diff --git a/volia/clustering_modules/kmeans_multidistance.py b/volia/clustering_modules/kmeans_multidistance.py
index 47fe159..2d2971d 100644
--- a/volia/clustering_modules/kmeans_multidistance.py
+++ b/volia/clustering_modules/kmeans_multidistance.py
@@ -3,7 +3,7 @@ import pickle
 from abstract_clustering import AbstractClustering
 from KMeans_Multidistance.KMeans_Class import KMeans
 from random import seed
-from random import random
+from random import randint
 import numpy as np
 from sklearn.metrics import pairwise_distances
 
@@ -61,7 +61,7 @@ class kmeansMultidistance():
 
         # Compute seeds before using seeds
         seed()
-        self.seeds = [random() for i in range(ninit)]
+        self.seeds = [randint(1, 100000) for i in range(ninit)]
 
         # Learning k-means model
         results = []
@@ -70,7 +70,7 @@ class kmeansMultidistance():
                            maxiter=maxiter,
                            distance=self.distance,
                            record_heterogeneity=[],
-                           verbose=True,
+                           verbose=debug,
                            seed=self.seeds[i])
             centroids, closest_cluster = model.fit(features)
 
@@ -92,6 +92,6 @@ class kmeansMultidistance():
             })
         losses = [result["loss"] for result in results]
         best = results[losses.index(min(losses))]
-        self.kmeans_model = results[best]["model"]
-        self.centroids = results[best]["centroids"]
-        self.seed = results[best]["seed"]
+        self.kmeans_model = best["model"]
+        self.centroids = best["centroids"]
+        self.seed = best["seed"]