From e52b6eb41228a7f8e381cf73c06e0dffba5773be Mon Sep 17 00:00:00 2001
From: Antoine Rolet <antoine.rolet@gmail.com>
Date: Tue, 12 Sep 2017 20:00:14 +0900
Subject: Renaming

---
 ot/lp/__init__.py  | 14 +++++++-------
 ot/lp/emd_wrap.pyx |  2 +-
 2 files changed, 8 insertions(+), 8 deletions(-)

(limited to 'ot/lp')

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index d0f682b..5c09da2 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, num_iter_max=100000, log=False):
+def emd(a, b, M, numItermax=100000, log=False):
     """Solves the Earth Movers distance problem and returns the OT matrix
 
 
@@ -41,7 +41,7 @@ def emd(a, b, M, num_iter_max=100000, log=False):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    num_iter_max : int, optional (default=100000)
+    numItermax : 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, num_iter_max=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, num_iter_max)
+    G, cost, u, v, result_code = emd_c(a, b, M, numItermax)
     result_code_string = check_result(result_code)
     if log:
         log = {}
@@ -107,7 +107,7 @@ def emd(a, b, M, num_iter_max=100000, log=False):
     return G
 
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False, return_matrix=False):
+def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False, return_matrix=False):
     """Solves the Earth Movers distance problem and returns the loss
 
     .. math::
@@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    num_iter_max : int, optional (default=100000)
+    numItermax : int, optional (default=100000)
         The maximum number of iterations before stopping the optimization
         algorithm if it has not converged.
     log: boolean, optional (default=False)
@@ -188,7 +188,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo
 
     if log or return_matrix:
         def f(b):
-            G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max)
+            G, cost, u, v, resultCode = emd_c(a, b, M, numItermax)
             result_code_string = check_result(resultCode)
             log = {}
             if return_matrix:
@@ -200,7 +200,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo
             return [cost, log]
     else:
         def f(b):
-            G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
+            G, cost, u, v, result_code = emd_c(a, b, M, numItermax)
             check_result(result_code)
             return cost
 
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx
index 45fc988..83ee6aa 100644
--- a/ot/lp/emd_wrap.pyx
+++ b/ot/lp/emd_wrap.pyx
@@ -29,7 +29,7 @@ def check_result(result_code):
     elif result_code == UNBOUNDED:
         message = "Problem unbounded"
     elif result_code == MAX_ITER_REACHED:
-        message = "num_iter_max reached before optimality. Try to increase num_iter_max."
+        message = "numItermax reached before optimality. Try to increase numItermax."
     warnings.warn(message)
     return message
 
-- 
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