Metadata-Version: 2.4
Name: polysolve
Version: 0.4.1
Summary: A Python library for representing, manipulating, and solving exponential functions using analytical methods and genetic algorithms, with optional CUDA acceleration.
Author-email: Jonathan Rampersad <jonathan@jono-rams.work>
License: MIT License
        
        Copyright (c) 2025 Jonathan Rampersad
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and 
        associated documentation files (the "Software"), to deal in the Software without restriction, including 
        without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 
        copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the 
        following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial 
        portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
        LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO 
        EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 
        IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE 
        USE OR OTHER DEALINGS IN THE SOFTWARE.
        
Project-URL: Homepage, https://polysolve.jono-rams.work
Project-URL: Documentation, https://polysolve.jono-rams.work/docs
Project-URL: Repository, https://github.com/jono-rams/PolySolve
Project-URL: Bug Tracker, https://github.com/jono-rams/PolySolve/issues
Keywords: math,polynomial,genetic algorithm,cuda,equation solver
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21
Provides-Extra: cuda12
Requires-Dist: cupy-cuda12x; extra == "cuda12"
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Dynamic: license-file

<p align="center">
  <img src="https://i.ibb.co/N22Gx6xq/Poly-Solve-Logo.png" alt="polysolve Logo" width="256">
</p>

[![PyPI version](https://img.shields.io/pypi/v/polysolve.svg)](https://pypi.org/project/polysolve/)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/polysolve.svg)](https://pypi.org/project/polysolve/)

A Python library for representing, manipulating, and solving polynomial equations using a high-performance genetic algorithm, with optional CUDA/GPU acceleration.

---

## Key Features

* **Create and Manipulate Polynomials**: Easily define polynomials of any degree using integer or float coefficients, and perform arithmetic operations like addition, subtraction, multiplication, and scaling.
* **Genetic Algorithm Solver**: Find approximate real roots for complex polynomials where analytical solutions are difficult or impossible.
* **CUDA Accelerated**: Leverage NVIDIA GPUs for a massive performance boost when finding roots in large solution spaces.
* **Analytical Solvers**: Includes standard, exact solvers for simple cases (e.g., `quadratic_solve`).
* **Simple API**: Designed to be intuitive and easy to integrate into any project.

---

## Installation

Install the base package from PyPI:

```bash
pip install polysolve
```

### CUDA Acceleration

To enable GPU acceleration, install the extra that matches your installed NVIDIA CUDA Toolkit version. This provides a significant speedup for the genetic algorithm.

**For CUDA 12.x users:**
```bash
pip install polysolve[cuda12]
```

---

## Quick Start

Here is a simple example of how to define a quadratic function, find its properties, and solve for its roots.

```python
from polysolve import Function, GA_Options

# 1. Define the function f(x) = 2x^2 - 3x - 5
#    Coefficients can be integers or floats.
f1 = Function(largest_exponent=2)
f1.set_coeffs([2, -3, -5])

print(f"Function f1: {f1}")
# > Function f1: 2x^2 - 3x - 5

# 2. Solve for y at a given x
y_val = f1.solve_y(5)
print(f"Value of f1 at x=5 is: {y_val}")
# > Value of f1 at x=5 is: 30.0

# 3. Get the derivative: 4x - 3
df1 = f1.derivative()
print(f"Derivative of f1: {df1}")
# > Derivative of f1: 4x - 3

# 4. Get the 2nd derivative: 4
ddf1 = f1.nth_derivative(2)
print(f"2nd Derivative of f1: {ddf1}")
# > Derivative of f1: 4

# 5. Find roots analytically using the quadratic formula
#    This is exact and fast for degree-2 polynomials.
roots_analytic = f1.quadratic_solve()
print(f"Analytic roots: {sorted(roots_analytic)}")
# > Analytic roots: [-1.0, 2.5]

# 6. Find roots with the genetic algorithm (CPU)
#    This can solve polynomials of any degree.
ga_opts = GA_Options(num_of_generations=20)
roots_ga = f1.get_real_roots(ga_opts, use_cuda=False)
print(f"Approximate roots from GA: {roots_ga[:2]}")
# > Approximate roots from GA: [-1.000..., 2.500...]

# If you installed a CUDA extra, you can run it on the GPU:
# roots_ga_gpu = f1.get_real_roots(ga_opts, use_cuda=True)
# print(f"Approximate roots from GA (GPU): {roots_ga_gpu[:2]}")

```

---

## Tuning the Genetic Algorithm

The `GA_Options` class gives you fine-grained control over the genetic algorithm's performance, letting you trade speed for accuracy.

The default options are balanced, but for very complex polynomials, you may want a more exhaustive search.

```python
from polysolve import GA_Options

# Create a config for a much deeper, more accurate search
# (slower, but better for high-degree, complex functions)
ga_accurate = GA_Options(
    num_of_generations=50,  # Run for more generations
    data_size=500000,       # Use a larger population
    elite_ratio=0.1,        # Keep the top 10%
    mutation_ratio=0.5      # Mutate 50%
)

# Pass the custom options to the solver
roots = f1.get_real_roots(ga_accurate)
```

For a full breakdown of all parameters, including crossover_ratio, mutation_strength, and more, please see [the full GA_Options API Documentation](https://polysolve.jono-rams.work/docs/ga-options-api).

---

## Development & Testing Environment

This project is automatically tested against a specific set of dependencies to ensure stability. Our Continuous Integration (CI) pipeline runs on an environment using **CUDA 12.5** on **Ubuntu 24.04**.

While the code may work on other configurations, all contributions must pass the automated tests in our reference environment. For detailed information on how to replicate the testing environment, please see our [**Contributing Guide**](CONTRIBUTING.md).

## Contributing

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
[![GitHub issues](https://img.shields.io/github/issues/jono-rams/PolySolve.svg?style=flat-square)](https://github.com/jono-rams/PolySolve/issues)
[![GitHub pull requests](https://img.shields.io/github/issues-pr/jono-rams/PolySolve.svg?style=flat-square)](https://github.com/jono-rams/PolySolve/pulls)

Contributions are welcome! Whether it's a bug report, a feature request, or a pull request, please feel free to get involved.

Please read our `CONTRIBUTING.md` file for details on our code of conduct and the process for submitting pull requests.

## Contributors

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      <td align="center" valign="top" width="14.28%"><a href="https://jono-rams.work"><img src="https://avatars.githubusercontent.com/u/29872001?v=4?s=100" width="100px;" alt="Jonathan Rampersad"/><br /><sub><b>Jonathan Rampersad</b></sub></a><br /><a href="https://github.com/jono-rams/PolySolve/commits?author=jono-rams" title="Maintenance">🚧</a> <a href="https://github.com/jono-rams/PolySolve/commits?author=jono-rams" title="Code">💻</a> <a href="https://github.com/jono-rams/PolySolve/commits?author=jono-rams" title="Documentation">📖</a> <a href="#infra-jono-rams" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td>
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## License

This project is licensed under the MIT License - see the `LICENSE` file for details.
