Changes:

 - Rename numItermax to max_iter
 - Default value to 100000 instead of 10000
 - Add max_iter to class SinkhornTransport(BaseTransport)
 - Add norm to all BaseTransport

1 files changed, 23 insertions, 23 deletions

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 5143a70..7bef648 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -14,8 +14,7 @@ from ..utils import parmap
 import multiprocessing
 
 
-
-def emd(a, b, M, numItermax=10000):
+def emd(a, b, M, max_iter=100000):
     """Solves the Earth Movers distance problem and returns the OT matrix
 
 
@@ -40,8 +39,9 @@ def emd(a, b, M, numItermax=10000):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    numItermax : int
-                 Maximum number of iterations made by the LP solver.
+    max_iter : int, optional (default=100000)
+        The maximum number of iterations before stopping the optimization
+        algorithm if it has not converged.
 
     Returns
     -------
@@ -54,7 +54,7 @@ def emd(a, b, M, numItermax=10000):
 
     Simple example with obvious solution. The function emd accepts lists and
     perform automatic conversion to numpy arrays
-    
+
     >>> import ot
     >>> a=[.5,.5]
     >>> b=[.5,.5]
@@ -86,10 +86,11 @@ def emd(a, b, M, numItermax=10000):
     if len(b) == 0:
         b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1]
 
-    return emd_c(a, b, M, numItermax)
+    return emd_c(a, b, M, max_iter)
+
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000):
-    """Solves the Earth Movers distance problem and returns the loss 
+def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000):
+    """Solves the Earth Movers distance problem and returns the loss
 
     .. math::
         \gamma = arg\min_\gamma <\gamma,M>_F
@@ -112,8 +113,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    numItermax : int
-                 Maximum number of iterations made by the LP solver.
+    max_iter : int, optional (default=100000)
+        The maximum number of iterations before stopping the optimization
+        algorithm if it has not converged.
 
     Returns
     -------
@@ -126,15 +128,15 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000):
 
     Simple example with obvious solution. The function emd accepts lists and
     perform automatic conversion to numpy arrays
-    
-    
+
+
     >>> import ot
     >>> a=[.5,.5]
     >>> b=[.5,.5]
     >>> M=[[0.,1.],[1.,0.]]
     >>> ot.emd2(a,b,M)
     0.0
-    
+
     References
     ----------
 
@@ -157,16 +159,14 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000):
         a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0]
     if len(b) == 0:
         b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1]
-        
-    if len(b.shape)==1:
-        return emd2_c(a, b, M, numItermax)
+
+    if len(b.shape) == 1:
+        return emd2_c(a, b, M, max_iter)
     else:
-        nb=b.shape[1]
-        #res=[emd2_c(a,b[:,i].copy(),M, numItermax) for i in range(nb)]
+        nb = b.shape[1]
+        # res = [emd2_c(a, b[:, i].copy(), M, max_iter) for i in range(nb)]
+
         def f(b):
-            return emd2_c(a,b,M, numItermax)
-        res= parmap(f, [b[:,i] for i in range(nb)],processes)
+            return emd2_c(a, b, M, max_iter)
+        res = parmap(f, [b[:, i] for i in range(nb)], processes)
         return np.array(res)
-        
-
-  
\ No newline at end of file