kdpee (version 1.0.0)

kdpee: 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))

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

# NOT RUN {
Xu <- matrix(runif(1000 * 100), ncol=100)
kdpee(Xu)

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

# }