# Changelog

All notable changes to the SVM Classifier C++ project will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [Unreleased]

### Planned
- Feature importance extraction for linear kernels
- Model serialization and persistence
- CUDA GPU acceleration support
- Python bindings via pybind11
- Sparse matrix support optimization
- Online learning capabilities

## [1.0.0] - 2024-12-XX

### Added
- Initial release of SVM Classifier C++
- **Core Features**
  - Support Vector Machine classifier with scikit-learn compatible API
  - Multiple kernel support: Linear, RBF, Polynomial, Sigmoid
  - Automatic library selection: liblinear for linear, libsvm for non-linear
  - Multiclass classification: One-vs-Rest (OvR) and One-vs-One (OvO) strategies
  - Native PyTorch tensor integration
  - JSON-based parameter configuration using nlohmann::json
  
- **API Methods**
  - `fit()`: Train the classifier on labeled data
  - `predict()`: Predict class labels for new samples
  - `predict_proba()`: Predict class probabilities (when supported)
  - `score()`: Calculate accuracy on test data
  - `decision_function()`: Get decision function values
  - `cross_validate()`: K-fold cross-validation
  - `grid_search()`: Hyperparameter optimization
  - `evaluate()`: Comprehensive evaluation metrics

- **Data Handling**
  - Efficient tensor to SVM format conversion
  - Automatic CPU/GPU tensor handling
  - Sparse feature support with configurable threshold
  - Memory-efficient data structures
  - Support for various tensor data types

- **Kernel Support**
  - **Linear**: Fast, optimized for high-dimensional data
  - **RBF**: Radial Basis Function with auto/manual gamma
  - **Polynomial**: Configurable degree and coefficients
  - **Sigmoid**: Neural network-like kernel

- **Multiclass Strategies**
  - **One-vs-Rest**: Faster training, good for many classes
  - **One-vs-One**: Better accuracy, voting-based prediction

- **Testing & Quality**
  - Comprehensive test suite with Catch2
  - Unit tests for all components
  - Integration tests for end-to-end workflows
  - Performance benchmarks and profiling
  - Memory leak detection with Valgrind
  - Code coverage analysis with lcov
  - Cross-platform compatibility (Linux, macOS, Windows)

- **Build System**
  - Modern CMake build system (3.15+)
  - Automatic dependency management
  - Multiple build configurations (Debug, Release, RelWithDebInfo)
  - Package generation with CPack
  - Docker support for containerized builds
  - Automated installation script

- **Documentation**
  - Comprehensive README with usage examples
  - Quick start guide for immediate productivity
  - Development guide for contributors
  - API documentation with Doxygen
  - Performance benchmarking results
  - Troubleshooting and FAQ sections

- **Examples & Demos**
  - Basic usage example with simple dataset
  - Advanced usage with hyperparameter tuning
  - Performance comparison between kernels
  - Cross-validation and model evaluation
  - Feature preprocessing demonstrations
  - Imbalanced dataset handling

- **CI/CD Pipeline**
  - GitHub Actions workflow
  - Multi-platform testing (Ubuntu, macOS)
  - Multiple compiler support (GCC, Clang)
  - Automated testing and validation
  - Code quality checks (formatting, static analysis)
  - Documentation generation and deployment
  - Release automation

- **Development Tools**
  - clang-format configuration for consistent code style
  - clang-tidy setup for static analysis
  - Doxygen configuration for documentation
  - Docker development environment
  - Comprehensive validation script
  - Performance profiling tools

### Technical Details

- **Language**: C++17 with modern C++ practices
- **Dependencies**: 
  - libtorch (PyTorch C++) for tensor operations
  - libsvm for non-linear SVM algorithms
  - liblinear for efficient linear classification
  - nlohmann::json for configuration management
  - Catch2 for testing framework
- **Architecture**: Modular design with clear separation of concerns
- **Memory Management**: RAII principles, automatic resource cleanup
- **Error Handling**: Exception-based with meaningful error messages
- **Performance**: Optimized data conversion, efficient memory usage

### Supported Platforms

- **Linux**: Ubuntu 18.04+, CentOS 7+, Debian 9+
- **macOS**: 10.14+ (Mojave and later)
- **Windows**: Windows 10 with Visual Studio 2019+

### Performance Characteristics

- **Linear Kernel**: Handles datasets up to 100K+ samples efficiently
- **RBF Kernel**: Optimized for datasets up to 10K samples
- **Memory Usage**: Scales linearly with dataset size
- **Training Speed**: Competitive with scikit-learn for equivalent operations
- **Prediction Speed**: Sub-millisecond prediction for individual samples

### Compatibility

- **Compiler Support**: GCC 7+, Clang 5+, MSVC 2019+
- **CMake**: Version 3.15 or higher required
- **PyTorch**: Compatible with libtorch 1.9+ and 2.x series
- **Standards**: Follows C++17 standard, forward compatible with C++20

## [0.9.0] - 2024-11-XX (Beta Release)

### Added
- Core SVM classifier implementation
- Basic kernel support (Linear, RBF)
- Initial multiclass support
- Proof-of-concept examples
- Basic test suite

### Known Issues
- Limited documentation
- Performance not optimized
- Missing advanced features

## [0.5.0] - 2024-10-XX (Alpha Release)

### Added
- Project structure and build system
- Initial CMake configuration
- Basic tensor conversion utilities
- Preliminary API design

### Development Notes
- Focus on architecture and design
- Establishing coding standards
- Setting up CI/CD pipeline

---

## Contributing

See [DEVELOPMENT.md](DEVELOPMENT.md) for information about contributing to this project.

## Migration Guide

### From scikit-learn

If you're migrating from scikit-learn, here are the key differences:

```python
# scikit-learn (Python)
from sklearn.svm import SVC
svm = SVC(kernel='rbf', C=1.0, gamma='auto')
svm.fit(X, y)
predictions = svm.predict(X_test)
probabilities = svm.predict_proba(X_test)
accuracy = svm.score(X_test, y_test)
```

```cpp
// SVM Classifier C++
#include <svm_classifier/svm_classifier.hpp>
using namespace svm_classifier;

json config = {{"kernel", "rbf"}, {"C", 1.0}, {"gamma", "auto"}};
SVMClassifier svm(config);
auto metrics = svm.fit(X, y);
auto predictions = svm.predict(X_test);
auto probabilities = svm.predict_proba(X_test);
double accuracy = svm.score(X_test, y_test);
```

### API Mapping

| scikit-learn | SVM Classifier C++ | Notes |
|--------------|-------------------|-------|
| `SVC()` | `SVMClassifier()` | Constructor with similar parameters |
| `fit(X, y)` | `fit(X, y)` | Returns training metrics |
| `predict(X)` | `predict(X)` | Returns torch::Tensor |
| `predict_proba(X)` | `predict_proba(X)` | Returns torch::Tensor |
| `score(X, y)` | `score(X, y)` | Returns double accuracy |
| `decision_function(X)` | `decision_function(X)` | Returns torch::Tensor |

## Acknowledgments

This project builds upon the excellent work of:

- **libsvm** by Chih-Chung Chang and Chih-Jen Lin
- **liblinear** by the LIBLINEAR Project team
- **PyTorch** by Facebook AI Research
- **nlohmann::json** by Niels Lohmann
- **Catch2** by the Catch2 team
- **scikit-learn** for API inspiration

Special thanks to the open-source community for their invaluable tools and libraries.