evtools-dst 0.1.0

Evidence Theory Tools — utilities for Dempster-Shafer theory of belief functions

evtools

Evidence Theory Tools — a growing Python library of utilities for working with belief functions in the Dempster-Shafer theory of evidence.

Modules

Module	Description
evtools.conversions	Conversions between all standard belief function representations

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evtools.conversions

Converts between all standard representations of belief functions using the Fast Möbius Transform (FMT) from Smets (2002) and Denoeux (2008).

Supported representations

Symbol	Name
m	Basic Belief Assignment (mass function)
bel	Belief function
pl	Plausibility function
b	Commonality function
q	Implicability function
v	Conjunctive weight function
w	Disjunctive weight function

Every conversion is available as a <source>to<target> function. For example mtob, pltom, qtow, beltov, etc.

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Installation

pip install evtools

Or from source:

git clone https://github.com/<your-username>/evtools.git
cd evtools
pip install -e .

Quick start

import numpy as np
from evtools.conversions import mtob, mtopl, mtobel, mtow

# Mass function over a 2-atom frame {a, b}
# Index order: ∅, {a}, {b}, {a,b}
m = np.array([0.0, 0.3, 0.5, 0.2])

print(mtob(m))    # commonality function
print(mtopl(m))   # plausibility function
print(mtobel(m))  # belief function
print(mtow(m))    # disjunctive weight function

Running tests

pip install -e ".[dev]"
pytest tests/

References

• Smets, P. (2002). The application of the transferable belief model to diagnostic problems. International Journal of Intelligent Systems.
• Denoeux, T. (2008). Conjunctive and disjunctive combination of belief functions induced by non-distinct bodies of evidence. Artificial Intelligence.

License

MIT