activationsCueSet: Calculate the change in activation for a specific cue or set...
In edl: Toolbox for Error-Driven Learning Simulations with Two-Layer Networks

Description Usage Arguments Value Notes Author(s) See Also Examples

Calculate the change in activation for a specific cue or set of cues for all outcomes (or a subset) in the weightmatrices.

activationsCueSet(
  wmlist,
  cueset,
  split = "_",
  select.outcomes = NULL,
  init.value = 0,
  normalize = FALSE
)

`wmlist`	A list with weightmatrices, generated by `RWlearning` or `updateWeights`, or a single weightmatrix (matrix).
`cueset`	String, specifying the cue set for which to calculate change in activation.
`split`	String, separator between cues and/or outcomes.
`select.outcomes`	Optional selection of outcomes to limit (or expand) the number of activations that are returned. The value of NULL (default) will return all activations (for each outcome in `wmlist`). Note that specified values that are not in the weightmatrix will return the initial value without error or warning. Please use `getValues` for returning all outcomes in the data.
`init.value`	Value of activations for non-existing connections. Typically set to 0.
`normalize`	Logical: whether or not the activation is normalized by dividing the total activation by the number of cues. Default is FALSE. If set to TRUE, the activation reflects the average activation per cue.

List of data frames. For each cueset defined in cueset, a dataframe of activation values is provided. These are returned as a list, with the cuesets as names.

The outcomes are selected based on the weightmatrices, and not necessarily all outcomes present in the training data.

Jacolien van Rij

getWeightsByCue, getWeightsByOutcome

Other functions for calculating activations: activationsEvents(), activationsMatrix(), activationsOutcomes(), getActivations()

# 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)

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

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

# Now we calculate the activations for all outcomes
# per event:
activations <- activationsCueSet(wm, cueset="BG_bicycle_red")
names(activations)
head(activations[[1]])

# plot:
a1 <- activations[[1]]
emptyPlot(nrow(a1), range(a1),
    xlab="Learning events", ylab="Activations",
    xmark=TRUE, ymark=TRUE, las=1)
for(i in 1:ncol(a1)){
    lines(a1[,i], col=i, lty=i)
}
legend_margin('topleft', legend=colnames(a1),
    col=1:ncol(a1), lty=1:ncol(a1),
    bty='n', cex=.75)