diff --git a/code/bolsonaro/models/omp_forest.py b/code/bolsonaro/models/omp_forest.py
index 2263e04943918d871a59127184f1f6f2da5bcaa2..5b211b0d2900a42ac5fedf95d1c98bee4f1fa5e9 100644
--- a/code/bolsonaro/models/omp_forest.py
+++ b/code/bolsonaro/models/omp_forest.py
@@ -33,8 +33,8 @@ class OmpForest(BaseEstimator, metaclass=ABCMeta):
 
     # sklearn baseestimator api methods
     def fit(self, X_forest, y_forest, X_omp, y_omp):
-        print(y_forest.shape)
-        print(set([type(y) for y in y_forest]))
+        # print(y_forest.shape)
+        # print(set([type(y) for y in y_forest]))
         self._base_forest_estimator.fit(X_forest, y_forest)
         self._extract_subforest(X_omp, y_omp) # type: OrthogonalMatchingPursuit
         return self
diff --git a/code/bolsonaro/models/omp_forest_classifier.py b/code/bolsonaro/models/omp_forest_classifier.py
index 36d12be6727c25fcc029c13b1a13490f24be1295..ccaf3ebc2b630798f62ab17a1285ab28b366ed95 100644
--- a/code/bolsonaro/models/omp_forest_classifier.py
+++ b/code/bolsonaro/models/omp_forest_classifier.py
@@ -129,7 +129,7 @@ class OmpForestMulticlassClassifier(OmpForest):
             omp_trees_indices = np.nonzero(weights)
             label_names.append(class_label)
             atoms_binary = (forest_predictions[num_class].T - 0.5) * 2 # centré réduit de 0/1 à -1/1
-            preds.append(np.sum(atoms_binary[omp_trees_indices], axis=0))
+            preds.append(np.sum(atoms_binary[omp_trees_indices], axis=0)/len(omp_trees_indices))
             num_class += 1
 
         preds = np.array(preds).T