cleanup variable name dense

Author: rflamary

ot/lp/__init__.py

@@ -107,19 +107,18 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True):
     b = np.asarray(b, dtype=np.float64)
     M = np.asarray(M, dtype=np.float64)
 
-    sparse= not dense
 
     # if empty array given then use uniform distributions
     if len(a) == 0:
         a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0]
     if len(b) == 0:
         b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
 
-    if sparse:
-        Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse)
-        G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0]))
+    if dense:
+        G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense)
     else:
-        G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse)
+        Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense)
+        G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0]))        
 
     result_code_string = check_result(result_code)
     if log:
@@ -217,8 +216,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
     b = np.asarray(b, dtype=np.float64)
     M = np.asarray(M, dtype=np.float64)
 
-    sparse=not dense
-
     # problem with pikling Forks
     if sys.platform.endswith('win32'):
         processes=1
@@ -231,12 +228,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(),
 
     if log or return_matrix:
         def f(b):
-
-            if sparse:
-                Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse)
-                G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0]))
+            if dense:
+                G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense)
             else:
-                G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse)
+                Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense)
+                G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0]))                
 
             result_code_string = check_result(result_code)
             log = {}
@@ -249,11 +245,13 @@ def f(b):
             return [cost, log]
     else:
         def f(b):
-            if sparse:
-                Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse)
-                G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0]))
+            if dense:
+                G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense)
             else:
-                G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse)
+                Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense)
+                G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0]))                
+
+            result_code_string = check_result(result_code)
             check_result(result_code)
             return cost
 

ot/lp/emd_wrap.pyx

@@ -46,7 +46,7 @@ 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 max_iter, bint sparse):
+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, bint dense):
     """
         Solves the Earth Movers distance problem and returns the optimal transport matrix
 
@@ -110,8 +110,19 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
     if not len(b):
         b=np.ones((n2,))/n2
 
-    if sparse:
+    if dense:
+        # init OT matrix
+        G=np.zeros([n1, n2])
+
+        # calling the function
+        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
+
 
+    else:
+        
+        # init sparse OT matrix
         Gv=np.zeros(nmax)
         iG=np.zeros(nmax,dtype=np.int)
         jG=np.zeros(nmax,dtype=np.int)
@@ -123,17 +134,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod
         return Gv[:nG], iG[:nG], jG[:nG], cost, alpha, beta, result_code
 
 
-    else:
-
-
-        G=np.zeros([n1, n2])
-
-
-        # calling the function
-        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
-
 
 @cython.boundscheck(False)
 @cython.wraparound(False)