# smax: The softmax function. In ohenery: Modeling of Ordinal Random Variables via Softmax Regression

## Description

The softmax function: exponentiate a vector and then normalize.

## Usage

 1 smax(eta, g = NULL) 

## Arguments

 eta numeric array of the odds. The odds are de-meaned within each group. g a vector giving the group indices. If NULL, then we assume only one group is in consideration.

## Details

Given vector η for a single group, essentially computes vector μ defined by

μ_i = \frac{\exp{η_i}}{∑_j \exp{η_j}}.

Note that this computation should be invariant with respect to level shifts of the η, and thus we de-mean the odds first.

## Value

the exponentiated data normalized. For the row-wise version, each row is soft maxed.

## Note

This function can deal with overflow in a semi-coherent way.

## Author(s)

Steven E. Pav shabbychef@gmail.com

normalize, inv_smax.
 1 2 3 4 # we can deal with large values: set.seed(2345) eta <- rnorm(12,sd=1000) smax(eta)