Metadata-Version: 2.1
Name: fuzzyops
Version: 1.0.16
Summary: Fuzzy math library
Home-page: https://github.com/Kotya2001/FuzzyOps
Author: Kotya2001
Author-email: your_email@example.com
License: MIT
Project-URL: Documentation, https://fuzzyops.readthedocs.io/en/latest/
Project-URL: Tutorial, https://github.com/Kotya2001/FuzzyOps/wiki/Instructions-for-using-the-FuzzyOps-library
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: setuptools>=60.2.0
Requires-Dist: numpy>=1.23.4
Requires-Dist: matplotlib>=3.6.2
Requires-Dist: pandas>=2.1.0
Requires-Dist: uncertainties>=3.1.7
Requires-Dist: torch>=2.0.0
Requires-Dist: scikit-learn>=1.4.1
Requires-Dist: cvxpy>=1.6.0
Requires-Dist: scipy>=1.11.2
Requires-Dist: numba>=0.56.4

# 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:
- https://github.com/Kotya2001/FuzzyOps/blob/main/cpp/README.md

It is also possible to implement a RESTful API in your software (the web service is deployed on your own resources), following these instructions:
 * https://github.com/Kotya2001/FuzzyOps-App - the source code of the web service for deployment;
 * https://github.com/Kotya2001/FuzzyOps-App/tree/main/posters - examples for accessing a web service via API (implemented in Python);
 * https://github.com/Kotya2001/FuzzyOps-App/wiki/Инструкция-по-использованию-алгоритмов-в-веб%E2%80%90серсиве-(по-API) - Instructions for using the library's algorithms through the RESTful API.


### 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](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token)

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

  - https://developer.nvidia.com/cuda-11-5-0-download-archive

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:

-  Instructions for working with the library - https://github.com/Kotya2001/FuzzyOps/wiki/Instructions-for-using-the-FuzzyOps-library;
-  Documentation for the library source code - https://fuzzyops.readthedocs.io/en/latest/

### Running tests

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

 - Instructions for running tests - https://github.com/Kotya2001/FuzzyOps/wiki/Instructions-for-running-FuzzyOps-library-tests
   

### Instructions for using the library in C++ programs

-  Instructions for using the library in C++ programs - https://github.com/Kotya2001/FuzzyOps/blob/main/cpp/README.md

### A web service for accessing the library's algorithms using the RESTful API:

- The source code of the web service and instructions for using it - https://github.com/Kotya2001/FuzzyOps-App


### Description of folders with library repository files

 * [cpp](https://github.com/Kotya2001/FuzzyOps/tree/main/cpp) - Instructions for using the library in C++ programs and examples of using the library in Python and C++;
 * [example](https://github.com/Kotya2001/FuzzyOps/tree/main/examples):
   * [common](https://github.com/Kotya2001/FuzzyOps/tree/main/examples/common) - Examples of using the library code;
   * The remaining files are practical examples of using the library code;
 * [src](https://github.com/Kotya2001/FuzzyOps/tree/main/src) - Library source codes:
   * [docs](https://github.com/Kotya2001/FuzzyOps/tree/main/src/docs) - Files, format .html with documentation for the source code (compiled using the library [sphinx](https://www.sphinx-doc.org/en/master/));
   * [fuzzyops](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops) - Library source codes:
     * [fan](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/fan) - Source codes of fuzzy analytical networks;
     * [fuzzy_logic](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/fuzzy_logic) Source codes of fuzzy logic algorithms;
     * [fuzzy_msa](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/fuzzy_msa) - Source codes of classical multicriteria analysis algorithms with fuzzy variables;
     * [fuzzy_nn](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/fuzzy_nn) - Source codes of algorithms for fuzzy neural networks (ANFIS Network);
     * [fuzzy_numbers](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/fuzzy_numbers/fuzzify) - Source codes for implementing fuzzy numbers (fuzzification, defuzzification, fuzzy arithmetic);
     * [fuzzygraphs](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/graphs/fuzzgraph) - Source codes for the implementation of fuzzy graphs;
     * [fuzzygraphs_algs](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/graphs/algorithms) - Source codes of algorithms on fuzzy graphs (Fuzzy dominance relations, fuzzy factor models, fuzzy transport graphs);
     * [fuzzy_pred](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/prediction) - Source codes of fuzzy prediction algorithms;
     * [sequencing_assignment](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/sequencing_assignment) - The source codes of algorithms on fuzzy graphs of the sequence of work in assignment tasks;
     * [tests](https://github.com/Kotya2001/FuzzyOps/tree/main/src/fuzzyops/tests) - Algorithm Test Codes.
 * [readthedocs](https://github.com/Kotya2001/FuzzyOps/blob/main/.readthedocs.yml) - A file for automatic assembly and placement of documentation on https://about.readthedocs.com;
 * [doc_reqs.txt](https://github.com/Kotya2001/FuzzyOps/blob/main/doc_reqs.txt) - A library dependency file for building documentation https://about.readthedocs.com;
 * [requirements](https://github.com/Kotya2001/FuzzyOps/blob/main/requirements.txt) - The dependency file for installing the library;
 * [setup.cfg](https://github.com/Kotya2001/FuzzyOps/blob/main/setup.cfg) - Configuration file for building the library distribution;
 * [setup.py](https://github.com/Kotya2001/FuzzyOps/blob/main/setup.py) - A file for building a library distribution using `setuptools`;
 * [LICENSE](https://github.com/Kotya2001/FuzzyOps/blob/main/LICENSE) - Library license file;
   
