# NOT RUN {
# 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$Frequency1
head(dat)
dim(dat)
# now use createTrainingData to sample from the specified frequencies:
train <- createTrainingData(dat)
# this training data can actually be used train network:
wm <- RWlearning(train)
# final weight matrix:
getWM(wm)
# Inspect the change in connection weights
# for cue=car
cueweights <- getWeightsByCue(wm, cue='car')
head(cueweights)
emptyPlot(nrow(cueweights), c(-.5,1), h0=0,
main="Cue='car'", ylab='connection weights', xlab='learning events')
lines(cueweights$vehicle)
lines(cueweights$plant, col='red', lty=4)
lines(cueweights$animal, col='red', lty=2)
legend_margin('topright', legend=c('animal', 'plant', 'vehicle'),
col=c(2,2,1), lty=c(2,4,1), lwd=1, bty='n')
# }
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