Added tests for euclidean distances.

Author: marcelcaraciolo

crab/metrics/pairwise.py

@@ -78,7 +78,7 @@ def check_pairwise_arrays(X, Y):
 
 # Distances
 def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False,
-        inverse=True):
+        inverse=False):
     """
     Considering the rows of X (and Y=X) as vectors, compute the
     distance matrix between each pair of vectors.
@@ -117,16 +117,16 @@ def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False,
     Examples
     --------
     >>> from crab.metrics.pairwise import euclidean_distances
-    >>> X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0],[3.0, 3.5, 1.5, 5.0, 3.5,3.0]]
+    >>> X = [[0, 1], [1, 1]]
     >>> # distrance between rows of X
     >>> euclidean_distances(X, X)
-    array([[ 1.        ,  0.29429806],
-           [ 0.29429806,  1.        ]])
+    array([[ 0.,  1.],
+           [ 1.,  0.]])
     >>> # get distance to origin
     >>> X = [[1.0, 0.0],[1.0,1.0]]
     >>> euclidean_distances(X, [[0.0, 0.0]])
-    array([[ 0.5       ],
-          [ 0.41421356]])
+    array([[ 1.      ],
+          [ 1.41421356]])
 
     """
     # should not need X_norm_squared because if you could precompute that as
@@ -173,5 +173,4 @@ def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False,
 
     return distances if squared else np.sqrt(distances)
 
-
 euclidian_distances = euclidean_distances  # both spelling for backward compat

crab/metrics/tests/test_pairwise.py

@@ -86,7 +86,7 @@ def test_euclidean_distances():
     #Idepontent Test
     X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0]]
     D = euclidean_distances(X, X)
-    assert_array_almost_equal(D, [[1.]])
+    assert_array_almost_equal(D, [[0.]])
 
     X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0]]
     D = euclidean_distances(X, X, inverse=False)
@@ -101,31 +101,31 @@ def test_euclidean_distances():
     X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0]]
     Y = [[3.0, 3.5, 1.5, 5.0, 3.5, 3.0]]
     D = euclidean_distances(X, Y)
-    assert_array_almost_equal(D, [[0.29429806]])
+    assert_array_almost_equal(D, [[2.39791576]])
 
     #Vector N x 1
     X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0], [2.5, 3.5, 3.0, 3.5, 2.5, 3.0]]
     Y = [[3.0, 3.5, 1.5, 5.0, 3.5, 3.0]]
     D = euclidean_distances(X, Y)
-    assert_array_almost_equal(D, [[0.29429806], [0.29429806]])
+    assert_array_almost_equal(D, [[2.39791576], [2.39791576]])
 
     #N-Dimmensional Vectors
     X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0], [2.5, 3.5, 3.0, 3.5, 2.5, 3.0]]
     Y = [[3.0, 3.5, 1.5, 5.0, 3.5, 3.0], [2.5, 3.5, 3.0, 3.5, 2.5, 3.0]]
     D = euclidean_distances(X, Y)
-    assert_array_almost_equal(D, [[0.29429806, 1.], [0.29429806,  1.]])
+    assert_array_almost_equal(D, [[2.39791576, 0.], [2.39791576,  0.]])
 
     X = [[2.5, 3.5, 3.0, 3.5, 2.5, 3.0], [3.0, 3.5, 1.5, 5.0, 3.5, 3.0]]
     D = euclidean_distances(X, X)
-    assert_array_almost_equal(D, [[1., 0.29429806], [0.29429806, 1.]])
+    assert_array_almost_equal(D, [[0., 2.39791576], [2.39791576, 0.]])
 
     X = [[1.0, 0.0], [1.0, 1.0]]
     Y = [[0.0, 0.0]]
     D = euclidean_distances(X, Y)
-    assert_array_almost_equal(D, [[0.5], [0.41421356]])
+    assert_array_almost_equal(D, [[1.], [1.41421356]])
 
     #Test Sparse Matrices
     X = csr_matrix(X)
     Y = csr_matrix(Y)
     D = euclidean_distances(X, Y)
-    assert_array_almost_equal(D, [[1., 2.]])
+    assert_array_almost_equal(D, [[1.], [1.41421356]])