diff --git a/lab3/assignment4.R b/lab3/assignment4.R
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+library(neuralnet)
+set.seed(1234567890)
+
+
+Var <- runif(500, 0, 10)
+
+
+mydata <- data.frame(Var, Sin=sin(Var))
+
+
+tr <- mydata[1:25,] # Training
+te <- mydata[26:500,] # Test
+
+
+# Random initialization of the weights in the interval [-1, 1]
+winit <- runif(10,-1,1)
+formula <- Sin ~ Var
+  nn <- neuralnet( formula , data = tr, hidden = c(10), startweights = winit )
+    # Plot of the training data (black), test data (blue), and predictions (red)
+    plot(tr, cex=2)
+    points(te, col = "blue", cex=1)
+    points(te[,1],predict(nn,te), col="red", cex=1)
+
+### PART 2 ###
+    
+    
+h1 <- function(x) {
+  x
+}
+    
+    
+h2 <- function(x) {
+  ifelse(x>0,x,0)
+}
+
+h3 <- function(x)  {
+  log(1 + exp(x))
+}
+
+
+
+nn_h1 <- neuralnet( formula , data = tr, hidden = c(10), startweights = t(winit), act.fct = h1 )
+# Plot of the training data (black), test data (blue), and predictions (red)
+plot(tr, cex=2, main = "h1")
+points(te, col = "blue", cex=1)
+points(te[,1],predict(nn_h1,te), col="red", cex=1)
+
+
+
+nn_h2 <- neuralnet( formula , data = tr, hidden = c(10), startweights = t(winit), act.fct = h2 )
+# Plot of the training data (black), test data (blue), and predictions (red)
+plot(tr, cex=2, main="h2")
+points(te, col = "blue", cex=1)
+points(te[,1],predict(nn_h2,te), col="red", cex=1)
+
+
+
+
+nn_h3 <- neuralnet( formula , data = tr, hidden = c(10), startweights = t(winit), act.fct = h3 )
+# Plot of the training data (black), test data (blue), and predictions (red)
+plot(tr, cex=2, main = "h3")
+points(te, col = "blue", cex=1)
+points(te[,1],predict(nn_h3,te), col="red", cex=1)
+
+
+# part 3
+
+
+Var1 <- runif(500, 0, 50)
+
+
+mydata1 <- data.frame(Var = Var1, Sin=sin(Var1))
+
+plot(mydata1, cex=2, main = "500 random points",ylim = c(-10,10))
+points(mydata1, col = "blue", cex=1)
+pred <- predict(nn,te)
+prediciton <- predict(nn,mydata1)
+points(mydata1[,1],prediciton, col="red", cex = 1)
+
+
+# Part 4
+
+