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Verified Commit 7cb7d644 authored by Laurent Modolo's avatar Laurent Modolo
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Practical b: fix typo

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......@@ -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>
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