From ab65f86304b03a967054eeeaf73b8c8277618d65 Mon Sep 17 00:00:00 2001
From: Antoine Rolet <antoine.rolet@gmail.com>
Date: Thu, 7 Sep 2017 14:35:35 +0900
Subject: Added log option to muliprocess emd

---
 ot/lp/__init__.py | 39 ++++++++++++++++++++++++++-------------
 1 file changed, 26 insertions(+), 13 deletions(-)

(limited to 'ot/lp')

diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py
index c15e6b9..8edd8ec 100644
--- a/ot/lp/__init__.py
+++ b/ot/lp/__init__.py
@@ -7,11 +7,13 @@ Solvers for the original linear program OT problem
 #
 # License: MIT License
 
+import multiprocessing
+
 import numpy as np
+
 # import compiled emd
 from .emd_wrap import emd_c
 from ..utils import parmap
-import multiprocessing
 
 
 def emd(a, b, M, numItermax=100000, log=False):
@@ -88,9 +90,9 @@ def emd(a, b, M, numItermax=100000, log=False):
 
     # if empty array given then use unifor distributions
     if len(a) == 0:
-        a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[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]
+        b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
 
     G, cost, u, v = emd_c(a, b, M, numItermax)
     if log:
@@ -101,7 +103,8 @@ def emd(a, b, M, numItermax=100000, log=False):
         return G, log
     return G
 
-def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000):
+
+def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False):
     """Solves the Earth Movers distance problem and returns the loss
 
     .. math::
@@ -168,16 +171,26 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000):
 
     # if empty array given then use unifor distributions
     if len(a) == 0:
-        a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[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 len(b.shape)==1:
-        return emd_c(a, b, M, numItermax)[1]
+        b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1]
+
+    if log:
+        def f(b):
+            G, cost, u, v = emd_c(a, b, M, numItermax)
+            log = {}
+            log['G'] = G
+            log['u'] = u
+            log['v'] = v
+            return [cost, log]
+    else:
+        def f(b):
+            return emd_c(a, b, M, numItermax)[1]
+
+    if len(b.shape) == 1:
+        return f(b)
     nb = b.shape[1]
     # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)]
 
-    def f(b):
-        return emd_c(a,b,M, numItermax)[1]
-    res= parmap(f, [b[:,i] for i in range(nb)],processes)
-    return np.array(res)
+    res = parmap(f, [b[:, i] for i in range(nb)], processes)
+    return res
-- 
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