From d130b55fd5845bf0848bb02cebc58ce1ae89f8a3 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Mon, 24 Oct 2016 15:51:31 +0200
Subject: bregman as module

---
 ot/__init__.py         |  2 +-
 ot/bregman/__init__.py |  4 ---
 ot/bregman/sink.py     | 91 --------------------------------------------------
 3 files changed, 1 insertion(+), 96 deletions(-)
 delete mode 100644 ot/bregman/__init__.py
 delete mode 100644 ot/bregman/sink.py

(limited to 'ot')

diff --git a/ot/__init__.py b/ot/__init__.py
index 0a9e89b..f5490f7 100644
--- a/ot/__init__.py
+++ b/ot/__init__.py
@@ -14,4 +14,4 @@ from bregman import sinkhorn
 # utils functions
 from utils import dist,dots,unif
 
-__all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dots']
+__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','dots']
diff --git a/ot/bregman/__init__.py b/ot/bregman/__init__.py
deleted file mode 100644
index e4016ea..0000000
--- a/ot/bregman/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-
-
-from .sink import sinkhorn
-
diff --git a/ot/bregman/sink.py b/ot/bregman/sink.py
deleted file mode 100644
index 8b97e1e..0000000
--- a/ot/bregman/sink.py
+++ /dev/null
@@ -1,91 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Oct 21 09:40:21 2016
-
-@author: rflamary
-"""
-
-import numpy as np
-
-
-def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9):
-    """
-    Solve the optimal transport problem (OT)
-    
-    .. math::
-        \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma)
-        
-        s.t. \gamma 1 = a
-        
-             \gamma^T 1= b 
-             
-             \gamma\geq 0
-    where :
-    
-    - M is the metric cost matrix
-    - Omega is the entropic regularization term
-    - a and b are the sample weights
-             
-    Parameters
-    ----------
-    a : (ns,) ndarray
-        samples in the source domain
-    b : (nt,) ndarray
-        samples in the target domain
-    M : (ns,nt) ndarray
-        loss matrix        
-    reg: float()
-        Regularization term >0
-  
-    
-    Returns
-    -------
-    gamma: (ns x nt) ndarray
-        Optimal transportation matrix for the given parameters
-        
-    """    
-    # init data
-    Nini = len(a)
-    Nfin = len(b)
-    
-    
-    cpt = 0
-    
-    # we assume that no distances are null except those of the diagonal of distances
-    u = np.ones(Nini)/Nini
-    v = np.ones(Nfin)/Nfin 
-    uprev=np.zeros(Nini)
-    vprev=np.zeros(Nini)
-
-    #print reg
- 
-    K = np.exp(-M/reg)
-    #print np.min(K)
-      
-    Kp = np.dot(np.diag(1/a),K)
-    transp = K
-    cpt = 0
-    err=1
-    while (err>stopThr and cpt<numItermax):
-        if np.any(np.dot(K.T,u)==0) or np.any(np.isnan(u)) or np.any(np.isnan(v)):
-            # we have reached the machine precision
-            # come back to previous solution and quit loop
-            print('Warning: numerical errrors')
-            if cpt!=0:
-                u = uprev
-                v = vprev     
-            break
-        uprev = u
-        vprev = v  
-        v = np.divide(b,np.dot(K.T,u))
-        u = 1./np.dot(Kp,v)
-        if cpt%10==0:
-            # we can speed up the process by checking for the error only all the 10th iterations
-            transp = np.dot(np.diag(u),np.dot(K,np.diag(v)))
-            err = np.linalg.norm((np.sum(transp,axis=0)-b))**2
-        cpt = cpt +1
-    #print 'err=',err,' cpt=',cpt  
-
-    return np.dot(np.diag(u),np.dot(K,np.diag(v)))
-
-
-- 
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