Basic Wordle solver that uses some information theory to try to optimize the number of guesses required. Currently supports only a simple command-line interface.
Compilation:
g++ -O3 main.cpp -o main
The program only takes one argument, the word file or a path to that file:
./main <filename/path>
Once running, the program will prompt you for your guess, to which you enter your 5 letter guess:
Enter guess: (e.g. slate)
Then, the program will prompt you for the result:
Enter result string: (e.g. ybgbb)
Enter a 5 letter string containing the following:
bif the box is black (wrong letter).yif the box is yellow (correct letter but wrong position).gif the box is green (correct letter and position).
The program then tells you how many possible words remain, together with what to try:
Words remaining: 13
Try: brash
And the process repeats until a solution is found.
For the python plotters, install the necessary modules if they are missing:
pip install matplotlib numpy
Here are the results of my strategy, which involves:
- Picking the guess which maximizes the result distribution entropy
- Ignoring this guess if it is part of the set of
bad_guessesand there are still more than 10 words remaining
This set of bad_guesses = {weird, price, antic, corny, baste} was made by first testing the entropy-maximization strategy on every word without any exclusion, then picking the first guesses that led to the total number of guesses exceeding 6.
For the 2315-words solution list, my strategy is close to the optimal which uses a more sophisticated method involving decision trees. With more tweaking to the bad_guesses set and word cutoff, my strategy can probably be improved.
For the full-length 12972-words list however, my strategy performs a lot worse compared to the optimal, although the computational time is much shorter. Perhaps this is due to a bad choice of bad_guesses.
Future developments may include updates to the strategy so that it never exceeds 6 guesses.