number of unique Morse code representations, as sets inherently prevent duplicate entries.

Which companies ask LeetCode 804. Unique Morse Code Words in interviews?

This problem is frequently asked at Wix.

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Unique Morse Code Words - LeetCode 804 Solution

Unique Morse Code Words - Complete Solution Guide

Unique Morse Code Words is LeetCode problem 804, a Easy level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

International Morse Code defines a standard encoding where each letter is mapped to a series of dots and dashes, as follows: 'a' maps to ".-" , 'b' maps to "-..." , 'c' maps to "-.-." , and so on. For convenience, the full table for the 26 letters of the English alphabet is given below: [".-","-...","-.-.","-..",".","..-.","--.","....","..",".---","-.-",".-..","--","-.","---",".--.","--.-",".-.","...","-","..-","...-",".--","-..-","-.--","--.."] Given an array of strings words where each word ca

Detailed Explanation

The problem asks us to take a list of words, convert each word into its Morse code representation by concatenating the Morse code equivalents of each letter, and then determine the number of *unique* Morse code representations that result from this conversion. We are provided with the mapping of each letter of the English alphabet to its corresponding Morse code.

Solution Approach

The solution iterates through the input array of words. For each word, it builds its Morse code transformation by iterating through each character in the word. For each character, it determines its corresponding Morse code representation using the provided mapping and appends it to a string. Finally, it adds the transformed word to a set. The size of the set is returned as the result, representing the number of unique Morse code representations.

Step-by-Step Algorithm

Step 1: Initialize a set to store the unique Morse code transformations.
Step 2: Iterate through each word in the input array `words`.
Step 3: For each word, initialize an empty string to store the Morse code transformation.
Step 4: Iterate through each character in the current word.
Step 5: Determine the Morse code representation for the current character by calculating its index (char - 'a') into the provided `morse_code` array and retrieve the morse representation.
Step 6: Append the Morse code representation to the transformation string.
Step 7: After processing all characters in the word, add the transformed string to the set.
Step 8: After processing all words, return the size of the set, which represents the number of unique Morse code transformations.

Key Insights

Insight 1: The core idea is to iterate through the words, transform each word into its Morse code representation, and store these representations in a set to ensure uniqueness.
Insight 2: Using a set data structure is crucial for efficiently determining the number of unique Morse code representations, as sets inherently prevent duplicate entries.
Insight 3: The mapping between a letter and its Morse code can be easily done using the ASCII value of the letter. Subtracting the ASCII value of 'a' from the ASCII value of the current letter gives its index in the `morse_code` array/list.

Complexity Analysis

Time Complexity: O(N*M)

Space Complexity: O(N*M)

Topics

This problem involves: Array, Hash Table, String.

Companies

Asked at: Wix.

Unique Morse Code Words | LeetCode 804

Easy

Array Hash Table String

Time: O(N*M)Space: O(N*M)

Solution Code

Available in:

class Solution {
    public int uniqueMorseRepresentations(String[] words) {
        String[] morse = {".-","-...","-.-.","-..",".","..-.","--.","....","..",".---","-.-",".-..","--","-.","---",".--.","--.-",".-.","...","-","..-","...-",".--","-..-","-.--","--.."};
        HashSet<String> set = new HashSet<>();
        for(String word : words){
            StringBuilder sb = new StringBuilder();
            for(char c : word.toCharArray()){
                sb.append(morse[c - 'a']);
            }
            set.add(sb.toString());
        }
        return set.size();
    }
}

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Problem Info

Description

Problem 804: Unique Morse Code Words - Detailed explanation and solution approaches.

Topics

Array Hash Table String

Companies

Wix

Stats

Difficulty:Easy

Languages:4

Companies:1

Problem #:804

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

8 steps

1
Step 1: Initialize a set to store the unique Morse code transformations.
2
Step 2: Iterate through each word in the input array `words`.
3
Step 3: For each word, initialize an empty string to store the Morse code transformation.
4
Step 4: Iterate through each character in the current word.
5
Step 5: Determine the Morse code representation for the current character by calculating its index (char - 'a') into the provided `morse_code` array and retrieve the morse representation.
6
Step 6: Append the Morse code representation to the transformation string.
7
Step 7: After processing all characters in the word, add the transformed string to the set.
8
Step 8: After processing all words, return the size of the set, which represents the number of unique Morse code transformations.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(N*M)

💾

Space Complexity

O(N*M)

📝

Why?

The time complexity is determined by the nested loops used to process each word and each character within each word. The space complexity is determined by the maximum possible size of the set which stores the morse code transformations of the words. Since the length of each word is limited to 12 and the number of words is limited to 100, the space complexity is not strictly O(N*M), but it depends on the specific distribution of characters and Morse code representations. However, N*M gives a reasonable upper bound on the space usage.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Precompute all possible Morse Code transformations

Instead of calculating the Morse code transformation for each word every time, you could precompute all possible transformations for words up to a certain length (e.g., 12, as per the problem constraints) and store them in a dictionary. This might provide a slight performance improvement if the Morse code generation is a significant bottleneck, although this is unlikely given the simplicity of the transformation.

Time: O(1) average case lookup time, O(M) precomputation. N lookups total would mean O(N) for all words plus the initial O(M)Space: O(26^M) to precompute all possible word transformations of length M

Trade-offs: This approach trades space for time. The precomputation could take significant time and space if M is large. It would only be beneficial if the Morse code transformation is a significant bottleneck or if the same words are processed repeatedly.

✨

Key Insights

1Insight 1: The core idea is to iterate through the words, transform each word into its Morse code representation, and store these representations in a set to ensure uniqueness.
2Insight 2: Using a set data structure is crucial for efficiently determining the number of unique Morse code representations, as sets inherently prevent duplicate entries.
3Insight 3: The mapping between a letter and its Morse code can be easily done using the ASCII value of the letter. Subtracting the ASCII value of 'a' from the ASCII value of the current letter gives its index in the `morse_code` array/list.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

String

A sequence of characters. Like a word or sentence in programming!

Edge Cases

Edge case 1: An empty input array `words`. The code handles this correctly as the outer loop will not execute, and the set will remain empty, returning 0.
Edge case 2: An input array with only one word, which consists of a single character. The code will correctly transform the character to its Morse code representation and store it in the set. The size of the set will be 1.
Edge case 3: An input array where all words map to the same Morse representation (e.g., words = ["a", "b", "c", ..., "z"]). The code will generate the Morse code for each character and add it to the set. Since all words result in a unique representation the size of the set will equal the length of the array.
Edge case 4: An input array where some words are duplicates. This is handled automatically by the set since the set stores only unique values and duplicates are automatically discarded.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to use a set to store the Morse code transformations. If a list is used instead, the code will count duplicate transformations, leading to an incorrect result.

✗Common mistake 2: Incorrectly mapping the characters to their Morse code representations. The index calculation (char - 'a') must be precise to retrieve the correct Morse code from the array.

✗Common mistake 3: Not considering the case where the input array is empty. While the code often works correctly in this case, it's good practice to explicitly handle this edge case.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Data compression. While Morse code itself isn't widely used for compression today, the concept of mapping frequently occurring symbols to shorter codes is fundamental to many compression algorithms (e.g., Huffman coding).

🏦Real-world scenario 2: Text encoding. The problem illustrates a basic form of encoding, which is relevant in various communication and data storage scenarios where characters need to be represented in a different format.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Even for easy problems, discuss time and space complexity. Interviewers want to see you think analytically.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: August 24, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJul 13

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 6

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

How should I approach easy-level algorithm problems?

Easy problems build foundational skills. Focus on: 1) Understanding the problem completely before coding, 2) Writing clean, readable code, 3) Handling all edge cases (empty input, single element, etc.), 4) Analyzing time and space complexity. Don't skip complexity analysis even for simple solutions.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Unique Morse Code Words"?

For this easy-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether hash table techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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