# GraffitiAI
GraffitiAI is a Python package for automated mathematical conjecturing, inspired by the legacy of GRAFFITI. It provides tools for exploring relationships between mathematical invariants and properties, with a focus on graph theory and polytopes. This package supports generating conjectures, applying heuristics, and visualizing results.
## Features
- Load and preprocess datasets with ease.
- Identify possible invariants and hypotheses for conjecturing.
- Generate upper and lower bounds for a target invariant.
- Apply customizable heuristics to refine conjectures.
- Export results to PDF for presentation and sharing.
- Includes a sample dataset of 3-regular polytopes for experimentation.
---
## Installation
To install GraffitiAI, use `pip`:
```bash
# Install GraffitiAI with pip
pip install graffitiai
```
---
## Quick Start
Here's a simple example to get you started:
```python
from graffitiai import TxGraffiti
# Initialize the Optimist instance
ai = TxGraffiti()
# Load a custom dataset
ai.read_csv("<path_to_your_data>.csv")
# Describe available invariants and hypotheses
ai.describe_invariants_and_hypotheses()
# Generate conjectures
ai.conjecture(
target_invariants=[
"zero_forcing_number",
"total_domination_number",
],
other_invariants=[
"independence_number",
"diameter",
"radius",
"domination_number"
],
hypothesis=[
"a_connected_cubic_and_diamond_free_graph",
"a_connected_and_cubic_graph_which_is_not_k_4",
],
complexity_range=(1, 3),
lower_b_max=None,
upper_b_max=2,
)
# Write the conjectures to the wall!
ai.write_on_the_wall()
# Save conjectures to a PDF
ai.save_conjectures_to_pdf("custom_conjectures.pdf")
```
---
## Contributing
Contributions are welcome! If you have suggestions, find bugs, or want to add features, feel free to create an issue or submit a pull request.
---
## License
This project is licensed under the MIT License. See the LICENSE file for details.
---
## Acknowledgments
GraffitiAI is inspired by the pioneering work of GRAFFITI and built using the ideas of *TxGraffiti* and the *Optimist*.
### Author
Randy R. Davila, PhD
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"description": "# GraffitiAI\n\nGraffitiAI is a Python package for automated mathematical conjecturing, inspired by the legacy of GRAFFITI. It provides tools for exploring relationships between mathematical invariants and properties, with a focus on graph theory and polytopes. This package supports generating conjectures, applying heuristics, and visualizing results.\n\n## Features\n- Load and preprocess datasets with ease.\n- Identify possible invariants and hypotheses for conjecturing.\n- Generate upper and lower bounds for a target invariant.\n- Apply customizable heuristics to refine conjectures.\n- Export results to PDF for presentation and sharing.\n- Includes a sample dataset of 3-regular polytopes for experimentation.\n\n---\n\n## Installation\n\nTo install GraffitiAI, use `pip`:\n\n```bash\n# Install GraffitiAI with pip\npip install graffitiai\n```\n\n---\n\n## Quick Start\n\nHere's a simple example to get you started:\n\n```python\nfrom graffitiai import TxGraffiti\n\n# Initialize the Optimist instance\nai = TxGraffiti()\n\n# Load a custom dataset\nai.read_csv(\"<path_to_your_data>.csv\")\n\n# Describe available invariants and hypotheses\nai.describe_invariants_and_hypotheses()\n\n# Generate conjectures\nai.conjecture(\n target_invariants=[\n \"zero_forcing_number\",\n \"total_domination_number\",\n ],\n other_invariants=[\n \"independence_number\",\n \"diameter\",\n \"radius\",\n \"domination_number\"\n ],\n hypothesis=[\n \"a_connected_cubic_and_diamond_free_graph\",\n \"a_connected_and_cubic_graph_which_is_not_k_4\",\n ],\n complexity_range=(1, 3),\n lower_b_max=None,\n upper_b_max=2,\n)\n\n# Write the conjectures to the wall!\nai.write_on_the_wall()\n\n# Save conjectures to a PDF\nai.save_conjectures_to_pdf(\"custom_conjectures.pdf\")\n```\n\n---\n\n## Contributing\n\nContributions are welcome! If you have suggestions, find bugs, or want to add features, feel free to create an issue or submit a pull request.\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See the LICENSE file for details.\n\n---\n\n## Acknowledgments\n\nGraffitiAI is inspired by the pioneering work of GRAFFITI and built using the ideas of *TxGraffiti* and the *Optimist*.\n\n### Author\n\nRandy R. Davila, PhD\n\n",
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