chore: add design docs and repository files#1
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## Core Implementation - Add complete differentiable convex hull computation using direction vector sampling - Implement ConvexHull class with comprehensive object-oriented API - Add geometric predicates: point inclusion, volume, surface area, distance computations - Ensure full JAX compatibility with jit, grad, vmap, and pmap transformations ## Algorithms & Methods - Direction vector sampling: uniform, icosphere (3D), adaptive (placeholder) - Soft selection using temperature-controlled softmax for differentiability - Multi-resolution hull computation with progressive refinement - Batch processing support for efficient parallel computation - Quality metrics for hull approximation assessment ## Documentation & Examples - Add comprehensive Sphinx documentation with RTD theme - Create getting started guide and installation instructions - Implement API reference with detailed function documentation - Add practical code examples: basic usage, JAX integration, optimization - Create interactive Jupyter notebook tutorials with visualizations ## Testing & Quality - Implement comprehensive test suite with 135+ tests - Add JAX compatibility tests ensuring JIT compilation support - Create unit tests for core utilities, algorithms, and predicates - Add integration tests for ConvexHull class and API consistency - Include edge case testing for numerical stability ## Fixes & Improvements - Fix JAX JIT compatibility issues with boolean array indexing - Resolve PyTree registration conflicts for multiple imports - Improve numerical stability in geometric computations - Enhance error handling and input validation - Ensure elite-level English consistency throughout codebase ## Infrastructure - Add Phase 1 implementation plan documents (EN/JP) - Update CHANGELOG with comprehensive feature documentation - Create professional examples directory with working code samples - Establish proper project structure for scalability This release provides a solid foundation for differentiable convex hull computation with focus on performance, usability, and JAX ecosystem integration. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Fix docstring punctuation and formatting issues - Remove unused imports and variables - Replace lambda expressions with proper function definitions - Fix function default argument issues with mutable types - Rename unused loop variables to follow conventions - Fix whitespace and formatting issues - Improve overall code quality and consistency All examples now pass linting checks and maintain functionality. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Fix pyproject.toml setuptools configuration - Add missing docs/build_docs.py for documentation builds - Disable codecov uploads (remove invalid repository condition) - Fix 604+ linting issues with ruff --fix - Add noqa comments for public API imports - Replace bare except with specific exception handling - Use contextlib.suppress for cleaner error handling 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Implement lazy loading in __init__.py files to break circular imports - Add __getattr__ methods for deferred imports in core and algorithms modules - Move direct imports to lazy loading with function-level imports - Fix myst-parser configuration in docs/conf.py - All core functionality now loads without circular dependency errors - Tests pass successfully with new import structure 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Replace if-elif chains with dictionary lookups in __getattr__ methods - Improves code readability and follows SIM116 linting rule - All linting checks now pass without errors 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Move direct import of approximate_convex_hull to lazy import in from_points method - This completes the circular import resolution - All module imports now work without circular dependency errors - Tests can now run successfully 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Add clear project description with badges and key features - Include practical code examples with both functional and OOP APIs - Show differentiable optimization example demonstrating JAX integration - Add comparison table highlighting advantages over existing libraries - Include roadmap, use cases, and contribution guidelines - Maintain concise but informative structure for better user engagement - All code examples verified to work with current implementation 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
- Fix input validation with NaN/infinite value detection - Resolve circular import issues with lazy loading pattern - Fix batch processing vmap axis specification errors - Update JAX compatibility (tree_map → tree.map) - Fix cache mechanism and pytree registration - Add comprehensive remediation plan documentation Tests now pass: - Input validation (NaN/inf detection) - Batch processing (all vmap operations) - JAX compatibility (tree transformations) - Cache mechanism (volume/surface area) - Predicates validation (dimension checking) 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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