source("perceptron.R")

maxit <- 100
learn.rate <- 0.05

# load data from the file 'leftright.dat'
	dat0 <- read.table("leftright.dat") 

# randomize the order
      rand = sample(nrow(dat0))
	dat = dat0[rand,]

# put the data in vector x, and the class labels (0 or 1) in vector y
in.dim <- dim(dat)[2] - 1
x <- as.matrix(dat[,1:in.dim])
y <- as.matrix((dat[,in.dim+1] == dat[1,in.dim+1]) * 1)

# split the dataset
splitdat <- split.data(x, y, 10)

# train a perceptron
model <- perceptron(splitdat$x.train, splitdat$y.train, 
					maxit = maxit, learn.rate = learn.rate)

# report results
plot(1:maxit, model$errors, type="l", xlab="iter", ylab="error")

#cat("train accuracy: ", compute.accuracy(model, splitdat$x.train, splitdat$y.train), "\n")
cat("test accuracy: ", compute.accuracy(model, splitdat$x.test, splitdat$y.test), "\n")