- Requirement: python 2 This is a Nonogram solver using simulated annealing
What is a Nongram : https://en.wikipedia.org/wiki/Nonogram
What is Simulated Annealing : When using local search optimization, the agent is likely to be stuck in a local maximum of reward function. Simulated Annealing can help the agent get out of local maxima by allowing the agent to make a bad move with a certain probability. For more information about Simulated Annealing: https://en.wikipedia.org/wiki/Simulated_annealing
- To solve a Nonogram, run:
python solve.py > FILENAME
You may want to output to a file like so because the output will be quite long. The script actually comes with a test nonogram that was hard-coded, it looks like:
| board | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1 | 1 | 0 | 1 | 1 | 1 |
| 2 | 0 | 0 | 0 | 1 | 1 |
| 3 | 1 | 1 | 0 | 1 | 0 |
| 4 | 1 | 1 | 0 | 0 | 0 |
| 5 | 1 | 1 | 0 | 0 | 0 |
Where 1's represent black pixels and 0's represent white pixels.
Feel free to try more test cases yourself!