ANN

install.packages('neuralnet')

library("neuralnet")

#Going to create a neural network to perform sqare rooting

#Type ?neuralnet for more information on the neuralnet library

#Generate 50 random numbers uniformly distributed between 0 and 100

#And store them as a dataframe

traininginput <-  as.data.frame(runif(50, min=0, max=100))

trainingoutput <- sqrt(traininginput)

#Column bind the data into one variable

trainingdata <- cbind(traininginput,trainingoutput)

colnames(trainingdata) <- c("Input","Output")

#Train the neural network

#Going to have 10 hidden layers

#Threshold is a numeric value specifying the threshold for the partial

#derivatives of the error function as stopping criteria.

net.sqrt <- neuralnet(Output~Input,trainingdata, hidden=10, threshold=0.01)

print(net.sqrt)

#Plot the neural network

plot(net.sqrt)

#Test the neural network on some training data

testdata <- as.data.frame((1:10)^2) #Generate some squared numbers

net.results <- compute(net.sqrt, testdata) #Run them through the neural network

#Lets see what properties net.sqrt has

ls(net.results)

#Lets see the results

print(net.results$net.result)

#Lets display a better version of the results

cleanoutput <- cbind(testdata,sqrt(testdata),

                         as.data.frame(net.results$net.result))

colnames(cleanoutput) <- c("Input","Expected Output","Neural Net Output")

print(cleanoutput)