etter doc for classes

1 files changed, 31 insertions, 30 deletions

diff --git a/ot/da.py b/ot/da.py
index 72ca3ac..90b96ba 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -151,7 +151,7 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos
 
     The algorithm used for solving the problem is the block coordinate
     descent that alternates between updates of G (using conditionnal gradient)
-    abd the update of L using a classical least square solver.
+    and the update of L using a classical least square solver.
 
 
     Parameters
@@ -320,7 +320,7 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b
 
     The algorithm used for solving the problem is the block coordinate
     descent that alternates between updates of G (using conditionnal gradient)
-    abd the update of L using a classical kernel least square solver.
+    and the update of L using a classical kernel least square solver.
 
 
     Parameters
@@ -492,7 +492,15 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b
 
 
 class OTDA(object):
-    """Class for domain adaptation with optimal transport"""
+    """Class for domain adaptation with optimal transport as proposed in [5]
+
+
+    References
+    ----------
+
+    .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1
+
+    """
 
     def __init__(self,metric='sqeuclidean'):
         """ Class initialization"""
@@ -504,8 +512,7 @@ class OTDA(object):
 
 
     def fit(self,xs,xt,ws=None,wt=None):
-        """ Fit domain adaptation between samples is xs and xt (with optional
-            weights)"""
+        """ Fit domain adaptation between samples is xs and xt (with optional weights)"""
         self.xs=xs
         self.xt=xt
 
@@ -522,7 +529,7 @@ class OTDA(object):
         self.computed=True
 
     def interp(self,direction=1):
-        """Barycentric interpolation for the source (1) or target (-1)
+        """Barycentric interpolation for the source (1) or target (-1) samples
 
         This Barycentric interpolation solves for each source (resp target)
         sample xs (resp xt) the following optimization problem:
@@ -558,10 +565,16 @@ class OTDA(object):
 
 
     def predict(self,x,direction=1):
-        """ Out of sample mapping using the formulation from Ferradans
+        """ Out of sample mapping using the formulation from [6]
+
+        For each sample x to map, it finds the nearest source sample xs and
+        map the samle x to the position xst+(x-xs) wher xst is the barycentric
+        interpolation of source sample xs.
+
+        References
+        ----------
 
-        It basically find the source sample the nearset to the nex sample and
-        apply the difference to the displaced source sample.
+        .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
 
         """
         if direction>0: # >0 then source to target
@@ -582,8 +595,7 @@ class OTDA_sinkhorn(OTDA):
     """Class for domain adaptation with optimal transport with entropic regularization"""
 
     def fit(self,xs,xt,reg=1,ws=None,wt=None,**kwargs):
-        """ Fit domain adaptation between samples is xs and xt (with optional
-            weights)"""
+        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)"""
         self.xs=xs
         self.xt=xt
 
@@ -601,12 +613,12 @@ class OTDA_sinkhorn(OTDA):
 
 
 class OTDA_lpl1(OTDA):
-    """Class for domain adaptation with optimal transport with entropic an group regularization"""
+    """Class for domain adaptation with optimal transport with entropic and group regularization"""
 
 
     def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs):
-        """ Fit domain adaptation between samples is xs and xt (with optional
-            weights)"""
+        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights),
+        See ot.da.sinkhorn_lpl1_mm for fit parameters""""
         self.xs=xs
         self.xt=xt
 
@@ -623,7 +635,7 @@ class OTDA_lpl1(OTDA):
         self.computed=True
 
 class OTDA_mapping_linear(OTDA):
-    """Class for optimal transport with joint linear mapping estimation"""
+    """Class for optimal transport with joint linear mapping estimation as in [8]"""
 
 
     def __init__(self):
@@ -657,12 +669,7 @@ class OTDA_mapping_linear(OTDA):
 
 
     def predict(self,x):
-        """ Out of sample mapping using the formulation from Ferradans
-
-        It basically find the source sample the nearset to the nex sample and
-        apply the difference to the displaced source sample.
-
-        """
+        """ Out of sample mapping estimated during the call to fit"""
         if self.computed:
             if self.bias:
                 x=np.hstack((x,np.ones((x.shape[0],1))))
@@ -672,13 +679,12 @@ class OTDA_mapping_linear(OTDA):
             return None
 
 class OTDA_mapping_kernel(OTDA_mapping_linear):
-    """Class for optimal transport with joint linear mapping estimation"""
+    """Class for optimal transport with joint nonlinear mapping estimation as in [8]"""
 
 
 
     def fit(self,xs,xt,mu=1,eta=1,bias=False,kerneltype='gaussian',sigma=1,**kwargs):
-        """ Fit domain adaptation between samples is xs and xt (with optional
-            weights)"""
+        """ Fit domain adaptation between samples is xs and xt """
         self.xs=xs
         self.xt=xt
         self.bias=bias
@@ -695,12 +701,7 @@ class OTDA_mapping_kernel(OTDA_mapping_linear):
 
 
     def predict(self,x):
-        """ Out of sample mapping using the formulation from Ferradans
-
-        It basically find the source sample the nearset to the nex sample and
-        apply the difference to the displaced source sample.
-
-        """
+        """ Out of sample mapping estimated during the call to fit"""
 
         if self.computed:
             K=kernel(x,self.xs,method=self.kernel,sigma=self.sigma,**self.kwargs)