[MRG] Distance calculation bug solve

ot/utils.py

@@ -182,7 +182,7 @@ def euclidean_distances(X, Y, squared=False):
     return c
 
 
-def dist(x1, x2=None, metric='sqeuclidean', p=2):
+def dist(x1, x2=None, metric='sqeuclidean', p=2, w=None):
     r"""Compute distance between samples in :math:`\mathbf{x_1}` and :math:`\mathbf{x_2}`
 
     .. note:: This function is backend-compatible and will work on arrays
@@ -202,6 +202,10 @@ def dist(x1, x2=None, metric='sqeuclidean', p=2):
         'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis',
         'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
         'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'.
+    p : float, optional
+        p-norm for the Minkowski and the Weighted Minkowski metrics. Default value is 2.
+    w : array-like, rank 1
+        Weights for the weighted metrics.
 
 
     Returns
@@ -221,7 +225,9 @@ def dist(x1, x2=None, metric='sqeuclidean', p=2):
         if not get_backend(x1, x2).__name__ == 'numpy':
             raise NotImplementedError()
         else:
-            return cdist(x1, x2, metric=metric, p=p)
+            if metric.endswith("minkowski"):
+                return cdist(x1, x2, metric=metric, p=p, w=w)
+            return cdist(x1, x2, metric=metric, w=w)
 
 
 def dist0(n, method='lin_square'):

test/test_utils.py

@@ -117,6 +117,26 @@ def test_dist():
     np.testing.assert_allclose(D, D2, atol=1e-14)
     np.testing.assert_allclose(D, D3, atol=1e-14)
 
+    # tests that every metric runs correctly
+    metrics_w = [
+        'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice',
+        'euclidean', 'hamming', 'jaccard', 'kulsinski',
+        'matching', 'minkowski', 'rogerstanimoto', 'russellrao',
+        'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'
+    ]  # those that support weights
+    metrics = ['mahalanobis', 'seuclidean']  # do not support weights depending on scipy's version
+
+    for metric in metrics_w:
+        print(metric)
+        ot.dist(x, x, metric=metric, p=3, w=np.random.random((2, )))
+    for metric in metrics:
+        print(metric)
+        ot.dist(x, x, metric=metric, p=3)
+
+    # weighted minkowski but with no weights
+    with pytest.raises(ValueError):
+        ot.dist(x, x, metric="wminkowski")
+
 
 def test_dist_backends(nx):