Install python 3.8 https://www.python.org/downloads/release/python-380/
- pip install numpy
- pip install pandas
- pip install matplotlib
- pip install Pillow
- pip install Cython
python setup.py build_ext --inplace
- SYN (synthetic dataset)
- DNA (the genome of Escherichia coli)
- TRU (Trucks)
- OLD (Movements)
I. SYN
1. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/SYN_2_Z.txt -s DataSet/SYN/pat_2.txt -k 2 -o 0.5 -c 50 -m 1000 -d 10 -t 3 -e 5
2. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/SYN_3_Z.txt -s DataSet/SYN/pat_3.txt -k 3 -o 0.5 -c 50 -m 1000 -d 10 -t 3 -e 5
3. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/SYN_4_Z.txt -s DataSet/SYN/pat_4.txt -k 4 -o 0.5 -c 50 -m 2000 -d 10 -t 3 -e 5
4. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/SYN_5_Z.txt -s DataSet/SYN/pat_5.txt -k 5 -o 0.5 -c 25 -m 3000 -d 10 -t 3 -e 5
5. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/S5_Z.txt -s DataSet/SYN/S5.txt -k 3 -o 0.5 -c 50 -m 2000 -d 10 -t 3 -e 5
6. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/S10_Z.txt -s DataSet/SYN/S10.txt -k 3 -o 0.5 -c 50 -m 1000 -d 10 -t 3 -e 5
7. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/S15_Z.txt -s DataSet/SYN/S15.txt -k 3 -o 0.5 -c 50 -m 3000 -d 10 -t 3 -e 5
8. python3 runner.py -w DataSet/SYN/SYN_W.txt -z DataSet/SYN/S20_Z.txt -s DataSet/SYN/S20.txt -k 3 -o 0.5 -c 50 -m 3000 -d 10 -t 3 -e 5
II. TRU
1. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/TRU_3_Z.txt -s DataSet/TRU/pat_3.txt -k 3 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
2. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/TRU_4_Z.txt -s DataSet/TRU/pat_4.txt -k 4 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
3. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/TRU_5_Z.txt -s DataSet/TRU/pat_5.txt -k 5 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
4. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/TRU_6_Z.txt -s DataSet/TRU/pat_6.txt -k 6 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
5. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/S20_Z.txt -s DataSet/TRU/S20.txt -k 4 -o 0.5 -c 25 -m 1000 -d 10 -t 5 -e 5
6. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/S40_Z.txt -s DataSet/TRU/S40.txt -k 4 -o 0.5 -c 25 -m 1000 -d 10 -t 5 -e 5
7. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/S60_Z.txt -s DataSet/TRU/S60.txt -k 4 -o 0.5 -c 25 -m 1000 -d 10 -t 5 -e 5
8. python3 runner.py -w DataSet/TRU/TRU_W.txt -z DataSet/TRU/S80_Z.txt -s DataSet/TRU/S80.txt -k 4 -o 0.5 -c 25 -m 1000 -d 10 -t 5 -e 5
III. DNA
1. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/DNA_2_Z.txt -s DataSet/DNA/pat_2.txt -k 2 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
2. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/DNA_3_Z.txt -s DataSet/DNA/pat_3.txt -k 3 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
3. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/DNA_4_Z.txt -s DataSet/DNA/pat_4.txt -k 4 -o 0.5 -c 50 -m 2000 -d 10 -t 10 -e 5
4. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/DNA_5_Z.txt -s DataSet/DNA/pat_5.txt -k 5 -o 0.5 -c 50 -m 3000 -d 10 -t 10 -e 5
5. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/S20_Z.txt -s DataSet/DNA/S20.txt -k 4 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
6. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/S40_Z.txt -s DataSet/DNA/S40.txt -k 4 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
7. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/S60_Z.txt -s DataSet/DNA/S60.txt -k 4 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
8. python3 runner.py -w DataSet/DNA/DNA_W.txt -z DataSet/DNA/S80_Z.txt -s DataSet/DNA/S80.txt -k 4 -o 0.5 -c 50 -m 1000 -d 10 -t 10 -e 5
- Bernardini, G., Chen, H., Conte, A., Grossi, R., Loukides, G., Pisanti, N., ... & Rosone, G. (2019, September). String sanitization: A combinatorial approach. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 627-644). Springer, Cham.
- Bernardini, Giulia, et al. "Hide and Mine in Strings: Hardness and Algorithms." International Conference on Data Mining (ICDM). 2020.
- Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., ... & Colton, S. (2012). A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1), 1-43.
- Run Tkinter python-based UI: python user_interface.py
- Run command-line interface: python runner.py