minmax: Minmax normalization

In kerntools: Kernel Functions and Tools for Machine Learning Applications

Minmax normalization

Description

Minmax normalization. Custom min/max values may be passed to the function.

Usage

minmax(X, rows = FALSE, values = NULL)

Arguments

X	Numeric matrix or data.frame of any size.
rows	If TRUE, the minmax normalization is done by row; otherwise, it is done by column. (Defaults: FALSE)
values	(optional) A list containing two elements, the "max" values and the "min" values. If no value is passed, the typical minmax normalization (which normalizes the dataset between 0 and 1) is computed with the observed maximum and minimum value in each column (or row) of X.

Value

Minmax-normalized X.

Examples

dat <- matrix(rnorm(100),ncol=10,nrow=10)
dat_minmax <- minmax(dat)
apply(dat_minmax,2,min) ## Min values = 0
apply(dat_minmax,2,max) ## Max values = 1
# We can also explicitly state the max and min values:
values <- list(min=apply(dat,2,min),max=apply(dat,2,max))
dat_minmax <- minmax(dat,values=values)

kerntools documentation built on April 3, 2025, 7:52 p.m.