Renamed variables

1 files changed, 17 insertions, 14 deletions

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 0f40c19..ab7cb97 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -12,11 +12,11 @@ import multiprocessing
 import numpy as np
 
 # import compiled emd
-from .emd_wrap import emd_c, checkResult
+from .emd_wrap import emd_c, check_result
 from ..utils import parmap
 
 
-def emd(a, b, M, numItermax=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, numItermax=100000, log=False):
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    numItermax : 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)
@@ -54,7 +54,7 @@ def emd(a, b, M, numItermax=100000, log=False):
         Optimal transportation matrix for the given parameters
     log: dict
         If input log is true, a dictionary containing the cost and dual
-        variables
+        variables and exit status
 
 
     Examples
@@ -94,20 +94,20 @@ def emd(a, b, M, numItermax=100000, log=False):
     if len(b) == 0:
         b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
 
-    G, cost, u, v, resultCode = emd_c(a, b, M, numItermax)
-    resultCodeString = checkResult(resultCode)
+    G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
+    resultCodeString = check_result(result_code)
     if log:
         log = {}
         log['cost'] = cost
         log['u'] = u
         log['v'] = v
         log['warning'] = resultCodeString
-        log['resultCode'] = resultCode
+        log['result_code'] = result_code
         return G, log
     return G
 
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=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::
@@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=
         Target histogram (uniform weigth if empty list)
     M : (ns,nt) ndarray, float64
         loss matrix
-    numItermax : 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.
 
@@ -139,6 +139,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=
     -------
     gamma: (ns x nt) ndarray
         Optimal transportation matrix for the given parameters
+    log: dict
+        If input log is true, a dictionary containing the cost and dual
+        variables and exit status
 
 
     Examples
@@ -180,19 +183,19 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=
 
     if log:
         def f(b):
-            G, cost, u, v, resultCode = emd_c(a, b, M, numItermax)
-            resultCodeString = checkResult(resultCode)
+            G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max)
+            resultCodeString = check_result(resultCode)
             log = {}
             log['G'] = G
             log['u'] = u
             log['v'] = v
             log['warning'] = resultCodeString
-            log['resultCode'] = resultCode
+            log['result_code'] = resultCode
             return [cost, log]
     else:
         def f(b):
-            G, cost, u, v, resultCode = emd_c(a, b, M, numItermax)
-            checkResult(resultCode)
+            G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max)
+            check_result(result_code)
             return cost
 
     if len(b.shape) == 1: