https://github.com/neuraladitya/polynomial_regression_c
A high-performance polynomial regression implementation in pure C with gradient descent optimization and visualization support.
https://github.com/neuraladitya/polynomial_regression_c
algorithm-implementation c-programming csv-processing data-science data-visualization high-performance-computing machine-learning numerical-computing polynomial-regression regression-analysis
Last synced: about 2 months ago
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A high-performance polynomial regression implementation in pure C with gradient descent optimization and visualization support.
- Host: GitHub
- URL: https://github.com/neuraladitya/polynomial_regression_c
- Owner: NeuralAditya
- Created: 2025-03-29T15:15:59.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-29T16:23:15.000Z (about 2 months ago)
- Last Synced: 2025-03-29T17:24:26.332Z (about 2 months ago)
- Topics: algorithm-implementation, c-programming, csv-processing, data-science, data-visualization, high-performance-computing, machine-learning, numerical-computing, polynomial-regression, regression-analysis
- Language: C
- Homepage:
- Size: 344 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Polynomial Regression from Scratch in C
[](https://opensource.org/licenses/MIT)
A high-performance implementation of polynomial regression in pure C, optimized for computational efficiency and embedded systems.
## Features
- **Fast Computation 🚀** - Highly optimized for speed and low memory usage
- **Zero Dependencies 🧹** - Uses only the C99 standard library
- **Polynomial Regression 📈** - Supports any degree polynomial fitting
- **Visualization Support 📊** - Python-based visualization of regression results
- **Educational & Modular 🎓** - Clear implementation for easy learning and extension
## Benchmarks
| Metric | This Implementation | Python (sklearn) |
|---------------------|---------------------|------------------|
| 100K samples (ms) | 18 | 470 |
| Memory Usage (MB) | 3.5 | 50.2 |
| Binary Size (KB) | 52 | N/A |
*Tested on Intel i7-1185G7 @ 3.00GHz*
## Installation
### Requirements
- GCC or Clang compiler
- Python 3.8+ (for visualization only)
### Build Instructions
```bash
# Clone repository
git clone https://github.com/NeuralAditya/Polynomial_Regression_C.git
cd Polynomial_Regression_C
# Compile with optimizations
gcc src/polynomial_regression.c -o polyreg -Wall -Wextra -lm
./polyreg
# Generate sample data (optional)
python scripts/generate_data.py
```
## Usage
### Basic Training
```bash
./lr data/synthetic.csv
```
### Visualization
```bash
python scripts/plot_results.py
```
### Command Line Options
| Flag | Description | Default |
|--------------|------------------------------|---------|
| `-d` | Polynomial Degree | 1000 |
| `-e` | Number of epochs | 1000 |
| `-l` | Learning rate | 0.01 |
| `-o` | Output predictions file | predictions.csv |
## Project Structure
```
Polynomial_Regression_C/
├── src/
│ ├── polynomial_regression.c # Core algorithm
│ ├── data_loader.c # CSV parser
│ └── polynomial_regression.h # Interface
├── scripts/
│ ├── plot_results.py # Plotting
│ ├── generate_data.py # Data generation
├── data/
│ ├── synthetic.csv # Sample dataset
│ ├── predictions.csv # Model predictions
├── docs/
│ └── regression_plot.png # Visualization output
└── tests/ # Unit tests (future)
```
## Algorithm Details
### Gradient Descent for Polynomial Coefficients
```c
void train(Model *model, Dataset *data, Hyperparams *params) {
for (int epoch = 0; epoch < params->epochs; epoch++) {
double gradients[MAX_DEGREE] = {0};
for (int i = 0; i < data->n_samples; i++) {
double prediction = predict(model, data->X[i]);
double error = prediction - data->y[i];
for (int j = 0; j <= model->degree; j++) {
gradients[j] += error * pow(data->X[i], j);
}
}
for (int j = 0; j <= model->degree; j++) {
model->theta[j] -= params->lr * (gradients[j] / data->n_samples);
}
}
}
```
### Key Optimizations
1. **Batch Processing** - Efficient computation for large datasets
2. **Matrix Formulations** - Utilizes matrix operations for normal equations
3. **Floating-Point Stability** - Reduces precision errors in higher-degree polynomials
## Applications
- Predicting trends in time-series data
- Stock market or financial forecasting
- Sensor data modeling
- Educational ML implementations
## Roadmap
- [x] Polynomial regression with gradient descent
- [x] CSV data loading and preprocessing
- [x] Prediction and model evaluation
- [x] Data visualization using Python
- [ ] Multi-threaded training for performance boost
- [ ] GPU acceleration with CUDA/OpenCL
- [ ] Support for higher-degree polynomials dynamically
- [ ] Model serialization and checkpointing
- [ ] Unit test framework for robustness
## Contributing
1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request
## License
Distributed under the MIT License. See `LICENSE` for more information.
## Contact
Aditya Arora - [email protected]
Project Link: [https://github.com/NeuralAditya/Polynomial_Regression_C](https://github.com/NeuralAditya/Polynomial_Regression_C)