Two implementations for score_pairs

Author: mvargas33

metric_learn/base_metric.py

@@ -180,8 +180,14 @@ def score_pairs(self, pairs):
       pairs = check_input(pairs, type_of_inputs='tuples',
                           preprocessor=self.preprocessor_,
                           estimator=self, tuple_size=2)
-
-      return [np.dot(np.dot(u, self.components_), v.T) for u,v in zip(pairs[:, 1, :], pairs[:, 0, :])]
+                          
+      # Note: For bilinear order matters, dist(a,b) != dist(b,a)
+      # We always choose first pair first, then second pair
+      # (In contrast with Mahalanobis implementation)
+      
+      # I dont know wich implementation performs better
+      return np.diagonal(np.dot(np.dot(pairs[:, 0, :], self.components_), pairs[:, 1, :].T))
+      return [np.dot(np.dot(u.T, self.components_), v) for u,v in zip(pairs[:, 0, :], pairs[:, 1, :])]
 
   def get_metric(self):
       check_is_fitted(self, 'components_')
@@ -206,7 +212,7 @@ def metric_fun(u, v):
           """
           u = validate_vector(u)
           v = validate_vector(v)
-          return np.dot(np.dot(u, components), v.T)
+          return np.dot(np.dot(u.T, components), v)
 
       return metric_fun