compute: Takes a pool of trained neural networks, selects the most fit...

In ahunteruk/RNeat: Neuroevolution of Augmenting Topologies - NEAT

Description

Takes a pool of trained neural networks, selects the most fit and uses it to predict the depend variable from the input data

Usage

compute(rneatsim, data)

Arguments

rneatsim	Is the class rneatneuralnet created using rneatneuralnet function
data	is input data

Value

data.frame class with predicted dependent variable

Examples

#Generate traing data y = sqrt(x)
trainingData <- as.data.frame(cbind(sqrt(seq(0.1,1,0.1)),seq(0.1,1,0.1)))
colnames(trainingData) <- c("y","x")

#Train the neural network for 5 generations, and plot the fitness
rneatsim <- rneatneuralnet(y~x,trainingData,5)
plot(rneatsim)

#Continue training the network for another 5 generations
rneatsim <- rneatneuralnetcontinuetraining(rneatsim,5)
plot(rneatsim)

#Construct some fresh data to stick through the neural network and hopefully get square rooted
liveData <- as.data.frame(seq(0.1,1,0.01))
colnames(liveData) <- c("x")

liveDataExpectedOutput <- sqrt(liveData)
colnames(liveDataExpectedOutput) <- "yExpected"

#Pass the data through the network
results <- compute(rneatsim,liveData)

#Calculate the difference between yPred the neural network output, and yExpected the actual square root of the input
error <- liveDataExpectedOutput[,"yExpected"] - results[,"yPred"]
results <- cbind(results,liveDataExpectedOutput,error)
print(results)

dev.new()
layout(matrix(c(3,3,3,1,4,2), 2, 3, byrow = TRUE),heights=c(1,2))
plot(x=results[,"x"],y=results[,"yExpected"],type="l", main="Neural Network y=sqrt(x) expected vs predicted",xlab="x",ylab="y")
lines(x=results[,"x"],y=results[,"yPred"],col="red",type="l")
legend(x='bottomright', c('yExpected','yPredicted'), col=c("black","red"), fill=1:2, bty='n')
plot(rneatsim)
plot(rneatsim$simulation)

ahunteruk/RNeat documentation built on May 12, 2019, 2:31 a.m.