luceChoice: Function implementing the Luce choice rule.
In edl: Toolbox for Error-Driven Learning Simulations with Two-Layer Networks

Description Usage Arguments Value Author(s) Examples

Function implementing the Luce choice rule.

1	luceChoice(value, all)

`value`	A positive value specifying a weight or activation (or comparable measure) of the choice option for which the choice probability is calculated
`all`	A positive array of the weights or activations of all possible choice options, including `value`

A value between [0,1]

Dorothee Hoppe

# load example data:
data(dat)

# add obligatory columns Cues, Outcomes, and Frequency:
dat$Cues <- paste("BG", dat$Shape, dat$Color, sep="_")
dat$Outcomes <- dat$Category
dat$Frequency <- dat$Frequency1
head(dat)
dim(dat)

# now use createTrainingData to sample from the specified frequencies:
train <- createTrainingData(dat)

# this training data can actually be used train network:
wm <- RWlearning(train)

# caculate activations of outcomes given the cue set blue_car
red_rabbit <- getActivations(getWM(wm), cueset = "red_rabbit")$red_rabbit

# caculate choice probability of outcomes given the cue set blue_car after 
# normalizing with rectified linear unit
luceChoice(red_rabbit["vehicle"], red_rabbit)
luceChoice(red_rabbit["plant"], red_rabbit)
luceChoice(red_rabbit["animal"], red_rabbit)

# note that when some activations are negative, this rule either should not be 
# applied, or negative values have to be corrected for, e.g., with applying a 
# rectified linear unit (relu)
blue_car <- getActivations(getWM(wm), cueset = "blue_car")$blue_car

## Not run: 
# this is should not be done without correction
luceChoice(blue_car["vehicle"], blue_car)
# use, e.g., function relu() on the raw values

## End(Not run)