diff --git a/volia/clustering_modules/kmeans_mahalanobis.py b/volia/clustering_modules/kmeans_mahalanobis.py
index 0779cf8..6848cdd 100644
--- a/volia/clustering_modules/kmeans_mahalanobis.py
+++ b/volia/clustering_modules/kmeans_mahalanobis.py
@@ -27,7 +27,6 @@ class kmeansMahalanobis():
         for n in range(N):
             for k in range(self.K):
                 distances[n][k] = self._dist(features[n], self.C[k], self.L[k])
-        print(distances)
         closest_cluster = np.argmin(distances, axis=1)
         return closest_cluster
 
@@ -119,7 +118,6 @@ class kmeansMahalanobis():
             for n in range(N):
                 for k in range(self.K):
                     distances[n][k] = self._dist(X[n], self.C[k], self.L[k])
-            print(distances)
             closest_cluster = np.argmin(distances, axis=1)
             if i % 1 == 0:
                 # -- Debug tool ----------------------
@@ -140,6 +138,8 @@ class kmeansMahalanobis():
                 # Find subset of X with values closed to the centroid c_k.
                 X_sub = np.where(closest_cluster == k)
                 X_sub = np.take(X, X_sub[0], axis=0)
+                if X_sub.shape[0] == 0:
+                    continue
                 np.mean(X_sub, axis=0)
                 C_new = np.mean(X_sub, axis=0)
 
@@ -150,7 +150,7 @@ class kmeansMahalanobis():
                     c_tmp = np.reshape(C_new, (-1, 1))
                     K_new = K_new + (x - c_tmp).dot((x - c_tmp).transpose())
                 K_new = K_new / X_sub.shape[0]
-                K_new = np.linalg.inv(K_new)
+                K_new = np.linalg.pinv(K_new)
 
                 if end_algo and (not (self.C[k] == C_new).all()):  # If the same stop
                     end_algo = False