santa-fe.r

require(nnet)
require(caret)

y = read.csv('http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe/A.dat', header=F)
y2 = read.csv('http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe/A.cont', header=F)
k = 40
n=100
y = y$V1/256
y2 = y2$V1/256
dat = sapply(1:k, function(a) c(rep(NA,a),y[1:(length(y)-a)]) )

model <- train(dat,
               y, 
                method='nnet', 
                linout=TRUE, 
                trace = FALSE)
ps <- predict(model, dat)
plot(seq(nrow(dat)), ps, type='l', col='blue')

# reinforced learning 
nr = 10
for (i in 1:nr){
  dat[1:(nrow(dat)-i),] = dat1[2:(nrow(dat)-i+1),] # shift up, overwrite oldest entry
  dat[1:(nrow(dat)-i),1] = model$finalModel$fitted.values[2:(nrow(dat)-i+1)]    # put newest predictions
  y[1:(nrow(y1)-i),] = y[2:(nrow(y1)-i+1),,drop=F] # drop 1st row of older targets
  model2 <- train(dat,
               y, 
                method='nnet',
                Wts = model$finalModel$weights,
                linout=TRUE, 
                trace = FALSE)
  print('if nmse of model2 increase stop and return model')
}



x = matrix(rev(y[(length(y)-k+1):length(y)]), nrow=1)

pred = rep(0, n)
for (i in seq(n)){
  ps <- predict(model, x)
  x[1,][2:k]=x[1,][1:(k-1)]
  x[1,1] = pred[i] =ps
}
plot(seq(n), pred, type='l', col='blue')
lines(y2[1:n])