library(ISLR)
library(MASS)
library(class)

auto = read.table("auto.data",header=T,na.strings="?")


auto$mpg01=rep(0,397)
auto$mpg01[auto$mpg>median(auto$mpg)]=1

sample(auto,size=length(mpg01)/2)

train_bools <- (auto$year %% 2 == 0)
train_data = auto[train_bools,]
test_data = auto[!train_bools,]



lda.fit=lda(mpg01 ~ horsepower + weight + cylinders + displacement,data=train_data)
lda.pred=predict(lda.fit, test_data)
mean(lda.pred$class!=test_data$mpg01,na.rm=T)

qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
qda.pred=predict(qda.fit,test_data,na.rm=T)
mean(qda.pred$class!=test_data$mpg01,na.rm=T)

glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
glm.probs=predict(glm.fit,test_data,type="response")
glm.pred=rep(0,199)
glm.pred[glm.probs>.5]=1
mean(glm.pred!=test_data$mpg01)

set.seed(1)
auto <- na.omit(auto)
train_bools <- (auto$year %% 2 == 0)
train_data = auto[train_bools,]
test_data = auto[!train_bools,]


train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
train.mpg01 = auto$mpg01[train_bools]

knn.pred = knn(train.X,test.X,train.mpg01,k=1)