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Optimal fixed or locally adaptive kernel density estimation

Project description

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. These methods are described in Shimazaki’s paper:

H. Shimazaki and S. Shinomoto, “Kernel Bandwidth Optimization in Spike Rate Estimation,” in Journal of Computational Neuroscience 29(1-2): 171–182, 2010 http://dx.doi.org/10.1007/s10827-009-0180-4.

License: All software in this package is licensed under the Apache License 2.0. See LICENSE.txt for more details.

Authors: Hideaki Shimazaki (shimazaki@jhu.edu) shimazaki on Github Lee A.D. Cooper (cooperle@gmail.com) cooperlab on GitHub Subhasis Ray (ray.subhasis@gmail.com)

Three methods are implemented in this package: 1. sshist - can be used to determine the optimal number of histogram bins for independent identically distributed samples from an underlying one-dimensional distribution. The principal here is to minimize the L2 norm of the difference between the histogram and the underlying distribution.

2. sskernel - implements kernel density estimation with a single globally-optimized bandwidth.

  1. ssvkernel - implements kernel density estimation with a locally variable bandwidth.

Dependencies: These functions in this package depend on NumPy for various operations including fast-fourier transforms and histogram generation.

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