diff --git a/hw3/.RData b/hw3/.RData
new file mode 100644
index 0000000..d058cb3
Binary files /dev/null and b/hw3/.RData differ
diff --git a/hw3/.Rhistory b/hw3/.Rhistory
new file mode 100644
index 0000000..c181128
--- /dev/null
+++ b/hw3/.Rhistory
@@ -0,0 +1,426 @@
+auto = read.table("auto.data",header=T,na.strings="?")
+length(x=auto$mpg)
+glm
+glm.pred
+help(rep)
+glm.pred=rep(FALSE,397)
+glm.pred
+medium(auto$mpg)
+median(auto$mpg)
+glm.pred[auto$mpg>median(auto$mpg)]=T
+glm.pred
+contour(auto)
+contour(glm.pred ~ auto$mpg)
+contour(glm.pred,auto$mpg)
+help(contour)
+contour(auto$mpg,auto$horsepower,glm.pred)
+glm.pred
+length(glm.pred)
+table(glm.pred,auto$mpg)
+table(glm.pred,auto$mpg,auto$horsepower)
+glm.pred=rep(0,397)
+glm.pred[auto$mpg>median(auto$mpg)]=1
+glm.pred
+auto$mpg01=rep(0,397)
+auto$mpg01[auto$mpg>median(auto$mpg)]=1
+auto$mpg01
+auto$mpg01
+auto$mpg01
+plots(auto)
+plot(auto)
+boxplot(auto)
+boxplot.matrix(auto)
+help(boxplot)
+boxplot(auto$mpg01,auto)
+boxplot(auto$mpg,auto)
+boxplot(auto$mpg)
+boxplot(auto)
+boxplot(mpg01 ~ auto)
+boxplot(mpg01 ~)
+boxplot(auto$mpg01 ~ auto)
+attach(auto)
+boxplot(mpg01)
+boxplot(mpg01 ~ auto)
+boxplot(mpg01 ~ auto,auto)
+boxplot(mpg01 ~ auto,data = auto)
+help(plot.table)
+plot.table(auto)
+help(plot.table)
+plot(auto)
+plot(auto,t="box")
+help(plot.table)
+help(plot.table,plot.frame=1)
+help(plot.table)
+help(plot.table,frame.plot=1)
+help(plot.table)
+help(plot.table,frame.plot=is.num)
+help(plot.table)
+plot(auto,t="box",frame.plot=1)
+plot(auto,frame.plot=1)
+plot(auto,frame.plot=1)
+plot(auto,frame.plot=is.num)
+plot(auto,frame.plot=0)
+plot(auto,frame.plot="0")
+plot(auto,frame.plot="1")
+plot(auto,frame.plot=TRUE)
+plot(auto,frame.plot=FALSE)
+plot(auto,frame.plot=TRUE)
+plot(auto,frame.plot=T)
+plot(auto,frame.plot=1)
+boxplot(mpg~mpg01,auto)
+boxplot(mpg01 ~ mpg,auto)
+boxplot(mpg01 ~ *,auto)
+boxplot(mpg01 ~ ,auto)
+boxplot(mpg01 ~ auto,auto)
+boxplot(mpg01,auto)
+boxplot(auto)
+boxplot(auto,y=mpg01)
+boxplot(auto,y=mpg)
+boxplot(data = auto)
+boxplot(auto)
+help(for)
+plot(auto,frame.plot=1)
+plot(auto)
+names(auto)
+auto$name
+help(sample)
+x <- 1:12
+x
+sample(x)
+help(sample)
+sample(x,replace=T)
+sample(x,replace=T)
+sample(x,replace=F)
+c
+x
+sample(x,replace=T)
+x
+help(sample)
+sample(x[x>9])
+sample(x[x>8])
+help(sample)
+x <- 1:10
+sample(x[x>8])
+sample(x[x>])
+help(sample)
+help(sample)
+help(sample)
+sample(auto,size=length(mpg01)/2)
+x <- length(mpg01)
+sample(x,size=length(mpg01)/2)
+auto[sample(x,size=length(mpg01)/2)]
+auto$mpg[sample(x,size=length(mpg01)/2)]
+help(data.frame)
+data.frame(
+help(data.frame)
+auto[sample(x,size=length(mpg01)/2)]
+train = sample(x,size=length(mpg01)/2)
+train = 
+auto[train]
+auto$mpg[train]
+auto$mpg[train,]
+auto$mpg[train]
+auto$mpg[23]
+auto$mpg[228]
+auto$mpg[391]
+auto.test=auto[!train]
+auto.train=auto[train]
+auto.test
+summary(auto.test)
+train=(mpg<15)
+train
+train = (sample(x,size=length(mpg01)/2))
+train
+head(auto)
+auto[,train[
+auto[,train]
+train
+help(contains)
+auto[1,train]
+train
+auto[[,train]]
+auto[[1,train]]
+autoi
+head(auto)
+head(auto[sample(nrow(auto),397/2)])
+head(auto[sample(nrow(auto),3)])
+data = data.frame(auto)
+data
+head(data[sample(nrow(data),3)])
+nrow(data)
+head(data[sample(ncol(data),3)])
+head(data[sample(ncol(data),397/2)])
+head(data[sample(ncol(data),3)])
+head(data[sample(ncol(data),3)])
+head(data[sample(ncol(data),3)])
+head(data[sample(ncol(data),3)])
+head(data[,sample(ncol(data),3)])
+head(data[,sample(ncol(data),3)])
+head(data[,sample(ncol(data),3)])
+head(data[,sample(ncol(data),3)])
+head(data[,sample(ncol(data),3)])
+head(data[sample(ncol(data),3),])
+head(data[sample(ncol(data),3),])
+head(data[sample(ncol(data),3),])
+head(data[sample(nrow(data),3),])
+head(data[sample(nrow(data),397/2),])
+head(data[sample(nrow(data),397/2),])
+head(data[sample(nrow(data),397/2),])
+head(data[sample(nrow(data),397/2),])
+head(data[sample(nrow(data),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+head(auto[sample(nrow(auto),397/2),])
+train = auto[sample(nrow(auto),397/2),]
+[sample(nrow(auto),397/2),]
+sample(nrow(auto),397/2)
+train sample(nrow(auto),397/2)
+train = sample(nrow(auto),397/2)
+autp[train,]
+auto[train,]
+train = sample(nrow(auto),397/2)
+head(auto[train,])
+head(auto[!train,])
+traindata = auto[train,]
+testdata = auto[!train,]
+testdata
+traindata
+length(traindata)
+length(traindata$mpg)
+198*2
+summary(testdata)
+testdata = auto[!train]
+testdata
+testdata = auto[!train,]
+train
+summary(train)
+names(train)
+head(traindata)
+
+testdata = auto[!train,]
+testdata
+!train
+train
+?sample
+sort(train)
+train_vals = train
+train = rep(false,397)
+train = rep(F,397)
+train
+help for
+?for
+?for
+help)for)
+help(for)
+help(for)
+help lapply()
+?lapply
+sapply(train,
+?sapply
+sapply(train,
+?sapply
+train[train_vals]=T
+train
+traindata = auto[train,]
+traindata
+length(auto)
+length(traindata)
+length(traindata$mpg)
+testdata=auto[!train,]
+length(testdate$mpg)
+length(testdata$mpg)
+training_indices = sample(nrow(auto),397/2)
+train_bools = rep(F,length(auto$mpg))
+train_bools[training_indices]=T
+head(train_bools)
+length(train_bools)
+train_data = auto[train_bools,]
+test_data = auto[!train_bools,]
+summary(train_data)
+summary(test_data)
+lda.fit
+library(MASS)
+lda.fit
+lda()
+detach(auto)
+mpg01
+mpg
+attach(test_data)
+mpg01
+names()
+names(test_data)
+ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
+detach(test_data)
+ldf.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
+lda.fit
+lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
+lda.fit
+summary(lda.fit)
+coefficients(lda.fit)
+plot(lda.fit)
+lda.pred=predict(lda.fit,test_data)
+lda.pred=predict(lda.fit, !training_bools)
+lda.pred=predict(lda.fit, !training_indices)
+test_data
+lda.pred=predict(lda.fit, test_data)
+lda.pred
+plot(lda.pred)
+names(lda.pred)
+lda.class=lda.pres$class
+lda.class=lda.pred$class
+table(lda.class,testdata)
+table(lda.class,test_data)
+length(lda.class)
+length(test_data)
+table(lda.class,test_data$mpg01)
+mean(lda.class==test_data$mpg01)
+sum(lda.pred$posterior[,1]>=.5)
+sum(lda.pred$posterior[,1]<.5)
+lda.pred$posterior[,1]
+sum(lda.pred$posterior<.5)
+lda.pred$posterior
+lda.pred$posterior<5
+lda.pred$posterior<.5
+sum(lda.pred$posterior<.5)
+sum(lda.pred$posterior<.5[,1])
+sum(lda.pred$posterior<.5[1])
+sum(lda.pred$posterior<.5[2])
+lda.pred$posterior<.5[2]
+lda.pred$posterior<.5
+lda.pred$posterior
+lda.pred$posterior[,1]
+lda.pred$posterior[1,]
+lda.pred$posterior[,2]
+lda.pred$posterior[,1]
+lda.pred$posterior[,1]>.5
+sum(lda.pred$posterior[,1]>.5)
+sum.bool(lda.pred$posterior[,1]>.5)
+?sum
+sum.bool(lda.pred$posterior[,1]>.5,na.rm=T)
+sum(lda.pred$posterior[,1]>.5,na.rm=T)
+sum(lda.pred$posterior[,1]>.5)
+sum(lda.pred$posterior[,1]>.5,na.rm=T)
+sum(lda.pred$posterior[,1]>=.5,na.rm=T)
+sum(lda.pred$posterior[,1]<.5,na.rm=T)
+mean(lda.pred$[,1]==test_data,na.rm=T)
+lda.pred
+lda.pred$class
+lda.pred$class==test_data$mpg01
+mean(lda.pred$class==test_data$mpg01,na.rm=T)
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
+lda.fit
+mean(lda.pred$class==test_data$mpg01,na.rm=T)
+lda.pred=predict(lda.fit, test_data)
+mean(lda.pred$class==test_data$mpg01,na.rm=T)
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+train_data == test_data
+train_data$mpg01 == test_data$mpg01
+lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
+lda.pred=predict(lda.fit, test_data)
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+lda.pred
+lda.pred$posterior[,1]
+summary(lda.fit)
+lda.fit
+lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=test_data)
+lda.fit
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+lda.pred=predict(lda.fit, test_data)
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+head(lda.pred)
+lda.fit=lda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
+lda.pred=predict(lda.fit, test_data)
+head(lda.pred)
+mean(lda.pred$class!=test_data$mpg01,na.rm=T)
+qda.fit=qda(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data)
+qda.fit
+qda.class=predict(qda.fit,test_data)$class
+qda.class=predict(qda.fit,test_data,na.rm=T)$class
+qda.class=predict(qda.fit,test_data)$class
+qda.class
+mean(qda.pred$class!=test_data$mpg01,na.rm=T)
+qda.pred=predict(qda.fit,test_data)
+qda.pred=predict(qda.fit,test_data,na.rm=T)
+mean(qda.pred$class!=test_data$mpg01,na.rm=T)
+glm.fit=glm(mpg01 ~ horsepower + weight + acceleration + displacement,data=train_data,family=binomial)
+glm.probs=predict(glm.fit,test_data,type="response")
+glm.pred=rep(0,199)
+glm.pred[glm.probs>.5]=1
+table(glm.pred,test_data$mpg01)
+mean(glm.pred!=test_data$mpg01)
+library(class)
+?cbind
+?knn
+knn.fit = knn(train_data,test_data,auto$mpg01[training_indices])
+knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1)
+knn.fit = knn(train_data,test_data,auto$mpg01[training_indices],k=1)
+?knn
+training_indices
+train_bools
+knn.fit = knn(train_data,test_data,auto$mpg01[train_bools],k=1)
+sdf = (mpg01<1)
+sdf = (auto$mpg01<1)
+sdf
+train_bools
+cbind(horsepower,displacement)
+cbind(train_data$horsepower,displacement)
+cbind(train_data$horsepower,train_data$displacement)
+cbind(auto$horsepower,auto$displacement)[train_bools]
+cbind(auto$horsepower,auto$displacement)[train_bools,]
+cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
+cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
+train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
+test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
+train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
+test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
+train.mpg01 = auto[train_bools]
+train.mpg01 = auto$mpg01[train_bools]
+test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!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$mpg01[train_bools]
+set.seed(56)
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)
+?cbind
+?Knn
+?knn
+train.X = cbind(auto$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]
+train.X = train.X[!is.na(train.X)]
+test.X = data.frame(test.X,
+train.mpg01 = train.mpg01[!is.na(train.mpg01)]
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)
+length(train.mpg01)
+length(test.X)
+text.X
+test.X
+test.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[!train_bools,]
+length(test.X)
+test.X
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)
+train.X
+train.X = cbind(auto$horsepower,auto$displacement,auto$weight,auto$acceleration)[train_bools,]
+train.X
+test.X
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)
+?knn
+length(train.X)
+length(train.X[1,])
+length(train.X[,1])
+?knn
+plot(auto)
+train.X = cbind(auto$horsepower,auto$displacement)[train_bools,]
+test.X = cbind(auto$horsepower,auto$displacement)[!train_bools,]
+train.mpg01 = auto$mpg01[train_bools]
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)
+train.X
+test.X
+train.mpg01
+knn.pred = knn(train.X,test.X,train.mpg01,k=1)
+q()