scikit-learn
diff --git a/‎sklearn/ensemble/weight_boosting.py
Lines changed: 6 additions & 14 deletions b/‎sklearn/ensemble/weight_boosting.py
Lines changed: 6 additions & 14 deletions
diff --git a/‎sklearn/utils/validation.py
Lines changed: 2 additions & 2 deletions b/‎sklearn/utils/validation.py
Lines changed: 2 additions & 2 deletions
@@ -38,6 +38,7 @@
 from ..utils.extmath import stable_cumsum
 from ..metrics import accuracy_score, r2_score
 from ..utils.validation import check_is_fitted
+from ..utils.validation import _check_sample_weight
 from ..utils.validation import has_fit_parameter
 from ..utils.validation import _num_samples
 
@@ -117,20 +118,11 @@ def fit(self, X, y, sample_weight=None):
 
         X, y = self._validate_data(X, y)
 
-        if sample_weight is None:
-            # Initialize weights to 1 / n_samples
-            sample_weight = np.empty(_num_samples(X), dtype=np.float64)
-            sample_weight[:] = 1. / _num_samples(X)
-        else:
-            sample_weight = check_array(sample_weight, ensure_2d=False)
-            # Normalize existing weights
-            sample_weight = sample_weight / sample_weight.sum(dtype=np.float64)
-
-            # Check that the sample weights sum is positive
-            if sample_weight.sum() <= 0:
-                raise ValueError(
-                    "Attempting to fit with a non-positive "
-                    "weighted number of samples.")
+        sample_weight = _check_sample_weight(sample_weight, X, np.float64)
+        sample_weight /= sample_weight.sum()
+        if sample_weight.sum() <= 0:
+            raise ValueError("Attempting to fit with a non-positive weighted "
+                             "number of samples.")
 
         # Check parameters
         self._validate_estimator()
 
@@ -1043,8 +1043,8 @@ def _check_sample_weight(sample_weight, X, dtype=None):
         if dtype is None:
             dtype = [np.float64, np.float32]
         sample_weight = check_array(
-                sample_weight, accept_sparse=False,
-                ensure_2d=False, dtype=dtype, order="C"
+            sample_weight, accept_sparse=False, ensure_2d=False, dtype=dtype,
+            order="C"
         )
         if sample_weight.ndim != 1:
             raise ValueError("Sample weights must be 1D array or scalar")