getActivations {edl}  R Documentation 
Calculate the activations for all or specific outcomes on the basis of a set of cues. This function combines the various functions to calculate the activations.
getActivations( wmlist, data = NULL, cueset = NULL, split = "_", select.outcomes = NULL, init.value = 0, normalize = FALSE )
wmlist 
A list with weightmatrices, generated by

data 
Data frame with columns 
cueset 
String, specifying the cue set for which to calculate
change in activation. Only will be used when 
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. See examples
for how to use this argument in combination with 
init.value 
Value of activations for nonexisting 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: when data
is provided, a list is returned with
the outcome activations for each learning event;
when cueset
is provided, a list is returned with data frames
of outcome activations. See examples.
Jacolien van Rij
getWeightsByCue
,
getWeightsByOutcome
Other functions for calculating activations:
activationsCueSet()
,
activationsEvents()
,
activationsMatrix()
,
activationsOutcomes()
# load example data: data(dat) # add obligatory columns Cues, Outcomes, and Frequency: dat < droplevels(dat[1:3,]) dat$Cues < paste("BG", dat$Shape, dat$Color, sep="_") dat$Outcomes < dat$Category dat$Frequency < dat$Frequency1 head(dat) # now use createTrainingData to sample from the specified frequencies: train < createTrainingData(dat) head(train) # this training data can actually be used train network: wm < RWlearning(train) # With this data we illustrate four different # ways to retrieve activation changes. # Situation I: return activations for each event act1 < getActivations(wm, data=train) head(act1) # plotting activations for each event doesn't provide very # useful info: plot(act1$Activation, type='l', ylim=c(0,1), col=alpha(1), ylab='Activation') # these lines may be more interpretable: n < which(act1$Outcomes=="animal") lines(n, act1$Activation[n], col=alpha(2), lwd=2) n < which(act1$Outcomes=="plant") lines(n, act1$Activation[n], col=alpha(3), lwd=2) # Situation II: return activations for each events # for all outcomes act2 < getActivations(wm, data=train, select.outcomes=TRUE) head(act2) plot(act2$plant, type='l', ylim=c(0,1), col=alpha(1), ylab='Activation') n < which(act2$Outcomes=="plant") rug(n, side=1) lines(n, act2$plant[n], lwd=2, col=alpha(3)) n < which(act2$Outcomes!="plant") lines(n, act2$plant[n], lwd=2, col=alpha(2)) legend('topright', legend=c("all events", "outcome present", "outcome absent"), col=c(1,alpha(3),alpha(2)), lwd=c(1,2,2), bty='n') # Situation III: return activations for specific cuesets # for all outcomes act3 < getActivations(wm, cueset=c("BG_cat_brown", "BG_flower_brown")) str(act3) a31 < act3[["BG_flower_brown"]] # or act3[[1]] plot(a31$plant, type='l', ylim=c(0,1), col=alpha(1), main="BG_flower_brown", ylab='Activation') lines(a31$animal,col=2) rug(which(train$Cues == "BG_flower_brown"), side=1) legend('topright', legend=c("plant", "animal"), col=c(1,2), lwd=1, bty='n') # Situation IV: return activations for a static weight matrix # Note: only with cueset (final < getWM(wm)) act4 < getActivations(final, cueset=unique(train$Cues)) act4