README.md
In bquast/sigmoid: Sigmoid Functions for Machine Learning

sigmoid

Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.

sigmoid demonstration

The stable version can be installed from CRAN using:

install.packages('sigmoid')

The development version, to be used at your peril, can be installed from GitHub using the remotes package.

if (!require('remotes')) install.packages('remotes')
remotes::install_github('bquast/sigmoid')

Following installation, the package can be loaded using:

The sigmoid() function returns the sigmoid value of the input(s), by default this is done using the standard logistic function.

library(sigmoid)
sigmoid(3)
#> [1] 0.9525741

Inputs can also be tensors, such as vectors, matrices, or arrays.

sigmoid(-5:5)
#>  [1] 0.006692851 0.017986210 0.047425873 0.119202922 0.268941421 0.500000000
#>  [7] 0.731058579 0.880797078 0.952574127 0.982013790 0.993307149
sigmoid( matrix(-3:5,nrow=3) ) # etc.
#>            [,1]      [,2]      [,3]
#> [1,] 0.04742587 0.5000000 0.9525741
#> [2,] 0.11920292 0.7310586 0.9820138
#> [3,] 0.26894142 0.8807971 0.9933071

The sigmoid() function is a wrapper, which by default uses the logistic() function, it can also use other methods.

sigmoid( -5:5, method='Gompertz' )
#>  [1] 3.507389e-65 1.942338e-24 1.892179e-09 6.179790e-04 6.598804e-02
#>  [6] 3.678794e-01 6.922006e-01 8.734230e-01 9.514320e-01 9.818511e-01
#> [11] 9.932847e-01

These functions can also be accessed directly.

logistic(1:5)
#> [1] 0.7310586 0.8807971 0.9525741 0.9820138 0.9933071
Gompertz(-1:-5)
#> [1] 6.598804e-02 6.179790e-04 1.892179e-09 1.942338e-24 3.507389e-65

These mappings are similar but not identical.

library(ggplot2)

input = -5:5

df = data.frame(input, logistic(input), Gompertz(input))

ggplot(df, aes(input, logistic(input))) + geom_line() +
  geom_line(aes(input,Gompertz(input)), colour='red')