Renamed variable

4 files changed, 14 insertions, 14 deletions

diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h
index bb486de..f42e222 100644
--- a/ot/lp/EMD.h
+++ b/ot/lp/EMD.h
@@ -30,6 +30,6 @@ enum ProblemType {
 	MAX_ITER_REACHED
 };
 
-int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int max_iter);
+int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter);
 
 #endif
diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp
index 92663dc..fc7ca63 100644
--- a/ot/lp/EMD_wrapper.cpp
+++ b/ot/lp/EMD_wrapper.cpp
@@ -16,7 +16,7 @@
 
 
 int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G,
-                double* alpha, double* beta, double *cost, int max_iter)  {
+                double* alpha, double* beta, double *cost, int maxIter)  {
 // beware M and C anre strored in row major C style!!!
     int n, m, i, cur;
 
@@ -48,7 +48,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G,
     std::vector<int> indI(n), indJ(m);
     std::vector<double> weights1(n), weights2(m);
     Digraph di(n, m);
-    NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m, max_iter);
+    NetworkSimplexSimple<Digraph,double,double, node_id_type> net(di, true, n+m, n*m, maxIter);
 
     // Set supply and demand, don't account for 0 values (faster)
 
diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 1238cdb..9a0cb1c 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, max_iter=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)
+    max_iter : 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, max_iter)
     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):
+def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=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(), 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)
+    max_iter : int, optional (default=100000)
         The maximum number of iterations before stopping the optimization
         algorithm if it has not converged.
 
@@ -183,7 +183,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo
 
     if log:
         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, max_iter)
             result_code_string = check_result(resultCode)
             log = {}
             log['G'] = G
@@ -194,7 +194,7 @@ 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, max_iter)
             check_result(result_code)
             return cost
 
diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx
index 7ebdd2a..83ee6aa 100644
--- a/ot/lp/emd_wrap.pyx
+++ b/ot/lp/emd_wrap.pyx
@@ -16,7 +16,7 @@ import warnings
 
 
 cdef extern from "EMD.h":
-    int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int numItermax)
+    int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter)
     cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED
 
 
@@ -36,7 +36,7 @@ def check_result(result_code):
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"]  b, np.ndarray[double, ndim=2, mode="c"]  M, int num_iter_max):
+def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"]  b, np.ndarray[double, ndim=2, mode="c"]  M, int max_iter):
     """
         Solves the Earth Movers distance problem and returns the optimal transport matrix
 
@@ -63,7 +63,7 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
         target histogram
     M : (ns,nt) ndarray, float64
         loss matrix
-    num_iter_max : int
+    max_iter : int
         The maximum number of iterations before stopping the optimization
         algorithm if it has not converged.
 
@@ -90,6 +90,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
         b=np.ones((n2,))/n2
 
     # calling the function
-    cdef int result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, num_iter_max)
+    cdef int result_code = EMD_wrap(n1, n2, <double*> a.data, <double*> b.data, <double*> M.data, <double*> G.data, <double*> alpha.data, <double*> beta.data, <double*> &cost, max_iter)
 
     return G, cost, alpha, beta, result_code