Lab 3: Updated distance computations for assignment 3

lab3/assignment2.R

@@ -29,39 +29,41 @@ times <- c(
 )
 
 
-st <- st[st$date <= date, ] # Filter out posterior dates
 st$time <- strptime(st$time, format = "%H:%M:%S")
 
 gaussian_kernel <- function(x, h) {
   exp(-(x ^ 2) / (2 * h ^ 2))
 }
 
-dist = seq(0, 300000, length.out = 1000)
+x = seq(0, 300000, length.out = 1000)
 plot(
-  gaussian_kernel(dist, h_distance),
+  gaussian_kernel(x, h_distance),
   type = 'l',
   xlab = "Physical distance",
   ylab = "Kernel value",
   main = "Gaussian distance kernel"
 )
+grid()
 
-dist = seq(0, 60)
+x = seq(0, 60)
 plot(
-  gaussian_kernel(dist, h_date),
+  gaussian_kernel(x, h_date),
   type = 'l',
   xlab = "Distance in days",
   ylab = "Kernel value",
   main = "Gaussian date kernel"
 )
+grid()
 
-dist = seq(0, 18)
+x = seq(0, 18)
 plot(
-  gaussian_kernel(dist, h_time),
+  gaussian_kernel(x, h_time),
   type = 'l',
   xlab = "Distance in hours",
   ylab = "Kernel value",
   main = "Gaussian time kernel"
 )
+grid()
 
 temp_add <- c()
 temp_mult <- c()
@@ -70,7 +72,7 @@ for (time in times) {
   time <- strptime(time, format = "%H:%M:%S")
   
   
-  # Filter out posterior time
+  # Filter out posterior dates and time
   st_temp <- st[st$date < date |
                   (st$date == date &
                      st$time <= time), ]
@@ -117,6 +119,7 @@ axis(
   at = 1:length(times),         
   labels = times                  
 )
+grid()
 
 plot(
   temp_mult,
@@ -130,4 +133,5 @@ axis(
   1,                              
   at = 1:length(times),         
   labels = times                  
-)
\ No newline at end of file
+)
+grid()
\ No newline at end of file

lab3/assignment3.R

@@ -90,10 +90,9 @@ err3
 # 3. Implementation of SVM predictions.
 
 gaussian_kernel <- function(x_i, x_star, sigma) {
-  return(exp(-sum((x_i - x_star)^2) / (2 * sigma^2)))
+  exp(-(dist(rbind(x_i, x_star)) ^ 2) / (2 * sigma ^ 2))
 }
 
-
 sv <- alphaindex(filter3)[[1]]
 co <- coef(filter3)[[1]]
 inte <- -b(filter3)
@@ -119,4 +118,4 @@ for (i in 1:10) {
 
 # Only first correct, close to decision boundary (0.006292512).
 k
-predict(filter3,spam[1:10,-58], type = "decision")
\ No newline at end of file
+predict(filter3, spam[1:10, -58], type = "decision")
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