Improved tests and docs for wasserstein_1d

3 files changed, 11 insertions, 10 deletions

diff --git a/ot/__init__.py b/ot/__init__.py
index f0e526c..5bd9bb3 100644
--- a/ot/__init__.py
+++ b/ot/__init__.py
@@ -22,7 +22,7 @@ from . import smooth
 from . import stochastic
 
 # OT functions
-from .lp import emd, emd2, emd_1d, emd2_1d
+from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d, wasserstein2_1d
 from .bregman import sinkhorn, sinkhorn2, barycenter
 from .da import sinkhorn_lpl1_mm
 
@@ -32,5 +32,6 @@ from .utils import dist, unif, tic, toc, toq
 __version__ = "0.5.1"
 
 __all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets',
-           'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'emd_1d', 'emd2_1d',
+           'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov',
+           'emd_1d', 'emd2_1d', 'wasserstein_1d', 'wasserstein2_1d',
            'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim']
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 719032b..76c9ec0 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -530,13 +530,13 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True,
 
 
 def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False):
-    """Solves the Wasserstein distance problem between 1d measures and returns
+    """Solves the p-Wasserstein distance problem between 1d measures and returns
     the OT matrix
 
 
     .. math::
-        \gamma = arg\min_\gamma \left(\sum_i \sum_j \gamma_{ij}
-            |x_a[i] - x_b[j]|^p \right)^{1/p}
+        \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij}
+            |x_a[i] - x_b[j]|^p \\right)^{1/p}
 
         s.t. \gamma 1 = a,
              \gamma^T 1= b,
@@ -617,15 +617,14 @@ def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False):
                   dense=dense, log=log)
 
 
-def wasserstein2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1.,
-                    dense=True, log=False):
-    """Solves the Wasserstein distance problem between 1d measures and returns
+def wasserstein2_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False):
+    """Solves the p-Wasserstein distance problem between 1d measures and returns
     the loss
 
 
     .. math::
         \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij}
-            |x_a[i] - x_b[j]|^p \right)^{1/p}
+            |x_a[i] - x_b[j]|^p \\right)^{1/p}
 
         s.t. \gamma 1 = a,
              \gamma^T 1= b,
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx
index 7134136..42b848f 100644
--- a/ot/lp/emd_wrap.pyx
+++ b/ot/lp/emd_wrap.pyx
@@ -13,6 +13,7 @@ cimport numpy as np
 from ..utils import dist
 
 cimport cython
+cimport libc.math as math
 
 import warnings
 
@@ -159,7 +160,7 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights,
         elif metric == 'cityblock' or metric == 'euclidean':
             m_ij = abs(u[i] - v[j])
         elif metric == 'minkowski':
-            m_ij = abs(u[i] - v[j]) ** p
+            m_ij = math.pow(abs(u[i] - v[j]), p)
         else:
             m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)),
                         metric=metric)[0, 0]