# STA 414/2104, Spring 2006, Assignment #1, Test script.
#
# R. M. Neal

source("ass1.r")

# Read the data.

x.train <- as.matrix(read.table("a1-x-train",head=F))
x.test <- as.matrix(read.table("a1-x-test",head=F))
y.train <- scan("a1-y-train")
y.test <- scan("a1-y-test")

# Set the values of k to look at.

kvec <- 3^(0:4)

# Function to compute average squred error of predictions on the test set.

sq.err <- function (pred) mean((pred-y.test)^2)

# Run the various functions.

pred.knn <- knn (kvec,x.train,y.train,x.test)
cv <- knncv(kvec,x.train,y.train)
pred.knnsel <- knnsel(kvec,x.train,y.train,x.test)
pred.knncombo <- knncombo(kvec,x.train,y.train,x.test)

# Display results on first ten test/training cases.

cat("\nknn:\n")
print(pred.knn[1:10])

cat("\nknncv:\n")
print(cv[1:10,])

cat("\nknnsel:\n")
print(pred.knnsel$results[1:10])

cat("\nknncombo:\n")
print(pred.knncombo$results[1:10])

# Display summary of squared error.

cat("\nAverage squared errors on test set:\n")
print (apply(
  cbind (pred.knn, knnsel=pred.knnsel$results, knncombo=pred.knncombo$results),
  2, sq.err))

# Print extra stuff.

cat("\nknnsel$cv.sq.err:\n")
print(pred.knnsel$cv.sq.err)

cat("\nknnsel$k:\n")
print(pred.knnsel$k)

cat("\nknncombo$beta:\n")
print(pred.knncombo$beta)