diff --git a/ot/da.py b/ot/da.py
index 5039fbd2f..db5f3ca5a 100644
--- a/ot/da.py
+++ b/ot/da.py
@@ -591,32 +591,64 @@ class OTDA(object):
 
     """
 
-    def __init__(self, metric='sqeuclidean'):
+    def __init__(self, metric='sqeuclidean', norm = None, 
+                 distribution_estimation='uniform', target_samples = None,
+                 mapping_type = 'barycentric_interpolation',
+                 reg = 1, eta = 1):
         """ Class initialization"""
-        self.xs = 0
-        self.xt = 0
-        self.G = 0
         self.metric = metric
-        self.computed = False
-
-    def fit(self, xs, xt, ws=None, wt=None, norm=None):
+        self.norm = norm
+        
+        self.distribution_estimation = distribution_estimation
+        self.target_samples = target_samples
+        self.mapping_type = mapping_type
+        
+        self.reg = reg
+        self.eta = eta
+
+
+    def _estimate_distribution(self,X):
+        
+        if self.distribution_estimation == "uniform":
+            return unif(X.shape[0])
+        
+        return X
+        
+    def fit(self, X, y=None):
         """ Fit domain adaptation between samples is xs and xt (with optional weights)"""
-        self.xs = xs
-        self.xt = xt
-
-        if wt is None:
-            wt = unif(xt.shape[0])
-        if ws is None:
-            ws = unif(xs.shape[0])
-
-        self.ws = ws
-        self.wt = wt
-
-        self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
-        self.G = emd(ws, wt, self.M)
+        self.xs = X
+       
+        self.ws = self._estimate_distribution(X)
+        
+        return self
+        
+    def transform(self,X):
+        """Scikitlearn compatible. Direction is determined by 
+        the existence of an a priori over the target distribution """
+        
+        return self._transport(X) if self._xor(np.array([(X == self.xs)]).all(),self.target_samples is None) else X
+        
+    def _transport(self,X):
+    
+        self.xt = X if self.target_samples is None else self.target_samples
+        self.wt = self._estimate_distribution(X)
+
+        self.M = dist(self.xs, self.xt, metric=self.metric)
+        self.normalizeM(self.norm)
+        self.G = emd(self.ws, self.wt, self.M)
         self.computed = True
-
+        
+        if self.mapping_type == 'OoS_mapping':
+            pass #TODO
+        
+        return self.interp(1) if self.target_samples is None else self.interp(-1) 
+
+
+    def _xor(self,logical_expression_1,logical_expression_2):
+        
+            return ((logical_expression_1 or logical_expression_2) 
+                     and ((not logical_expression_1) or (not logical_expression_2)))
+                     
     def interp(self, direction=1):
         """Barycentric interpolation for the source (1) or target (-1) samples
 
@@ -652,7 +684,8 @@ def interp(self, direction=1):
         else:
             print("Warning, model not fitted yet, returning None")
             return None
-
+                     
+                     
     def predict(self, x, direction=1):
         """ Out of sample mapping using the formulation from [6]
 
@@ -703,71 +736,79 @@ def normalizeM(self, norm):
 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, norm=None, **kwargs):
-        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)"""
-        self.xs = xs
-        self.xt = xt
-
-        if wt is None:
-            wt = unif(xt.shape[0])
-        if ws is None:
-            ws = unif(xs.shape[0])
-
-        self.ws = ws
-        self.wt = wt
-
-        self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
-        self.G = sinkhorn(ws, wt, self.M, reg, **kwargs)
+    
+    def _transport(self,X):
+        """ Regularized domain adaptation between samples xs and xt """
+        self.xt = X if self.target_samples is None else self.target_samples
+        self.wt = self._estimate_distribution(X)
+
+        self.M = dist(self.xs, self.xt, metric=self.metric)
+        self.normalizeM(self.norm)
+        self.G = sinkhorn(self.ws, self.wt, self.M, self.reg)
         self.computed = True
-
-
+        
+        if self.mapping_type == 'OoS_mapping':
+            pass #TODO
+        
+        return self.interp(1) if self.target_samples is None else self.interp(-1) 
+      
 class OTDA_lpl1(OTDA):
 
     """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, norm=None, **kwargs):
-        """ 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
-
-        if wt is None:
-            wt = unif(xt.shape[0])
-        if ws is None:
-            ws = unif(xs.shape[0])
-
-        self.ws = ws
-        self.wt = wt
-
-        self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
-        self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs)
+        
+    def fit(self, X, y):
+        """ Regularized domain adaptation between samples xs and xt  """
+        self.xs = X
+        self.ys = y
+        
+        self.ws = self._estimate_distribution(X)
+        
+        return self
+            
+    def _transport(self,X):
+    
+        self.xt = X if self.target_samples is None else self.target_samples
+        self.wt = self._estimate_distribution(X)
+
+        self.M = dist(self.xs, self.xt, metric=self.metric)
+        self.normalizeM(self.norm)
+        self.G = sinkhorn_lpl1_mm(self.ws, self.ys, self.wt, self.M, self.reg, self.eta)
         self.computed = True
-
+        
+        if self.mapping_type == 'OoS_mapping':
+            pass #TODO
+        
+        return self.interp(1) if self.target_samples is None else self.interp(-1) 
+        
 
 class OTDA_l1l2(OTDA):
 
     """Class for domain adaptation with optimal transport with entropic and group lasso regularization"""
 
-    def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs):
-        """ Fit regularized domain adaptation between samples is xs and xt (with optional weights),  See ot.da.sinkhorn_lpl1_gl for fit parameters"""
-        self.xs = xs
-        self.xt = xt
-
-        if wt is None:
-            wt = unif(xt.shape[0])
-        if ws is None:
-            ws = unif(xs.shape[0])
-
-        self.ws = ws
-        self.wt = wt
-
-        self.M = dist(xs, xt, metric=self.metric)
-        self.normalizeM(norm)
-        self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs)
+    def fit(self, X, y):
+        """ Regularized domain adaptation between samples xs and xt  """
+        self.xs = X
+        self.ys = y
+        
+        self.ws = self._estimate_distribution(X)
+        
+        return self
+            
+    def _transport(self,X):
+    
+        self.xt = X if self.target_samples is None else self.target_samples
+        self.wt = self._estimate_distribution(X)
+
+        self.M = dist(self.xs, self.xt, metric=self.metric)
+        self.normalizeM(self.norm)
+        self.G = sinkhorn_l1l2_gl(self.ws, self.ys, self.wt, self.M, self.reg, self.eta)
         self.computed = True
-
+        
+        if self.mapping_type == 'OoS_mapping':
+            pass #TODO
+        
+        return self.interp(1) if self.target_samples is None else self.interp(-1) 
 
 class OTDA_mapping_linear(OTDA):