Returned to old variable name to follow repo convention

1 files changed, 6 insertions, 6 deletions

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index ae5b08c..17f5bb4 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -16,7 +16,7 @@ from .emd_wrap import emd_c, check_result
 from ..utils import parmap
 
 
-def emd(a, b, M, max_iter=100000, log=False):
+def emd(a, b, M, num_iter_max=100000, log=False):
     """Solves the Earth Movers distance problem and returns the OT matrix
 
 
@@ -41,7 +41,7 @@ def emd(a, b, M, max_iter=100000, log=False):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    max_iter : int, optional (default=100000)
+    num_iter_max : int, optional (default=100000)
         The maximum number of iterations before stopping the optimization
         algorithm if it has not converged.
     log: boolean, optional (default=False)
@@ -94,7 +94,7 @@ def emd(a, b, M, max_iter=100000, log=False):
     if len(b) == 0:
         b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
 
-    G, cost, u, v, result_code = emd_c(a, b, M, max_iter)
+    G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
     result_code_string = check_result(result_code)
     if log:
         log = {}
@@ -107,7 +107,7 @@ def emd(a, b, M, max_iter=100000, log=False):
     return G
 
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=False):
+def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False):
     """Solves the Earth Movers distance problem and returns the loss
 
     .. math::
@@ -183,7 +183,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=Fa
 
     if log:
         def f(b):
-            G, cost, u, v, resultCode = emd_c(a, b, M, max_iter)
+            G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max)
             result_code_string = check_result(resultCode)
             log = {}
             log['G'] = G
@@ -194,7 +194,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=Fa
             return [cost, log]
     else:
         def f(b):
-            G, cost, u, v, result_code = emd_c(a, b, M, max_iter)
+            G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
             check_result(result_code)
             return cost