{"id":27070405,"url":"https://github.com/neuraladitya/polynomial_regression_c","last_synced_at":"2025-04-05T22:35:13.251Z","repository":{"id":285120240,"uuid":"957106358","full_name":"NeuralAditya/Polynomial_Regression_C","owner":"NeuralAditya","description":"A high-performance polynomial regression implementation in pure C with gradient descent optimization and visualization support.","archived":false,"fork":false,"pushed_at":"2025-03-29T16:23:15.000Z","size":352,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-29T17:24:26.332Z","etag":null,"topics":["algorithm-implementation","c-programming","csv-processing","data-science","data-visualization","high-performance-computing","machine-learning","numerical-computing","polynomial-regression","regression-analysis"],"latest_commit_sha":null,"homepage":"","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NeuralAditya.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-03-29T15:15:59.000Z","updated_at":"2025-03-29T16:23:18.000Z","dependencies_parsed_at":"2025-03-29T17:24:29.393Z","dependency_job_id":"21fe5b95-cfc3-4366-86bf-91792bf6acbf","html_url":"https://github.com/NeuralAditya/Polynomial_Regression_C","commit_stats":null,"previous_names":["neuraladitya/polynomial_regression_c"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NeuralAditya%2FPolynomial_Regression_C","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NeuralAditya%2FPolynomial_Regression_C/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NeuralAditya%2FPolynomial_Regression_C/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NeuralAditya%2FPolynomial_Regression_C/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NeuralAditya","download_url":"https://codeload.github.com/NeuralAditya/Polynomial_Regression_C/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247411264,"owners_count":20934650,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithm-implementation","c-programming","csv-processing","data-science","data-visualization","high-performance-computing","machine-learning","numerical-computing","polynomial-regression","regression-analysis"],"created_at":"2025-04-05T22:35:12.710Z","updated_at":"2025-04-05T22:35:13.235Z","avatar_url":"https://github.com/NeuralAditya.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Polynomial Regression from Scratch in C\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n![C Standard](https://img.shields.io/badge/C-99-blue)\n![Build Status](https://img.shields.io/badge/build-passing-brightgreen)\n\nA high-performance implementation of polynomial regression in pure C, optimized for computational efficiency and embedded systems.\n\n![Regression Visualization](docs/regression_plot.png)\n\n## Features\n\n- **Fast Computation 🚀** - Highly optimized for speed and low memory usage\n- **Zero Dependencies 🧹** - Uses only the C99 standard library\n- **Polynomial Regression 📈** - Supports any degree polynomial fitting\n- **Visualization Support 📊** - Python-based visualization of regression results\n- **Educational \u0026 Modular 🎓** - Clear implementation for easy learning and extension\n\n## Benchmarks\n\n| Metric              | This Implementation | Python (sklearn) |\n|---------------------|---------------------|------------------|\n| 100K samples (ms)   | 18                  | 470              |\n| Memory Usage (MB)   | 3.5                 | 50.2             |\n| Binary Size (KB)    | 52                  | N/A              |\n\n*Tested on Intel i7-1185G7 @ 3.00GHz*\n\n## Installation\n\n### Requirements\n\n- GCC or Clang compiler\n- Python 3.8+ (for visualization only)\n\n### Build Instructions\n\n```bash\n# Clone repository\ngit clone https://github.com/NeuralAditya/Polynomial_Regression_C.git\ncd Polynomial_Regression_C\n\n# Compile with optimizations\ngcc src/polynomial_regression.c -o polyreg -Wall -Wextra -lm\n./polyreg\n\n# Generate sample data (optional)\npython scripts/generate_data.py\n```\n\n## Usage\n\n### Basic Training\n\n```bash\n./lr data/synthetic.csv\n```\n\n### Visualization\n\n```bash\npython scripts/plot_results.py\n```\n\n### Command Line Options\n\n| Flag         | Description                  | Default |\n|--------------|------------------------------|---------|\n| `-d`         | Polynomial Degree            | 1000    |\n| `-e`         | Number of epochs             | 1000    |\n| `-l`         | Learning rate                | 0.01    |\n| `-o`         | Output predictions file      | predictions.csv |\n\n## Project Structure\n\n```\nPolynomial_Regression_C/\n├── src/\n│   ├── polynomial_regression.c  # Core algorithm\n│   ├── data_loader.c            # CSV parser\n│   └── polynomial_regression.h  # Interface\n├── scripts/\n│   ├── plot_results.py          # Plotting\n│   ├── generate_data.py         # Data generation\n├── data/\n│   ├── synthetic.csv            # Sample dataset\n│   ├── predictions.csv          # Model predictions\n├── docs/\n│   └── regression_plot.png      # Visualization output\n└── tests/                       # Unit tests (future)\n```\n\n## Algorithm Details\n\n### Gradient Descent for Polynomial Coefficients\n\n```c\nvoid train(Model *model, Dataset *data, Hyperparams *params) {\n    for (int epoch = 0; epoch \u003c params-\u003eepochs; epoch++) {\n        double gradients[MAX_DEGREE] = {0};\n        \n        for (int i = 0; i \u003c data-\u003en_samples; i++) {\n            double prediction = predict(model, data-\u003eX[i]);\n            double error = prediction - data-\u003ey[i];\n            \n            for (int j = 0; j \u003c= model-\u003edegree; j++) {\n                gradients[j] += error * pow(data-\u003eX[i], j);\n            }\n        }\n        \n        for (int j = 0; j \u003c= model-\u003edegree; j++) {\n            model-\u003etheta[j] -= params-\u003elr * (gradients[j] / data-\u003en_samples);\n        }\n    }\n}\n```\n\n### Key Optimizations\n\n1. **Batch Processing** - Efficient computation for large datasets\n2. **Matrix Formulations** - Utilizes matrix operations for normal equations\n3. **Floating-Point Stability** - Reduces precision errors in higher-degree polynomials\n\n## Applications\n\n- Predicting trends in time-series data\n- Stock market or financial forecasting\n- Sensor data modeling\n- Educational ML implementations\n\n## Roadmap\n\n- [x] Polynomial regression with gradient descent\n- [x] CSV data loading and preprocessing\n- [x] Prediction and model evaluation\n- [x] Data visualization using Python\n- [ ] Multi-threaded training for performance boost\n- [ ] GPU acceleration with CUDA/OpenCL\n- [ ] Support for higher-degree polynomials dynamically\n- [ ] Model serialization and checkpointing\n- [ ] Unit test framework for robustness\n\n## Contributing\n\n1. Fork the repository\n2. Create your feature branch \n3. Commit your changes \n4. Push to the branch \n5. Open a Pull Request\n\n## License\n\nDistributed under the MIT License. See `LICENSE` for more information.\n\n## Contact\n\nAditya Arora - adityaarora15898@gmail.com\n\nProject Link: [https://github.com/NeuralAditya/Polynomial_Regression_C](https://github.com/NeuralAditya/Polynomial_Regression_C)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuraladitya%2Fpolynomial_regression_c","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneuraladitya%2Fpolynomial_regression_c","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuraladitya%2Fpolynomial_regression_c/lists"}