Package 'kdpee' reference manual

Title:	Fast Multidimensional Entropy Estimation by k-d Partitioning
Description:	Estimate entropy of multidimensional data set.
Authors:	Olaf Mersmann [aut, cre] , Dan Stowell [aut, cph], Queen Mary University of London [cph]
Maintainer:	Olaf Mersmann <[email protected]>
License:	GPL (>= 3)
Version:	1.0.0
Built:	2024-12-08 06:48:57 UTC
Source:	CRAN

Fast Entropy Estimation of Multi-Dimensional Data

Description

Non-parametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning.

Usage

kdpee(X, ci = 0.95, lower = apply(X, 2, min), upper = apply(X, 2, max))
kdpee(X, ci = 0.95, lower = apply(X, 2, min), upper = apply(X, 2, max))

Arguments

`X`	[`matrix`] Data, one observation per row.
`ci`	[`numeric(1)`] Confidence threshold used to decide if a cell should be divided further. Defaults to 95%.
`lower`	[`numeric(n)`] Lower bound of the support of `X`.
`upper`	[`numeric(n)`] Upper bound of the support of `X`.

Value

Differential entropy estimate.

References

D. Stowell and M. D. Plumbley Fast multidimensional entropy estimation by k-d partitioning. IEEE Signal Processing Letters 16 (6), 537–540, June 2009. http://dx.doi.org/10.1109/LSP.2009.2017346

Examples

Xu <- matrix(runif(1000 * 100), ncol=100)
kdpee(Xu)

Xn <- matrix(rnorm(1000 * 100), ncol=100)
kdpee(Xn)

Xu <- matrix(runif(1000 * 100), ncol=100)
kdpee(Xu)

Xn <- matrix(rnorm(1000 * 100), ncol=100)
kdpee(Xn)

Package 'kdpee'