LeetCode Maximum Consecutive Ones III #195
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📝 WalkthroughWalkthroughThe changes reorganize the Max Consecutive Ones III problem by relocating its README to the correct directory structure and exporting the Changes
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🧰 Additional context used📓 Path-based instructions (11)leetcode/**/README.md📄 CodeRabbit inference engine (.github/copilot-instructions.md)
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leetcode/**📄 CodeRabbit inference engine (.github/copilot-instructions.md)
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**/README.md📄 CodeRabbit inference engine (CLAUDE.md)
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**/*.ts📄 CodeRabbit inference engine (.github/copilot-instructions.md)
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**/*.{ts,js}📄 CodeRabbit inference engine (.github/copilot-instructions.md)
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**/*-*.{ts,js,md}📄 CodeRabbit inference engine (.github/copilot-instructions.md)
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leetcode/**/*.{ts,js}📄 CodeRabbit inference engine (.github/copilot-instructions.md)
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**/*.{js,ts}📄 CodeRabbit inference engine (CLAUDE.md)
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leetcode/**/*.{js,ts}📄 CodeRabbit inference engine (AGENTS.md)
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leetcode/**/*-*.{js,ts}📄 CodeRabbit inference engine (AGENTS.md)
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leetcode/**/*.ts📄 CodeRabbit inference engine (AGENTS.md)
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📚 Learning: 2025-12-30T13:41:58.622ZApplied to files:
📚 Learning: 2025-12-30T13:41:24.644ZApplied to files:
🔇 Additional comments (1)
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Claude finished @pertrai1's task in 1m 11s —— View job 📋 Review Checklist
✅ Algorithm CorrectnessThe solution is correct and handles all test cases properly. The sliding window approach correctly:
Edge cases handled:
📊 Complexity AnalysisTime Complexity: O(n) ✓ The analysis is accurate. Although there's a nested loop structure, each element is visited at most twice:
This gives us O(2n) = O(n) amortized time complexity. Space Complexity: O(1) ✓ Correct. Only constant extra space is used ( 🎯 DSA Pattern: Variable Sliding WindowPattern: Variable Sliding Window (also known as Flexible Sliding Window) Why this pattern is suitable:
Key Insight: 🔑 The crucial insight is that we can reframe the problem: Instead of "flipping k zeros to ones," think of it as "finding the longest subarray with at most k zeros." This reframing eliminates the need to actually flip anything - we just need to count zeros in our current window. The sliding window works because:
💻 Code QualityStrengths:
Minor suggestions:
🔗 Related Problems for PracticeThese problems use the same variable sliding window pattern:
Pattern family: All "longest subarray with at most X constraint" problems use this technique. 🎓 Learning SummaryCore Concepts Mastered:
Why This Approach Beats Brute Force:
Common Mistakes with This Pattern:
Interview Tip: When you see "longest/maximum subarray with constraint," immediately think sliding window! 📝 File OrganizationNice cleanup! The PR correctly reorganizes the solution into the proper directory structure:
🎉 Overall AssessmentExcellent solution! This is a textbook implementation of the variable sliding window pattern. The code is clean, efficient, and well-commented. Your complexity analysis is spot-on, and the solution handles all edge cases correctly. Rating: 5/5 - Optimal solution with great code quality. |
🧮 Solution AnalysisPR: LeetCode Maximum Consecutive Ones III 📊 Code Quality ResultsESLint Analysis
🔍 Complexity Analysis💡 Analysis Tips
📁 Analysis Files CreatedDetailed analysis files have been automatically generated and committed to 🎯 Next Steps
Analysis generated for PR files only |
✅ Test ResultsTests have been executed for this pull request. 📊 Coverage Summary
Check the workflow logs for detailed test output. |
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🤖 Gemini Code Review (Model: ## Code Review: Max Consecutive Ones III
This is a well-implemented solution for the "Max Consecutive Ones III" problem. It correctly utilizes a common algorithmic pattern for this type of problem.
---
### 1. Pattern Identification
This solution uses the **Sliding Window** pattern. Specifically, it's a *variable-size sliding window* approach. The window expands by moving the `right` pointer, and it shrinks by moving the `left` pointer when the window condition (at most `k` zeros) is violated.
### 2. Complexity Verification
* **Time Complexity**: The stated time complexity is `O(n)`, where `n` is the length of `nums`. This is **accurate**. Both the `left` and `right` pointers traverse the array at most once. Each element is processed in constant time.
* **Space Complexity**: The stated space complexity is `O(1)`. This is **accurate**. The solution uses a fixed number of variables (`maxLength`, `left`, `zeroCount`, `n`) regardless of the input array size.
### 3. Key Insight
The key insight for this problem, and for many sliding window problems, is to efficiently maintain a "valid" window. Here, a "valid" window `[left, right]` is one that contains at most `k` zeros.
The "aha!" moment is realizing that:
1. We can expand the window greedily by moving the `right` pointer.
2. As we expand, we keep track of the number of zeros (`zeroCount`) within the current window.
3. If `zeroCount` ever exceeds `k`, our current window is invalid. To make it valid again, we must shrink it from the `left`. We move the `left` pointer forward, decrementing `zeroCount` if the element at `nums[left]` was a zero, until `zeroCount` is back to `k` or less.
4. At each step where the window is valid (or made valid after shrinking), we calculate its length (`right - left + 1`) and update `maxLength`. Because we are always trying to extend the `right` pointer and only shrinking `left` when necessary, we are effectively finding the longest possible valid window at each `right` position.
### 4. Edge Case Analysis
The current solution appears to handle common edge cases correctly:
* **`k = 0`**: No flips allowed. The algorithm will correctly identify the longest sequence of consecutive `1`s, shrinking the window every time a `0` is encountered.
* **`k >= nums.length`**: We can flip all `0`s. `zeroCount` will never exceed `k`, so the `left` pointer will never move, and `maxLength` will eventually be `n`.
* **`nums` contains all `1`s**: `zeroCount` will always be `0`. `maxLength` will correctly become `n`.
* **`nums` contains all `0`s**:
* If `k = 0`, `maxLength` will be `0`.
* If `k = 1`, `maxLength` will be `1` (one `0` flipped to `1`).
* If `k >= n`, `maxLength` will be `n`.
* **Smallest input (`nums.length = 1`)**:
* `nums = [0], k = 0`: `maxLength = 0`.
* `nums = [0], k = 1`: `maxLength = 1`.
* `nums = [1], k = 0`: `maxLength = 1`.
The logic holds for these scenarios.
### 5. Learning Points
* **Similar Problems using this pattern**:
* **Longest Substring Without Repeating Characters**: Find the longest substring with unique characters.
* **Minimum Window Substring**: Find the smallest window in `S` that contains all characters from `T`.
* **Fruit Into Baskets (or Longest Substring with At Most K Distinct Characters)**: Find the longest subarray with at most `K` distinct elements.
* **Binary Subarrays With Sum**: Problems asking for counts of subarrays meeting a condition often use a variation of the sliding window, sometimes combined with a prefix sum or hash map.
* Generally, any problem asking for the "longest/shortest subarray/substring that satisfies a condition" is a strong candidate for the sliding window technique.
* **Common Mistakes with this pattern**:
* **Incorrect window shrinking logic**: Moving the `left` pointer too far, or not far enough, or not correctly updating the count of elements that violate the condition (e.g., `zeroCount`).
* **Off-by-one errors**: Especially when calculating the window length (`right - left + 1`).
* **Forgetting to update the result**: The `maxLength` (or `minLength`, `count`, etc.) should be updated *after* the window has been deemed valid for the current `right` position.
* **Edge case oversights**: Not considering empty inputs, single-element inputs, or inputs where `k` is at its minimum or maximum allowed values.
* **Variations of this problem**:
* Find the *minimum* length subarray that contains at least `k` zeros. (This would involve a slightly different condition for updating `minLength`).
* Find the *count* of subarrays that have at most `k` zeros. (This often requires a more nuanced approach, sometimes using the "at most K" minus "at most K-1" trick, or adding `right - left + 1` to the count at each valid step).
* Instead of zeros, what if we need to flip at most `k` *other* characters to make a longest sequence of a *specific* character (e.g., 'A's in a string of 'A's and 'B's)?
### 6. Code Quality
* **Variable Naming**: `maxLength`, `left`, `zeroCount`, `n` are all clear and descriptive.
* **Code Structure**: The sliding window logic is standard and easy to follow. The `if (nums[right] === 0)` block clearly separates the logic for handling zeros.
* **Readability**: The code is very readable.
* **TypeScript Best Practices**:
* Using `const` for `n` and `maxLength` (initially `0`) is appropriate. `left` and `zeroCount` correctly use `let` as they are reassigned.
* The `export` keyword is a good addition for modularity within a repository.
* The addition of a newline at the end of the file (as seen in the diff) is a minor but good practice for file hygiene.
### 7. Alternative Approaches
1. **Brute Force (O(N^2) or O(N^3))**:
* Iterate through all possible subarrays using nested loops (outer loop for start `i`, inner loop for end `j`).
* For each subarray `nums[i...j]`, count the number of zeros.
* If the zero count is less than or equal to `k`, update the maximum length.
* **Trade-offs**: Much simpler to conceptualize and implement initially, but significantly less efficient for larger `n`, making it unsuitable for the given constraints (`10^5`).
2. **Binary Search on Answer (O(N log N))**:
* The problem asks for the *maximum length*. We can binary search for this maximum length `L` between `1` and `n`.
* For a given `L` (the "potential answer"), we can create a helper function `canAchieveLength(L)` that checks if there exists *any* subarray of length `L` that can be made all ones by flipping at most `k` zeros. This check can be done in `O(N)` time using a fixed-size sliding window (of size `L`) to count zeros.
* **Trade-offs**: More complex to implement than the optimal sliding window. While it can be a useful technique when a direct sliding window isn't immediately obvious, for this specific problem, the `O(N)` sliding window is more efficient and direct. It doesn't offer a performance advantage here.
The chosen sliding window approach is the optimal and most idiomatic solution for this problem. |
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🤖 Gemini Code Review (Model: Here's a detailed review of your LeetCode solution for "Max Consecutive Ones III": 1. Pattern IdentificationThis solution effectively uses the Sliding Window algorithmic pattern. Specifically, it's a variable-size sliding window where the window expands from the right and contracts from the left based on a condition (the number of zeros within the window). 2. Complexity Verification
3. Key InsightThe key insight for this problem, which makes the sliding window approach optimal, is: "To find the longest subarray containing at most The "aha!" moment is realizing that since we're looking for the maximum length, we don't need to find the "perfect" smallest window that satisfies the condition. Instead, we just need to ensure the current window is valid (has at most 4. Edge Case AnalysisThe current solution appears robust for various edge cases. Let's consider a few:
The solution handles these edge cases correctly due to the robust nature of the sliding window logic. 5. Learning Points
6. Code Quality
7. Alternative Approaches
Overall, this is a well-implemented and optimal solution for the problem. The code is clean, efficient, and correctly applies the sliding window pattern. Good work! |
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leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts (1)
5-30: Rename directory from1046-max-consecutive-ones-iiito1004-max-consecutive-ones-iiito match the correct LeetCode problem number.The algorithm implementation is correct with proper sliding window technique, O(n) time complexity, and O(1) space complexity. Code follows TypeScript best practices with explicit type annotations, const/let usage, and strict equality checks. However, LeetCode problem "Max Consecutive Ones III" is problem
#1004, not#1046. Update the directory name to match the repository's naming convention and the actual problem reference.
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📒 Files selected for processing (3)
1046-max-consecutive-ones-iii/README.mdleetcode/medium/1046-max-consecutive-ones-iii/README.mdleetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
💤 Files with no reviewable changes (1)
- 1046-max-consecutive-ones-iii/README.md
🧰 Additional context used
📓 Path-based instructions (11)
leetcode/**/README.md
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
Problem README files should include: problem title with difficulty badge, problem description, examples with inputs and outputs, and constraints
Keep README files up to date with problem counts and organization
Files:
leetcode/medium/1046-max-consecutive-ones-iii/README.md
leetcode/**
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
Don't require tests for all LeetCode solutions (validated on platform)
Files:
leetcode/medium/1046-max-consecutive-ones-iii/README.mdleetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
**/README.md
📄 CodeRabbit inference engine (CLAUDE.md)
Include problem title and difficulty badge, link to the problem on the respective platform (LeetCode, GreatFrontEnd, etc.), problem description, examples and constraints
Files:
leetcode/medium/1046-max-consecutive-ones-iii/README.md
**/*.ts
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
**/*.ts: Use explicit type annotations for function parameters and return types
Follow strict TypeScript configuration (defined intsconfig.json)
Target ES2020 with CommonJS modules
Strict mode enabled with ES2020 target and CommonJS modules for TypeScript
**/*.ts: Use explicit type annotations for function parameters and return types in TypeScript solutions
Follow strict TypeScript configuration with ES2020 target and CommonJS modules
Avoidanytype unless absolutely necessary in TypeScript solutions
Use interfaces for object types in TypeScript solutions
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
**/*.{ts,js}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
**/*.{ts,js}: Test complex algorithms with helper functions, data structure implementations, solutions with multiple edge cases, GreatFrontEnd API implementations, and reusable utility functions
Use descriptive variable names (single letters likei,j,kare acceptable for loop iterators and standard mathematical contexts)
Don't use deprecated JavaScript features
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
**/*-*.{ts,js,md}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
Use kebab-case for file names matching problem names
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
leetcode/**/*.{ts,js}
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
Don't deviate from LeetCode's expected function signatures
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
**/*.{js,ts}
📄 CodeRabbit inference engine (CLAUDE.md)
**/*.{js,ts}: Verify the solution correctly solves the problem for all test cases, including edge cases: empty arrays, single elements, negative numbers, null values
Document and verify time and space complexity using Big O notation, typically in code comments or docs/analysis/ files
Use clear, descriptive variable names (not just i, j, k unless in simple loops) and add comments for non-obvious logic or algorithm steps
Use const by default, let only when reassignment is needed; avoid var except for LeetCode solution function definitions
Use strict equality (===) over loose equality (==)
Use modern ES6+ syntax (arrow functions, destructuring, spread operators) where appropriate and prefer built-in methods (.map(), .filter(), .reduce())
**/*.{js,ts}: Useconstby default,letonly when reassignment is needed in JavaScript/TypeScript
Use strict equality (===) over loose equality (==) in JavaScript/TypeScript
Use descriptive variable names; single letters likei,j,kacceptable only for standard loop contexts
Prefer built-in array methods (.map(),.filter(),.reduce()) in JavaScript/TypeScript
Fix ESLint errors withnpm run quality:fixand ensure proper code formatting with Prettier
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
leetcode/**/*.{js,ts}
📄 CodeRabbit inference engine (AGENTS.md)
leetcode/**/*.{js,ts}: Keep LeetCode solutions concise and focused on the algorithm
Always document both time and space complexity in Big O notation with clear reasoning
Explain WHY the complexity is what it is, including amortized analysis when applicable
Document the algorithmic pattern used in solution code comments
Include examples in comments for complex algorithms in solution files
Review complexity analysis reports for algorithm files after running quality checks
Test edge cases if adding or modifying solution code
Do not add unnecessary dependencies or imports to solutions
Do not over-engineer simple solutions
Do not ignore time/space complexity considerations in code reviews
Do not deviate from LeetCode's expected function signatures in solution files
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
leetcode/**/*-*.{js,ts}
📄 CodeRabbit inference engine (AGENTS.md)
Solution files should use kebab-case naming:
{problem-name}.{js|ts}
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
leetcode/**/*.ts
📄 CodeRabbit inference engine (AGENTS.md)
Use TypeScript type annotations for all parameters and return types with complexity analysis in code comments
Files:
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts
🧠 Learnings (3)
📚 Learning: 2025-12-30T13:41:07.657Z
Learnt from: CR
Repo: pertrai1/coding-challenges PR: 0
File: .github/copilot-instructions.md:0-0
Timestamp: 2025-12-30T13:41:07.657Z
Learning: Applies to leetcode/**/README.md : Problem README files should include: problem title with difficulty badge, problem description, examples with inputs and outputs, and constraints
Applied to files:
leetcode/medium/1046-max-consecutive-ones-iii/README.md
📚 Learning: 2025-12-30T13:41:58.622Z
Learnt from: CR
Repo: pertrai1/coding-challenges PR: 0
File: AGENTS.md:0-0
Timestamp: 2025-12-30T13:41:58.622Z
Learning: Applies to leetcode/**/README.md : Keep README files up to date with problem counts and organization
Applied to files:
leetcode/medium/1046-max-consecutive-ones-iii/README.md
📚 Learning: 2025-12-30T13:41:24.644Z
Learnt from: CR
Repo: pertrai1/coding-challenges PR: 0
File: CLAUDE.md:0-0
Timestamp: 2025-12-30T13:41:24.644Z
Learning: Applies to **/README.md : Include problem title and difficulty badge, link to the problem on the respective platform (LeetCode, GreatFrontEnd, etc.), problem description, examples and constraints
Applied to files:
leetcode/medium/1046-max-consecutive-ones-iii/README.md
🔇 Additional comments (1)
leetcode/medium/1046-max-consecutive-ones-iii/README.md (1)
1-27: Well-structured README following all guidelines.The README includes all required elements per coding guidelines: problem title with difficulty badge, link to LeetCode, problem description, examples with inputs/outputs, and constraints. Based on learnings, this follows the expected format for problem README files.
✏️ Tip: You can disable this entire section by setting review_details to false in your review settings.
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Pull request overview
This pull request reorganizes the "Max Consecutive Ones III" LeetCode problem by moving it to the correct directory structure and updating the solution from a function expression to an exported function declaration.
Changes:
- Converted the solution function from a non-exported function expression to an exported TypeScript function declaration
- Added a properly formatted README.md with clean markdown formatting following repository standards
- Removed incorrectly placed README.md from the root-level directory
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
leetcode/medium/1046-max-consecutive-ones-iii/max-consecutive-ones-iii.ts |
Changed from function expression to exported function declaration, aligning with repository TypeScript conventions |
leetcode/medium/1046-max-consecutive-ones-iii/README.md |
Added clean markdown-formatted problem description replacing HTML-heavy format |
1046-max-consecutive-ones-iii/README.md |
Removed incorrectly placed file from root directory |
The changes look good overall. The solution correctly implements the sliding window pattern with proper time and space complexity. The README follows the cleaner markdown format seen in similar problems like problem 0340. The export pattern aligns with other TypeScript solutions in the repository.
📅 Spaced Repetition Reviews Scheduled!Great job solving #1046 - Max Consecutive Ones Iii! 🎉 To help you retain this knowledge long-term, I've scheduled 5 review sessions using spaced repetition:
What to expect:
🧠 Why Spaced Repetition?Research shows that reviewing material at increasing intervals dramatically improves retention. This adaptive scheduling ensures optimal spacing based on when you actually complete each review! Check |
Problem: #1046 - Max Consecutive Ones Iii PR: #195 First review scheduled (subsequent reviews auto-scheduled on completion) [skip ci]
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