lab1: part 1 code cleanup

lab1/assignment1.R

@@ -19,7 +19,7 @@ missclass=function(X,Xfit){
   n=length(X)
   return(1-sum(diag(table(X,Xfit)))/n)
 }
-
+# -------- part 2 ---------
 # Create model from training data
 model_train <- kknn(as.factor(V65)~., train, train, k=30, kernel="rectangular")
 model_test <- kknn(as.factor(V65)~., train, test, k=30, kernel="rectangular")
@@ -42,6 +42,8 @@ missclass_test <- missclass(test$V65,fitted_test)
 print(missclass_train)
 print(missclass_test)
 
+# -------- part 3 ---------
+
 # Get all cases where the target is 8
 digit_8_cases <- which(train$V65 == "8")
 
@@ -65,7 +67,7 @@ for (case in hardest_cases) {
   plot_case(case, train)
 }
 
-
+# -------- part 4 ---------
 # Initialize numeric vectors for missclassification rates
 train_missclassification <- numeric(30)
 valid_missclassification <- numeric(30)
@@ -82,6 +84,8 @@ for (i in 1:30) {
 print(train_missclassification)
 print(valid_missclassification)
 
+# -------- part 5 ---------
+
 # Plot missclassification rates
 plot(1:30,valid_missclassification, ylim = c(0, max(valid_missclassification)), col="red", type="l")
 points(1:30, train_missclassification, col="blue", type="l")