SoftMax: Normalize a set of continuous values using SoftMax

In DMwR2: Functions and Data for the Second Edition of "Data Mining with R"

Description

Function for normalizing the range of values of a continuous variable using the SoftMax function (Pyle, 199).

Usage

SoftMax(x, lambda = 2, avg = mean(x, na.rm = T), std = sd(x, na.rm = T))

Arguments

x	A vector with numeric values
lambda	A numeric value entering the formula of the soft max function (see Details). Defaults to 2.
avg	The statistic of centrality of the continuous variable being normalized (defaults to the mean of the values in x).
std	The statistic of spread of the continuous variable being normalized (defaults to the standard deviation of the values in x).

Details

The Soft Max normalization consist in transforming the value x into

1 / [ 1+ exp( (x-AVG(x))/(LAMBDA*SD(X)/2*PI) ) ]

Value

An object with the same dimensions as x but with the values normalized

Author(s)

Luis Torgo [e-mail]

References

Pyle, D. (1999). Data preparation for data mining. Morgan Kaufmann.

Torgo, L. (2016) Data Mining using R: learning with case studies, second edition, Chapman & Hall/CRC (ISBN-13: 978-1482234893).

http://ltorgo.github.io/DMwR2

See Also

scale

Examples

## A simple example with the iris data set
data(iris)
summary(SoftMax(iris[["Petal.Length"]]))
summary(iris[["Petal.Length"]])

DMwR2 documentation built on May 2, 2019, 1:42 p.m.