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Fuzzy math library

Project description

FuzzyOps

Library of algorithms for fuzzy forecasting and decision support

The library is intended for use:

  • in scientific laboratories engaged in research in the field of multi-criteria analysis, optimal planning and management;
  • in companies engaged in the development of decision support systems. In fact, the library should be used in the creation of both full-featured software products and experimental mock-ups of software systems designed to work with fuzzy factors.

The library can also be used by directly calling functions in C++ programs, following the instructions:

How to install the library

To install the library as a pip package, use the command: pip install git+https://{login}:{token}@github.com/Kotya2001/FuzzyOps.git by substituting the appropriate values:

  • login: your login on GitHub
  • token: how to create a token - here

Or

  • pip install fuzzyops

Before installation

Create a virtual environment with Python >= 3.10

Full path to the Python 3.10 executable file -m venv env

Activating the environment

  • Macos: source env/bin/activate
  • Windows: .\env\Scripts\activate
  • Linux: source env/bin/activate

Installing the Cuda Toolkit 11.5

Install PyTorch depending on your operating system

  • Windows: pip3 install torch --index-url https://download.pytorch.org/whl/cu117
  • Macos: pip3 install torch
  • Linux: pip3 install torch

Minimum technical requirements

  • RAM capacity of at least 2 GB;
  • For CUDA calculations, an Nvidia GeForce RTX 3060 or higher graphics output device
  • Installed Python version 3.10 or higher

Instructions for using the library and documentation for the library's source code:

Running tests

After installation, the tests are run according to the instructions.:

Instructions for using the library in C++ programs

Описание папок с файлами репозитория библиотеки

  • cpp - Instructions for using the library in C++ programs and examples of using the library in Python and C++;
  • example:
    • common - Examples of using the library code;
    • The remaining files are practical examples of using the library code;
  • src - Library source codes:
    • docs - Files, format .html with documentation for the source code (compiled using the library sphinx);
    • fuzzyops - Library source codes:
      • fan - Source codes of fuzzy analytical networks;
      • fuzzy_logic Source codes of fuzzy logic algorithms;
      • fuzzy_msa - Source codes of classical multicriteria analysis algorithms with fuzzy variables;
      • fuzzy_nn - Source codes of algorithms for fuzzy neural networks (ANFIS Network);
      • fuzzy_numbers - Source codes for implementing fuzzy numbers (fuzzification, defuzzification, fuzzy arithmetic);
      • fuzzygraphs - Source codes for the implementation of fuzzy graphs;
      • fuzzygraphs_algs - Source codes of algorithms on fuzzy graphs (Fuzzy dominance relations, fuzzy factor models, fuzzy transport graphs);
      • fuzzy_pred - Source codes of fuzzy prediction algorithms;
      • sequencing_assignment - The source codes of algorithms on fuzzy graphs of the sequence of work in assignment tasks;
      • tests - Algorithm Test Codes.
  • readthedocs - A file for automatic assembly and placement of documentation on https://about.readthedocs.com;
  • doc_reqs.txt - A library dependency file for building documentation https://about.readthedocs.com;
  • requirements - The dependency file for installing the library;
  • setup.cfg - Configuration file for building the library distribution;
  • setup.py - A file for building a library distribution using setuptools;
  • LICENSE - Library license file;

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