rneatneuralnet: Create a new pool of neural networks trained using the NEAT...

Description Usage Arguments Value Examples

Description

Create a new pool of neural networks trained using the NEAT algorithm using formula notation

Usage

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rneatneuralnet(formula, trainingData, nTrainingGenerations = 10,
  maxNumberOfNodes = 500, speciesPopulation = 200)

Arguments

formula

specifies the dependent and explantory varibles using a formula

trainingData

Is the data used to train the networks

nTrainingGenerations

Number of generations / breeding cycles to use in the genetic mating

maxNumberOfNodes

The maximum number of neural network nodes

speciesPopulation

The maximum bumber of species

Value

rneatneuralnet class with pool of genomes and training data

Examples

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#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.