edl (version 1.1)

activationsOutcomes: Calculate the activations for all outcomes in the data.

Description

Calculate the activations for all outcomes in the data per learning event. The activation values are returned as data frame.

Usage

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

Arguments

wmlist

A list with weightmatrices, generated by RWlearning or updateWeights, or a single weightmatrix (matrix).

data

Data frame with columns Cues and Outcomes. Number of rows should be the same as the number of weightmatrices in wmlist.

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 data). 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.

Value

Vector or list of activation values (see return.list and fun for the specific conditions, and the examples below).

Notes

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.

See Also

getWeightsByCue, getWeightsByOutcome

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

Examples

# 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)

# 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)

# }