From 750d287bb91f17f1d1bc5824b6c0645bee410007 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Felix=20Ramnel=C3=B6v?= <felra653@student.liu.se>
Date: Fri, 13 Dec 2024 09:44:27 +0100
Subject: [PATCH] Lab 3: Code cleanup
---
lab3/assignment2.R | 13 ++++++-------
lab3/assignment3.R | 6 ++----
2 files changed, 8 insertions(+), 11 deletions(-)
diff --git a/lab3/assignment2.R b/lab3/assignment2.R
index f71fbf8..0f0353d 100644
--- a/lab3/assignment2.R
+++ b/lab3/assignment2.R
@@ -28,6 +28,7 @@ times <- c(
"24:00:00"
)
+# Convert time strings to POSIXlt
st$time <- strptime(st$time, format = "%H:%M:%S")
gaussian_kernel <- function(x, h) {
@@ -80,9 +81,7 @@ date_diff_ignoring_year <- function(date1, date2) {
diff <- abs(as.numeric(date1-date2))
# If difference is greater than half a year, go other way
- if (diff >= 183) {
- diff <- 366 - diff
- }
+ if (diff >= 183) diff <- 366 - diff
return(diff)
}
@@ -106,14 +105,14 @@ for (time in times) {
}, st_temp$latitude, st_temp$longitude)
- date_kernels <- mapply(function(x_i) {
- dist <- date_diff_ignoring_year(date, x_i)
+ date_kernels <- mapply(function(date_i) {
+ dist <- date_diff_ignoring_year(date, date_i)
gaussian_kernel(dist, h_date)
}, st_temp$date)
- time_kernels <- mapply(function(x_i) {
- dist <- as.numeric(difftime(time, x_i, units = "hours"))
+ time_kernels <- mapply(function(time_i) {
+ dist <- as.numeric(difftime(time, time_i, units = "hours"))
gaussian_kernel(dist, h_time)
}, st_temp$time)
diff --git a/lab3/assignment3.R b/lab3/assignment3.R
index 18d9602..bc09c53 100644
--- a/lab3/assignment3.R
+++ b/lab3/assignment3.R
@@ -98,16 +98,14 @@ k <- NULL
for (i in 1:10) {
# We produce predictions for just the first 10 points in the dataset.
k2 <- inte
- data_point <- as.numeric(spam[i, -58])
+ new_data_point <- as.numeric(spam[i, -58])
for (j in 1:length(sv)) {
support_vector <- as.numeric(spam[sv[j], -58])
- kernel_value <- rbf_kernel(support_vector, data_point)
+ kernel_value <- rbf_kernel(support_vector, new_data_point)
k2 <- k2 + co[j] * kernel_value
}
print(k2)
k <- c(k, sign(k2))
}
-
-# Only first correct, close to decision boundary (0.006292512).
k
predict(filter3, spam[1:10, -58], type = "decision")
\ No newline at end of file
--
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