diff --git a/lab2/assignment1.R b/lab2/assignment1.R
index 4923521cdd767f38a6563705c3e58471bc1e633b..b706dd3ddd11555b28535a73fd93e24c35d868ed 100644
--- a/lab2/assignment1.R
+++ b/lab2/assignment1.R
@@ -44,9 +44,11 @@ lambda_with_k_features = function(x_train, y_train, alpha, k = 3) {
   
   lambda_values <- fit_train$lambda
   
-  num_non_zero <- apply(coef_matrix != 0, 2, sum) # sum of num of cols wit non zero coeff, i.e. number of features
+  # sum of num of cols wit non zero coeff, i.e. number of features
+  num_non_zero <- apply(coef_matrix != 0, 2, sum)
   
-  lambda_values[num_non_zero == k] # lambda value with k non zero coeff, i.e. number of features
+  # lambda value with k non zero coeff, i.e. number of features
+  lambda_values[num_non_zero == k]
   
 }
 
diff --git a/lab2/assignment2.R b/lab2/assignment2.R
index c9039778de9b4d5dfedc6c4b2fbccef7546b729b..f1c95f1dbfadb59a2a8845f04969aec8f9b600ed 100644
--- a/lab2/assignment2.R
+++ b/lab2/assignment2.R
@@ -149,7 +149,8 @@ accuracy <- function(x, x_pred) {
 }
 
 F1 <- function(x, x_pred) {
-  return(2 * precision(x, x_pred) * recall(x, x_pred) / (precision(x, x_pred) + recall(x, x_pred)))
+  return(2 * precision(x, x_pred) * recall(x, x_pred) / (precision(x, x_pred) +
+                                                           recall(x, x_pred)))
 }
 
 y_hat4 <- predict(optimal_tree, newdata = test, type = "class")
diff --git a/lab2/assignment3.R b/lab2/assignment3.R
index 291fa3fbbdd68708dffb4dcea96f64996b663be9..6441e7a1d526b5ff96aee21aa57df5c26c993f8d 100644
--- a/lab2/assignment3.R
+++ b/lab2/assignment3.R
@@ -67,9 +67,8 @@ test = data[-id, ]
 
 scaler3 <- preProcess(train)
 
-train_scaled<- predict(scaler3, train)
+train_scaled <- predict(scaler3, train)
 
-test_scaled <- test
 test_scaled <- predict(scaler3, test)
 
 X_train <- train_scaled[, feature_column_names]
@@ -99,17 +98,45 @@ test_errors <- c()
 
 cost_function <- function(theta) {
   train_error <- MSE(y_train, as.matrix(X_train) %*% theta)
+  
+  
+  return(train_error)
+}
+
+theta <- rep(0, ncol(X_train))
+
+# res <- optim(
+#   par = theta,
+#   fn = cost_function,
+#   method = "BFGS",
+#   control = list(trace = 1, maxit = 200, REPORT = 1),
+# )
+
+for (i in 1:5000) {
+  
+  print(i)
+  res <- optim(
+    par = theta,
+    fn = cost_function,
+    method = "BFGS",
+    control = list(maxit = 1),
+  )
+  
+  print(res$value)
+  
+  theta <- res$par
+  
   test_error <- MSE(y_test, as.matrix(X_test) %*% theta)
   
-  train_errors <<- c(train_errors, train_error)
+  train_errors <<- c(train_errors, res$value)
   test_errors <<- c(test_errors, test_error)
   
-  return(train_error)
+  if (res$convergence == 0) {
+    break
+  }
 }
 
-res <- optim(par = rep(0, ncol(X_train)),
-             fn = cost_function,
-             method = "BFGS",)
+res$counts
 
 range <- c(train_errors[501:length(train_errors)], test_errors[501:length(test_errors)])
 
@@ -122,7 +149,7 @@ plot(
   main = "Training and Test Errors over Iterations",
   lty = 1,
   ylim = c(min(range), max(range)),
-  xlim = c(501,length(train_errors)),
+  xlim = c(501, length(train_errors)),
 )
 
 points(test_errors,
diff --git a/lab2/figures/assignment3-4.eps b/lab2/figures/assignment3-4.eps
index f156ce98a9a7f9e7654665b39c4bffc8d57ee9d5..484234983a2e80b0e0b3d9955ee694ed472fe41e 100644
--- a/lab2/figures/assignment3-4.eps
+++ b/lab2/figures/assignment3-4.eps
@@ -131,7 +131,6 @@ dup length dict begin
   end
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+290.94 240.96 128.82 -37.12 r p3
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+302.15 228.59 m
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-350.79 224.28 (T) 0 ta
+335.81 224.28 (T) 0 ta
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-350.79 211.90 (T) 0 ta
+335.81 211.90 (T) 0 ta
 -1.440 (est Error) tb gr
 ep
 %%Trailer
diff --git a/lab2/figures/assignment3-4.png b/lab2/figures/assignment3-4.png
index cc6b1d311f37cafd53dadedd31f61a5223c3e9a6..0e747b8b028581d3385f2bdc04d9e874dc61f97a 100644
Binary files a/lab2/figures/assignment3-4.png and b/lab2/figures/assignment3-4.png differ