🧩 NeetCode Practice Framework¶

🎯 Stop Memorizing. Start Engineering.¶

A scalable Python framework that transforms LeetCode-style algorithm practice into a knowledge-graph-driven, data-driven, testable, and high-performance workflow — with AI-powered mind maps, industrial-strength testing, and pattern-based learning to help developers grow faster and understand algorithms more deeply.

📚 Docs • 🤖 AI Mind Maps • 🧠 Interactive Mind Maps • 🚀 Quick Start • 📐 Patterns

English | 繁體中文

Topics: knowledge-graph ai-powered mind-map pattern-recognition leetcode neetcode-150 blind-75 stress-testing algorithm-engineering performance-benchmarking data-driven-testing random-test-generation judge-function algorithm-debugging competitive-programming python vscode-integration test-automation coding-interview

💎 Core Philosophy¶

"Algorithm mastery is not about memorizing 300 solutions — it's about internalizing 15 fundamental patterns and knowing precisely when to apply each one."

This framework embodies three transformative principles:

🧬 Knowledge Graph Architecture¶

Traditional LeetCode practice treats problems as isolated units. We built an interconnected ontology system where:

API Kernels define reusable algorithmic primitives (SubstringSlidingWindow, GridBFS, BacktrackExplore)
Patterns compose kernels into higher-level strategies
Problem Families reveal structural relationships across 300+ problems
AI Synthesis discovers non-obvious connections humans miss

This is how experts think — in abstractions, not in solutions.

⚙️ Production-Grade Validation¶

Your solution passes LeetCode's tests. But is it correct? Is it optimal? We provide ICPC/Codeforces-caliber testing infrastructure:

Capability	What It Proves
🎲 Seeded Random Generation	Your code handles cases you never imagined
⚖️ Custom Judge Functions	Multiple valid answers are all accepted
📊 Multi-Solution Benchmarking	Which approach is actually faster
📈 Empirical Complexity Estimation	Your O(n log n) claim is verified

This is how Google engineers validate — through exhaustive, reproducible testing.

🤖 AI-Augmented Understanding¶

We don't just store knowledge — we synthesize insight:

AI analyzes the entire ontology to generate creative, interconnected mind maps
Multi-perspective synthesis: Architect × Professor × Engineer × Competitor
Problems link to GitHub solutions (when available) or LeetCode (fallback)

This is how the next generation learns — with AI as a thinking partner.

🌟 What Sets Us Apart¶

💡 "The difference between a good programmer and a great one isn't the algorithm they choose — it's how they prove it works."

📦 Other LeetCode Repos	🚀 NeetCode
❌ Copy solutions, hope they work	✅ Prove your solution is correct
❌ Manual test cases only	✅ Auto-generate 1000+ test cases
❌ No way to compare approaches	✅ Benchmark N solutions side-by-side
❌ Memorize patterns blindly	✅ Visualize patterns with mind maps
❌ No systematic learning path	✅ Structured roadmaps (NeetCode 150, Blind 75)

🧠 The Knowledge Graph Advantage¶

Most people practice algorithms in isolation. We built an interconnected knowledge system:

Mind Map	Description	Link
🤖 AI Ontology Analysis	AI-powered deep pattern synthesis	🔗 EN · 🔗 中文
📐 Pattern Hierarchy	API kernels → patterns → solutions	🔗
👨‍👩‍👧‍👦 Family Derivation	Base templates → derived variants	🔗
⚡ Algorithm Usage	Know which algorithm applies where	🔗
🏢 Company Coverage	Target preparation for specific companies	🔗
🗺️ Learning Roadmaps	NeetCode 150, Blind 75, etc.	🔗

→ Explore 10+ Interactive Mind Maps

⚙️ Industrial-Strength Testing¶

Built on principles from Codeforces, ICPC, and Google's engineering practices:

Capability	What It Does	Why It Matters
🎲 Random Test Generation	Seeded generators for reproducibility	Find edge cases you never imagined
⚖️ Custom Judge Functions	ICPC-style validation logic	Multiple correct answers? No problem
📊 Multi-Solution Benchmark	Compare N approaches automatically	Know which is actually faster
📈 Complexity Estimation	Empirical Big-O analysis	Verify your theoretical claims
🔧 VS Code Integration	One-click debug, tasks, shortcuts	Debug algorithms like real software

⭐ Why This Framework?¶

The Problem with Traditional Practice¶

You solve a problem on LeetCode. It passes. But do you really know if your solution is correct? What about:

That edge case with empty input you didn't test?
The subtle off-by-one error that only appears with large N?
Whether your O(n log n) claim is actually true?

Traditional practice leaves these questions unanswered. This framework answers them definitively.

What Makes Us Different¶

Capability	This Framework	Typical Repos
Reproducible Random Tests	✅ Seeded generators	❌ Manual only
Custom Judge Functions	✅ ICPC/Codeforces style	❌ String match
Multi-Solution Benchmarking	✅ Compare N approaches	❌ Single solution
VS Code Integration	✅ Tasks, Debug, Shortcuts	❌ CLI only
Stress Testing	✅ Generate 1000+ cases	❌ Limited
Complexity Estimation	✅ Automatic Big-O	❌ None

Built For Excellence¶

Audience	How We Help
🏆 Competitive Programmers	Train like Codeforces grandmasters — stress test until you break your code, then fix it
💼 FAANG Engineers	Build interview confidence by proving your solutions work, not just hoping they do
🎓 CS Students	Learn algorithms the right way — through experimentation, not memorization
👨‍🏫 Educators	Give students industrial-grade tools to validate their understanding
🔬 Researchers	Benchmark algorithm variants at scale with reproducible methodology

🚀 Quick Start¶

1. Setup Environment¶

Windows (PowerShell)

# Clone and navigate to project
cd C:\path\to\neetcode

# Install Python 3.11 (if needed)
py install 3.11

# Create and activate virtual environment
py -3.11 -m venv leetcode
leetcode\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Linux / macOS

# Using pyenv (recommended)
pyenv install 3.11
pyenv local 3.11

# Create and activate virtual environment
python -m venv leetcode
source leetcode/bin/activate

# Install dependencies
pip install -r requirements.txt

# Make scripts executable
chmod +x run_tests.sh run_case.sh new_problem.sh

2. Create Your First Problem¶

# Windows
new_problem.bat 0001_two_sum

# Linux/macOS
./new_problem.sh 0001_two_sum

This creates: - solutions/0001_two_sum.py — Your solution file - tests/0001_two_sum_1.in — Test input - tests/0001_two_sum_1.out — Expected output

3. Run Tests¶

# Windows
run_tests.bat 0001_two_sum

# Linux/macOS
./run_tests.sh 0001_two_sum

4. Debug in VS Code¶

Open any solution file in solutions/
Press F5 to debug with test case #1
Or press Ctrl+Shift+B to run all tests

That's it! You're ready to solve problems. 🎉

✨ Key Features¶

Feature	Description
🤖 AI Ontology Analysis	AI-powered knowledge graph synthesis — discover pattern relationships humans miss
🧪 Automated Testing	Run multiple test cases automatically with clear pass/fail reporting and timing
🎲 Random Test Generation	Seeded generators for reproducibility, stress test with 1000+ cases, auto-save failing cases
⚖️ Custom Judge Functions	Validate multiple correct answers, ICPC-style validation, works without expected output
📊 Performance Analysis	Benchmark multiple solutions, automatic time complexity estimation, side-by-side comparison
🔧 VS Code Integration	One-click test execution, integrated debugging, custom tasks and shortcuts
🧠 Interactive Mind Maps	Visualize algorithm patterns, track learning progress — Explore →

🧠 Interactive Mind Maps¶

Visualize algorithm patterns, problem relationships, and learning paths:

🤖 AI-Powered Ontology Analysis (NEW!)¶

"Let AI synthesize what takes humans years to internalize."

Our AI Ontology Analyzer processes the entire knowledge graph — API Kernels, Patterns, Algorithms, Data Structures, Problem Families — and generates creative, interconnected mind maps that reveal insights human-curated lists miss.

Language	Description	Links
English	AI-synthesized pattern relationships	Static · Interactive ✨
繁體中文	AI 智能分析模式關聯	Static · Interactive ✨

What makes it special: - 🧬 Deep Pattern Synthesis — AI identifies non-obvious connections between patterns - 🎯 Smart Linking — Problems link to GitHub solutions (when available) or LeetCode - 🌐 Multi-language — Generate in English and 繁體中文 - ♻️ Regeneratable — Run python tools/generate_mindmaps_ai.py to create fresh insights

📚 Curated Mind Maps¶

Mind Map	Description	Links
📐 Pattern Hierarchy	API Kernels → Patterns → Problems	Static · Interactive ✨
👨‍👩‍👧‍👦 Family Derivation	Base templates → Derived variants	Static · Interactive ✨
⚡ Algorithm Usage	Problems by algorithm	Static · Interactive ✨
🏗️ Data Structure Usage	Problems by data structure	Static · Interactive ✨
🏢 Company Coverage	Company-specific problems	Static · Interactive ✨
🗺️ Learning Roadmaps	NeetCode 150, Blind 75, etc.	Static · Interactive ✨
🔗 Problem Relations	Related problems network	Static · Interactive ✨
🔀 Solution Variants	Multiple approaches	Static · Interactive ✨
📊 Difficulty × Topics	Topics by difficulty	Static · Interactive ✨

👉 View All Interactive Mind Maps

🤖 AI Mind Map Generation¶

"The synthesis of a Software Architect's system thinking, an Algorithm Professor's pedagogical wisdom, a Principal Engineer's battle-tested experience, and a Competitive Programming Champion's pattern recognition — all unified through AI."

The Vision¶

Traditional algorithm learning resources present knowledge in isolation. Our AI Ontology Analyzer takes a fundamentally different approach:

Traditional Approach	Our AI Approach
Static problem lists	Dynamic knowledge graph synthesis
Manual categorization	AI-discovered pattern relationships
Single perspective	Multi-perspective expert synthesis
Memorize solutions	Understand interconnections

How It Works¶

┌─────────────────────────────────────────────────────────────────┐
│                    KNOWLEDGE GRAPH INPUT                        │
├─────────────────────────────────────────────────────────────────┤
│  ontology/          │  meta/problems/     │  docs/patterns/     │
│  ├── api_kernels    │  ├── 0001_*.toml    │  ├── sliding_window │
│  ├── patterns       │  ├── 0003_*.toml    │  └── ...            │
│  ├── algorithms     │  └── ...            │                     │
│  └── ...            │                     │                     │
└─────────────────────┴─────────────────────┴─────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    AI SYNTHESIS ENGINE                          │
├─────────────────────────────────────────────────────────────────┤
│  🏗️ Software Architect    → System-level pattern organization   │
│  📚 Algorithm Professor   → Pedagogical structure & progression │
│  ⚙️ Principal Engineer    → Practical applicability & trade-offs│
│  🏆 Competitive Champion  → Pattern recognition shortcuts       │
│  🎨 API Designer          → Clean knowledge interfaces          │
└─────────────────────────────────────────────────────────────────┘
                              │
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    INTELLIGENT OUTPUT                           │
├─────────────────────────────────────────────────────────────────┤
│  ✅ Smart Links: GitHub solution (if exists) → LeetCode fallback│
│  ✅ Multi-language: EN / 繁體中文 / 简体中文                      │
│  ✅ Markmap format: Interactive, collapsible, beautiful         │
│  ✅ Custom goals: Interview prep / Systematic learning / Review │
└─────────────────────────────────────────────────────────────────┘

Quick Start¶

# Interactive mode (recommended)
python tools/generate_mindmaps_ai.py

# Specific goals
python tools/generate_mindmaps_ai.py --goal interview        # Interview preparation
python tools/generate_mindmaps_ai.py --goal systematic       # Learning roadmap
python tools/generate_mindmaps_ai.py --goal pattern_mastery  # Deep pattern analysis

# Focus on specific topic
python tools/generate_mindmaps_ai.py --topic sliding_window
python tools/generate_mindmaps_ai.py --topic dynamic_programming

# Multiple languages
# Configure in tools/mindmap_ai_config.toml:
# language = ["en", "zh-TW"]

Configuration¶

Edit tools/mindmap_ai_config.toml to customize:

Section	What You Can Configure
`[model]`	LLM model, temperature, max tokens
`[output]`	Directory, filename, HTML generation
`[ontology]`	Which knowledge graph data to include
`[problems]`	Problem filters (difficulty, topics, roadmaps)
`[generation]`	Goal, style, custom instructions
`[links]`	GitHub repo URL, branch, link format
`[advanced]`	Output language(s), complexity inclusion

The Intelligence Behind It¶

The AI doesn't just reorganize data — it synthesizes understanding from multiple expert perspectives:

Perspective	Contribution to Mind Map
🏗️ Software Architect	Identifies abstraction layers, sees patterns as reusable components
📚 Algorithm Professor	Structures learning progression, explains "why" not just "how"
⚙️ Principal Engineer	Highlights production trade-offs, real-world applicability
🏆 Competitive Champion	Surfaces pattern-matching shortcuts, time-pressure optimizations
🎨 API Designer	Creates clean knowledge interfaces, consistent naming
👥 Open Source Advocate	Makes knowledge discoverable, contribution-friendly

Output Examples¶

With Solution (links to GitHub):

- [LeetCode 3 - Longest Substring Without Repeating](https://github.com/lufftw/neetcode/blob/main/solutions/0003_longest_substring_without_repeating_characters.py)

Without Solution (links to LeetCode):

- [LeetCode 121 - Best Time to Buy and Sell Stock](https://leetcode.com/problems/best-time-to-buy-and-sell-stock/)

No API Key? No Problem¶

The generator saves the complete prompt to tools/prompts/generated/mindmap_prompt.md. Copy and paste it into ChatGPT, Claude, or any LLM web interface.

📐 Pattern Documentation¶

"Don't memorize 200 problems. Master 10 patterns."

Each API Kernel has a dedicated pattern guide with base template, variations, and copy-paste ready code.

API Kernel	Guide	Problems
`SubstringSlidingWindow`	📖	LeetCode 3, 76, 159, 209, 340, 438, 567
`GridBFSMultiSource`	soon	LeetCode 994, 286, 542
`BacktrackingExploration`	soon	LeetCode 51, 52, 46, 78
`KWayMerge`	soon	LeetCode 23, 21, 88
`BinarySearchBoundary`	soon	LeetCode 4, 33, 34, 35

👉 View All Pattern Guides →

📖 Usage Guide¶

⌨️ VS Code Integration¶

Keyboard Shortcuts:

Shortcut	Action
`Ctrl+Shift+B`	Run all tests for current file
`F5`	Debug with test case #1

Note: Open a solution file in solutions/ before using shortcuts.

Available Tasks (Ctrl+Shift+P → "Tasks: Run Task"):

Task	Description
Run all tests	Execute all test cases
Run case #1 / #2 / #3	Run specific test case
Benchmark	Show execution times
Run all solutions	Compare all implementations
Run with generated (10)	Static + 10 generated cases
Run generated only	Skip static tests
Save failed cases	Auto-save failing inputs

💻 Command Line Interface¶

# Run all test cases
python runner/test_runner.py <problem_name>

# Run specific test case
python runner/case_runner.py <problem_name> <case_number>

# Run with benchmarking
python runner/test_runner.py <problem_name> --benchmark

# Run all solutions
python runner/test_runner.py <problem_name> --all

# Generate random tests
python runner/test_runner.py <problem_name> --generate 10

# Estimate time complexity
python runner/test_runner.py <problem_name> --estimate

📝 Solution File Format¶

# solutions/0001_two_sum.py
from typing import List

class Solution:
    def twoSum(self, nums: List[int], target: int) -> List[int]:
        seen = {}
        for i, num in enumerate(nums):
            complement = target - num
            if complement in seen:
                return [seen[complement], i]
            seen[num] = i
        return []

def solve():
    import sys
    lines = sys.stdin.read().strip().split('\n')

    # Parse input
    nums = list(map(int, lines[0].split(',')))
    target = int(lines[1])

    # Run solution
    result = Solution().twoSum(nums, target)
    print(result)

if __name__ == "__main__":
    solve()

📋 Test File Format¶

Specification	Requirement
Line Ending	LF (Unix format, `\n`)
Encoding	UTF-8
File Ending	Single newline at end
Naming	`{number}_{name}_{case}.in/.out`

Input file (tests/0001_two_sum_1.in):

2,7,11,15
9

Output file (tests/0001_two_sum_1.out):

[0, 1]

🔧 Advanced Features¶

🚀 Multi-Solution Benchmarking¶

Compare multiple approaches for the same problem:

# solutions/0023_merge_k_sorted_lists.py

SOLUTIONS = {
    "default": {
        "method": "mergeKLists_heap",
        "complexity": "O(N log k)",
        "description": "Min Heap approach"
    },
    "divide": {
        "method": "mergeKLists_divide",
        "complexity": "O(N log k)",
        "description": "Divide and Conquer"
    },
    "greedy": {
        "method": "mergeKLists_greedy",
        "complexity": "O(kN)",
        "description": "Greedy comparison"
    },
}

class Solution:
    def mergeKLists_heap(self, lists):
        # Heap implementation
        pass

    def mergeKLists_divide(self, lists):
        # Divide & Conquer implementation
        pass

    def mergeKLists_greedy(self, lists):
        # Greedy implementation
        pass

Run commands:

# Run specific solution
python runner/test_runner.py 0023_merge_k_sorted_lists --method heap

# Compare all solutions
python runner/test_runner.py 0023_merge_k_sorted_lists --all --benchmark

Output:

============================================================
📊 Performance Comparison
============================================================
Method               Avg Time     Complexity      Pass Rate
------------------------------------------------------------
heap                    44.36ms   O(N log k)      3/3
divide                  44.48ms   O(N log k)      3/3
greedy                  44.82ms   O(kN)           3/3
============================================================

Advanced: Wrapper Pattern for Multiple Classes

When you need separate classes with the same method name:

class SolutionRecursive:
    def reverseKGroup(self, head, k):
        pass  # Recursive implementation

class SolutionIterative:
    def reverseKGroup(self, head, k):
        pass  # Iterative implementation

# Wrapper functions
def solve_recursive(head, k):
    return SolutionRecursive().reverseKGroup(head, k)

def solve_iterative(head, k):
    return SolutionIterative().reverseKGroup(head, k)

SOLUTIONS = {
    "default": {"method": "solve_iterative", ...},
    "recursive": {"method": "solve_recursive", ...},
}

Create with template: `new_problem.bat 0025_reverse_nodes --wrapper`

🔀 Flexible Output Validation¶

For problems with multiple valid answers ("return in any order"):

Validation Modes:

Mode	Description	Requires `.out`
`[judge]`	Custom validation with reference	✅
`[judge-only]`	Custom validation only	❌
`[exact]`	Exact string match	✅
`[sorted]`	Sort before comparison	✅
`[set]`	Set comparison	✅

JUDGE_FUNC (Recommended):

def judge(actual: list, expected, input_data: str) -> bool:
    """Validate N-Queens solution."""
    n = int(input_data.strip())

    # Validate each board
    for board in actual:
        if not is_valid_n_queens(board, n):
            return False

    # Check count if expected exists
    if expected is not None:
        return len(actual) == len(expected)

    return True

JUDGE_FUNC = judge

COMPARE_MODE (Simple Cases):

COMPARE_MODE = "sorted"  # Options: "exact" | "sorted" | "set"

🎲 Random Test Generation¶

Create a generator file with the same name as your solution:

# generators/0004_median_of_two_sorted_arrays.py
import random
from typing import Iterator, Optional

def generate(count: int = 10, seed: Optional[int] = None) -> Iterator[str]:
    """Generate random test cases."""
    if seed is not None:
        random.seed(seed)

    # Edge cases first
    yield "[]\n[1]"
    yield "[1]\n[]"

    # Random cases
    for _ in range(count - 2):
        m = random.randint(0, 1000)
        n = random.randint(0, 1000)
        nums1 = sorted(random.randint(-10**6, 10**6) for _ in range(m))
        nums2 = sorted(random.randint(-10**6, 10**6) for _ in range(n))
        yield f"{list(nums1)}\n{list(nums2)}".replace(' ', '')

Usage:

# Run static + generated tests
python runner/test_runner.py 0004_median --generate 10

# Only generated tests
python runner/test_runner.py 0004_median --generate-only 100

# Reproducible with seed
python runner/test_runner.py 0004_median --generate 10 --seed 42

# Save failing cases
python runner/test_runner.py 0004_median --generate 10 --save-failed

📈 Time Complexity Estimation¶

Add a complexity generator function:

# generators/0004_median_of_two_sorted_arrays.py

def generate_for_complexity(n: int) -> str:
    """Generate test case with specific size n."""
    m = random.randint(0, n)
    return _generate_case(m, n - m)

Run estimation:

python runner/test_runner.py 0004_median --estimate

Output:

📈 Running complexity estimation...
   Sizes: [10, 20, 50, 100, 200, 500, 1000, 2000]
   n=   10: 0.0040ms
   n=  100: 0.0082ms
   n= 1000: 0.0685ms
   n= 2000: 0.1796ms

✅ Estimated: O(n log n)
   Confidence: 1.00

📁 Project Architecture¶

neetcode/
│
├── solutions/                 # 📝 Your solution files
│   └── 0001_two_sum.py
│
├── tests/                     # 📋 Test cases
│   ├── 0001_two_sum_1.in      # Input file
│   ├── 0001_two_sum_1.out     # Expected output
│   └── *_failed_*.in          # Auto-saved failed cases (--save-failed)
│
├── generators/                # 🎲 Random test generators (optional)
│   └── 0001_two_sum.py        # generate(count, seed) function
│
├── runner/                    # ⚙️ Test execution engine
│   ├── test_runner.py         # CLI entry point & main orchestration
│   ├── case_runner.py         # Single case runner (for debugging)
│   ├── executor.py            # Test case execution (subprocess)
│   ├── compare.py             # Output comparison (exact/sorted/set/judge)
│   ├── reporter.py            # Result formatting & benchmark display
│   ├── module_loader.py       # Dynamic module loading
│   ├── complexity_estimator.py # Time complexity estimation (big_O)
│   ├── paths.py               # Path utilities
│   ├── io_utils.py            # File I/O operations
│   └── util.py                # Re-exports (backward compatible)
│
├── templates/                 # 📄 Problem templates
│   ├── template_solution.py          # Single solution
│   ├── template_solution_multi.py    # Multi-solution (one class)
│   └── template_solution_wrapper.py  # Multi-solution (wrapper pattern)
│
├── .vscode/                   # 🔧 VS Code integration
│   ├── settings.json          # Python environment settings
│   ├── tasks.json             # Ctrl+Shift+B shortcuts
│   └── launch.json            # F5 debug configurations
│
├── docs/                      # 📚 Documentation (MkDocs)
│   ├── index.md               # Homepage (English)
│   ├── index_zh-TW.md         # Homepage (繁體中文)
│   ├── mindmaps/              # Generated mind map markdown
│   ├── patterns/              # Generated pattern documentation
│   ├── pages/                 # Generated HTML (gitignored)
│   └── stylesheets/           # Custom CSS
│
├── tools/                     # 🛠️ Utility scripts
│   ├── generate_mindmaps_ai.py   # 🤖 AI mind map generator
│   ├── mindmap_ai_config.toml    # AI generation configuration
│   ├── generate_mindmaps.py      # Rule-based mind map generator
│   ├── generate_mindmaps.toml    # Rule-based configuration
│   ├── generate_pattern_docs.py  # Generate pattern docs
│   └── prompts/                  # AI prompt management
│       ├── README.md             # Prompt documentation
│       └── generated/            # Auto-generated prompts
│
├── ontology/                  # 🧬 Algorithm ontology (TOML)
│   ├── api_kernels.toml       # API kernel definitions
│   ├── patterns.toml          # Pattern definitions
│   ├── algorithms.toml        # Algorithm definitions
│   ├── data_structures.toml   # Data structure definitions
│   ├── companies.toml         # Company definitions
│   ├── topics.toml            # Topic definitions
│   ├── difficulties.toml      # Difficulty levels
│   ├── families.toml          # Problem family definitions
│   └── roadmaps.toml          # Roadmap definitions
│
├── meta/                      # 📊 Problem & pattern metadata
│   ├── problems/              # Problem metadata (one TOML per problem)
│   │   └── *.toml
│   └── patterns/              # Pattern documentation sources
│       └── <pattern_name>/    # Pattern-specific markdown
│
├── roadmaps/                  # 🗺️ Learning path definitions
│   ├── neetcode_150.toml
│   ├── blind_75.toml
│   └── sliding_window_path.toml
│
├── .dev/                      # 🧪 Maintainer zone (unit tests)
│   ├── tests/                 # Unit test suite (150+ cases)
│   ├── run_tests.bat/.sh      # Run unit tests
│   ├── TESTING.md             # Testing documentation
│   └── README.md              # Maintainer guide
│
├── .github/                   # 🚀 GitHub configuration
│   └── workflows/
│       └── deploy-pages.yml   # GitHub Pages deployment
│
├── leetcode/                  # 🐍 Python virtual environment (3.11)
│
├── run_tests.bat / .sh        # Run all tests for a problem
├── run_case.bat / .sh         # Run single test case
├── new_problem.bat / .sh      # Create new problem from template
│
├── requirements.txt           # Python dependencies
├── mkdocs.yml                 # MkDocs configuration
├── pytest.ini                 # pytest configuration
├── README.md                  # This file (English)
└── README_zh-TW.md            # 繁體中文版

Directory Guide¶

Directory	Purpose	Target Audience
`solutions/`	Write your solutions here	✅ All users
`tests/`	Add test cases (.in/.out)	✅ All users
`generators/`	Random test generators	✅ All users
`runner/`	Test execution engine	🔧 Contributors
`templates/`	Problem templates	✅ All users
`.vscode/`	VS Code configuration	✅ All users
`docs/`	MkDocs documentation	🔧 Contributors
`tools/`	Documentation generators	🔧 Contributors
`ontology/`	Algorithm ontology data	🔧 Contributors
`meta/`	Problem/pattern metadata	🔧 Contributors
`.dev/`	Unit tests (150+ cases)	🔧 Maintainers

📝 Note: Files in docs/mindmaps/, docs/patterns/, and docs/pages/ are auto-generated. Edit the source files in ontology/, meta/, and tools/ instead.

❓ Frequently Asked Questions¶

What problems does this framework solve?

- Running multiple algorithm implementations automatically - Generating reproducible random test data for stress testing - Benchmarking solutions to identify performance differences - Debugging LeetCode-style problems with VS Code integration - Validating outputs using custom logic beyond simple file comparison

How is this different from copying LeetCode solutions?

This is not a solution collection — it's a **testing infrastructure**. You write solutions, and the framework: 1. Runs them against static test cases 2. Generates random test cases automatically 3. Validates correctness using custom judge functions 4. Benchmarks multiple solutions against each other 5. Estimates time complexity empirically

Can I use this for interview preparation?

Absolutely! The framework is perfect for interview prep: - Practice writing solutions in **real LeetCode format** - Find **edge cases you might miss** with random test generation - See which approach is **actually faster** with benchmarking - **Debug easily** with VS Code integration

What Python version is required?

Python 3.11 — matching the [LeetCode official environment](https://support.leetcode.com/hc/en-us/articles/360011833974-What-are-the-environments-for-the-programming-languages).

🛠️ For Contributors¶

Running Unit Tests¶

# Activate virtual environment
leetcode\Scripts\activate  # Windows
source leetcode/bin/activate  # Linux/macOS

# Run all tests
python -m pytest .dev/tests -v

# With coverage
python -m pytest .dev/tests --cov=runner --cov-report=html

Generate Mind Maps Locally¶

AI-Powered (Recommended):

# Interactive mode
python tools/generate_mindmaps_ai.py

# With specific goal
python tools/generate_mindmaps_ai.py --goal interview

# Generate multiple languages
# Edit tools/mindmap_ai_config.toml: language = ["en", "zh-TW"]
python tools/generate_mindmaps_ai.py

Configuration: tools/mindmap_ai_config.toml

Rule-Based:

# Generate Markdown mind maps
python tools/generate_mindmaps.py

# Generate HTML (interactive) mind maps
python tools/generate_mindmaps.py --html

Configuration: tools/generate_mindmaps.toml

Documentation¶

.dev/README.md — Maintainer guide
.dev/TESTING.md — Testing documentation
docs/GITHUB_PAGES_SETUP.md — Deployment guide

📜 License¶

MIT License — Free for personal learning and educational use.

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