From 12d9b3ff72e9669ccc0162e82b7a33beb51d3e25 Mon Sep 17 00:00:00 2001
From: Antoine Rolet <antoine.rolet@gmail.com>
Date: Thu, 7 Sep 2017 13:50:41 +0900
Subject: Return dual variables in an optional dictionary

Also removed some code duplication
---
 ot/lp/__init__.py | 24 +++++++++++++++++-------
 1 file changed, 17 insertions(+), 7 deletions(-)

(limited to 'ot/lp/__init__.py')

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index 6048f60..c15e6b9 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -9,12 +9,12 @@ Solvers for the original linear program OT problem
 
 import numpy as np
 # import compiled emd
-from .emd_wrap import emd_c, emd2_c
+from .emd_wrap import emd_c
 from ..utils import parmap
 import multiprocessing
 
 
-def emd(a, b, M, numItermax=100000, dual_variables=False):
+def emd(a, b, M, numItermax=100000, log=False):
     """Solves the Earth Movers distance problem and returns the OT matrix
 
 
@@ -42,11 +42,17 @@ def emd(a, b, M, numItermax=100000, dual_variables=False):
     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)
+        If True, returns a dictionary containing the cost and dual
+        variables. Otherwise returns only the optimal transportation matrix.
 
     Returns
     -------
     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
 
 
     Examples
@@ -86,9 +92,13 @@ def emd(a, b, M, numItermax=100000, dual_variables=False):
     if len(b) == 0:
         b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1]
 
-    G, alpha, beta = emd_c(a, b, M, numItermax)
-    if dual_variables:
-        return G, alpha, beta
+    G, cost, u, v = emd_c(a, b, M, numItermax)
+    if log:
+        log = {}
+        log['cost'] = cost
+        log['u'] = u
+        log['v'] = v
+        return G, log
     return G
 
 def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000):
@@ -163,11 +173,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000):
         b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1]
     
     if len(b.shape)==1:
-        return emd2_c(a, b, M, numItermax)[0]
+        return emd_c(a, b, M, numItermax)[1]
     nb = b.shape[1]
     # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)]
 
     def f(b):
-        return emd2_c(a,b,M, numItermax)[0]
+        return emd_c(a,b,M, numItermax)[1]
     res= parmap(f, [b[:,i] for i in range(nb)],processes)
     return np.array(res)
-- 
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