From 0316d552fa6005aaf0f6231eb9ca20441d5a2532 Mon Sep 17 00:00:00 2001
From: aje <leo_g_autheron@hotmail.fr>
Date: Wed, 30 Aug 2017 10:53:31 +0200
Subject: Move normalize function in utils.py

---
 ot/da.py | 52 ++++++----------------------------------------------
 1 file changed, 6 insertions(+), 46 deletions(-)

(limited to 'ot/da.py')

diff --git a/ot/da.py b/ot/da.py
index b4a69b1..61a3ba0 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -13,7 +13,7 @@ import numpy as np
 
 from .bregman import sinkhorn
 from .lp import emd
-from .utils import unif, dist, kernel
+from .utils import unif, dist, kernel, cost_normalization
 from .utils import check_params, deprecated, BaseEstimator
 from .optim import cg
 from .optim import gcg
@@ -673,7 +673,7 @@ class OTDA(object):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, norm)
         self.G = emd(ws, wt, self.M, max_iter)
         self.computed = True
 
@@ -741,26 +741,6 @@ class OTDA(object):
         # aply the delta to the interpolation
         return xf[idx, :] + x - x0[idx, :]
 
-    def normalizeM(self, norm):
-        """ Apply normalization to the loss matrix
-
-
-        Parameters
-        ----------
-        norm : str
-            type of normalization from 'median','max','log','loglog'
-
-        """
-
-        if norm == "median":
-            self.M /= float(np.median(self.M))
-        elif norm == "max":
-            self.M /= float(np.max(self.M))
-        elif norm == "log":
-            self.M = np.log(1 + self.M)
-        elif norm == "loglog":
-            self.M = np.log(1 + np.log(1 + self.M))
-
 
 @deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be"
             " removed in 0.5 \nUse class SinkhornTransport instead.")
@@ -787,7 +767,7 @@ class OTDA_sinkhorn(OTDA):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, norm)
         self.G = sinkhorn(ws, wt, self.M, reg, **kwargs)
         self.computed = True
 
@@ -816,7 +796,7 @@ class OTDA_lpl1(OTDA):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, norm)
         self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs)
         self.computed = True
 
@@ -845,7 +825,7 @@ class OTDA_l1l2(OTDA):
         self.wt = wt
 
         self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
+        self.M = cost_normalization(self.M, norm)
         self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs)
         self.computed = True
 
@@ -1001,7 +981,7 @@ class BaseTransport(BaseEstimator):
 
             # pairwise distance
             self.cost_ = dist(Xs, Xt, metric=self.metric)
-            self.normalizeCost_(self.norm)
+            self.cost_ = cost_normalization(self.cost_, self.norm)
 
             if (ys is not None) and (yt is not None):
 
@@ -1183,26 +1163,6 @@ class BaseTransport(BaseEstimator):
 
             return transp_Xt
 
-    def normalizeCost_(self, norm):
-        """ Apply normalization to the loss matrix
-
-
-        Parameters
-        ----------
-        norm : str
-            type of normalization from 'median','max','log','loglog'
-
-        """
-
-        if norm == "median":
-            self.cost_ /= float(np.median(self.cost_))
-        elif norm == "max":
-            self.cost_ /= float(np.max(self.cost_))
-        elif norm == "log":
-            self.cost_ = np.log(1 + self.cost_)
-        elif norm == "loglog":
-            self.cost_ = np.log(1 + np.log(1 + self.cost_))
-
 
 class SinkhornTransport(BaseTransport):
     """Domain Adapatation OT method based on Sinkhorn Algorithm
-- 
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