Description Usage Arguments Value Examples
Runs a genetic algorithm to find optimal parameter settings based on expert alignment determinations.
1 2 | optimize.features(set, ranking, num = 200, step = 45, replication = 5,
list = FALSE)
|
set |
the output from set.generation function, which is a two element list containing the original word pairs and possible alignments. |
ranking |
a vector specifying the correct alignment in the candidate alignments generated. |
num |
number of populations in the genetic algorithm |
step |
number of iterations in the genetic algorithm |
replication |
number of independent genetic algorithm optimizations. |
list |
Whether or not to return the entire result of the genetic algorithm which contains a big list of possible parameters and corresponding performance in each independent replication |
If list=FALSE, the function returns a single vector representing the optimal parameter values.
If list=TRUE, the function returns a list where each top-level element corresponds to the number of replications. Within each replicate, two elements are returned:
performance |
Performance values for each population. |
optimized_parameters |
Feature values at each step in the optimization process. |
1 2 3 4 5 6 7 8 9 10 | # This simplified example illustrates the supervised learning workflow
# some cognate data
data<-data.frame(dog=c('dog','perro'),cat=c('cat','gato'),rat=c('rat','rata'))
# generate training data for linguist (not written)
M1<-generate.training(raw.data=data, search.size=100)
# optimize features using expert determinations: 1,1,1
optimize.features(set=M1, ranking=c(1,1,1),
num=20, step=5, replication=2, list=FALSE)
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