A python package of evidence theory
Project description
evidence_theory
简介
dst是一个用于证据理论的Python包,提供了一系列工具和函数,帮助用户处理和应用证据理论。
安装
使用pip安装dst:
pip install dst
例子
- 见
./example/conflict_example.py中实现了一个经典的证据冲突的融合。 - 见
./example/ppt.py中实现了一个pignistic probability transformation。 - 见
./example/moment.py中实现了一个计算证据对应的矩的例子。 - 见
./example/space_example.py中实现了一个计算证据对应的矩的例子。 - 见
./example/rps_example.py中实现了随机排列集左交融合的例子。 - 见
./example/wang_orthogonal_example.py中实现了论文Wang, Y., Li, Z., & Deng, Y. (2024). A new orthogonal sum in Random Permutation Set. Fuzzy Sets and Systems, 109034中的正交rps融合规则。
文档
完整的API文档和使用指南可在项目主页上找到。
支持与交流
有任何问题或建议,欢迎在GitHub Issues页面提交。
许可证
本项目遵循MIT License。
作者:Tianxiang Zhan 电子邮件:zhantianxianguestc@hotmail.com
致谢
感谢所有贡献者和社区成员的帮助和支持。
相关论文
本软件包的编程思想基于Zhan等人的论文,如果涉及相关内容,请引用相关文献。
@article{zhan2024generalized,
title={Generalized information entropy and generalized information dimension},
author={Zhan, Tianxiang and Zhou, Jiefeng and Li, Zhen and Deng, Yong},
journal={Chaos, Solitons \& Fractals},
volume={184},
pages={114976},
year={2024},
publisher={Elsevier}
}
@article{zhan2024random,
title={Random Graph Set and Evidence Pattern Reasoning Model},
author={Zhan, Tianxiang and Li, Zhen and Deng, Yong},
journal={arXiv preprint arXiv:2402.13058},
year={2024}
}
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