Open Source Python Artificial Intelligence Software for Mac

The Simple Medical Imaging Library Interface (SMILI), pronounced 'smilie', is an open-source, light-weight and easy-to-use medical imaging viewer and library for all major operating systems. The main sMILX application features for viewing n-D images, vector images, DICOMs, anonymizing, shape analysis and models/surfaces with easy drag and drop functions. It also features a number of standard processing algorithms for smoothing, thresholding, masking etc. images and models, both with graphical user interfaces and/or via the command-line. See our YouTube channel for tutorial videos via the homepage. The applications are all built out of a uniform user-interface framework that provides a very high level (Qt) interface to powerful image processing and scientific visualisation algorithms from the Insight Toolkit (ITK) and Visualisation Toolkit (VTK). The framework allows one to build stand-alone medical imaging applications quickly and easily.

The Player Project: Player is a networked interface to robots and sensors. Stage and Gazebo are Player-friendly multiple-robot simulators. The software aims for POSIX compliance and runs on most UNIX-like OS's. Some parts also work on Windows.

A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We are interested in reuse, and we focus on common NLP tasks that are broadly useful for textmining.

We experiment with Evolution of Artifical Neural Networks, combining the two fields of Evolutionary Computation and ANNs. Our methods are applied to a variety of interesting problems. To learn more, click on "Home Page", "Mail", or "Files".

The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.

An IRC bot, similar to the Blootbot and Infobot, written in python.

Our project harnesses the power of Graph Neural Network (GNN) to estimate pure-component parameters of the state-of-the-art Equation of State, PC-SAFT. We aim to empower users to leverage this robust equation without the need for prior experimental data, revolutionizing the calculation of thermodynamic properties and enhancing process simulations. FeOS is used for the PC-SAFT calculations. The estimated parameters can be used in DWSIM and Aspen HYSYS process simulators.

This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed to address problems in epidemiological data mining to identify complex patterns relating predictive attributes in noisy datasets to disease phenotypes of interest. ExSTraCS combines a number of recent advancements into a single algorithmic platform. It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7.

Bangla text to speech Multilingual (Bangla, English) real-time ([almost] in a GPU) speech synthesis library. Installation -------------------------------------- * Install Anaconda * conda create -n new_virtual_env python==3.6.8 * conda activate new_virtual_env * pip install -r requirements.txt * While running for the first time, keep your internet connection on to download the weights of the speech synthesis models (>500 MB) * For fast inference, you must install tensorflow-gpu and have a NVidia GPU. Project link: https://github.com/zabir-nabil/bangla-tts

Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior. Researchers all over the world, both from the fields of artificial intelligence and cognitive science, are using Soar for a variety of tasks.

Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.

RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition. full installation and usage instructions given at http://sourceforge.net/p/rnnl/wiki/Home/

Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.

The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information.

An AIML based chat bot building on the original Alice AIML 1.0.1 set produced by Dr. Wallace and the ALICE AI Foundation and the PyAIML code base written by Cort Stratton, the ANGie project incorporates additional AIML sets, adds its own AIML to the set, adds new AIML tags and additional code to provide more dynamic responses and more logical case-based-reasoning. Reading through most AIML sets it seems like the authors' intention was to have a response to every input that a bot has ever seen. The ANGie project strives to have intelligent and sensible responses, but to allow the bot to have no response when the meaning of the input is inconclusive, when additional context would be required to properly respond, or in general for questions for which the bot is unprepared - in order to create a bot that is capable of carrying on basic conversations with a human similar to the sort of small talk that two humans might have. Requires PyAIMLng, PyGOAPng, and aimlGOAP.

Simple program for artificial neural network users. Right now the program can manipulate with Feed forward back propagation network.

Using this plugin-based framework, you can instantly start working on the *brain* of your bot (irc bot, chatterbot, robot, ...). With support for db, irc, logging and programming-language independent plugins, users can easily enhance the functionality.

This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.

The goal of this project is to learn about and develop an AI. Current path is using a bot on AIM.

A three-step approach towards experimental brain-computer-interfaces, based on the OCZ nia device for EEG-data acquisition and artificial neural networks for signal-interpretation.

This project is a quest for conscious artificial intelligence. A number of prototypes will be developed as the project progresses. This project has 2 subprojects: Object Pascal based CAI NEURAL API - https://github.com/joaopauloschuler/neural-api Python based K-CAI NEURAL API - https://github.com/joaopauloschuler/k-neural-api A video from the first prototype has been made: http://www.youtube.com/watch?v=qH-IQgYy9zg Above video shows a popperian agent collecting mining ore from 3 mining sites and bringing to the base. At the time the agent is born, it doesn't know how to walk nor it knows that it feels pleasure by mining. He has tact only (blind agent). The video shows learning, planning, executing and plan optimization.

Redundancy due to cut-paste operations in text creates bias in machine learning for NLP. This module takes a directory and produces a subset of the files in that directory (in a list) with an upper bound on similarity between two files.

Decision Analysis is an easily-extensible expert system to help users make decisions of all types. Written entirely in Python, Decision Analysis, at this time, contains a general decsion module, which uses a weighted average technique to evaluate use

Educational Learning Classifier System (eLCS) is a set of learning classifier system (LCS) educational demos designed to introduce students or researchers to the basics of a modern Michigan-style LCS algorithm. This eLCS package includes 5 different implementations of a basic LCS algorithm, as part of a 6 stage set of demos that will be paired with the first introductory LCS textbook. Each eLCS implementations (from demo 2 up to demo 6) progressively add major components of the entire LCS algorithm in order to illustrate how work, how they are coded, and what impact they have on how an LCS algorithm runs. The Demo 6 version of eLCS is most similar to the UCS algorithm. Each version only includes the minimum code needed to perform the functions they were designed for. This way users can start by examining the simplest version of the code and progress forward. This code is intended to be used as an educational tool, or as algorithmic code building blocks.

It is use AI running localy and model you can choose. Supproted two API first is as default is Llama, second if full LM Studio api. Skrypt have a tool for automatic add to scheduler or cron-not tested enought. Scrypt now not working with Microsoft outlook and Google gmail, for certifications and api polici reasons . Fuly tested on Seznam.cz* services provider, if you have difrent provier with same type of security or autentification it will be working. *Email is using standart IMAP *Calendaruse iCalendar api and aut method. Setup: 1. Download and unpack archyve 2. Download model** and put into same folder as main.py 3. Run run_setings.bat and set your authentificators for email*** and calendar, time of peridical start 4. Push button SAVE 5. Push button Plan 6. Check if working **Model must understand your language, test before use! ***In emal seting(usualy on web) create a new folder and set auto COPY! recive emails in to it, bacause they will be deleted.