This is a Python / Numba implementation of kernel density estimation (KDE) with the polynomial-exponential kernel (Hofmeyr, 2019), which enables linear-complexity runtime.
Accuracy
Hofmeyr, David P. "Fast exact evaluation of univariate kernel sums." IEEE transactions on pattern analysis and machine intelligence 43.2 (2019): 447-458.
See also https://github.com/DavidHofmeyr/FKSUM for the original implementation in R/C++.
pip install polyexpkde
Alternatively, install from source. After cloning this repo, install the dependencies on the command-line, then install polyexpkde:
pip install -r requirements.txt
pip install -e .
pytest
See examples/basic-example.ipynb for an example. This uses a sklearn-compatible BaseEstimator API, but we follow the example of scipy.stats.gaussian_kde by allowing you to specify the bw_method, which produces the factor that is multiplied with the standard deviation of the data. Sampling and 1d integration are also supported.