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, 42 insertions, 40 deletions

diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx
index 6039e1f..26d3330 100644
--- a/ot/lp/emd_wrap.pyx
+++ b/ot/lp/emd_wrap.pyx
@@ -15,56 +15,57 @@ cimport cython
 
 
 cdef extern from "EMD.h":
-    int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax)
+    int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter)
     cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED
 
 
 
 @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 numItermax):
+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
-        
+
         gamm=emd(a,b,M)
-    
+
     .. math::
-        \gamma = arg\min_\gamma <\gamma,M>_F 
-        
+        \gamma = arg\min_\gamma <\gamma,M>_F
+
         s.t. \gamma 1 = a
-        
-             \gamma^T 1= b 
-             
+
+             \gamma^T 1= b
+
              \gamma\geq 0
     where :
-    
+
     - M is the metric cost matrix
     - a and b are the sample weights
-             
+
     Parameters
     ----------
     a : (ns,) ndarray, float64
-        source histogram 
+        source histogram
     b : (nt,) ndarray, float64
         target histogram
     M : (ns,nt) ndarray, float64
-        loss matrix        
-    numItermax : int
-                 Maximum number of iterations made by the LP solver.
-  
-    
+        loss matrix
+    max_iter : int
+        The maximum number of iterations before stopping the optimization
+        algorithm if it has not converged.
+
+
     Returns
     -------
     gamma: (ns x nt) ndarray
         Optimal transportation matrix for the given parameters
- 
+
     """
     cdef int n1= M.shape[0]
     cdef int n2= M.shape[1]
 
     cdef float cost=0
     cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2])
-    
+
     if not len(a):
         a=np.ones((n1,))/n1
 
@@ -72,7 +73,7 @@ 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 resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, numItermax)
+    cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, max_iter)
     if resultSolver != OPTIMAL:
         if resultSolver == INFEASIBLE:
             print("Problem infeasible. Try to increase numItermax.")
@@ -83,49 +84,50 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def emd2_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 numItermax):
+def emd2_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 loss
-        
+
         gamm=emd(a,b,M)
-    
+
     .. math::
-        \gamma = arg\min_\gamma <\gamma,M>_F 
-        
+        \gamma = arg\min_\gamma <\gamma,M>_F
+
         s.t. \gamma 1 = a
-        
-             \gamma^T 1= b 
-             
+
+             \gamma^T 1= b
+
              \gamma\geq 0
     where :
-    
+
     - M is the metric cost matrix
     - a and b are the sample weights
-             
+
     Parameters
     ----------
     a : (ns,) ndarray, float64
-        source histogram 
+        source histogram
     b : (nt,) ndarray, float64
         target histogram
     M : (ns,nt) ndarray, float64
-        loss matrix        
-    numItermax : int
-                 Maximum number of iterations made by the LP solver.
-  
-    
+        loss matrix
+    max_iter : int
+        The maximum number of iterations before stopping the optimization
+        algorithm if it has not converged.
+
+
     Returns
     -------
     gamma: (ns x nt) ndarray
         Optimal transportation matrix for the given parameters
- 
+
     """
     cdef int n1= M.shape[0]
     cdef int n2= M.shape[1]
 
     cdef float cost=0
     cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2])
-    
+
     if not len(a):
         a=np.ones((n1,))/n1
 
@@ -133,13 +135,13 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo
         b=np.ones((n2,))/n2
 
     # calling the function
-    cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, numItermax)
+    cdef int resultSolver = EMD_wrap(n1,n2,<double*> a.data,<double*> b.data,<double*> M.data,<double*> G.data,<double*> &cost, max_iter)
     if resultSolver != OPTIMAL:
         if resultSolver == INFEASIBLE:
             print("Problem infeasible. Try to inscrease numItermax.")
         elif resultSolver == UNBOUNDED:
             print("Problem unbounded")
-    
+
     cost=0
     for i in range(n1):
         for j in range(n2):