add mapping estimation (still debugging)

1 files changed, 29 insertions, 22 deletions

diff --git a/ot/utils.py b/ot/utils.py
index 24f65a8..47fe77f 100644
--- a/ot/utils.py
+++ b/ot/utils.py
@@ -6,28 +6,34 @@ import numpy as np
 from scipy.spatial.distance import cdist
 
 
+def kernel(x1,x2,method='gaussian',sigma=1,**kwargs):
+    """Compute kernel matrix"""
+    if method.lower() in ['gaussian','gauss','rbf']:
+        K=np.exp(dist(x1,x2)/(2*sigma**2))
+    return K
+
 def unif(n):
-    """ return a uniform histogram of length n (simplex) 
-    
+    """ return a uniform histogram of length n (simplex)
+
     Parameters
     ----------
 
     n : int
         number of bins in the histogram
-  
+
     Returns
     -------
     h : np.array (n,)
-        histogram of length n such that h_i=1/n for all i    
-    
-    
+        histogram of length n such that h_i=1/n for all i
+
+
     """
     return np.ones((n,))/n
 
 
 def dist(x1,x2=None,metric='sqeuclidean'):
     """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist
-    
+
     Parameters
     ----------
 
@@ -36,28 +42,29 @@ def dist(x1,x2=None,metric='sqeuclidean'):
     x2 : np.array (n2,d), optional
         matrix with n2 samples of size d (if None then x2=x1)
     metric : str, fun, optional
-        name of the metric to be computed (full list in the doc of scipy),  If a string, 
+        name of the metric to be computed (full list in the doc of scipy),  If a string,
         the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’,
         ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’,
         ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’,
         ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’.
 
-  
+
     Returns
     -------
-    
+
     M : np.array (n1,n2)
         distance matrix computed with given metric
-    
+
     """
     if x2 is None:
-        return cdist(x1,x1,metric=metric)
-    else:
-        return cdist(x1,x2,metric=metric)  
-        
+        x2=x1
+
+    return cdist(x1,x2,metric=metric)
+
+
 def dist0(n,method='lin_square'):
     """Compute standard cost matrices of size (n,n) for OT problems
-    
+
     Parameters
     ----------
 
@@ -68,21 +75,21 @@ def dist0(n,method='lin_square'):
 
         * 'lin_square' : linear sampling between 0 and n-1, quadratic loss
 
-  
+
     Returns
     -------
-    
+
     M : np.array (n1,n2)
-        distance matrix computed with given metric    
-    
-    
+        distance matrix computed with given metric
+
+
     """
     res=0
     if method=='lin_square':
         x=np.arange(n,dtype=np.float64).reshape((n,1))
         res=dist(x,x)
     return res
-    
+
 
 def dots(*args):
     """ dots function for multiple matrix multiply """