add mapping estimation with kernels (still debugging)

Author: rflamary

ot/da.py

@@ -247,7 +247,7 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b
 
     def loss(L,G):
         """Compute full loss"""
-        return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L)**2)
+        return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.trace(L.T.dot(K0).dot(L))
 
     def solve_L_nobias(G):
         """ solve L problem with fixed G (least square)"""
@@ -450,11 +450,11 @@ def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs):
         self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs)
         self.computed=True
 
-class OTDA_mapping(OTDA):
+class OTDA_mapping_linear(OTDA):
     """Class for optimal transport with joint linear mapping estimation"""
 
 
-    def __init__(self,metric='sqeuclidean'):
+    def __init__(self):
         """ Class initialization"""
 
 
@@ -463,8 +463,8 @@ def __init__(self,metric='sqeuclidean'):
         self.G=0
         self.L=0
         self.bias=False
-        self.metric=metric
         self.computed=False
+        self.metric='sqeuclidean'
 
     def fit(self,xs,xt,mu=1,eta=1,bias=False,**kwargs):
         """ Fit domain adaptation between samples is xs and xt (with optional
@@ -473,6 +473,7 @@ def fit(self,xs,xt,mu=1,eta=1,bias=False,**kwargs):
         self.xt=xt
         self.bias=bias
 
+
         self.ws=unif(xs.shape[0])
         self.wt=unif(xt.shape[0])
 
@@ -498,3 +499,42 @@ def predict(self,x):
             print("Warning, model not fitted yet, returning None")
             return None
 
+class OTDA_mapping_kernel(OTDA_mapping_linear):
+    """Class for optimal transport with joint linear mapping estimation"""
+
+
+
+    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)"""
+        self.xs=xs
+        self.xt=xt
+        self.bias=bias
+
+        self.ws=unif(xs.shape[0])
+        self.wt=unif(xt.shape[0])
+        self.kernel=kerneltype
+        self.sigma=sigma
+        self.kwargs=kwargs
+
+
+        self.G,self.L=joint_OT_mapping_kernel(xs,xt,mu=mu,eta=eta,bias=bias,**kwargs)
+        self.computed=True
+
+
+    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.
+
+        """
+
+        if self.computed:
+            K=kernel(x,self.xs,method=self.kernel,sigma=self.sigma,**self.kwargs)
+            if self.bias:
+                K=np.hstack((K,np.ones((x.shape[0],1))))
+            return K.dot(self.L)
+        else:
+            print("Warning, model not fitted yet, returning None")
+            return None

ot/datasets.py

@@ -108,9 +108,9 @@ def get_data_classif(dataset,n,nz=.5,theta=0,**kwargs):
         x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2)
 
     elif  dataset.lower()=='gaussrot'      :
-        rot=np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]])
-        m1=np.array([-1,-1])
-        m2=np.array([1,1])
+        rot=np.array([[np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]])
+        m1=np.array([-1,1])
+        m2=np.array([1,-1])
         y=np.floor((np.arange(n)*1.0/n*2))+1
         n1=np.sum(y==1)
         n2=np.sum(y==2)