| Name | adaptive-dynamics JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Adaptive Dynamics Toolkit: Adaptive π geometry, ARP optimizers, simulations, and compression. |
| upload_time | 2025-09-03 21:15:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2025 Ryan McKenna and contributors
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.
Note: This license applies to the Adaptive Dynamics Toolkit Community Edition.
The ADT Pro features located in src/adaptive_dynamics/pro/ are licensed under
a separate commercial license. |
| keywords |
compression
geometry
optimization
physics
torch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Adaptive Dynamics Toolkit (ADT)
<p align="center">
<a href="https://github.com/RDM3DC/adaptive-dynamics-toolkit/actions">
<img alt="CI" src="https://img.shields.io/github/actions/workflow/status/RDM3DC/adaptive-dynamics-toolkit/ci.yml?label=CI">
</a>
<a href="https://pypi.org/project/adaptive-dynamics/">
<img alt="PyPI" src="https://img.shields.io/pypi/v/adaptive-dynamics.svg">
</a>
<a href="https://pypi.org/project/adaptive-dynamics/">
<img alt="Python" src="https://img.shields.io/pypi/pyversions/adaptive-dynamics.svg">
</a>
<a href="LICENSE">
<img alt="License" src="https://img.shields.io/badge/license-MIT-blue.svg">
</a>
<a href="https://github.com/RDM3DC/adaptive-dynamics-toolkit/issues">
<img alt="Issues" src="https://img.shields.io/github/issues/RDM3DC/adaptive-dynamics-toolkit.svg">
</a>
<a href="https://github.com/sponsors/RDM3DC">
<img alt="Sponsors" src="https://img.shields.io/badge/sponsor-%E2%9D%A4-ff69b4.svg">
</a>
</p>
<p align="center">
<img src="docs/assets/hero.gif" alt="Adaptive π visualization" width="640"><br>
<img src="docs/assets/loss.gif" alt="ARP vs Adam loss (synthetic)" width="640">
</p>
A unified framework for adaptive computing paradigms, including adaptive π geometry, ARP optimization, physics simulations, and compression algorithms.
## Installation
```bash
pip install adaptive-dynamics
```
For development or to include optional dependencies:
```bash
pip install "adaptive-dynamics[torch,sympy,dev]"
# or with uv
uv venv && uv pip install -e ".[dev,docs,torch,sympy]"
```
## Quick Examples
### Curved Geometry with Adaptive π (πₐ)
```python
from adaptive_dynamics.pi.geometry import AdaptivePi
# Create an instance with gentle positive curvature
pi = AdaptivePi(curvature_fn=lambda x, y: 1e-3)
# Calculate circumference in curved space
circumference = pi.circle_circumference(1.0)
print(f"Circumference of unit circle: {circumference:.6f}")
# Output: Circumference of unit circle: 3.144159
```
### Neural Network Training with ARP Optimizer
```python
import torch
import torch.nn as nn
from adaptive_dynamics.arp.optimizers import ARP
# Define a simple model
model = nn.Sequential(nn.Flatten(), nn.Linear(28*28, 10))
# Use ARP optimizer
opt = ARP(model.parameters(), lr=3e-3, alpha=0.01, mu=0.001)
# Training loop (example)
# X, y = ... load a batch ...
loss_fn = nn.CrossEntropyLoss()
loss = loss_fn(model(X), y)
loss.backward()
opt.step()
opt.zero_grad()
```
## Examples
- [πₐ Curved Circles](examples/pi_a_curved_circles.ipynb)
- [ARP Optimizer on MNIST](examples/arp_mnist.ipynb)
## Documentation
Full documentation is available at [https://RDM3DC.github.io/adaptive-dynamics-toolkit](https://RDM3DC.github.io/adaptive-dynamics-toolkit)
- [Getting Started Guide](https://RDM3DC.github.io/adaptive-dynamics-toolkit/getting-started)
- [API Reference](https://RDM3DC.github.io/adaptive-dynamics-toolkit/api)
- [Tutorials](https://RDM3DC.github.io/adaptive-dynamics-toolkit/tutorials)
## Features
- **Adaptive π Geometry**: Curved space mathematics and Gauss-Bonnet inspired algorithms
- **ARP Optimization**: Resistance-conductance model for neural network optimization
- **Physics Simulations**: Gravity, beams, and ringdown simulations with adaptive precision
- **Compression Tools**: Adaptive compression for text, curves, and tensors
- **TSP Solvers**: Tools for 3D printing toolpath optimization
## Pro Features
ADT Pro extends the toolkit with advanced features for enterprise and research:
- Advanced CUDA acceleration
- Premium simulation capabilities
- Enterprise-grade dashboards
- Specialized slicer algorithms
Contact us at [contact@example.com](mailto:contact@example.com) for licensing information.
## Contributing
Contributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for details.
## License
- Adaptive Dynamics Toolkit (Community Edition): [MIT License](LICENSE)
- ADT Pro: Commercial license (see [pro/README_PRO.md](src/adaptive_dynamics/pro/README_PRO.md))
## Support & Services
- [Integration & Research Consulting](https://yourusername.github.io/adaptive-dynamics-toolkit/services)
- [Training & Workshops](https://yourusername.github.io/adaptive-dynamics-toolkit/training)
- [GitHub Sponsors](https://github.com/sponsors/yourusername)
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"description": "# Adaptive Dynamics Toolkit (ADT)\n\n<p align=\"center\">\n <a href=\"https://github.com/RDM3DC/adaptive-dynamics-toolkit/actions\">\n <img alt=\"CI\" src=\"https://img.shields.io/github/actions/workflow/status/RDM3DC/adaptive-dynamics-toolkit/ci.yml?label=CI\">\n </a>\n <a href=\"https://pypi.org/project/adaptive-dynamics/\">\n <img alt=\"PyPI\" src=\"https://img.shields.io/pypi/v/adaptive-dynamics.svg\">\n </a>\n <a href=\"https://pypi.org/project/adaptive-dynamics/\">\n <img alt=\"Python\" src=\"https://img.shields.io/pypi/pyversions/adaptive-dynamics.svg\">\n </a>\n <a href=\"LICENSE\">\n <img alt=\"License\" src=\"https://img.shields.io/badge/license-MIT-blue.svg\">\n </a>\n <a href=\"https://github.com/RDM3DC/adaptive-dynamics-toolkit/issues\">\n <img alt=\"Issues\" src=\"https://img.shields.io/github/issues/RDM3DC/adaptive-dynamics-toolkit.svg\">\n </a>\n <a href=\"https://github.com/sponsors/RDM3DC\">\n <img alt=\"Sponsors\" src=\"https://img.shields.io/badge/sponsor-%E2%9D%A4-ff69b4.svg\">\n </a>\n</p>\n\n<p align=\"center\">\n <img src=\"docs/assets/hero.gif\" alt=\"Adaptive \u03c0 visualization\" width=\"640\"><br>\n <img src=\"docs/assets/loss.gif\" alt=\"ARP vs Adam loss (synthetic)\" width=\"640\">\n</p>\n\nA unified framework for adaptive computing paradigms, including adaptive \u03c0 geometry, ARP optimization, physics simulations, and compression algorithms.\n\n## Installation\n\n```bash\npip install adaptive-dynamics\n```\n\nFor development or to include optional dependencies:\n\n```bash\npip install \"adaptive-dynamics[torch,sympy,dev]\"\n# or with uv\nuv venv && uv pip install -e \".[dev,docs,torch,sympy]\"\n```\n\n## Quick Examples\n\n### Curved Geometry with Adaptive \u03c0 (\u03c0\u2090)\n\n```python\nfrom adaptive_dynamics.pi.geometry import AdaptivePi\n\n# Create an instance with gentle positive curvature\npi = AdaptivePi(curvature_fn=lambda x, y: 1e-3)\n\n# Calculate circumference in curved space\ncircumference = pi.circle_circumference(1.0)\nprint(f\"Circumference of unit circle: {circumference:.6f}\")\n# Output: Circumference of unit circle: 3.144159\n```\n\n### Neural Network Training with ARP Optimizer\n\n```python\nimport torch\nimport torch.nn as nn\nfrom adaptive_dynamics.arp.optimizers import ARP\n\n# Define a simple model\nmodel = nn.Sequential(nn.Flatten(), nn.Linear(28*28, 10))\n\n# Use ARP optimizer\nopt = ARP(model.parameters(), lr=3e-3, alpha=0.01, mu=0.001)\n\n# Training loop (example)\n# X, y = ... load a batch ...\nloss_fn = nn.CrossEntropyLoss()\nloss = loss_fn(model(X), y)\nloss.backward()\nopt.step()\nopt.zero_grad()\n```\n\n## Examples\n\n- [\u03c0\u2090 Curved Circles](examples/pi_a_curved_circles.ipynb)\n- [ARP Optimizer on MNIST](examples/arp_mnist.ipynb)\n\n## Documentation\n\nFull documentation is available at [https://RDM3DC.github.io/adaptive-dynamics-toolkit](https://RDM3DC.github.io/adaptive-dynamics-toolkit)\n\n- [Getting Started Guide](https://RDM3DC.github.io/adaptive-dynamics-toolkit/getting-started)\n- [API Reference](https://RDM3DC.github.io/adaptive-dynamics-toolkit/api)\n- [Tutorials](https://RDM3DC.github.io/adaptive-dynamics-toolkit/tutorials)\n\n## Features\n\n- **Adaptive \u03c0 Geometry**: Curved space mathematics and Gauss-Bonnet inspired algorithms\n- **ARP Optimization**: Resistance-conductance model for neural network optimization\n- **Physics Simulations**: Gravity, beams, and ringdown simulations with adaptive precision\n- **Compression Tools**: Adaptive compression for text, curves, and tensors\n- **TSP Solvers**: Tools for 3D printing toolpath optimization\n\n## Pro Features\n\nADT Pro extends the toolkit with advanced features for enterprise and research:\n- Advanced CUDA acceleration\n- Premium simulation capabilities\n- Enterprise-grade dashboards\n- Specialized slicer algorithms\n\nContact us at [contact@example.com](mailto:contact@example.com) for licensing information.\n\n## Contributing\n\nContributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for details.\n\n## License\n\n- Adaptive Dynamics Toolkit (Community Edition): [MIT License](LICENSE)\n- ADT Pro: Commercial license (see [pro/README_PRO.md](src/adaptive_dynamics/pro/README_PRO.md))\n\n## Support & Services\n\n- [Integration & Research Consulting](https://yourusername.github.io/adaptive-dynamics-toolkit/services)\n- [Training & Workshops](https://yourusername.github.io/adaptive-dynamics-toolkit/training)\n- [GitHub Sponsors](https://github.com/sponsors/yourusername)",
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