demo+sinkhorn

6 files changed, 142 insertions, 0 deletions

diff --git a/ot/__init__.py b/ot/__init__.py
index d0ab3f7..beeae7f 100644
--- a/ot/__init__.py
+++ b/ot/__init__.py
@@ -1,6 +1,12 @@
 
 
+
 from emd import emd
+from bregman import sinkhorn
+
+import utils
+import datasets
 
+from utils import dist,dots
 
 __all__ = ["emd"]
diff --git a/ot/bregman/__init__.py b/ot/bregman/__init__.py
new file mode 100644
index 0000000..e4016ea
--- /dev/null
+++ b/ot/bregman/__init__.py
@@ -0,0 +1,4 @@
+
+
+from .sink import sinkhorn
+
diff --git a/ot/bregman/sinkhorn.py b/ot/bregman/sinkhorn.py
new file mode 100644
index 0000000..798ac97
--- /dev/null
+++ b/ot/bregman/sinkhorn.py
@@ -0,0 +1,91 @@
+# -*- 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(-reg*M)
+    #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)))
+
+
diff --git a/ot/datasets.py b/ot/datasets.py
new file mode 100644
index 0000000..bb10ba4
--- /dev/null
+++ b/ot/datasets.py
@@ -0,0 +1,10 @@
+
+import numpy as np
+
+
+
+def get_1D_gauss(n,m,s):
+    "return a 1D histogram for a gaussian distribution (n bins, mean m and std s) "
+    x=np.arange(n,dtype=np.float64)
+    h=np.exp(-(x-m)**2/(2*s^2))
+    return h/h.sum()
\ No newline at end of file
diff --git a/ot/emd/emd.pyx b/ot/emd/emd.pyx
index d090cea..e5ac8e0 100644
--- a/ot/emd/emd.pyx
+++ b/ot/emd/emd.pyx
@@ -19,6 +19,22 @@ cdef extern from "EMD.h":
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"]  b,np.ndarray[double, ndim=2, mode="c"]  M):
+    """
+        Solves the Earth Movers distance problem and returns the optimal transport matrix
+        
+        
+ 
+        :param a: m weights of the source distribution (must sum to one)
+        :param b: n weights of the target distribution (must sum to one)
+        :param M: m x n cost matrix
+        :type a: np.ndarray
+        :type b: np.ndarray
+        :type M: np.ndarray
+        :return: Optimal transport matrix
+        :rtype: np.ndarray
+ 
+ 
+    """
     cdef int n1= M.shape[0]
     cdef int n2= M.shape[1]
 
diff --git a/ot/utils.py b/ot/utils.py
new file mode 100644
index 0000000..1a1c6b8
--- /dev/null
+++ b/ot/utils.py
@@ -0,0 +1,15 @@
+
+import numpy as np
+from scipy.spatial.distance import cdist, pdist
+
+
+def dist(x1,x2=None,metric='sqeuclidean'):
+    """Compute distance between samples in x1 and x2"""
+    if x2 is None:
+        return pdist(x1,metric=metric)
+    else:
+        return cdist(x1,x2,metric=metric)  
+
+def dots(*args):
+    """ Stupid but nice dots function for multiple matrix multiply """
+    return reduce(np.dot,args)
\ No newline at end of file