From 3e67bb6862450d2c1acd4bccccaab9200b38791d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Felix=20Ramnel=C3=B6v?= <felra653@student.liu.se>
Date: Wed, 11 Dec 2024 08:10:35 +0100
Subject: [PATCH] Lab 3: Updated distance computations for assignment 3
---
lab3/assignment2.R | 22 +++++++++++++---------
lab3/assignment3.R | 5 ++---
2 files changed, 15 insertions(+), 12 deletions(-)
diff --git a/lab3/assignment2.R b/lab3/assignment2.R
index b0e655d..259d892 100644
--- a/lab3/assignment2.R
+++ b/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
diff --git a/lab3/assignment3.R b/lab3/assignment3.R
index 53af0fe..47b8abc 100644
--- a/lab3/assignment3.R
+++ b/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")
\ No newline at end of file
--
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