Open cbergmeir opened 4 years ago
when using, e.g.: size=c(2,2), the function exits with a seg fault, but should better report that Jordan networks with more than one hidden layer don't make much sense, as their feedback comes directly from the output layer.
library(RSNNS)
data(snnsData) inputs <- snnsData$laser_1000.pat[,inputColumns(snnsData$laser_1000.pat)] outputs <- snnsData$laser_1000.pat[,outputColumns(snnsData$laser_1000.pat)]
patterns <- splitForTrainingAndTest(inputs, outputs, ratio=0.15)
modelJordan <- jordan(patterns$inputsTrain, patterns$targetsTrain, size=c(2,2), learnFuncParams=c(0.1), maxit=100, inputsTest=patterns$inputsTest, targetsTest=patterns$targetsTest, linOut=FALSE)
when using, e.g.: size=c(2,2), the function exits with a seg fault, but should better report that Jordan networks with more than one hidden layer don't make much sense, as their feedback comes directly from the output layer.
library(RSNNS)
data(snnsData) inputs <- snnsData$laser_1000.pat[,inputColumns(snnsData$laser_1000.pat)] outputs <- snnsData$laser_1000.pat[,outputColumns(snnsData$laser_1000.pat)]
patterns <- splitForTrainingAndTest(inputs, outputs, ratio=0.15)
modelJordan <- jordan(patterns$inputsTrain, patterns$targetsTrain, size=c(2,2), learnFuncParams=c(0.1), maxit=100, inputsTest=patterns$inputsTest, targetsTest=patterns$targetsTest, linOut=FALSE)