Python script(s) for various MDS algorithms and experiments
git clone https://github.com/mkoledoye/mds_experiments/
cd mds_experiments
pip install -r requirements.txt
Run any of the experiments with:
python run.py --exp_no EXP_NO --nruns NRUNS
NRUNS is the number of times an experiment should be repeated each initialized with a random configuration X. Pass NRUNS >= 100 to have a sufficient amount of trials.
EXP_NO values are described below.
The experiments are dividing into two groups:
- Comparisons:
compares the performance of MDS variants changing the amount of noise (
EXP_NO=1) or number of anchors (EXP_NO=2). - Missing Data:
check the effects of missing data in the distance matrix varying the amount of noise (
EXP_NO=3) or the number of tags (EXP_NO=4).
A sample animation of the computed configuration using any of the MDS variants can be viewed by running:
python animation.py
NOTE: The animation requires Python 3
- M. A. Koledoye, T. Facchinetti and L. Almeida, "MDS-based localization with known anchor locations and missing tag-to-tag distances," 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, 2017, pp. 1-4. doi: 10.1109/ETFA.2017.8247768
- C. Di Franco, E. Bini, M. Marinoni and G. C. Buttazzo, "Multidimensional scaling localization with anchors," 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Coimbra, 2017, pp. 49-54. doi: 10.1109/ICARSC.2017.7964051