Handmade tests incorporated

Author: mvargas33

metric_learn/oasis.py

@@ -17,10 +17,6 @@ def fit(self, X, y):
         """
         X = self._prepare_inputs(X, y, ensure_min_samples=2)
 
-        # Handmade dummy fit
-        #self.components_ = np.identity(np.shape(X[0])[-1]) # Identity matrix
-        #self.components_ = np.array([[2,4,6], [6,4,2], [1, 2, 3]])
-
         # Dummy fit
         self.components_ = np.random.rand(np.shape(X[0])[-1], np.shape(X[0])[-1])
         return self

test_bilinear.py

@@ -3,6 +3,7 @@
 from numpy.testing import assert_array_almost_equal
 
 def test_toy_distance():
+    # Random generalized test for 2 points
     d = 100
 
     u = np.random.rand(d)
@@ -22,4 +23,20 @@ def test_toy_distance():
     assert_array_almost_equal(dist1, desired)
     assert_array_almost_equal(dist2, desired)
 
+    # Handmade example
+    u = np.array([0, 1 ,2])
+    v = np.array([3, 4, 5])
+
+    mixin.components_= np.array([[2,4,6], [6,4,2], [1, 2, 3]])
+    dists = mixin.score_pairs([[u, v], [v, u]])
+    assert_array_almost_equal(dists, [96, 120])
+
+    # Symetric example
+    u = np.array([0, 1 ,2])
+    v = np.array([3, 4, 5])
+
+    mixin.components_= np.identity(3) # Identity matrix
+    dists = mixin.score_pairs([[u, v], [v, u]])
+    assert_array_almost_equal(dists, [14, 14])
+
 test_toy_distance()