activationsOutcomes {edl}  R Documentation 
Calculate the activations for all outcomes in the data per learning event. The activation values are returned as data frame.
activationsOutcomes(
wmlist,
data,
split = "_",
select.outcomes = NULL,
init.value = 0,
normalize = FALSE
)
wmlist 
A list with weightmatrices, generated by

data 
Data frame with columns 
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 
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. 
Vector or list of activation values (see return.list
and fun
for the specific conditions, and the examples below).
The outcomes are selected based on the data with events, and not
necessarily all outcomes present in the weightmatrices. For example,
when the weightmatrices were first trained on another data set, some
outcomes may be present in the weightmatrices but not in the current
training data. To include these as well, the user can specify these
extra outcomes with the argument select.outcomes
.
Jacolien van Rij
getWeightsByCue
,
getWeightsByOutcome
Other functions for calculating activations:
activationsCueSet()
,
activationsEvents()
,
activationsMatrix()
,
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 < activationsOutcomes(wm, train)
head(activations)
# Now with selection of outcomes (note that 'dog' does
# not occur as outcome in the data):
activations2 < activationsOutcomes(wm, train,
select.outcomes = c("plant", "vehicle", "dog"))
head(activations2)
tail(activations2)