What is the time complexity of Camelcase Matching?

The optimal solution has a time complexity of O(n*m).

What data structures are used to solve Camelcase Matching?

This problem uses Array, Two Pointers, String, Trie, String Matching. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Camelcase Matching?

Insight 1: The core idea is to iterate through each query string and the pattern simultaneously, checking for matching characters. If a character in the query is uppercase and doesn't match the current pattern character, the query doesn't match. Insight 2: Lowercase characters in the query can be skipped over, as they represent the inserted lowercase letters. This allows for flexibility in how the query string is built from the pattern.

Which companies ask LeetCode 1023. Camelcase Matching in interviews?

This problem is frequently asked at Compass.

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Camelcase Matching - LeetCode 1023 Solution

Camelcase Matching - Complete Solution Guide

Camelcase Matching is LeetCode problem 1023, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

Given an array of strings queries and a string pattern , return a boolean array answer where answer[i] is true if queries[i] matches pattern , and false otherwise. A query word queries[i] matches pattern if you can insert lowercase English letters into the pattern so that it equals the query. You may insert a character at any position in pattern or you may choose not to insert any characters at all . Example 1: Input: queries = ["FooBar","FooBarTest","FootBall","FrameBuffer","ForceFeedBack"], pa

Detailed Explanation

The problem asks us to determine, for each string in a given array of strings (`queries`), whether it matches a given `pattern` string. A string matches the pattern if it can be formed by inserting lowercase letters (zero or more) into the pattern. Crucially, uppercase letters in the query string *must* match corresponding letters in the pattern, and any additional uppercase letters in the query that are not in the pattern means the string *doesn't* match. The input consists of an array of query strings and a pattern string. The output should be a boolean array where each element represents whether the corresponding query string matches the pattern.

Solution Approach

The solution iterates through each query string and checks if it matches the pattern using a helper function (or a lambda in C++ and Python). The helper function uses two pointers: one for the query string and one for the pattern string. It iterates through the query string. If the current character in the query matches the current character in the pattern, both pointers are incremented. If the current character in the query is uppercase and *doesn't* match the pattern, then the query is not a match. If it matches, we increment our pattern pointer. If the current character in the query is lowercase, it's skipped. After iterating through the query string, it checks if the pattern pointer has reached the end of the pattern string. If it has, then the query matches the pattern; otherwise, it doesn't.

Step-by-Step Algorithm

Step 1: Iterate through each query string in the input array.
Step 2: For each query, initialize a pattern index to 0.
Step 3: Iterate through the characters of the query string.
Step 4: If the pattern index is within the bounds of the pattern length and the current character of the query matches the character at the pattern index in the pattern, increment the pattern index.
Step 5: If the current character of the query is uppercase and does NOT match the character in pattern at the current pattern index (or if the pattern index has reached the end of the pattern string), the query does not match, so return false.
Step 6: After the loop, check if the pattern index has reached the end of the pattern. If it has, then all the pattern's characters have been found in order, and the query matches. Return true.
Step 7: Store the boolean result of the match in a result array and return.

Key Insights

Insight 1: The core idea is to iterate through each query string and the pattern simultaneously, checking for matching characters. If a character in the query is uppercase and doesn't match the current pattern character, the query doesn't match.
Insight 2: Lowercase characters in the query can be skipped over, as they represent the inserted lowercase letters. This allows for flexibility in how the query string is built from the pattern.
Insight 3: The pattern must be fully consumed for a query to be considered a match. If the pattern index hasn't reached the end of the pattern string, it means the query isn't long enough to fulfill the pattern.

Complexity Analysis

Time Complexity: O(n*m)

Space Complexity: O(n)

Topics

This problem involves: Array, Two Pointers, String, Trie, String Matching.

Companies

Asked at: Compass.

Camelcase Matching | LeetCode 1023

Medium

Array Two Pointers String Trie String Matching

Time: O(n*m)Space: O(n)

Solution Code

Available in:

import java.util.ArrayList;
import java.util.List;

class Solution {
    public List<Boolean> camelMatch(String[] queries, String pattern) {
        List<Boolean> result = new ArrayList<>();
        for (String query : queries) {
            result.add(isMatch(query, pattern));
        }
        return result;
    }

    private boolean isMatch(String query, String pattern) {
        int patternIndex = 0;
        for (char char_q : query.toCharArray()) {
            if (patternIndex < pattern.length() && pattern.charAt(patternIndex) == char_q) {
                patternIndex++;
            } else if (Character.isUpperCase(char_q)) {
                return false;
            }
        }
        return patternIndex == pattern.length();
    }
}

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Problem Info

Description

Problem 1023: Camelcase Matching - Detailed explanation and solution approaches.

Topics

Array Two Pointers String Trie String Matching

Companies

Compass

Stats

Difficulty:Medium

Languages:4

Companies:1

Problem #:1023

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

7 steps

1
Step 1: Iterate through each query string in the input array.
2
Step 2: For each query, initialize a pattern index to 0.
3
Step 3: Iterate through the characters of the query string.
4
Step 4: If the pattern index is within the bounds of the pattern length and the current character of the query matches the character at the pattern index in the pattern, increment the pattern index.
5
Step 5: If the current character of the query is uppercase and does NOT match the character in pattern at the current pattern index (or if the pattern index has reached the end of the pattern string), the query does not match, so return false.
6
Step 6: After the loop, check if the pattern index has reached the end of the pattern. If it has, then all the pattern's characters have been found in order, and the query matches. Return true.
7
Step 7: Store the boolean result of the match in a result array and return.

💡

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)

📦 Linear space - Memory grows with input size.

📝

Why?

The time complexity is determined by the nested loops: the outer loop iterating through the queries and the inner loop iterating through each query string. The space complexity comes from storing the result boolean array whose size scales linearly with the number of queries.

📖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

Regular Expression

Construct a regular expression dynamically from the pattern by inserting `[a-z]*` between each character. Then test each query against the regex.

Time: O(n*k), where n is the number of queries, and k is the time to evaluate the regex on a single string, which depends on regex engine implementation but often includes backtracking.Space: O(m), where m is the length of the pattern string (to build the regex).

Trade-offs: This approach can be more concise in code but potentially less efficient due to the overhead of regex compilation and matching. Regex can also become complex and hard to debug.

✨

Key Insights

1Insight 1: The core idea is to iterate through each query string and the pattern simultaneously, checking for matching characters. If a character in the query is uppercase and doesn't match the current pattern character, the query doesn't match.
2Insight 2: Lowercase characters in the query can be skipped over, as they represent the inserted lowercase letters. This allows for flexibility in how the query string is built from the pattern.
3Insight 3: The pattern must be fully consumed for a query to be considered a match. If the pattern index hasn't reached the end of the pattern string, it means the query isn't long enough to fulfill the pattern.

📚

Key Terms Explained

For Students

Array

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

Two Pointers

Using two indices to traverse data from different positions simultaneously.

String

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

Edge Cases

Edge case 1: Empty pattern string. If the pattern is empty, any query consisting only of lowercase letters should match. If a query contains any uppercase letters, it will not match. This is handled correctly because the code checks `pattern_index < len(pattern)` before accessing `pattern[pattern_index]`.
Edge case 2: Query string is shorter than the pattern. If a query string is shorter than the pattern string, the `pattern_index` will never reach the end of the pattern, and the function will correctly return `false`.
Edge case 3: Pattern appears multiple times in the query string. The algorithm correctly handles this by advancing the `pattern_index` whenever a matching character is found.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Failing to check if the pattern index is within the bounds of the pattern string before accessing `pattern[pattern_index]`. This can lead to an out-of-bounds error.

✗Common mistake 2: Not checking if the pattern index reaches the end of the pattern string after iterating through the query string. This can lead to incorrectly identifying a non-matching query as a match.

✗Common mistake 3: Assuming only lowercase letters can be added. This leads to the incorrect conclusion that the uppercase letters have to be contiguous. This is wrong, as only the pattern letters have to appear in the right order.

✅

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: Code completion/IntelliSense. This pattern matching algorithm can be used to suggest code completions based on a partial input (the pattern).

🏦Real-world scenario 2: Searching for files or data based on a camel case pattern. For example, searching for 'FB' could return 'FooBar', 'FrameBuffer', etc.

💡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

Medium problems are the bread and butter of technical interviews. Practice explaining your approach before coding.

⚠️ 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: October 2, 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

Try solving this without using extra space first. It's a great exercise for understanding the underlying pattern.

@CodeNinjaJul 13

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

@TechLeadJul 6

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

@DevExpert92Jun 29

❓

Frequently Asked Questions

When should I use in-place modification vs creating a new array?

Use in-place modification when space complexity matters (O(1) space requirement) and the original array can be modified. Create a new array when you need to preserve the original data or when the problem involves significant restructuring that would complicate in-place logic.

How do I avoid infinite loops with two pointers?

Always ensure your loop condition guarantees termination (typically left < right or slow != fast). Move at least one pointer in each iteration, and handle edge cases where pointers might not move (like skipping duplicates). Draw out pointer movement on paper first.

How much time should I spend on a medium problem in interviews?

In a 45-minute interview, aim for: 5-10 minutes understanding and clarifying, 5-10 minutes discussing approach, 20-25 minutes coding, and 5-10 minutes testing. If stuck after 15 minutes of thinking, verbalize your thought process and ask for hints.

What is the importance of time and space complexity analysis?

Complexity analysis is crucial because: 1) Interviewers always ask about it, 2) It helps you choose between approaches, 3) It demonstrates CS fundamentals. Always state both time and space complexity, and be prepared to explain how you derived them.

What is the optimal approach for "Camelcase Matching"?

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

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