A simple methodology to optimize the candidate model by searching through an optimized clustered graph based on levenshtein distance.
This is an attempt to optimize the search of words for candidate model while building auto-correct applications. The idea is to minimize the calculation the Ld(Levenshtein distance) as it is computationally expensive. Simple implementation of DBSCAN clustering is being used to form clusters to be queried further.
Use the package manager pip to install the requirements.
pip install numpy
jupyter notebook
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.