From 7cb7d644c7c1f918de2b5655b28a12086f32097d Mon Sep 17 00:00:00 2001
From: Laurent Modolo <laurent.modolo@ens-lyon.fr>
Date: Thu, 12 Oct 2023 10:18:02 +0200
Subject: [PATCH] Practical b: fix typo
---
Practical_b.Rmd | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/Practical_b.Rmd b/Practical_b.Rmd
index 01ff3eb..14146c5 100644
--- a/Practical_b.Rmd
+++ b/Practical_b.Rmd
@@ -493,13 +493,13 @@ Think about the starting state of your algorithm and the stopping condition
<details><summary>Solution</summary>
<p>
-We have no prior information about the centroid, we can randomly draw them
-We are going to iterate over the two step of the algorithm until the centroids stay the same
+We have no prior information about the centroid, we can randomly draw them.
+We are going to iterate over the two-step of the algorithm until the centroids stay the same.
</p>
</details>
<div class="pencadre">
-Start by implementing an `kmeans_initiation(x, k)` function for your algorithm, returning $k$ centroids
+Start by implementing a `kmeans_initiation(x, k)` function for your algorithm, returning $k$ centroids
</div>
<details><summary>Solution</summary>
@@ -517,7 +517,7 @@ kmeans_initiation <- function(x, k) {
</details>
<div class="pencadre">
-Implement an `compute_distance(x, centroid)` function for your algorithm, the distance of each point (row of x) to each centroid, based on the squared Euclidian distance
+Implement a `compute_distance(x, centroid)` function for your algorithm, the distance of each point (row of x) to each centroid, based on the squared Euclidian distance.
</div>
<details><summary>Solution</summary>
@@ -535,7 +535,7 @@ compute_distance <- function(x, centroid) {
</details>
<div class="pencadre">
-Implement an `cluster_assignment(distance)` function for your algorithm, returning the assignment of each point (row of x), based on the squared Euclidian distance
+Implement a `cluster_assignment(distance)` function for your algorithm, returning the assignment of each point (row of x), based on the squared Euclidian distance.
</div>
<details><summary>Solution</summary>
@@ -554,7 +554,7 @@ cluster_assignment <- function(distance) {
<div class="pencadre">
-Implement an `centroid_update(x, cluster, k)` function for your algorithm, returning the updated centroid for your clusters
+Implement a `centroid_update(x, cluster, k)` function for your algorithm, returning the updated centroid for your clusters.
</div>
<details><summary>Solution</summary>
@@ -572,7 +572,7 @@ centroid_update <- function(x, cluster, k) {
</details>
<div class="pencadre">
-Implement an `kmeans_example(x, k)` function for your algorithm, wrapping everything and test it
+Implement a `kmeans_example(x, k)` function for your algorithm, wrapping everything and test it.
</div>
<details><summary>Solution</summary>
--
GitLab