CalcJSDivergence: Calculate Jensen-Shannon Divergence

In textmineR: Functions for Text Mining and Topic Modeling

Description

This function calculates the Jensen Shannon Divergence for the rows or columns of a numeric matrix or for two numeric vectors.

Usage

CalcJSDivergence(x, y = NULL, by_rows = TRUE)

Arguments

x	A numeric matrix or numeric vector
y	A numeric vector. y must be specified if x is a numeric vector.
by_rows	Logical. If x is a matrix, should distances be calculated by rows?

Value

If x is a matrix, this returns an square and symmetric matrix. The i,j entries correspond to the Hellinger Distance between the rows of x (or the columns of x if by_rows = FALSE). If x and y are vectors, this returns a numeric scalar whose value is the Hellinger Distance between x and y.

Examples

x <- rchisq(n = 100, df = 8)
y <- x^2
CalcJSDivergence(x = x, y = y)

mymat <- rbind(x, y)
CalcJSDivergence(x = mymat)

Example output

Loading required package: Matrix
[1] 0.02806598
           x          y
x 0.00000000 0.02806598
y 0.02806598 0.00000000

textmineR documentation built on June 28, 2021, 9:08 a.m.