MATLAB Implementation for Calculating Jousselme Evidence Distance in Evidence Theory

This repository provides an original MATLAB implementation for calculating Jousselme evidence distance in evidence theory. The code follows these key computational steps: 1. Reads input evidence matrices representing basic probability assignments 2. Computes pairwise Jaccard similarity coefficients between evidence bodies using set intersection and union operations 3. Transforms Jaccard similarity values into Jousselme evidence distances through mathematical conversion The implementation utilizes MATLAB's matrix operations for efficient similarity calculations and includes proper normalization handling for evidence vectors. Key functions involve: - Matrix manipulation for evidence representation - Vectorized similarity computation using element-wise operations - Distance transformation with linear algebraic methods This code serves as both a practical tool for evidence theory applications and an educational resource for understanding distance metrics in uncertainty reasoning. Researchers can use it to analyze evidence conflicts, combine information from multiple sources, and study Dempster-Shafer theory implementations. The commented code structure makes it suitable for learning algorithmic approaches to evidence distance calculations. Hope this implementation proves valuable for your evidence theory research and applications!