Skip to main content

A Python package for automated mathematical conjecturing.

Project description

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:

# Install GraffitiAI with pip
pip install graffitiai

Quick Start

Here's a simple example to get you started:

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

graffitiai-0.1.6.tar.gz (15.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

graffitiai-0.1.6-py3-none-any.whl (15.1 kB view details)

Uploaded Python 3

File details

Details for the file graffitiai-0.1.6.tar.gz.

File metadata

  • Download URL: graffitiai-0.1.6.tar.gz
  • Upload date:
  • Size: 15.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for graffitiai-0.1.6.tar.gz
Algorithm Hash digest
SHA256 a9393d0517c9a3e59ef5bddeff1ad2e1744a6f61fa927774a511b0cfc91cb859
MD5 b96991e2b7f294ae923d8892d4a43e3a
BLAKE2b-256 c4be16afc234acb7acbc43ecc25dad43c8b89ff1046075b6e7a158277c330ec6

See more details on using hashes here.

File details

Details for the file graffitiai-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: graffitiai-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 15.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.7

File hashes

Hashes for graffitiai-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 23e147b62bdd7b899a873175d2490a74ad4c097e35e65188f200b93e10d6fee6
MD5 2e93a4363d289f9bcbbc2c679215a7e0
BLAKE2b-256 b2dc066b57a559f525480f96156ac6af149c5551eb8488d7c8426618ea9fc0e8

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page