From 9265faccf53706f61c043b21754dbf9736dc042f Mon Sep 17 00:00:00 2001
From: "thibault.capt" <thibault.capt@etu.hesge.ch>
Date: Mon, 9 Oct 2023 18:35:41 +0200
Subject: [PATCH] show variance
---
main.py | 20 ++++++++++++++++++--
1 file changed, 18 insertions(+), 2 deletions(-)
diff --git a/main.py b/main.py
index 63cc38f..03e4755 100644
--- a/main.py
+++ b/main.py
@@ -21,7 +21,7 @@ if __name__ == '__main__':
centroids = X[np.random.choice(X.shape[0], k, replace=False)]
max_iter = 100
-
+ total_variances = []
# Algorithme K-Means
for i in range(max_iter):
# Créer des clusters vides
@@ -40,6 +40,14 @@ if __name__ == '__main__':
if len(clusters[j]) > 0:
centroids[j] = np.mean(clusters[j], axis=0)
+ # Calculer la variance totale à cette itération
+ total_variance = 0.0
+ for j in range(k):
+ for point in clusters[j]:
+ total_variance += manhattan_distance(point, centroids[j]) ** 2
+
+ total_variances.append(total_variance)
+
# Afficher les centroides finaux et les clusters
for iteration, centroid in enumerate(centroids):
cluster_points = np.array(clusters[iteration])
@@ -54,4 +62,12 @@ if __name__ == '__main__':
# Convergence ?
if np.all(old_centroids == centroids):
- break
\ No newline at end of file
+ break
+
+ # Afficher les variances totales à chaque itération
+ plt.plot(range(1, len(total_variances) + 1), total_variances, marker='o')
+ plt.xlabel('Itération')
+ plt.ylabel('Variance totale')
+ plt.title('Variance totale à chaque itération de K-Means')
+ plt.grid(True)
+ plt.show()
--
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