diff --git a/pyod/models/copod.py b/pyod/models/copod.py
index 3d357fd66..1877bd902 100644
--- a/pyod/models/copod.py
+++ b/pyod/models/copod.py
@@ -136,7 +136,10 @@ def decision_function(self, X):
         skewness = np.sign(skew(X, axis=0))
         self.U_skew = self.U_l * -1 * np.sign(
             skewness - 1) + self.U_r * np.sign(skewness + 1)
-        self.O = np.maximum(self.U_skew, np.add(self.U_l, self.U_r) / 2)
+
+        self.O = np.maximum(self.U_l, self.U_r)
+        self.O = np.maximum(self.U_skew, self.O)
+
         if hasattr(self, 'X_train'):
             decision_scores_ = self.O.sum(axis=1)[-original_size:]
         else:
@@ -195,7 +198,10 @@ def _decision_function_parallel(self, X):
         skewness = np.sign(skew(X, axis=0))
         self.U_skew = self.U_l * -1 * np.sign(
             skewness - 1) + self.U_r * np.sign(skewness + 1)
-        self.O = np.maximum(self.U_skew, np.add(self.U_l, self.U_r) / 2)
+
+        self.O = np.maximum(self.U_l, self.U_r)
+        self.O = np.maximum(self.U_skew, self.O)
+
         if hasattr(self, 'X_train'):
             decision_scores_ = self.O.sum(axis=1)[-original_size:]
         else:
diff --git a/pyod/models/ecod.py b/pyod/models/ecod.py
index fe278b454..00ade951a 100644
--- a/pyod/models/ecod.py
+++ b/pyod/models/ecod.py
@@ -138,10 +138,7 @@ def decision_function(self, X):
         skewness = np.sign(skew(X, axis=0))
         self.U_skew = self.U_l * -1 * np.sign(
             skewness - 1) + self.U_r * np.sign(skewness + 1)
-
-        self.O = np.maximum(self.U_l, self.U_r)
-        self.O = np.maximum(self.U_skew, self.O)
-
+        self.O = np.maximum(self.U_skew, np.add(self.U_l, self.U_r) / 2)
         if hasattr(self, 'X_train'):
             decision_scores_ = self.O.sum(axis=1)[-original_size:]
         else:
@@ -200,10 +197,7 @@ def _decision_function_parallel(self, X):
         skewness = np.sign(skew(X, axis=0))
         self.U_skew = self.U_l * -1 * np.sign(
             skewness - 1) + self.U_r * np.sign(skewness + 1)
-
-        self.O = np.maximum(self.U_l, self.U_r)
-        self.O = np.maximum(self.U_skew, self.O)
-
+        self.O = np.maximum(self.U_skew, np.add(self.U_l, self.U_r) / 2)
         if hasattr(self, 'X_train'):
             decision_scores_ = self.O.sum(axis=1)[-original_size:]
         else: