NMI {CommKern}

Normalized mutual information (NMI)

Description

Description of the normalized mutual information function.

Usage

NMI(a, b, variant = c("max", "min", "sqrt", "sum", "joint"))

Arguments

a

a vector of classifications; this must be a vector of characters, integers, numerics, or a factor, but not a list.

b

a vector of classifications

variant

a string in ('max', 'min', 'sqrt', 'sum', 'joint') that calculates different variants of the NMI. The default use is 'max'.

Details

In information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between two variables, or the quantification of the 'amount of information' obtained about one random variable by observing the other random variable. The normalization of the MI score scales the results between 0 (no mutual information) and 1 (perfect correlation). The five options for the variant - max, min, square root, sum, and joint - all relate to the denominator of the NMI = MI / D.

Value

a scalar with the normalized mutual information (NMI).

See Also

adj_RI, purity

Examples

x <- c(1, 3, 1, 2, 3, 3, 3, 2, 1, 2, 1, 2)
y <- c(1, 1, 2, 3, 2, 1, 3, 1, 2, 3, 3, 2)

NMI(x, y, variant = 'max')
NMI(x, y, variant = 'min')
NMI(x, y, variant = 'sqrt')
NMI(x, y, variant = 'sum')
NMI(x, y, variant = 'joint')

x <- c("A", "A", "A", "A", "B", "C", "A", "B", "B", "C")
y <- c("B", "A", "A", "A", "C", "C", "B", "C", "D", "D")
NMI(x, y, variant = 'max')
NMI(x, y, variant = 'min')
NMI(x, y, variant = 'sqrt')
NMI(x, y, variant = 'sum')
NMI(x, y, variant = 'joint')