search for: actfn

...a <- mlptrain(inp=trainInput, neurons=trainNeurons, out=trainAnswers, it=1000) ## To call this network, we can see how it works on a set of known positive and negative values testInput <- matrix(-2:2) mlp(testInput, trainingData$weight, trainingData$dist, trainingData$neurons, trainingData$actfns) Will vary - but output on my computer was: [,1] [1,] 0.001043291 [2,] 0.001045842 [3,] 0.072451270 [4,] 0.950744548 [5,] 0.950931168 So it's instead classifying the negatives as 0 and positives as 1 (getting close to, anyhow - increasing the number of iterations, ie it=5000, mak...