This package implements adaptive kernel density estimation algorithms for 1-dimensional
signals developed by Hideaki Shimazaki. This enables the generation of smoothed histograms that preserve important density features at multiple scales, as opposed to naive single-bandwidth kernel density methods that can either over or under smooth density estimates.
The kernel bandwith is choosen locally to account for variations in the density of the data. Areas with large density gets smaller kernels and vice versa. This smoothes the tails and gets high resolution in high statistics regions.
This uses the awesome pybind11 package which makes creating C++ bindings super convenient. Only the evaluation is written in a small C++ snippet to speed it up, the rest is a pure python implementation.
This Python 3.5+ package implements various kernel density estimators (KDE). Three algorithms are implemented through the same API: NaiveKDE, TreeKDE and FFTKDE. The class FFTKDE outperforms other popular implementations, see the comparison page.
我在Python中搜索可变/自适应内核密度估计包时遇到了这个问题。我知道手术可能已经很久了,但我还是发现了:
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