Practical b: fix typo

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>