Open Source Python Artificial Intelligence Software - Page 90

A toolkit for the optical recognition of Psaltiki 19th century music notation. It is based on and requires the Gamera document image analysis framework (http://gamera.sf.net).

Purple Llama is an umbrella safety initiative that aggregates tools, benchmarks, and mitigations to help developers build responsibly with open generative AI. Its scope spans input and output safeguards, cybersecurity-focused evaluations, and reference shields that can be inserted at inference time. The project evolves as a hub for safety research artifacts like Llama Guard and Code Shield, along with dataset specs and how-to guides for integrating checks into applications. CyberSecEval, one of its flagship components, provides repeatable evaluations for security risk, including agent-oriented tasks such as automated patching benchmarks. The aim is to make safety practical: ship testable baselines, publish metrics, and provide drop-in implementations that reduce friction for teams adopting Llama. Documentation and sites attached to the repo walk through setup, usage, and the rationale behind each safeguard, encouraging community contributions.

A Python framework to build artificial neural networks

PyAline is a python implementation of Grzegorz Kondrak's ALINE phonetic alighment algorithm.

PyArmadillo - streamlined linear algebra library for Python, with emphasis on ease of use. Alternative to NumPy / SciPy. * Main page: https://pyarma.sourceforge.io * Documentation: https://pyarma.sourceforge.io/docs.html * Bug reports: https://pyarma.sourceforge.io/faq.html * Git repo: https://gitlab.com/jason-rumengan/pyarma

PyBact is an open source software written in Python for Bacterial Identification. The software generates simulated data matrix which accurately represents the probabilistic positive/negative results of the tested biochemical test.

Case base reasoninig is one of the primitive AI techniques in existance. Infact it's one of the laziest. Implementation however takes some effort. Python is one of the most to used languages that is becoming popular in every community for its simplicity & ease of learning. It has an interface for wordnet (through nltk tools) which brings us why pyCBR exists. This script is still in its infant stage of CBR & script matching. Nontheless you could use it to do many crazy stuff. Enjoy!!!

PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.

pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are first-class, enabling fast experiments on multi-GPU setups with simple, declarative configs. Model definitions are concise and modular, making it easy to prototype new blocks or swap backbones while keeping the rest of the pipeline unchanged. Pretrained weights and evaluation scripts cover common datasets, and the logging/metric stack is designed for quick comparison across runs. Practitioners use pycls both as a baseline factory and as a scaffold for new classification backbones.

Turn your scanner into a free document reader for invoices (e.g. for e-banking) with the help of tesseract-ocr available for many unix (and also windows) platforms.

An attempt at offering machine learning and data mining algorithms at the finest grain we are able to, easy to combine together through Python scripting to glue together the Lego-like bricks.

A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour).

An easy-to-use python interface to GENESIS3.

A library for implementing GOAP in an AI agent. Based on pygoap v3 by Leif Theden et al. Updated code to work without having pygame installed, bug-fixed functions to make them implement the behaviors that were expected, and implemented desired behaviors so that the Pirate demo works properly for all known actions and goals.

A flexible and easy-to-use toolkit for implementing genetic algorithm in Python

PyLife is an implementation of the game of life algorithm featuring parallel programming. It uses MPI and python to achieve a consistent software architecture and reliably performance.

PyML is an interactive object oriented framework for machine learning written in python. PyML focuses on kernel classifiers, providing tools for feature selection, model selection, and methods for assessing classifier performance.

PyOP (Python Optimus Prime bot) is an Open Source IRC bot written in Python. It's intended to be simple, fast and modular. Support for jabber/MSN is planned.

Allows approximate mapping between two different ontologies. The mapping is obtained combining sets of matchers that compare different features of elements from the two ontologies.

PyPR - Python Pattern Recognition. A small collection of useful pattern recognition methods. The code is still in its early stages.

PyPlayground is an environment for developing algorithms involving movement in a space of up to three dimensions using Python.

2D robotic simulator in Python 2.x, tested in 2.6 and 2.7, under Win7(64) and Ubuntu 10.04 (Lucid). Needs the matching version of Pygame installed! (www.pygame.org) Download the .py file and two .bmp files: -->back2_800_600 for background -->any of the robo1.bmp through robo4.bmp for robot image (different colors). The map of the "room" can be changed by modifying the list of obstacles. Read more in ReadMe.txt and in the .py file comments.

PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks.

PyTextRank is a Python implementation of TextRank as a spaCy pipeline extension, for graph-based natural language work -- and related knowledge graph practices.